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mm: introduce Data Access MONitor (DAMON) Patch series "Introduce Data Access MONitor (DAMON)", v34. Introduction ============ DAMON is a data access monitoring framework for the Linux kernel. The core mechanisms of DAMON called 'region based sampling' and 'adaptive regions adjustment' (refer to 'mechanisms.rst' in the 11th patch of this patchset for the detail) make it - accurate (The monitored information is useful for DRAM level memory management. It might not appropriate for Cache-level accuracy, though.), - light-weight (The monitoring overhead is low enough to be applied online while making no impact on the performance of the target workloads.), and - scalable (the upper-bound of the instrumentation overhead is controllable regardless of the size of target workloads.). Using this framework, therefore, several memory management mechanisms such as reclamation and THP can be optimized to aware real data access patterns. Experimental access pattern aware memory management optimization works that incurring high instrumentation overhead will be able to have another try. Though DAMON is for kernel subsystems, it can be easily exposed to the user space by writing a DAMON-wrapper kernel subsystem. Then, user space users who have some special workloads will be able to write personalized tools or applications for deeper understanding and specialized optimizations of their systems. DAMON is also merged in two public Amazon Linux kernel trees that based on v5.4.y[1] and v5.10.y[2]. [1] https://github.com/amazonlinux/linux/tree/amazon-5.4.y/master/mm/damon [2] https://github.com/amazonlinux/linux/tree/amazon-5.10.y/master/mm/damon The userspace tool[1] is available, released under GPLv2, and actively being maintained. I am also planning to implement another basic user interface in perf[2]. Also, the basic test suite for DAMON is available under GPLv2[3]. [1] https://github.com/awslabs/damo [2] https://lore.kernel.org/linux-mm/20210107120729.22328-1-sjpark@amazon.com/ [3] https://github.com/awslabs/damon-tests Long-term Plan -------------- DAMON is a part of a project called Data Access-aware Operating System (DAOS). As the name implies, I want to improve the performance and efficiency of systems using fine-grained data access patterns. The optimizations are for both kernel and user spaces. I will therefore modify or create kernel subsystems, export some of those to user space and implement user space library / tools. Below shows the layers and components for the project. --------------------------------------------------------------------------- Primitives: PTE Accessed bit, PG_idle, rmap, (Intel CMT), ... Framework: DAMON Features: DAMOS, virtual addr, physical addr, ... Applications: DAMON-debugfs, (DARC), ... ^^^^^^^^^^^^^^^^^^^^^^^ KERNEL SPACE ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ Raw Interface: debugfs, (sysfs), (damonfs), tracepoints, (sys_damon), ... vvvvvvvvvvvvvvvvvvvvvvv USER SPACE vvvvvvvvvvvvvvvvvvvvvvvvvvvvvvvv Library: (libdamon), ... Tools: DAMO, (perf), ... --------------------------------------------------------------------------- The components in parentheses or marked as '...' are not implemented yet but in the future plan. IOW, those are the TODO tasks of DAOS project. For more detail, please refer to the plans: https://lore.kernel.org/linux-mm/20201202082731.24828-1-sjpark@amazon.com/ Evaluations =========== We evaluated DAMON's overhead, monitoring quality and usefulness using 24 realistic workloads on my QEMU/KVM based virtual machine running a kernel that v24 DAMON patchset is applied. DAMON is lightweight. It increases system memory usage by 0.39% and slows target workloads down by 1.16%. DAMON is accurate and useful for memory management optimizations. An experimental DAMON-based operation scheme for THP, namely 'ethp', removes 76.15% of THP memory overheads while preserving 51.25% of THP speedup. Another experimental DAMON-based 'proactive reclamation' implementation, 'prcl', reduces 93.38% of residential sets and 23.63% of system memory footprint while incurring only 1.22% runtime overhead in the best case (parsec3/freqmine). NOTE that the experimental THP optimization and proactive reclamation are not for production but only for proof of concepts. Please refer to the official document[1] or "Documentation/admin-guide/mm: Add a document for DAMON" patch in this patchset for detailed evaluation setup and results. [1] https://damonitor.github.io/doc/html/latest-damon/admin-guide/mm/damon/eval.html Real-world User Story ===================== In summary, DAMON has used on production systems and proved its usefulness. DAMON as a profiler ------------------- We analyzed characteristics of a large scale production systems of our customers using DAMON. The systems utilize 70GB DRAM and 36 CPUs. From this, we were able to find interesting things below. There were obviously different access pattern under idle workload and active workload. Under the idle workload, it accessed large memory regions with low frequency, while the active workload accessed small memory regions with high freuqnecy. DAMON found a 7GB memory region that showing obviously high access frequency under the active workload. We believe this is the performance-effective working set and need to be protected. There was a 4KB memory region that showing highest access frequency under not only active but also idle workloads. We think this must be a hottest code section like thing that should never be paged out. For this analysis, DAMON used only 0.3-1% of single CPU time. Because we used recording-based analysis, it consumed about 3-12 MB of disk space per 20 minutes. This is only small amount of disk space, but we can further reduce the disk usage by using non-recording-based DAMON features. I'd like to argue that only DAMON can do such detailed analysis (finding 4KB highest region in 70GB memory) with the light overhead. DAMON as a system optimization tool ----------------------------------- We also found below potential performance problems on the systems and made DAMON-based solutions. The system doesn't want to make the workload suffer from the page reclamation and thus it utilizes enough DRAM but no swap device. However, we found the system is actively reclaiming file-backed pages, because the system has intensive file IO. The file IO turned out to be not performance critical for the workload, but the customer wanted to ensure performance critical file-backed pages like code section to not mistakenly be evicted. Using direct IO should or `mlock()` would be a straightforward solution, but modifying the user space code is not easy for the customer. Alternatively, we could use DAMON-based operation scheme[1]. By using it, we can ask DAMON to track access frequency of each region and make 'process_madvise(MADV_WILLNEED)[2]' call for regions having specific size and access frequency for a time interval. We also found the system is having high number of TLB misses. We tried 'always' THP enabled policy and it greatly reduced TLB misses, but the page reclamation also been more frequent due to the THP internal fragmentation caused memory bloat. We could try another DAMON-based operation scheme that applies 'MADV_HUGEPAGE' to memory regions having >=2MB size and high access frequency, while applying 'MADV_NOHUGEPAGE' to regions having <2MB size and low access frequency. We do not own the systems so we only reported the analysis results and possible optimization solutions to the customers. The customers satisfied about the analysis results and promised to try the optimization guides. [1] https://lore.kernel.org/linux-mm/20201006123931.5847-1-sjpark@amazon.com/ [2] https://lore.kernel.org/linux-api/20200622192900.22757-4-minchan@kernel.org/ Comparison with Idle Page Tracking ================================== Idle Page Tracking allows users to set and read idleness of pages using a bitmap file which represents each page with each bit of the file. One recommended usage of it is working set size detection. Users can do that by 1. find PFN of each page for workloads in interest, 2. set all the pages as idle by doing writes to the bitmap file, 3. wait until the workload accesses its working set, and 4. read the idleness of the pages again and count pages became not idle. NOTE: While Idle Page Tracking is for user space users, DAMON is primarily designed for kernel subsystems though it can easily exposed to the user space. Hence, this section only assumes such user space use of DAMON. For what use cases Idle Page Tracking would be better? ------------------------------------------------------ 1. Flexible usecases other than hotness monitoring. Because Idle Page Tracking allows users to control the primitive (Page idleness) by themselves, Idle Page Tracking users can do anything they want. Meanwhile, DAMON is primarily designed to monitor the hotness of each memory region. For this, DAMON asks users to provide sampling interval and aggregation interval. For the reason, there could be some use case that using Idle Page Tracking is simpler. 2. Physical memory monitoring. Idle Page Tracking receives PFN range as input, so natively supports physical memory monitoring. DAMON is designed to be extensible for multiple address spaces and use cases by implementing and using primitives for the given use case. Therefore, by theory, DAMON has no limitation in the type of target address space as long as primitives for the given address space exists. However, the default primitives introduced by this patchset supports only virtual address spaces. Therefore, for physical memory monitoring, you should implement your own primitives and use it, or simply use Idle Page Tracking. Nonetheless, RFC patchsets[1] for the physical memory address space primitives is already available. It also supports user memory same to Idle Page Tracking. [1] https://lore.kernel.org/linux-mm/20200831104730.28970-1-sjpark@amazon.com/ For what use cases DAMON is better? ----------------------------------- 1. Hotness Monitoring. Idle Page Tracking let users know only if a page frame is accessed or not. For hotness check, the user should write more code and use more memory. DAMON do that by itself. 2. Low Monitoring Overhead DAMON receives user's monitoring request with one step and then provide the results. So, roughly speaking, DAMON require only O(1) user/kernel context switches. In case of Idle Page Tracking, however, because the interface receives contiguous page frames, the number of user/kernel context switches increases as the monitoring target becomes complex and huge. As a result, the context switch overhead could be not negligible. Moreover, DAMON is born to handle with the monitoring overhead. Because the core mechanism is pure logical, Idle Page Tracking users might be able to implement the mechanism on their own, but it would be time consuming and the user/kernel context switching will still more frequent than that of DAMON. Also, the kernel subsystems cannot use the logic in this case. 3. Page granularity working set size detection. Until v22 of this patchset, this was categorized as the thing Idle Page Tracking could do better, because DAMON basically maintains additional metadata for each of the monitoring target regions. So, in the page granularity working set size detection use case, DAMON would incur (number of monitoring target pages * size of metadata) memory overhead. Size of the single metadata item is about 54 bytes, so assuming 4KB pages, about 1.3% of monitoring target pages will be additionally used. All essential metadata for Idle Page Tracking are embedded in 'struct page' and page table entries. Therefore, in this use case, only one counter variable for working set size accounting is required if Idle Page Tracking is used. There are more details to consider, but roughly speaking, this is true in most cases. However, the situation changed from v23. Now DAMON supports arbitrary types of monitoring targets, which don't use the metadata. Using that, DAMON can do the working set size detection with no additional space overhead but less user-kernel context switch. A first draft for the implementation of monitoring primitives for this usage is available in a DAMON development tree[1]. An RFC patchset for it based on this patchset will also be available soon. Since v24, the arbitrary type support is dropped from this patchset because this patchset doesn't introduce real use of the type. You can still get it from the DAMON development tree[2], though. [1] https://github.com/sjp38/linux/tree/damon/pgidle_hack [2] https://github.com/sjp38/linux/tree/damon/master 4. More future usecases While Idle Page Tracking has tight coupling with base primitives (PG_Idle and page table Accessed bits), DAMON is designed to be extensible for many use cases and address spaces. If you need some special address type or want to use special h/w access check primitives, you can write your own primitives for that and configure DAMON to use those. Therefore, if your use case could be changed a lot in future, using DAMON could be better. Can I use both Idle Page Tracking and DAMON? -------------------------------------------- Yes, though using them concurrently for overlapping memory regions could result in interference to each other. Nevertheless, such use case would be rare or makes no sense at all. Even in the case, the noise would bot be really significant. So, you can choose whatever you want depending on the characteristics of your use cases. More Information ================ We prepared a showcase web site[1] that you can get more information. There are - the official documentations[2], - the heatmap format dynamic access pattern of various realistic workloads for heap area[3], mmap()-ed area[4], and stack[5] area, - the dynamic working set size distribution[6] and chronological working set size changes[7], and - the latest performance test results[8]. [1] https://damonitor.github.io/_index [2] https://damonitor.github.io/doc/html/latest-damon [3] https://damonitor.github.io/test/result/visual/latest/rec.heatmap.0.png.html [4] https://damonitor.github.io/test/result/visual/latest/rec.heatmap.1.png.html [5] https://damonitor.github.io/test/result/visual/latest/rec.heatmap.2.png.html [6] https://damonitor.github.io/test/result/visual/latest/rec.wss_sz.png.html [7] https://damonitor.github.io/test/result/visual/latest/rec.wss_time.png.html [8] https://damonitor.github.io/test/result/perf/latest/html/index.html Baseline and Complete Git Trees =============================== The patches are based on the latest -mm tree, specifically v5.14-rc1-mmots-2021-07-15-18-47 of https://github.com/hnaz/linux-mm. You can also clone the complete git tree: $ git clone git://github.com/sjp38/linux -b damon/patches/v34 The web is also available: https://github.com/sjp38/linux/releases/tag/damon/patches/v34 Development Trees ----------------- There are a couple of trees for entire DAMON patchset series and features for future release. - For latest release: https://github.com/sjp38/linux/tree/damon/master - For next release: https://github.com/sjp38/linux/tree/damon/next Long-term Support Trees ----------------------- For people who want to test DAMON but using LTS kernels, there are another couple of trees based on two latest LTS kernels respectively and containing the 'damon/master' backports. - For v5.4.y: https://github.com/sjp38/linux/tree/damon/for-v5.4.y - For v5.10.y: https://github.com/sjp38/linux/tree/damon/for-v5.10.y Amazon Linux Kernel Trees ------------------------- DAMON is also merged in two public Amazon Linux kernel trees that based on v5.4.y[1] and v5.10.y[2]. [1] https://github.com/amazonlinux/linux/tree/amazon-5.4.y/master/mm/damon [2] https://github.com/amazonlinux/linux/tree/amazon-5.10.y/master/mm/damon Git Tree for Diff of Patches ============================ For easy review of diff between different versions of each patch, I prepared a git tree containing all versions of the DAMON patchset series: https://github.com/sjp38/damon-patches You can clone it and use 'diff' for easy review of changes between different versions of the patchset. For example: $ git clone https://github.com/sjp38/damon-patches && cd damon-patches $ diff -u damon/v33 damon/v34 Sequence Of Patches =================== First three patches implement the core logics of DAMON. The 1st patch introduces basic sampling based hotness monitoring for arbitrary types of targets. Following two patches implement the core mechanisms for control of overhead and accuracy, namely regions based sampling (patch 2) and adaptive regions adjustment (patch 3). Now the essential parts of DAMON is complete, but it cannot work unless someone provides monitoring primitives for a specific use case. The following two patches make it just work for virtual address spaces monitoring. The 4th patch makes 'PG_idle' can be used by DAMON and the 5th patch implements the virtual memory address space specific monitoring primitives using page table Accessed bits and the 'PG_idle' page flag. Now DAMON just works for virtual address space monitoring via the kernel space api. To let the user space users can use DAMON, following four patches add interfaces for them. The 6th patch adds a tracepoint for monitoring results. The 7th patch implements a DAMON application kernel module, namely damon-dbgfs, that simply wraps DAMON and exposes DAMON interface to the user space via the debugfs interface. The 8th patch further exports pid of monitoring thread (kdamond) to user space for easier cpu usage accounting, and the 9th patch makes the debugfs interface to support multiple contexts. Three patches for maintainability follows. The 10th patch adds documentations for both the user space and the kernel space. The 11th patch provides unit tests (based on the kunit) while the 12th patch adds user space tests (based on the kselftest). Finally, the last patch (13th) updates the MAINTAINERS file. This patch (of 13): DAMON is a data access monitoring framework for the Linux kernel. The core mechanisms of DAMON make it - accurate (the monitoring output is useful enough for DRAM level performance-centric memory management; It might be inappropriate for CPU cache levels, though), - light-weight (the monitoring overhead is normally low enough to be applied online), and - scalable (the upper-bound of the overhead is in constant range regardless of the size of target workloads). Using this framework, hence, we can easily write efficient kernel space data access monitoring applications. For example, the kernel's memory management mechanisms can make advanced decisions using this. Experimental data access aware optimization works that incurring high access monitoring overhead could again be implemented on top of this. Due to its simple and flexible interface, providing user space interface would be also easy. Then, user space users who have some special workloads can write personalized applications for better understanding and optimizations of their workloads and systems. === Nevertheless, this commit is defining and implementing only basic access check part without the overhead-accuracy handling core logic. The basic access check is as below. The output of DAMON says what memory regions are how frequently accessed for a given duration. The resolution of the access frequency is controlled by setting ``sampling interval`` and ``aggregation interval``. In detail, DAMON checks access to each page per ``sampling interval`` and aggregates the results. In other words, counts the number of the accesses to each region. After each ``aggregation interval`` passes, DAMON calls callback functions that previously registered by users so that users can read the aggregated results and then clears the results. This can be described in below simple pseudo-code:: init() while monitoring_on: for page in monitoring_target: if accessed(page): nr_accesses[page] += 1 if time() % aggregation_interval == 0: for callback in user_registered_callbacks: callback(monitoring_target, nr_accesses) for page in monitoring_target: nr_accesses[page] = 0 if time() % update_interval == 0: update() sleep(sampling interval) The target regions constructed at the beginning of the monitoring and updated after each ``regions_update_interval``, because the target regions could be dynamically changed (e.g., mmap() or memory hotplug). The monitoring overhead of this mechanism will arbitrarily increase as the size of the target workload grows. The basic monitoring primitives for actual access check and dynamic target regions construction aren't in the core part of DAMON. Instead, it allows users to implement their own primitives that are optimized for their use case and configure DAMON to use those. In other words, users cannot use current version of DAMON without some additional works. Following commits will implement the core mechanisms for the overhead-accuracy control and default primitives implementations. Link: https://lkml.kernel.org/r/20210716081449.22187-1-sj38.park@gmail.com Link: https://lkml.kernel.org/r/20210716081449.22187-2-sj38.park@gmail.com Signed-off-by: SeongJae Park <sjpark@amazon.de> Reviewed-by: Leonard Foerster <foersleo@amazon.de> Reviewed-by: Fernand Sieber <sieberf@amazon.com> Acked-by: Shakeel Butt <shakeelb@google.com> Cc: Jonathan Cameron <Jonathan.Cameron@huawei.com> Cc: Alexander Shishkin <alexander.shishkin@linux.intel.com> Cc: Amit Shah <amit@kernel.org> Cc: Benjamin Herrenschmidt <benh@kernel.crashing.org> Cc: Jonathan Corbet <corbet@lwn.net> Cc: David Hildenbrand <david@redhat.com> Cc: David Woodhouse <dwmw@amazon.com> Cc: Marco Elver <elver@google.com> Cc: Fan Du <fan.du@intel.com> Cc: Greg Kroah-Hartman <greg@kroah.com> Cc: Greg Thelen <gthelen@google.com> Cc: Joe Perches <joe@perches.com> Cc: Mel Gorman <mgorman@suse.de> Cc: Maximilian Heyne <mheyne@amazon.de> Cc: Minchan Kim <minchan@kernel.org> Cc: Ingo Molnar <mingo@redhat.com> Cc: Namhyung Kim <namhyung@kernel.org> Cc: Peter Zijlstra <peterz@infradead.org> Cc: Rik van Riel <riel@surriel.com> Cc: David Rientjes <rientjes@google.com> Cc: Steven Rostedt (VMware) <rostedt@goodmis.org> Cc: Shuah Khan <shuah@kernel.org> Cc: Vlastimil Babka <vbabka@suse.cz> Cc: Vladimir Davydov <vdavydov.dev@gmail.com> Cc: Brendan Higgins <brendanhiggins@google.com> Cc: Markus Boehme <markubo@amazon.de> Signed-off-by: Andrew Morton <akpm@linux-foundation.org> Signed-off-by: Linus Torvalds <torvalds@linux-foundation.org>
2021-09-08 10:56:28 +08:00
/* SPDX-License-Identifier: GPL-2.0 */
/*
* DAMON api
*
* Author: SeongJae Park <sjpark@amazon.de>
*/
#ifndef _DAMON_H_
#define _DAMON_H_
#include <linux/mutex.h>
#include <linux/time64.h>
#include <linux/types.h>
#include <linux/random.h>
mm: introduce Data Access MONitor (DAMON) Patch series "Introduce Data Access MONitor (DAMON)", v34. Introduction ============ DAMON is a data access monitoring framework for the Linux kernel. The core mechanisms of DAMON called 'region based sampling' and 'adaptive regions adjustment' (refer to 'mechanisms.rst' in the 11th patch of this patchset for the detail) make it - accurate (The monitored information is useful for DRAM level memory management. It might not appropriate for Cache-level accuracy, though.), - light-weight (The monitoring overhead is low enough to be applied online while making no impact on the performance of the target workloads.), and - scalable (the upper-bound of the instrumentation overhead is controllable regardless of the size of target workloads.). Using this framework, therefore, several memory management mechanisms such as reclamation and THP can be optimized to aware real data access patterns. Experimental access pattern aware memory management optimization works that incurring high instrumentation overhead will be able to have another try. Though DAMON is for kernel subsystems, it can be easily exposed to the user space by writing a DAMON-wrapper kernel subsystem. Then, user space users who have some special workloads will be able to write personalized tools or applications for deeper understanding and specialized optimizations of their systems. DAMON is also merged in two public Amazon Linux kernel trees that based on v5.4.y[1] and v5.10.y[2]. [1] https://github.com/amazonlinux/linux/tree/amazon-5.4.y/master/mm/damon [2] https://github.com/amazonlinux/linux/tree/amazon-5.10.y/master/mm/damon The userspace tool[1] is available, released under GPLv2, and actively being maintained. I am also planning to implement another basic user interface in perf[2]. Also, the basic test suite for DAMON is available under GPLv2[3]. [1] https://github.com/awslabs/damo [2] https://lore.kernel.org/linux-mm/20210107120729.22328-1-sjpark@amazon.com/ [3] https://github.com/awslabs/damon-tests Long-term Plan -------------- DAMON is a part of a project called Data Access-aware Operating System (DAOS). As the name implies, I want to improve the performance and efficiency of systems using fine-grained data access patterns. The optimizations are for both kernel and user spaces. I will therefore modify or create kernel subsystems, export some of those to user space and implement user space library / tools. Below shows the layers and components for the project. --------------------------------------------------------------------------- Primitives: PTE Accessed bit, PG_idle, rmap, (Intel CMT), ... Framework: DAMON Features: DAMOS, virtual addr, physical addr, ... Applications: DAMON-debugfs, (DARC), ... ^^^^^^^^^^^^^^^^^^^^^^^ KERNEL SPACE ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ Raw Interface: debugfs, (sysfs), (damonfs), tracepoints, (sys_damon), ... vvvvvvvvvvvvvvvvvvvvvvv USER SPACE vvvvvvvvvvvvvvvvvvvvvvvvvvvvvvvv Library: (libdamon), ... Tools: DAMO, (perf), ... --------------------------------------------------------------------------- The components in parentheses or marked as '...' are not implemented yet but in the future plan. IOW, those are the TODO tasks of DAOS project. For more detail, please refer to the plans: https://lore.kernel.org/linux-mm/20201202082731.24828-1-sjpark@amazon.com/ Evaluations =========== We evaluated DAMON's overhead, monitoring quality and usefulness using 24 realistic workloads on my QEMU/KVM based virtual machine running a kernel that v24 DAMON patchset is applied. DAMON is lightweight. It increases system memory usage by 0.39% and slows target workloads down by 1.16%. DAMON is accurate and useful for memory management optimizations. An experimental DAMON-based operation scheme for THP, namely 'ethp', removes 76.15% of THP memory overheads while preserving 51.25% of THP speedup. Another experimental DAMON-based 'proactive reclamation' implementation, 'prcl', reduces 93.38% of residential sets and 23.63% of system memory footprint while incurring only 1.22% runtime overhead in the best case (parsec3/freqmine). NOTE that the experimental THP optimization and proactive reclamation are not for production but only for proof of concepts. Please refer to the official document[1] or "Documentation/admin-guide/mm: Add a document for DAMON" patch in this patchset for detailed evaluation setup and results. [1] https://damonitor.github.io/doc/html/latest-damon/admin-guide/mm/damon/eval.html Real-world User Story ===================== In summary, DAMON has used on production systems and proved its usefulness. DAMON as a profiler ------------------- We analyzed characteristics of a large scale production systems of our customers using DAMON. The systems utilize 70GB DRAM and 36 CPUs. From this, we were able to find interesting things below. There were obviously different access pattern under idle workload and active workload. Under the idle workload, it accessed large memory regions with low frequency, while the active workload accessed small memory regions with high freuqnecy. DAMON found a 7GB memory region that showing obviously high access frequency under the active workload. We believe this is the performance-effective working set and need to be protected. There was a 4KB memory region that showing highest access frequency under not only active but also idle workloads. We think this must be a hottest code section like thing that should never be paged out. For this analysis, DAMON used only 0.3-1% of single CPU time. Because we used recording-based analysis, it consumed about 3-12 MB of disk space per 20 minutes. This is only small amount of disk space, but we can further reduce the disk usage by using non-recording-based DAMON features. I'd like to argue that only DAMON can do such detailed analysis (finding 4KB highest region in 70GB memory) with the light overhead. DAMON as a system optimization tool ----------------------------------- We also found below potential performance problems on the systems and made DAMON-based solutions. The system doesn't want to make the workload suffer from the page reclamation and thus it utilizes enough DRAM but no swap device. However, we found the system is actively reclaiming file-backed pages, because the system has intensive file IO. The file IO turned out to be not performance critical for the workload, but the customer wanted to ensure performance critical file-backed pages like code section to not mistakenly be evicted. Using direct IO should or `mlock()` would be a straightforward solution, but modifying the user space code is not easy for the customer. Alternatively, we could use DAMON-based operation scheme[1]. By using it, we can ask DAMON to track access frequency of each region and make 'process_madvise(MADV_WILLNEED)[2]' call for regions having specific size and access frequency for a time interval. We also found the system is having high number of TLB misses. We tried 'always' THP enabled policy and it greatly reduced TLB misses, but the page reclamation also been more frequent due to the THP internal fragmentation caused memory bloat. We could try another DAMON-based operation scheme that applies 'MADV_HUGEPAGE' to memory regions having >=2MB size and high access frequency, while applying 'MADV_NOHUGEPAGE' to regions having <2MB size and low access frequency. We do not own the systems so we only reported the analysis results and possible optimization solutions to the customers. The customers satisfied about the analysis results and promised to try the optimization guides. [1] https://lore.kernel.org/linux-mm/20201006123931.5847-1-sjpark@amazon.com/ [2] https://lore.kernel.org/linux-api/20200622192900.22757-4-minchan@kernel.org/ Comparison with Idle Page Tracking ================================== Idle Page Tracking allows users to set and read idleness of pages using a bitmap file which represents each page with each bit of the file. One recommended usage of it is working set size detection. Users can do that by 1. find PFN of each page for workloads in interest, 2. set all the pages as idle by doing writes to the bitmap file, 3. wait until the workload accesses its working set, and 4. read the idleness of the pages again and count pages became not idle. NOTE: While Idle Page Tracking is for user space users, DAMON is primarily designed for kernel subsystems though it can easily exposed to the user space. Hence, this section only assumes such user space use of DAMON. For what use cases Idle Page Tracking would be better? ------------------------------------------------------ 1. Flexible usecases other than hotness monitoring. Because Idle Page Tracking allows users to control the primitive (Page idleness) by themselves, Idle Page Tracking users can do anything they want. Meanwhile, DAMON is primarily designed to monitor the hotness of each memory region. For this, DAMON asks users to provide sampling interval and aggregation interval. For the reason, there could be some use case that using Idle Page Tracking is simpler. 2. Physical memory monitoring. Idle Page Tracking receives PFN range as input, so natively supports physical memory monitoring. DAMON is designed to be extensible for multiple address spaces and use cases by implementing and using primitives for the given use case. Therefore, by theory, DAMON has no limitation in the type of target address space as long as primitives for the given address space exists. However, the default primitives introduced by this patchset supports only virtual address spaces. Therefore, for physical memory monitoring, you should implement your own primitives and use it, or simply use Idle Page Tracking. Nonetheless, RFC patchsets[1] for the physical memory address space primitives is already available. It also supports user memory same to Idle Page Tracking. [1] https://lore.kernel.org/linux-mm/20200831104730.28970-1-sjpark@amazon.com/ For what use cases DAMON is better? ----------------------------------- 1. Hotness Monitoring. Idle Page Tracking let users know only if a page frame is accessed or not. For hotness check, the user should write more code and use more memory. DAMON do that by itself. 2. Low Monitoring Overhead DAMON receives user's monitoring request with one step and then provide the results. So, roughly speaking, DAMON require only O(1) user/kernel context switches. In case of Idle Page Tracking, however, because the interface receives contiguous page frames, the number of user/kernel context switches increases as the monitoring target becomes complex and huge. As a result, the context switch overhead could be not negligible. Moreover, DAMON is born to handle with the monitoring overhead. Because the core mechanism is pure logical, Idle Page Tracking users might be able to implement the mechanism on their own, but it would be time consuming and the user/kernel context switching will still more frequent than that of DAMON. Also, the kernel subsystems cannot use the logic in this case. 3. Page granularity working set size detection. Until v22 of this patchset, this was categorized as the thing Idle Page Tracking could do better, because DAMON basically maintains additional metadata for each of the monitoring target regions. So, in the page granularity working set size detection use case, DAMON would incur (number of monitoring target pages * size of metadata) memory overhead. Size of the single metadata item is about 54 bytes, so assuming 4KB pages, about 1.3% of monitoring target pages will be additionally used. All essential metadata for Idle Page Tracking are embedded in 'struct page' and page table entries. Therefore, in this use case, only one counter variable for working set size accounting is required if Idle Page Tracking is used. There are more details to consider, but roughly speaking, this is true in most cases. However, the situation changed from v23. Now DAMON supports arbitrary types of monitoring targets, which don't use the metadata. Using that, DAMON can do the working set size detection with no additional space overhead but less user-kernel context switch. A first draft for the implementation of monitoring primitives for this usage is available in a DAMON development tree[1]. An RFC patchset for it based on this patchset will also be available soon. Since v24, the arbitrary type support is dropped from this patchset because this patchset doesn't introduce real use of the type. You can still get it from the DAMON development tree[2], though. [1] https://github.com/sjp38/linux/tree/damon/pgidle_hack [2] https://github.com/sjp38/linux/tree/damon/master 4. More future usecases While Idle Page Tracking has tight coupling with base primitives (PG_Idle and page table Accessed bits), DAMON is designed to be extensible for many use cases and address spaces. If you need some special address type or want to use special h/w access check primitives, you can write your own primitives for that and configure DAMON to use those. Therefore, if your use case could be changed a lot in future, using DAMON could be better. Can I use both Idle Page Tracking and DAMON? -------------------------------------------- Yes, though using them concurrently for overlapping memory regions could result in interference to each other. Nevertheless, such use case would be rare or makes no sense at all. Even in the case, the noise would bot be really significant. So, you can choose whatever you want depending on the characteristics of your use cases. More Information ================ We prepared a showcase web site[1] that you can get more information. There are - the official documentations[2], - the heatmap format dynamic access pattern of various realistic workloads for heap area[3], mmap()-ed area[4], and stack[5] area, - the dynamic working set size distribution[6] and chronological working set size changes[7], and - the latest performance test results[8]. [1] https://damonitor.github.io/_index [2] https://damonitor.github.io/doc/html/latest-damon [3] https://damonitor.github.io/test/result/visual/latest/rec.heatmap.0.png.html [4] https://damonitor.github.io/test/result/visual/latest/rec.heatmap.1.png.html [5] https://damonitor.github.io/test/result/visual/latest/rec.heatmap.2.png.html [6] https://damonitor.github.io/test/result/visual/latest/rec.wss_sz.png.html [7] https://damonitor.github.io/test/result/visual/latest/rec.wss_time.png.html [8] https://damonitor.github.io/test/result/perf/latest/html/index.html Baseline and Complete Git Trees =============================== The patches are based on the latest -mm tree, specifically v5.14-rc1-mmots-2021-07-15-18-47 of https://github.com/hnaz/linux-mm. You can also clone the complete git tree: $ git clone git://github.com/sjp38/linux -b damon/patches/v34 The web is also available: https://github.com/sjp38/linux/releases/tag/damon/patches/v34 Development Trees ----------------- There are a couple of trees for entire DAMON patchset series and features for future release. - For latest release: https://github.com/sjp38/linux/tree/damon/master - For next release: https://github.com/sjp38/linux/tree/damon/next Long-term Support Trees ----------------------- For people who want to test DAMON but using LTS kernels, there are another couple of trees based on two latest LTS kernels respectively and containing the 'damon/master' backports. - For v5.4.y: https://github.com/sjp38/linux/tree/damon/for-v5.4.y - For v5.10.y: https://github.com/sjp38/linux/tree/damon/for-v5.10.y Amazon Linux Kernel Trees ------------------------- DAMON is also merged in two public Amazon Linux kernel trees that based on v5.4.y[1] and v5.10.y[2]. [1] https://github.com/amazonlinux/linux/tree/amazon-5.4.y/master/mm/damon [2] https://github.com/amazonlinux/linux/tree/amazon-5.10.y/master/mm/damon Git Tree for Diff of Patches ============================ For easy review of diff between different versions of each patch, I prepared a git tree containing all versions of the DAMON patchset series: https://github.com/sjp38/damon-patches You can clone it and use 'diff' for easy review of changes between different versions of the patchset. For example: $ git clone https://github.com/sjp38/damon-patches && cd damon-patches $ diff -u damon/v33 damon/v34 Sequence Of Patches =================== First three patches implement the core logics of DAMON. The 1st patch introduces basic sampling based hotness monitoring for arbitrary types of targets. Following two patches implement the core mechanisms for control of overhead and accuracy, namely regions based sampling (patch 2) and adaptive regions adjustment (patch 3). Now the essential parts of DAMON is complete, but it cannot work unless someone provides monitoring primitives for a specific use case. The following two patches make it just work for virtual address spaces monitoring. The 4th patch makes 'PG_idle' can be used by DAMON and the 5th patch implements the virtual memory address space specific monitoring primitives using page table Accessed bits and the 'PG_idle' page flag. Now DAMON just works for virtual address space monitoring via the kernel space api. To let the user space users can use DAMON, following four patches add interfaces for them. The 6th patch adds a tracepoint for monitoring results. The 7th patch implements a DAMON application kernel module, namely damon-dbgfs, that simply wraps DAMON and exposes DAMON interface to the user space via the debugfs interface. The 8th patch further exports pid of monitoring thread (kdamond) to user space for easier cpu usage accounting, and the 9th patch makes the debugfs interface to support multiple contexts. Three patches for maintainability follows. The 10th patch adds documentations for both the user space and the kernel space. The 11th patch provides unit tests (based on the kunit) while the 12th patch adds user space tests (based on the kselftest). Finally, the last patch (13th) updates the MAINTAINERS file. This patch (of 13): DAMON is a data access monitoring framework for the Linux kernel. The core mechanisms of DAMON make it - accurate (the monitoring output is useful enough for DRAM level performance-centric memory management; It might be inappropriate for CPU cache levels, though), - light-weight (the monitoring overhead is normally low enough to be applied online), and - scalable (the upper-bound of the overhead is in constant range regardless of the size of target workloads). Using this framework, hence, we can easily write efficient kernel space data access monitoring applications. For example, the kernel's memory management mechanisms can make advanced decisions using this. Experimental data access aware optimization works that incurring high access monitoring overhead could again be implemented on top of this. Due to its simple and flexible interface, providing user space interface would be also easy. Then, user space users who have some special workloads can write personalized applications for better understanding and optimizations of their workloads and systems. === Nevertheless, this commit is defining and implementing only basic access check part without the overhead-accuracy handling core logic. The basic access check is as below. The output of DAMON says what memory regions are how frequently accessed for a given duration. The resolution of the access frequency is controlled by setting ``sampling interval`` and ``aggregation interval``. In detail, DAMON checks access to each page per ``sampling interval`` and aggregates the results. In other words, counts the number of the accesses to each region. After each ``aggregation interval`` passes, DAMON calls callback functions that previously registered by users so that users can read the aggregated results and then clears the results. This can be described in below simple pseudo-code:: init() while monitoring_on: for page in monitoring_target: if accessed(page): nr_accesses[page] += 1 if time() % aggregation_interval == 0: for callback in user_registered_callbacks: callback(monitoring_target, nr_accesses) for page in monitoring_target: nr_accesses[page] = 0 if time() % update_interval == 0: update() sleep(sampling interval) The target regions constructed at the beginning of the monitoring and updated after each ``regions_update_interval``, because the target regions could be dynamically changed (e.g., mmap() or memory hotplug). The monitoring overhead of this mechanism will arbitrarily increase as the size of the target workload grows. The basic monitoring primitives for actual access check and dynamic target regions construction aren't in the core part of DAMON. Instead, it allows users to implement their own primitives that are optimized for their use case and configure DAMON to use those. In other words, users cannot use current version of DAMON without some additional works. Following commits will implement the core mechanisms for the overhead-accuracy control and default primitives implementations. Link: https://lkml.kernel.org/r/20210716081449.22187-1-sj38.park@gmail.com Link: https://lkml.kernel.org/r/20210716081449.22187-2-sj38.park@gmail.com Signed-off-by: SeongJae Park <sjpark@amazon.de> Reviewed-by: Leonard Foerster <foersleo@amazon.de> Reviewed-by: Fernand Sieber <sieberf@amazon.com> Acked-by: Shakeel Butt <shakeelb@google.com> Cc: Jonathan Cameron <Jonathan.Cameron@huawei.com> Cc: Alexander Shishkin <alexander.shishkin@linux.intel.com> Cc: Amit Shah <amit@kernel.org> Cc: Benjamin Herrenschmidt <benh@kernel.crashing.org> Cc: Jonathan Corbet <corbet@lwn.net> Cc: David Hildenbrand <david@redhat.com> Cc: David Woodhouse <dwmw@amazon.com> Cc: Marco Elver <elver@google.com> Cc: Fan Du <fan.du@intel.com> Cc: Greg Kroah-Hartman <greg@kroah.com> Cc: Greg Thelen <gthelen@google.com> Cc: Joe Perches <joe@perches.com> Cc: Mel Gorman <mgorman@suse.de> Cc: Maximilian Heyne <mheyne@amazon.de> Cc: Minchan Kim <minchan@kernel.org> Cc: Ingo Molnar <mingo@redhat.com> Cc: Namhyung Kim <namhyung@kernel.org> Cc: Peter Zijlstra <peterz@infradead.org> Cc: Rik van Riel <riel@surriel.com> Cc: David Rientjes <rientjes@google.com> Cc: Steven Rostedt (VMware) <rostedt@goodmis.org> Cc: Shuah Khan <shuah@kernel.org> Cc: Vlastimil Babka <vbabka@suse.cz> Cc: Vladimir Davydov <vdavydov.dev@gmail.com> Cc: Brendan Higgins <brendanhiggins@google.com> Cc: Markus Boehme <markubo@amazon.de> Signed-off-by: Andrew Morton <akpm@linux-foundation.org> Signed-off-by: Linus Torvalds <torvalds@linux-foundation.org>
2021-09-08 10:56:28 +08:00
mm/damon: adaptively adjust regions Even somehow the initial monitoring target regions are well constructed to fulfill the assumption (pages in same region have similar access frequencies), the data access pattern can be dynamically changed. This will result in low monitoring quality. To keep the assumption as much as possible, DAMON adaptively merges and splits each region based on their access frequency. For each ``aggregation interval``, it compares the access frequencies of adjacent regions and merges those if the frequency difference is small. Then, after it reports and clears the aggregated access frequency of each region, it splits each region into two or three regions if the total number of regions will not exceed the user-specified maximum number of regions after the split. In this way, DAMON provides its best-effort quality and minimal overhead while keeping the upper-bound overhead that users set. Link: https://lkml.kernel.org/r/20210716081449.22187-4-sj38.park@gmail.com Signed-off-by: SeongJae Park <sjpark@amazon.de> Reviewed-by: Leonard Foerster <foersleo@amazon.de> Reviewed-by: Fernand Sieber <sieberf@amazon.com> Acked-by: Shakeel Butt <shakeelb@google.com> Cc: Alexander Shishkin <alexander.shishkin@linux.intel.com> Cc: Amit Shah <amit@kernel.org> Cc: Benjamin Herrenschmidt <benh@kernel.crashing.org> Cc: Brendan Higgins <brendanhiggins@google.com> Cc: David Hildenbrand <david@redhat.com> Cc: David Rientjes <rientjes@google.com> Cc: David Woodhouse <dwmw@amazon.com> Cc: Fan Du <fan.du@intel.com> Cc: Greg Kroah-Hartman <greg@kroah.com> Cc: Greg Thelen <gthelen@google.com> Cc: Ingo Molnar <mingo@redhat.com> Cc: Joe Perches <joe@perches.com> Cc: Jonathan Cameron <Jonathan.Cameron@huawei.com> Cc: Jonathan Corbet <corbet@lwn.net> Cc: Marco Elver <elver@google.com> Cc: Markus Boehme <markubo@amazon.de> Cc: Maximilian Heyne <mheyne@amazon.de> Cc: Mel Gorman <mgorman@suse.de> Cc: Minchan Kim <minchan@kernel.org> Cc: Namhyung Kim <namhyung@kernel.org> Cc: Peter Zijlstra <peterz@infradead.org> Cc: Rik van Riel <riel@surriel.com> Cc: Shuah Khan <shuah@kernel.org> Cc: Steven Rostedt (VMware) <rostedt@goodmis.org> Cc: Vladimir Davydov <vdavydov.dev@gmail.com> Cc: Vlastimil Babka <vbabka@suse.cz> Signed-off-by: Andrew Morton <akpm@linux-foundation.org> Signed-off-by: Linus Torvalds <torvalds@linux-foundation.org>
2021-09-08 10:56:36 +08:00
/* Minimal region size. Every damon_region is aligned by this. */
#define DAMON_MIN_REGION PAGE_SIZE
2021-11-06 04:47:33 +08:00
/* Max priority score for DAMON-based operation schemes */
#define DAMOS_MAX_SCORE (99)
mm/damon: adaptively adjust regions Even somehow the initial monitoring target regions are well constructed to fulfill the assumption (pages in same region have similar access frequencies), the data access pattern can be dynamically changed. This will result in low monitoring quality. To keep the assumption as much as possible, DAMON adaptively merges and splits each region based on their access frequency. For each ``aggregation interval``, it compares the access frequencies of adjacent regions and merges those if the frequency difference is small. Then, after it reports and clears the aggregated access frequency of each region, it splits each region into two or three regions if the total number of regions will not exceed the user-specified maximum number of regions after the split. In this way, DAMON provides its best-effort quality and minimal overhead while keeping the upper-bound overhead that users set. Link: https://lkml.kernel.org/r/20210716081449.22187-4-sj38.park@gmail.com Signed-off-by: SeongJae Park <sjpark@amazon.de> Reviewed-by: Leonard Foerster <foersleo@amazon.de> Reviewed-by: Fernand Sieber <sieberf@amazon.com> Acked-by: Shakeel Butt <shakeelb@google.com> Cc: Alexander Shishkin <alexander.shishkin@linux.intel.com> Cc: Amit Shah <amit@kernel.org> Cc: Benjamin Herrenschmidt <benh@kernel.crashing.org> Cc: Brendan Higgins <brendanhiggins@google.com> Cc: David Hildenbrand <david@redhat.com> Cc: David Rientjes <rientjes@google.com> Cc: David Woodhouse <dwmw@amazon.com> Cc: Fan Du <fan.du@intel.com> Cc: Greg Kroah-Hartman <greg@kroah.com> Cc: Greg Thelen <gthelen@google.com> Cc: Ingo Molnar <mingo@redhat.com> Cc: Joe Perches <joe@perches.com> Cc: Jonathan Cameron <Jonathan.Cameron@huawei.com> Cc: Jonathan Corbet <corbet@lwn.net> Cc: Marco Elver <elver@google.com> Cc: Markus Boehme <markubo@amazon.de> Cc: Maximilian Heyne <mheyne@amazon.de> Cc: Mel Gorman <mgorman@suse.de> Cc: Minchan Kim <minchan@kernel.org> Cc: Namhyung Kim <namhyung@kernel.org> Cc: Peter Zijlstra <peterz@infradead.org> Cc: Rik van Riel <riel@surriel.com> Cc: Shuah Khan <shuah@kernel.org> Cc: Steven Rostedt (VMware) <rostedt@goodmis.org> Cc: Vladimir Davydov <vdavydov.dev@gmail.com> Cc: Vlastimil Babka <vbabka@suse.cz> Signed-off-by: Andrew Morton <akpm@linux-foundation.org> Signed-off-by: Linus Torvalds <torvalds@linux-foundation.org>
2021-09-08 10:56:36 +08:00
/* Get a random number in [l, r) */
static inline unsigned long damon_rand(unsigned long l, unsigned long r)
{
return l + prandom_u32_max(r - l);
}
mm/damon/core: implement region-based sampling To avoid the unbounded increase of the overhead, DAMON groups adjacent pages that are assumed to have the same access frequencies into a region. As long as the assumption (pages in a region have the same access frequencies) is kept, only one page in the region is required to be checked. Thus, for each ``sampling interval``, 1. the 'prepare_access_checks' primitive picks one page in each region, 2. waits for one ``sampling interval``, 3. checks whether the page is accessed meanwhile, and 4. increases the access count of the region if so. Therefore, the monitoring overhead is controllable by adjusting the number of regions. DAMON allows both the underlying primitives and user callbacks to adjust regions for the trade-off. In other words, this commit makes DAMON to use not only time-based sampling but also space-based sampling. This scheme, however, cannot preserve the quality of the output if the assumption is not guaranteed. Next commit will address this problem. Link: https://lkml.kernel.org/r/20210716081449.22187-3-sj38.park@gmail.com Signed-off-by: SeongJae Park <sjpark@amazon.de> Reviewed-by: Leonard Foerster <foersleo@amazon.de> Reviewed-by: Fernand Sieber <sieberf@amazon.com> Acked-by: Shakeel Butt <shakeelb@google.com> Cc: Alexander Shishkin <alexander.shishkin@linux.intel.com> Cc: Amit Shah <amit@kernel.org> Cc: Benjamin Herrenschmidt <benh@kernel.crashing.org> Cc: Brendan Higgins <brendanhiggins@google.com> Cc: David Hildenbrand <david@redhat.com> Cc: David Rientjes <rientjes@google.com> Cc: David Woodhouse <dwmw@amazon.com> Cc: Fan Du <fan.du@intel.com> Cc: Greg Kroah-Hartman <greg@kroah.com> Cc: Greg Thelen <gthelen@google.com> Cc: Ingo Molnar <mingo@redhat.com> Cc: Joe Perches <joe@perches.com> Cc: Jonathan Cameron <Jonathan.Cameron@huawei.com> Cc: Jonathan Corbet <corbet@lwn.net> Cc: Marco Elver <elver@google.com> Cc: Markus Boehme <markubo@amazon.de> Cc: Maximilian Heyne <mheyne@amazon.de> Cc: Mel Gorman <mgorman@suse.de> Cc: Minchan Kim <minchan@kernel.org> Cc: Namhyung Kim <namhyung@kernel.org> Cc: Peter Zijlstra <peterz@infradead.org> Cc: Rik van Riel <riel@surriel.com> Cc: Shuah Khan <shuah@kernel.org> Cc: Steven Rostedt (VMware) <rostedt@goodmis.org> Cc: Vladimir Davydov <vdavydov.dev@gmail.com> Cc: Vlastimil Babka <vbabka@suse.cz> Signed-off-by: Andrew Morton <akpm@linux-foundation.org> Signed-off-by: Linus Torvalds <torvalds@linux-foundation.org>
2021-09-08 10:56:32 +08:00
/**
* struct damon_addr_range - Represents an address region of [@start, @end).
* @start: Start address of the region (inclusive).
* @end: End address of the region (exclusive).
*/
struct damon_addr_range {
unsigned long start;
unsigned long end;
};
/**
* struct damon_region - Represents a monitoring target region.
* @ar: The address range of the region.
* @sampling_addr: Address of the sample for the next access check.
* @nr_accesses: Access frequency of this region.
* @list: List head for siblings.
mm/damon/core: account age of target regions Patch series "Implement Data Access Monitoring-based Memory Operation Schemes". Introduction ============ DAMON[1] can be used as a primitive for data access aware memory management optimizations. For that, users who want such optimizations should run DAMON, read the monitoring results, analyze it, plan a new memory management scheme, and apply the new scheme by themselves. Such efforts will be inevitable for some complicated optimizations. However, in many other cases, the users would simply want the system to apply a memory management action to a memory region of a specific size having a specific access frequency for a specific time. For example, "page out a memory region larger than 100 MiB keeping only rare accesses more than 2 minutes", or "Do not use THP for a memory region larger than 2 MiB rarely accessed for more than 1 seconds". To make the works easier and non-redundant, this patchset implements a new feature of DAMON, which is called Data Access Monitoring-based Operation Schemes (DAMOS). Using the feature, users can describe the normal schemes in a simple way and ask DAMON to execute those on its own. [1] https://damonitor.github.io Evaluations =========== DAMOS is accurate and useful for memory management optimizations. An experimental DAMON-based operation scheme for THP, 'ethp', removes 76.15% of THP memory overheads while preserving 51.25% of THP speedup. Another experimental DAMON-based 'proactive reclamation' implementation, 'prcl', reduces 93.38% of residential sets and 23.63% of system memory footprint while incurring only 1.22% runtime overhead in the best case (parsec3/freqmine). NOTE that the experimental THP optimization and proactive reclamation are not for production but only for proof of concepts. Please refer to the showcase web site's evaluation document[1] for detailed evaluation setup and results. [1] https://damonitor.github.io/doc/html/v34/vm/damon/eval.html Long-term Support Trees ----------------------- For people who want to test DAMON but using LTS kernels, there are another couple of trees based on two latest LTS kernels respectively and containing the 'damon/master' backports. - For v5.4.y: https://git.kernel.org/sj/h/damon/for-v5.4.y - For v5.10.y: https://git.kernel.org/sj/h/damon/for-v5.10.y Sequence Of Patches =================== The 1st patch accounts age of each region. The 2nd patch implements the core of the DAMON-based operation schemes feature. The 3rd patch makes the default monitoring primitives for virtual address spaces to support the schemes. From this point, the kernel space users can use DAMOS. The 4th patch exports the feature to the user space via the debugfs interface. The 5th patch implements schemes statistics feature for easier tuning of the schemes and runtime access pattern analysis, and the 6th patch adds selftests for these changes. Finally, the 7th patch documents this new feature. This patch (of 7): DAMON can be used for data access pattern aware memory management optimizations. For that, users should run DAMON, read the monitoring results, analyze it, plan a new memory management scheme, and apply the new scheme by themselves. It would not be too hard, but still require some level of effort. For complicated cases, this effort is inevitable. That said, in many cases, users would simply want to apply an actions to a memory region of a specific size having a specific access frequency for a specific time. For example, "page out a memory region larger than 100 MiB but having a low access frequency more than 10 minutes", or "Use THP for a memory region larger than 2 MiB having a high access frequency for more than 2 seconds". For such optimizations, users will need to first account the age of each region themselves. To reduce such efforts, this implements a simple age account of each region in DAMON. For each aggregation step, DAMON compares the access frequency with that from last aggregation and reset the age of the region if the change is significant. Else, the age is incremented. Also, in case of the merge of regions, the region size-weighted average of the ages is set as the age of merged new region. Link: https://lkml.kernel.org/r/20211001125604.29660-1-sj@kernel.org Link: https://lkml.kernel.org/r/20211001125604.29660-2-sj@kernel.org Signed-off-by: SeongJae Park <sj@kernel.org> Cc: Jonathan Cameron <Jonathan.Cameron@huawei.com> Cc: Amit Shah <amit@kernel.org> Cc: Benjamin Herrenschmidt <benh@kernel.crashing.org> Cc: Jonathan Corbet <corbet@lwn.net> Cc: David Hildenbrand <david@redhat.com> Cc: David Woodhouse <dwmw@amazon.com> Cc: Marco Elver <elver@google.com> Cc: Leonard Foerster <foersleo@amazon.de> Cc: Greg Thelen <gthelen@google.com> Cc: Markus Boehme <markubo@amazon.de> Cc: David Rienjes <rientjes@google.com> Cc: Shakeel Butt <shakeelb@google.com> Cc: Shuah Khan <shuah@kernel.org> Signed-off-by: Andrew Morton <akpm@linux-foundation.org> Signed-off-by: Linus Torvalds <torvalds@linux-foundation.org>
2021-11-06 04:46:18 +08:00
* @age: Age of this region.
*
* @age is initially zero, increased for each aggregation interval, and reset
* to zero again if the access frequency is significantly changed. If two
* regions are merged into a new region, both @nr_accesses and @age of the new
* region are set as region size-weighted average of those of the two regions.
mm/damon/core: implement region-based sampling To avoid the unbounded increase of the overhead, DAMON groups adjacent pages that are assumed to have the same access frequencies into a region. As long as the assumption (pages in a region have the same access frequencies) is kept, only one page in the region is required to be checked. Thus, for each ``sampling interval``, 1. the 'prepare_access_checks' primitive picks one page in each region, 2. waits for one ``sampling interval``, 3. checks whether the page is accessed meanwhile, and 4. increases the access count of the region if so. Therefore, the monitoring overhead is controllable by adjusting the number of regions. DAMON allows both the underlying primitives and user callbacks to adjust regions for the trade-off. In other words, this commit makes DAMON to use not only time-based sampling but also space-based sampling. This scheme, however, cannot preserve the quality of the output if the assumption is not guaranteed. Next commit will address this problem. Link: https://lkml.kernel.org/r/20210716081449.22187-3-sj38.park@gmail.com Signed-off-by: SeongJae Park <sjpark@amazon.de> Reviewed-by: Leonard Foerster <foersleo@amazon.de> Reviewed-by: Fernand Sieber <sieberf@amazon.com> Acked-by: Shakeel Butt <shakeelb@google.com> Cc: Alexander Shishkin <alexander.shishkin@linux.intel.com> Cc: Amit Shah <amit@kernel.org> Cc: Benjamin Herrenschmidt <benh@kernel.crashing.org> Cc: Brendan Higgins <brendanhiggins@google.com> Cc: David Hildenbrand <david@redhat.com> Cc: David Rientjes <rientjes@google.com> Cc: David Woodhouse <dwmw@amazon.com> Cc: Fan Du <fan.du@intel.com> Cc: Greg Kroah-Hartman <greg@kroah.com> Cc: Greg Thelen <gthelen@google.com> Cc: Ingo Molnar <mingo@redhat.com> Cc: Joe Perches <joe@perches.com> Cc: Jonathan Cameron <Jonathan.Cameron@huawei.com> Cc: Jonathan Corbet <corbet@lwn.net> Cc: Marco Elver <elver@google.com> Cc: Markus Boehme <markubo@amazon.de> Cc: Maximilian Heyne <mheyne@amazon.de> Cc: Mel Gorman <mgorman@suse.de> Cc: Minchan Kim <minchan@kernel.org> Cc: Namhyung Kim <namhyung@kernel.org> Cc: Peter Zijlstra <peterz@infradead.org> Cc: Rik van Riel <riel@surriel.com> Cc: Shuah Khan <shuah@kernel.org> Cc: Steven Rostedt (VMware) <rostedt@goodmis.org> Cc: Vladimir Davydov <vdavydov.dev@gmail.com> Cc: Vlastimil Babka <vbabka@suse.cz> Signed-off-by: Andrew Morton <akpm@linux-foundation.org> Signed-off-by: Linus Torvalds <torvalds@linux-foundation.org>
2021-09-08 10:56:32 +08:00
*/
struct damon_region {
struct damon_addr_range ar;
unsigned long sampling_addr;
unsigned int nr_accesses;
struct list_head list;
mm/damon/core: account age of target regions Patch series "Implement Data Access Monitoring-based Memory Operation Schemes". Introduction ============ DAMON[1] can be used as a primitive for data access aware memory management optimizations. For that, users who want such optimizations should run DAMON, read the monitoring results, analyze it, plan a new memory management scheme, and apply the new scheme by themselves. Such efforts will be inevitable for some complicated optimizations. However, in many other cases, the users would simply want the system to apply a memory management action to a memory region of a specific size having a specific access frequency for a specific time. For example, "page out a memory region larger than 100 MiB keeping only rare accesses more than 2 minutes", or "Do not use THP for a memory region larger than 2 MiB rarely accessed for more than 1 seconds". To make the works easier and non-redundant, this patchset implements a new feature of DAMON, which is called Data Access Monitoring-based Operation Schemes (DAMOS). Using the feature, users can describe the normal schemes in a simple way and ask DAMON to execute those on its own. [1] https://damonitor.github.io Evaluations =========== DAMOS is accurate and useful for memory management optimizations. An experimental DAMON-based operation scheme for THP, 'ethp', removes 76.15% of THP memory overheads while preserving 51.25% of THP speedup. Another experimental DAMON-based 'proactive reclamation' implementation, 'prcl', reduces 93.38% of residential sets and 23.63% of system memory footprint while incurring only 1.22% runtime overhead in the best case (parsec3/freqmine). NOTE that the experimental THP optimization and proactive reclamation are not for production but only for proof of concepts. Please refer to the showcase web site's evaluation document[1] for detailed evaluation setup and results. [1] https://damonitor.github.io/doc/html/v34/vm/damon/eval.html Long-term Support Trees ----------------------- For people who want to test DAMON but using LTS kernels, there are another couple of trees based on two latest LTS kernels respectively and containing the 'damon/master' backports. - For v5.4.y: https://git.kernel.org/sj/h/damon/for-v5.4.y - For v5.10.y: https://git.kernel.org/sj/h/damon/for-v5.10.y Sequence Of Patches =================== The 1st patch accounts age of each region. The 2nd patch implements the core of the DAMON-based operation schemes feature. The 3rd patch makes the default monitoring primitives for virtual address spaces to support the schemes. From this point, the kernel space users can use DAMOS. The 4th patch exports the feature to the user space via the debugfs interface. The 5th patch implements schemes statistics feature for easier tuning of the schemes and runtime access pattern analysis, and the 6th patch adds selftests for these changes. Finally, the 7th patch documents this new feature. This patch (of 7): DAMON can be used for data access pattern aware memory management optimizations. For that, users should run DAMON, read the monitoring results, analyze it, plan a new memory management scheme, and apply the new scheme by themselves. It would not be too hard, but still require some level of effort. For complicated cases, this effort is inevitable. That said, in many cases, users would simply want to apply an actions to a memory region of a specific size having a specific access frequency for a specific time. For example, "page out a memory region larger than 100 MiB but having a low access frequency more than 10 minutes", or "Use THP for a memory region larger than 2 MiB having a high access frequency for more than 2 seconds". For such optimizations, users will need to first account the age of each region themselves. To reduce such efforts, this implements a simple age account of each region in DAMON. For each aggregation step, DAMON compares the access frequency with that from last aggregation and reset the age of the region if the change is significant. Else, the age is incremented. Also, in case of the merge of regions, the region size-weighted average of the ages is set as the age of merged new region. Link: https://lkml.kernel.org/r/20211001125604.29660-1-sj@kernel.org Link: https://lkml.kernel.org/r/20211001125604.29660-2-sj@kernel.org Signed-off-by: SeongJae Park <sj@kernel.org> Cc: Jonathan Cameron <Jonathan.Cameron@huawei.com> Cc: Amit Shah <amit@kernel.org> Cc: Benjamin Herrenschmidt <benh@kernel.crashing.org> Cc: Jonathan Corbet <corbet@lwn.net> Cc: David Hildenbrand <david@redhat.com> Cc: David Woodhouse <dwmw@amazon.com> Cc: Marco Elver <elver@google.com> Cc: Leonard Foerster <foersleo@amazon.de> Cc: Greg Thelen <gthelen@google.com> Cc: Markus Boehme <markubo@amazon.de> Cc: David Rienjes <rientjes@google.com> Cc: Shakeel Butt <shakeelb@google.com> Cc: Shuah Khan <shuah@kernel.org> Signed-off-by: Andrew Morton <akpm@linux-foundation.org> Signed-off-by: Linus Torvalds <torvalds@linux-foundation.org>
2021-11-06 04:46:18 +08:00
unsigned int age;
/* private: Internal value for age calculation. */
unsigned int last_nr_accesses;
mm/damon/core: implement region-based sampling To avoid the unbounded increase of the overhead, DAMON groups adjacent pages that are assumed to have the same access frequencies into a region. As long as the assumption (pages in a region have the same access frequencies) is kept, only one page in the region is required to be checked. Thus, for each ``sampling interval``, 1. the 'prepare_access_checks' primitive picks one page in each region, 2. waits for one ``sampling interval``, 3. checks whether the page is accessed meanwhile, and 4. increases the access count of the region if so. Therefore, the monitoring overhead is controllable by adjusting the number of regions. DAMON allows both the underlying primitives and user callbacks to adjust regions for the trade-off. In other words, this commit makes DAMON to use not only time-based sampling but also space-based sampling. This scheme, however, cannot preserve the quality of the output if the assumption is not guaranteed. Next commit will address this problem. Link: https://lkml.kernel.org/r/20210716081449.22187-3-sj38.park@gmail.com Signed-off-by: SeongJae Park <sjpark@amazon.de> Reviewed-by: Leonard Foerster <foersleo@amazon.de> Reviewed-by: Fernand Sieber <sieberf@amazon.com> Acked-by: Shakeel Butt <shakeelb@google.com> Cc: Alexander Shishkin <alexander.shishkin@linux.intel.com> Cc: Amit Shah <amit@kernel.org> Cc: Benjamin Herrenschmidt <benh@kernel.crashing.org> Cc: Brendan Higgins <brendanhiggins@google.com> Cc: David Hildenbrand <david@redhat.com> Cc: David Rientjes <rientjes@google.com> Cc: David Woodhouse <dwmw@amazon.com> Cc: Fan Du <fan.du@intel.com> Cc: Greg Kroah-Hartman <greg@kroah.com> Cc: Greg Thelen <gthelen@google.com> Cc: Ingo Molnar <mingo@redhat.com> Cc: Joe Perches <joe@perches.com> Cc: Jonathan Cameron <Jonathan.Cameron@huawei.com> Cc: Jonathan Corbet <corbet@lwn.net> Cc: Marco Elver <elver@google.com> Cc: Markus Boehme <markubo@amazon.de> Cc: Maximilian Heyne <mheyne@amazon.de> Cc: Mel Gorman <mgorman@suse.de> Cc: Minchan Kim <minchan@kernel.org> Cc: Namhyung Kim <namhyung@kernel.org> Cc: Peter Zijlstra <peterz@infradead.org> Cc: Rik van Riel <riel@surriel.com> Cc: Shuah Khan <shuah@kernel.org> Cc: Steven Rostedt (VMware) <rostedt@goodmis.org> Cc: Vladimir Davydov <vdavydov.dev@gmail.com> Cc: Vlastimil Babka <vbabka@suse.cz> Signed-off-by: Andrew Morton <akpm@linux-foundation.org> Signed-off-by: Linus Torvalds <torvalds@linux-foundation.org>
2021-09-08 10:56:32 +08:00
};
/**
* struct damon_target - Represents a monitoring target.
* @pid: The PID of the virtual address space to monitor.
mm/damon: adaptively adjust regions Even somehow the initial monitoring target regions are well constructed to fulfill the assumption (pages in same region have similar access frequencies), the data access pattern can be dynamically changed. This will result in low monitoring quality. To keep the assumption as much as possible, DAMON adaptively merges and splits each region based on their access frequency. For each ``aggregation interval``, it compares the access frequencies of adjacent regions and merges those if the frequency difference is small. Then, after it reports and clears the aggregated access frequency of each region, it splits each region into two or three regions if the total number of regions will not exceed the user-specified maximum number of regions after the split. In this way, DAMON provides its best-effort quality and minimal overhead while keeping the upper-bound overhead that users set. Link: https://lkml.kernel.org/r/20210716081449.22187-4-sj38.park@gmail.com Signed-off-by: SeongJae Park <sjpark@amazon.de> Reviewed-by: Leonard Foerster <foersleo@amazon.de> Reviewed-by: Fernand Sieber <sieberf@amazon.com> Acked-by: Shakeel Butt <shakeelb@google.com> Cc: Alexander Shishkin <alexander.shishkin@linux.intel.com> Cc: Amit Shah <amit@kernel.org> Cc: Benjamin Herrenschmidt <benh@kernel.crashing.org> Cc: Brendan Higgins <brendanhiggins@google.com> Cc: David Hildenbrand <david@redhat.com> Cc: David Rientjes <rientjes@google.com> Cc: David Woodhouse <dwmw@amazon.com> Cc: Fan Du <fan.du@intel.com> Cc: Greg Kroah-Hartman <greg@kroah.com> Cc: Greg Thelen <gthelen@google.com> Cc: Ingo Molnar <mingo@redhat.com> Cc: Joe Perches <joe@perches.com> Cc: Jonathan Cameron <Jonathan.Cameron@huawei.com> Cc: Jonathan Corbet <corbet@lwn.net> Cc: Marco Elver <elver@google.com> Cc: Markus Boehme <markubo@amazon.de> Cc: Maximilian Heyne <mheyne@amazon.de> Cc: Mel Gorman <mgorman@suse.de> Cc: Minchan Kim <minchan@kernel.org> Cc: Namhyung Kim <namhyung@kernel.org> Cc: Peter Zijlstra <peterz@infradead.org> Cc: Rik van Riel <riel@surriel.com> Cc: Shuah Khan <shuah@kernel.org> Cc: Steven Rostedt (VMware) <rostedt@goodmis.org> Cc: Vladimir Davydov <vdavydov.dev@gmail.com> Cc: Vlastimil Babka <vbabka@suse.cz> Signed-off-by: Andrew Morton <akpm@linux-foundation.org> Signed-off-by: Linus Torvalds <torvalds@linux-foundation.org>
2021-09-08 10:56:36 +08:00
* @nr_regions: Number of monitoring target regions of this target.
mm/damon/core: implement region-based sampling To avoid the unbounded increase of the overhead, DAMON groups adjacent pages that are assumed to have the same access frequencies into a region. As long as the assumption (pages in a region have the same access frequencies) is kept, only one page in the region is required to be checked. Thus, for each ``sampling interval``, 1. the 'prepare_access_checks' primitive picks one page in each region, 2. waits for one ``sampling interval``, 3. checks whether the page is accessed meanwhile, and 4. increases the access count of the region if so. Therefore, the monitoring overhead is controllable by adjusting the number of regions. DAMON allows both the underlying primitives and user callbacks to adjust regions for the trade-off. In other words, this commit makes DAMON to use not only time-based sampling but also space-based sampling. This scheme, however, cannot preserve the quality of the output if the assumption is not guaranteed. Next commit will address this problem. Link: https://lkml.kernel.org/r/20210716081449.22187-3-sj38.park@gmail.com Signed-off-by: SeongJae Park <sjpark@amazon.de> Reviewed-by: Leonard Foerster <foersleo@amazon.de> Reviewed-by: Fernand Sieber <sieberf@amazon.com> Acked-by: Shakeel Butt <shakeelb@google.com> Cc: Alexander Shishkin <alexander.shishkin@linux.intel.com> Cc: Amit Shah <amit@kernel.org> Cc: Benjamin Herrenschmidt <benh@kernel.crashing.org> Cc: Brendan Higgins <brendanhiggins@google.com> Cc: David Hildenbrand <david@redhat.com> Cc: David Rientjes <rientjes@google.com> Cc: David Woodhouse <dwmw@amazon.com> Cc: Fan Du <fan.du@intel.com> Cc: Greg Kroah-Hartman <greg@kroah.com> Cc: Greg Thelen <gthelen@google.com> Cc: Ingo Molnar <mingo@redhat.com> Cc: Joe Perches <joe@perches.com> Cc: Jonathan Cameron <Jonathan.Cameron@huawei.com> Cc: Jonathan Corbet <corbet@lwn.net> Cc: Marco Elver <elver@google.com> Cc: Markus Boehme <markubo@amazon.de> Cc: Maximilian Heyne <mheyne@amazon.de> Cc: Mel Gorman <mgorman@suse.de> Cc: Minchan Kim <minchan@kernel.org> Cc: Namhyung Kim <namhyung@kernel.org> Cc: Peter Zijlstra <peterz@infradead.org> Cc: Rik van Riel <riel@surriel.com> Cc: Shuah Khan <shuah@kernel.org> Cc: Steven Rostedt (VMware) <rostedt@goodmis.org> Cc: Vladimir Davydov <vdavydov.dev@gmail.com> Cc: Vlastimil Babka <vbabka@suse.cz> Signed-off-by: Andrew Morton <akpm@linux-foundation.org> Signed-off-by: Linus Torvalds <torvalds@linux-foundation.org>
2021-09-08 10:56:32 +08:00
* @regions_list: Head of the monitoring target regions of this target.
* @list: List head for siblings.
*
* Each monitoring context could have multiple targets. For example, a context
* for virtual memory address spaces could have multiple target processes. The
mm/damon: rename damon_primitives to damon_operations Patch series "Allow DAMON user code independent of monitoring primitives". In-kernel DAMON user code is required to configure the monitoring context (struct damon_ctx) with proper monitoring primitives (struct damon_primitive). This makes the user code dependent to all supporting monitoring primitives. For example, DAMON debugfs interface depends on both DAMON_VADDR and DAMON_PADDR, though some users have interest in only one use case. As more monitoring primitives are introduced, the problem will be bigger. To minimize such unnecessary dependency, this patchset makes monitoring primitives can be registered by the implemnting code and later dynamically searched and selected by the user code. In addition to that, this patchset renames monitoring primitives to monitoring operations, which is more easy to intuitively understand what it means and how it would be structed. This patch (of 8): DAMON has a set of callback functions called monitoring primitives and let it can be configured with various implementations for easy extension for different address spaces and usages. However, the word 'primitive' is not so explicit. Meanwhile, many other structs resembles similar purpose calls themselves 'operations'. To make the code easier to be understood, this commit renames 'damon_primitives' to 'damon_operations' before it is too late to rename. Link: https://lkml.kernel.org/r/20220215184603.1479-1-sj@kernel.org Link: https://lkml.kernel.org/r/20220215184603.1479-2-sj@kernel.org Signed-off-by: SeongJae Park <sj@kernel.org> Cc: Xin Hao <xhao@linux.alibaba.com> Cc: David Rientjes <rientjes@google.com> Signed-off-by: Andrew Morton <akpm@linux-foundation.org> Signed-off-by: Linus Torvalds <torvalds@linux-foundation.org>
2022-03-23 05:48:46 +08:00
* @pid should be set for appropriate &struct damon_operations including the
* virtual address spaces monitoring operations.
mm/damon/core: implement region-based sampling To avoid the unbounded increase of the overhead, DAMON groups adjacent pages that are assumed to have the same access frequencies into a region. As long as the assumption (pages in a region have the same access frequencies) is kept, only one page in the region is required to be checked. Thus, for each ``sampling interval``, 1. the 'prepare_access_checks' primitive picks one page in each region, 2. waits for one ``sampling interval``, 3. checks whether the page is accessed meanwhile, and 4. increases the access count of the region if so. Therefore, the monitoring overhead is controllable by adjusting the number of regions. DAMON allows both the underlying primitives and user callbacks to adjust regions for the trade-off. In other words, this commit makes DAMON to use not only time-based sampling but also space-based sampling. This scheme, however, cannot preserve the quality of the output if the assumption is not guaranteed. Next commit will address this problem. Link: https://lkml.kernel.org/r/20210716081449.22187-3-sj38.park@gmail.com Signed-off-by: SeongJae Park <sjpark@amazon.de> Reviewed-by: Leonard Foerster <foersleo@amazon.de> Reviewed-by: Fernand Sieber <sieberf@amazon.com> Acked-by: Shakeel Butt <shakeelb@google.com> Cc: Alexander Shishkin <alexander.shishkin@linux.intel.com> Cc: Amit Shah <amit@kernel.org> Cc: Benjamin Herrenschmidt <benh@kernel.crashing.org> Cc: Brendan Higgins <brendanhiggins@google.com> Cc: David Hildenbrand <david@redhat.com> Cc: David Rientjes <rientjes@google.com> Cc: David Woodhouse <dwmw@amazon.com> Cc: Fan Du <fan.du@intel.com> Cc: Greg Kroah-Hartman <greg@kroah.com> Cc: Greg Thelen <gthelen@google.com> Cc: Ingo Molnar <mingo@redhat.com> Cc: Joe Perches <joe@perches.com> Cc: Jonathan Cameron <Jonathan.Cameron@huawei.com> Cc: Jonathan Corbet <corbet@lwn.net> Cc: Marco Elver <elver@google.com> Cc: Markus Boehme <markubo@amazon.de> Cc: Maximilian Heyne <mheyne@amazon.de> Cc: Mel Gorman <mgorman@suse.de> Cc: Minchan Kim <minchan@kernel.org> Cc: Namhyung Kim <namhyung@kernel.org> Cc: Peter Zijlstra <peterz@infradead.org> Cc: Rik van Riel <riel@surriel.com> Cc: Shuah Khan <shuah@kernel.org> Cc: Steven Rostedt (VMware) <rostedt@goodmis.org> Cc: Vladimir Davydov <vdavydov.dev@gmail.com> Cc: Vlastimil Babka <vbabka@suse.cz> Signed-off-by: Andrew Morton <akpm@linux-foundation.org> Signed-off-by: Linus Torvalds <torvalds@linux-foundation.org>
2021-09-08 10:56:32 +08:00
*/
struct damon_target {
struct pid *pid;
mm/damon: adaptively adjust regions Even somehow the initial monitoring target regions are well constructed to fulfill the assumption (pages in same region have similar access frequencies), the data access pattern can be dynamically changed. This will result in low monitoring quality. To keep the assumption as much as possible, DAMON adaptively merges and splits each region based on their access frequency. For each ``aggregation interval``, it compares the access frequencies of adjacent regions and merges those if the frequency difference is small. Then, after it reports and clears the aggregated access frequency of each region, it splits each region into two or three regions if the total number of regions will not exceed the user-specified maximum number of regions after the split. In this way, DAMON provides its best-effort quality and minimal overhead while keeping the upper-bound overhead that users set. Link: https://lkml.kernel.org/r/20210716081449.22187-4-sj38.park@gmail.com Signed-off-by: SeongJae Park <sjpark@amazon.de> Reviewed-by: Leonard Foerster <foersleo@amazon.de> Reviewed-by: Fernand Sieber <sieberf@amazon.com> Acked-by: Shakeel Butt <shakeelb@google.com> Cc: Alexander Shishkin <alexander.shishkin@linux.intel.com> Cc: Amit Shah <amit@kernel.org> Cc: Benjamin Herrenschmidt <benh@kernel.crashing.org> Cc: Brendan Higgins <brendanhiggins@google.com> Cc: David Hildenbrand <david@redhat.com> Cc: David Rientjes <rientjes@google.com> Cc: David Woodhouse <dwmw@amazon.com> Cc: Fan Du <fan.du@intel.com> Cc: Greg Kroah-Hartman <greg@kroah.com> Cc: Greg Thelen <gthelen@google.com> Cc: Ingo Molnar <mingo@redhat.com> Cc: Joe Perches <joe@perches.com> Cc: Jonathan Cameron <Jonathan.Cameron@huawei.com> Cc: Jonathan Corbet <corbet@lwn.net> Cc: Marco Elver <elver@google.com> Cc: Markus Boehme <markubo@amazon.de> Cc: Maximilian Heyne <mheyne@amazon.de> Cc: Mel Gorman <mgorman@suse.de> Cc: Minchan Kim <minchan@kernel.org> Cc: Namhyung Kim <namhyung@kernel.org> Cc: Peter Zijlstra <peterz@infradead.org> Cc: Rik van Riel <riel@surriel.com> Cc: Shuah Khan <shuah@kernel.org> Cc: Steven Rostedt (VMware) <rostedt@goodmis.org> Cc: Vladimir Davydov <vdavydov.dev@gmail.com> Cc: Vlastimil Babka <vbabka@suse.cz> Signed-off-by: Andrew Morton <akpm@linux-foundation.org> Signed-off-by: Linus Torvalds <torvalds@linux-foundation.org>
2021-09-08 10:56:36 +08:00
unsigned int nr_regions;
mm/damon/core: implement region-based sampling To avoid the unbounded increase of the overhead, DAMON groups adjacent pages that are assumed to have the same access frequencies into a region. As long as the assumption (pages in a region have the same access frequencies) is kept, only one page in the region is required to be checked. Thus, for each ``sampling interval``, 1. the 'prepare_access_checks' primitive picks one page in each region, 2. waits for one ``sampling interval``, 3. checks whether the page is accessed meanwhile, and 4. increases the access count of the region if so. Therefore, the monitoring overhead is controllable by adjusting the number of regions. DAMON allows both the underlying primitives and user callbacks to adjust regions for the trade-off. In other words, this commit makes DAMON to use not only time-based sampling but also space-based sampling. This scheme, however, cannot preserve the quality of the output if the assumption is not guaranteed. Next commit will address this problem. Link: https://lkml.kernel.org/r/20210716081449.22187-3-sj38.park@gmail.com Signed-off-by: SeongJae Park <sjpark@amazon.de> Reviewed-by: Leonard Foerster <foersleo@amazon.de> Reviewed-by: Fernand Sieber <sieberf@amazon.com> Acked-by: Shakeel Butt <shakeelb@google.com> Cc: Alexander Shishkin <alexander.shishkin@linux.intel.com> Cc: Amit Shah <amit@kernel.org> Cc: Benjamin Herrenschmidt <benh@kernel.crashing.org> Cc: Brendan Higgins <brendanhiggins@google.com> Cc: David Hildenbrand <david@redhat.com> Cc: David Rientjes <rientjes@google.com> Cc: David Woodhouse <dwmw@amazon.com> Cc: Fan Du <fan.du@intel.com> Cc: Greg Kroah-Hartman <greg@kroah.com> Cc: Greg Thelen <gthelen@google.com> Cc: Ingo Molnar <mingo@redhat.com> Cc: Joe Perches <joe@perches.com> Cc: Jonathan Cameron <Jonathan.Cameron@huawei.com> Cc: Jonathan Corbet <corbet@lwn.net> Cc: Marco Elver <elver@google.com> Cc: Markus Boehme <markubo@amazon.de> Cc: Maximilian Heyne <mheyne@amazon.de> Cc: Mel Gorman <mgorman@suse.de> Cc: Minchan Kim <minchan@kernel.org> Cc: Namhyung Kim <namhyung@kernel.org> Cc: Peter Zijlstra <peterz@infradead.org> Cc: Rik van Riel <riel@surriel.com> Cc: Shuah Khan <shuah@kernel.org> Cc: Steven Rostedt (VMware) <rostedt@goodmis.org> Cc: Vladimir Davydov <vdavydov.dev@gmail.com> Cc: Vlastimil Babka <vbabka@suse.cz> Signed-off-by: Andrew Morton <akpm@linux-foundation.org> Signed-off-by: Linus Torvalds <torvalds@linux-foundation.org>
2021-09-08 10:56:32 +08:00
struct list_head regions_list;
struct list_head list;
};
mm/damon/core: implement DAMON-based Operation Schemes (DAMOS) In many cases, users might use DAMON for simple data access aware memory management optimizations such as applying an operation scheme to a memory region of a specific size having a specific access frequency for a specific time. For example, "page out a memory region larger than 100 MiB but having a low access frequency more than 10 minutes", or "Use THP for a memory region larger than 2 MiB having a high access frequency for more than 2 seconds". Most simple form of the solution would be doing offline data access pattern profiling using DAMON and modifying the application source code or system configuration based on the profiling results. Or, developing a daemon constructed with two modules (one for access monitoring and the other for applying memory management actions via mlock(), madvise(), sysctl, etc) is imaginable. To avoid users spending their time for implementation of such simple data access monitoring-based operation schemes, this makes DAMON to handle such schemes directly. With this change, users can simply specify their desired schemes to DAMON. Then, DAMON will automatically apply the schemes to the user-specified target processes. Each of the schemes is composed with conditions for filtering of the target memory regions and desired memory management action for the target. Specifically, the format is:: <min/max size> <min/max access frequency> <min/max age> <action> The filtering conditions are size of memory region, number of accesses to the region monitored by DAMON, and the age of the region. The age of region is incremented periodically but reset when its addresses or access frequency has significantly changed or the action of a scheme was applied. For the action, current implementation supports a few of madvise()-like hints, ``WILLNEED``, ``COLD``, ``PAGEOUT``, ``HUGEPAGE``, and ``NOHUGEPAGE``. Because DAMON supports various address spaces and application of the actions to a monitoring target region is dependent to the type of the target address space, the application code should be implemented by each primitives and registered to the framework. Note that this only implements the framework part. Following commit will implement the action applications for virtual address spaces primitives. Link: https://lkml.kernel.org/r/20211001125604.29660-3-sj@kernel.org Signed-off-by: SeongJae Park <sj@kernel.org> Cc: Amit Shah <amit@kernel.org> Cc: Benjamin Herrenschmidt <benh@kernel.crashing.org> Cc: David Hildenbrand <david@redhat.com> Cc: David Rienjes <rientjes@google.com> Cc: David Woodhouse <dwmw@amazon.com> Cc: Greg Thelen <gthelen@google.com> Cc: Jonathan Cameron <Jonathan.Cameron@huawei.com> Cc: Jonathan Corbet <corbet@lwn.net> Cc: Leonard Foerster <foersleo@amazon.de> Cc: Marco Elver <elver@google.com> Cc: Markus Boehme <markubo@amazon.de> Cc: Shakeel Butt <shakeelb@google.com> Cc: Shuah Khan <shuah@kernel.org> Signed-off-by: Andrew Morton <akpm@linux-foundation.org> Signed-off-by: Linus Torvalds <torvalds@linux-foundation.org>
2021-11-06 04:46:22 +08:00
/**
* enum damos_action - Represents an action of a Data Access Monitoring-based
* Operation Scheme.
*
* @DAMOS_WILLNEED: Call ``madvise()`` for the region with MADV_WILLNEED.
* @DAMOS_COLD: Call ``madvise()`` for the region with MADV_COLD.
* @DAMOS_PAGEOUT: Call ``madvise()`` for the region with MADV_PAGEOUT.
* @DAMOS_HUGEPAGE: Call ``madvise()`` for the region with MADV_HUGEPAGE.
* @DAMOS_NOHUGEPAGE: Call ``madvise()`` for the region with MADV_NOHUGEPAGE.
mm/damon/schemes: implement statistics feature To tune the DAMON-based operation schemes, knowing how many and how large regions are affected by each of the schemes will be helful. Those stats could be used for not only the tuning, but also monitoring of the working set size and the number of regions, if the scheme does not change the program behavior too much. For the reason, this implements the statistics for the schemes. The total number and size of the regions that each scheme is applied are exported to users via '->stat_count' and '->stat_sz' of 'struct damos'. Admins can also check the number by reading 'schemes' debugfs file. The last two integers now represents the stats. To allow collecting the stats without changing the program behavior, this also adds new scheme action, 'DAMOS_STAT'. Note that 'DAMOS_STAT' is not only making no memory operation actions, but also does not reset the age of regions. Link: https://lkml.kernel.org/r/20211001125604.29660-6-sj@kernel.org Signed-off-by: SeongJae Park <sj@kernel.org> Cc: Amit Shah <amit@kernel.org> Cc: Benjamin Herrenschmidt <benh@kernel.crashing.org> Cc: David Hildenbrand <david@redhat.com> Cc: David Rienjes <rientjes@google.com> Cc: David Woodhouse <dwmw@amazon.com> Cc: Greg Thelen <gthelen@google.com> Cc: Jonathan Cameron <Jonathan.Cameron@huawei.com> Cc: Jonathan Corbet <corbet@lwn.net> Cc: Leonard Foerster <foersleo@amazon.de> Cc: Marco Elver <elver@google.com> Cc: Markus Boehme <markubo@amazon.de> Cc: Shakeel Butt <shakeelb@google.com> Cc: Shuah Khan <shuah@kernel.org> Signed-off-by: Andrew Morton <akpm@linux-foundation.org> Signed-off-by: Linus Torvalds <torvalds@linux-foundation.org>
2021-11-06 04:46:32 +08:00
* @DAMOS_STAT: Do nothing but count the stat.
* @NR_DAMOS_ACTIONS: Total number of DAMOS actions
mm/damon/core: implement DAMON-based Operation Schemes (DAMOS) In many cases, users might use DAMON for simple data access aware memory management optimizations such as applying an operation scheme to a memory region of a specific size having a specific access frequency for a specific time. For example, "page out a memory region larger than 100 MiB but having a low access frequency more than 10 minutes", or "Use THP for a memory region larger than 2 MiB having a high access frequency for more than 2 seconds". Most simple form of the solution would be doing offline data access pattern profiling using DAMON and modifying the application source code or system configuration based on the profiling results. Or, developing a daemon constructed with two modules (one for access monitoring and the other for applying memory management actions via mlock(), madvise(), sysctl, etc) is imaginable. To avoid users spending their time for implementation of such simple data access monitoring-based operation schemes, this makes DAMON to handle such schemes directly. With this change, users can simply specify their desired schemes to DAMON. Then, DAMON will automatically apply the schemes to the user-specified target processes. Each of the schemes is composed with conditions for filtering of the target memory regions and desired memory management action for the target. Specifically, the format is:: <min/max size> <min/max access frequency> <min/max age> <action> The filtering conditions are size of memory region, number of accesses to the region monitored by DAMON, and the age of the region. The age of region is incremented periodically but reset when its addresses or access frequency has significantly changed or the action of a scheme was applied. For the action, current implementation supports a few of madvise()-like hints, ``WILLNEED``, ``COLD``, ``PAGEOUT``, ``HUGEPAGE``, and ``NOHUGEPAGE``. Because DAMON supports various address spaces and application of the actions to a monitoring target region is dependent to the type of the target address space, the application code should be implemented by each primitives and registered to the framework. Note that this only implements the framework part. Following commit will implement the action applications for virtual address spaces primitives. Link: https://lkml.kernel.org/r/20211001125604.29660-3-sj@kernel.org Signed-off-by: SeongJae Park <sj@kernel.org> Cc: Amit Shah <amit@kernel.org> Cc: Benjamin Herrenschmidt <benh@kernel.crashing.org> Cc: David Hildenbrand <david@redhat.com> Cc: David Rienjes <rientjes@google.com> Cc: David Woodhouse <dwmw@amazon.com> Cc: Greg Thelen <gthelen@google.com> Cc: Jonathan Cameron <Jonathan.Cameron@huawei.com> Cc: Jonathan Corbet <corbet@lwn.net> Cc: Leonard Foerster <foersleo@amazon.de> Cc: Marco Elver <elver@google.com> Cc: Markus Boehme <markubo@amazon.de> Cc: Shakeel Butt <shakeelb@google.com> Cc: Shuah Khan <shuah@kernel.org> Signed-off-by: Andrew Morton <akpm@linux-foundation.org> Signed-off-by: Linus Torvalds <torvalds@linux-foundation.org>
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*/
enum damos_action {
DAMOS_WILLNEED,
DAMOS_COLD,
DAMOS_PAGEOUT,
DAMOS_HUGEPAGE,
DAMOS_NOHUGEPAGE,
mm/damon/schemes: implement statistics feature To tune the DAMON-based operation schemes, knowing how many and how large regions are affected by each of the schemes will be helful. Those stats could be used for not only the tuning, but also monitoring of the working set size and the number of regions, if the scheme does not change the program behavior too much. For the reason, this implements the statistics for the schemes. The total number and size of the regions that each scheme is applied are exported to users via '->stat_count' and '->stat_sz' of 'struct damos'. Admins can also check the number by reading 'schemes' debugfs file. The last two integers now represents the stats. To allow collecting the stats without changing the program behavior, this also adds new scheme action, 'DAMOS_STAT'. Note that 'DAMOS_STAT' is not only making no memory operation actions, but also does not reset the age of regions. Link: https://lkml.kernel.org/r/20211001125604.29660-6-sj@kernel.org Signed-off-by: SeongJae Park <sj@kernel.org> Cc: Amit Shah <amit@kernel.org> Cc: Benjamin Herrenschmidt <benh@kernel.crashing.org> Cc: David Hildenbrand <david@redhat.com> Cc: David Rienjes <rientjes@google.com> Cc: David Woodhouse <dwmw@amazon.com> Cc: Greg Thelen <gthelen@google.com> Cc: Jonathan Cameron <Jonathan.Cameron@huawei.com> Cc: Jonathan Corbet <corbet@lwn.net> Cc: Leonard Foerster <foersleo@amazon.de> Cc: Marco Elver <elver@google.com> Cc: Markus Boehme <markubo@amazon.de> Cc: Shakeel Butt <shakeelb@google.com> Cc: Shuah Khan <shuah@kernel.org> Signed-off-by: Andrew Morton <akpm@linux-foundation.org> Signed-off-by: Linus Torvalds <torvalds@linux-foundation.org>
2021-11-06 04:46:32 +08:00
DAMOS_STAT, /* Do nothing but only record the stat */
NR_DAMOS_ACTIONS,
mm/damon/core: implement DAMON-based Operation Schemes (DAMOS) In many cases, users might use DAMON for simple data access aware memory management optimizations such as applying an operation scheme to a memory region of a specific size having a specific access frequency for a specific time. For example, "page out a memory region larger than 100 MiB but having a low access frequency more than 10 minutes", or "Use THP for a memory region larger than 2 MiB having a high access frequency for more than 2 seconds". Most simple form of the solution would be doing offline data access pattern profiling using DAMON and modifying the application source code or system configuration based on the profiling results. Or, developing a daemon constructed with two modules (one for access monitoring and the other for applying memory management actions via mlock(), madvise(), sysctl, etc) is imaginable. To avoid users spending their time for implementation of such simple data access monitoring-based operation schemes, this makes DAMON to handle such schemes directly. With this change, users can simply specify their desired schemes to DAMON. Then, DAMON will automatically apply the schemes to the user-specified target processes. Each of the schemes is composed with conditions for filtering of the target memory regions and desired memory management action for the target. Specifically, the format is:: <min/max size> <min/max access frequency> <min/max age> <action> The filtering conditions are size of memory region, number of accesses to the region monitored by DAMON, and the age of the region. The age of region is incremented periodically but reset when its addresses or access frequency has significantly changed or the action of a scheme was applied. For the action, current implementation supports a few of madvise()-like hints, ``WILLNEED``, ``COLD``, ``PAGEOUT``, ``HUGEPAGE``, and ``NOHUGEPAGE``. Because DAMON supports various address spaces and application of the actions to a monitoring target region is dependent to the type of the target address space, the application code should be implemented by each primitives and registered to the framework. Note that this only implements the framework part. Following commit will implement the action applications for virtual address spaces primitives. Link: https://lkml.kernel.org/r/20211001125604.29660-3-sj@kernel.org Signed-off-by: SeongJae Park <sj@kernel.org> Cc: Amit Shah <amit@kernel.org> Cc: Benjamin Herrenschmidt <benh@kernel.crashing.org> Cc: David Hildenbrand <david@redhat.com> Cc: David Rienjes <rientjes@google.com> Cc: David Woodhouse <dwmw@amazon.com> Cc: Greg Thelen <gthelen@google.com> Cc: Jonathan Cameron <Jonathan.Cameron@huawei.com> Cc: Jonathan Corbet <corbet@lwn.net> Cc: Leonard Foerster <foersleo@amazon.de> Cc: Marco Elver <elver@google.com> Cc: Markus Boehme <markubo@amazon.de> Cc: Shakeel Butt <shakeelb@google.com> Cc: Shuah Khan <shuah@kernel.org> Signed-off-by: Andrew Morton <akpm@linux-foundation.org> Signed-off-by: Linus Torvalds <torvalds@linux-foundation.org>
2021-11-06 04:46:22 +08:00
};
/**
* struct damos_quota - Controls the aggressiveness of the given scheme.
* @ms: Maximum milliseconds that the scheme can use.
* @sz: Maximum bytes of memory that the action can be applied.
* @reset_interval: Charge reset interval in milliseconds.
*
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* @weight_sz: Weight of the region's size for prioritization.
* @weight_nr_accesses: Weight of the region's nr_accesses for prioritization.
* @weight_age: Weight of the region's age for prioritization.
*
* To avoid consuming too much CPU time or IO resources for applying the
* &struct damos->action to large memory, DAMON allows users to set time and/or
* size quotas. The quotas can be set by writing non-zero values to &ms and
* &sz, respectively. If the time quota is set, DAMON tries to use only up to
* &ms milliseconds within &reset_interval for applying the action. If the
* size quota is set, DAMON tries to apply the action only up to &sz bytes
* within &reset_interval.
*
* Internally, the time quota is transformed to a size quota using estimated
* throughput of the scheme's action. DAMON then compares it against &sz and
* uses smaller one as the effective quota.
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*
* For selecting regions within the quota, DAMON prioritizes current scheme's
mm/damon: rename damon_primitives to damon_operations Patch series "Allow DAMON user code independent of monitoring primitives". In-kernel DAMON user code is required to configure the monitoring context (struct damon_ctx) with proper monitoring primitives (struct damon_primitive). This makes the user code dependent to all supporting monitoring primitives. For example, DAMON debugfs interface depends on both DAMON_VADDR and DAMON_PADDR, though some users have interest in only one use case. As more monitoring primitives are introduced, the problem will be bigger. To minimize such unnecessary dependency, this patchset makes monitoring primitives can be registered by the implemnting code and later dynamically searched and selected by the user code. In addition to that, this patchset renames monitoring primitives to monitoring operations, which is more easy to intuitively understand what it means and how it would be structed. This patch (of 8): DAMON has a set of callback functions called monitoring primitives and let it can be configured with various implementations for easy extension for different address spaces and usages. However, the word 'primitive' is not so explicit. Meanwhile, many other structs resembles similar purpose calls themselves 'operations'. To make the code easier to be understood, this commit renames 'damon_primitives' to 'damon_operations' before it is too late to rename. Link: https://lkml.kernel.org/r/20220215184603.1479-1-sj@kernel.org Link: https://lkml.kernel.org/r/20220215184603.1479-2-sj@kernel.org Signed-off-by: SeongJae Park <sj@kernel.org> Cc: Xin Hao <xhao@linux.alibaba.com> Cc: David Rientjes <rientjes@google.com> Signed-off-by: Andrew Morton <akpm@linux-foundation.org> Signed-off-by: Linus Torvalds <torvalds@linux-foundation.org>
2022-03-23 05:48:46 +08:00
* target memory regions using the &struct damon_operations->get_scheme_score.
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* You could customize the prioritization logic by setting &weight_sz,
mm/damon: rename damon_primitives to damon_operations Patch series "Allow DAMON user code independent of monitoring primitives". In-kernel DAMON user code is required to configure the monitoring context (struct damon_ctx) with proper monitoring primitives (struct damon_primitive). This makes the user code dependent to all supporting monitoring primitives. For example, DAMON debugfs interface depends on both DAMON_VADDR and DAMON_PADDR, though some users have interest in only one use case. As more monitoring primitives are introduced, the problem will be bigger. To minimize such unnecessary dependency, this patchset makes monitoring primitives can be registered by the implemnting code and later dynamically searched and selected by the user code. In addition to that, this patchset renames monitoring primitives to monitoring operations, which is more easy to intuitively understand what it means and how it would be structed. This patch (of 8): DAMON has a set of callback functions called monitoring primitives and let it can be configured with various implementations for easy extension for different address spaces and usages. However, the word 'primitive' is not so explicit. Meanwhile, many other structs resembles similar purpose calls themselves 'operations'. To make the code easier to be understood, this commit renames 'damon_primitives' to 'damon_operations' before it is too late to rename. Link: https://lkml.kernel.org/r/20220215184603.1479-1-sj@kernel.org Link: https://lkml.kernel.org/r/20220215184603.1479-2-sj@kernel.org Signed-off-by: SeongJae Park <sj@kernel.org> Cc: Xin Hao <xhao@linux.alibaba.com> Cc: David Rientjes <rientjes@google.com> Signed-off-by: Andrew Morton <akpm@linux-foundation.org> Signed-off-by: Linus Torvalds <torvalds@linux-foundation.org>
2022-03-23 05:48:46 +08:00
* &weight_nr_accesses, and &weight_age, because monitoring operations are
2021-11-06 04:47:33 +08:00
* encouraged to respect those.
*/
struct damos_quota {
unsigned long ms;
unsigned long sz;
unsigned long reset_interval;
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unsigned int weight_sz;
unsigned int weight_nr_accesses;
unsigned int weight_age;
/* private: */
/* For throughput estimation */
unsigned long total_charged_sz;
unsigned long total_charged_ns;
unsigned long esz; /* Effective size quota in bytes */
/* For charging the quota */
unsigned long charged_sz;
unsigned long charged_from;
struct damon_target *charge_target_from;
unsigned long charge_addr_from;
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/* For prioritization */
unsigned long histogram[DAMOS_MAX_SCORE + 1];
unsigned int min_score;
};
mm/damon/schemes: activate schemes based on a watermarks mechanism DAMON-based operation schemes need to be manually turned on and off. In some use cases, however, the condition for turning a scheme on and off would depend on the system's situation. For example, schemes for proactive pages reclamation would need to be turned on when some memory pressure is detected, and turned off when the system has enough free memory. For easier control of schemes activation based on the system situation, this introduces a watermarks-based mechanism. The client can describe the watermark metric (e.g., amount of free memory in the system), watermark check interval, and three watermarks, namely high, mid, and low. If the scheme is deactivated, it only gets the metric and compare that to the three watermarks for every check interval. If the metric is higher than the high watermark, the scheme is deactivated. If the metric is between the mid watermark and the low watermark, the scheme is activated. If the metric is lower than the low watermark, the scheme is deactivated again. This is to allow users fall back to traditional page-granularity mechanisms. Link: https://lkml.kernel.org/r/20211019150731.16699-12-sj@kernel.org Signed-off-by: SeongJae Park <sj@kernel.org> Cc: Amit Shah <amit@kernel.org> Cc: Benjamin Herrenschmidt <benh@kernel.crashing.org> Cc: David Hildenbrand <david@redhat.com> Cc: David Rientjes <rientjes@google.com> Cc: David Woodhouse <dwmw@amazon.com> Cc: Greg Thelen <gthelen@google.com> Cc: Jonathan Cameron <Jonathan.Cameron@huawei.com> Cc: Jonathan Corbet <corbet@lwn.net> Cc: Leonard Foerster <foersleo@amazon.de> Cc: Marco Elver <elver@google.com> Cc: Markus Boehme <markubo@amazon.de> Cc: Shakeel Butt <shakeelb@google.com> Cc: Shuah Khan <shuah@kernel.org> Signed-off-by: Andrew Morton <akpm@linux-foundation.org> Signed-off-by: Linus Torvalds <torvalds@linux-foundation.org>
2021-11-06 04:47:47 +08:00
/**
* enum damos_wmark_metric - Represents the watermark metric.
*
* @DAMOS_WMARK_NONE: Ignore the watermarks of the given scheme.
* @DAMOS_WMARK_FREE_MEM_RATE: Free memory rate of the system in [0,1000].
* @NR_DAMOS_WMARK_METRICS: Total number of DAMOS watermark metrics
mm/damon/schemes: activate schemes based on a watermarks mechanism DAMON-based operation schemes need to be manually turned on and off. In some use cases, however, the condition for turning a scheme on and off would depend on the system's situation. For example, schemes for proactive pages reclamation would need to be turned on when some memory pressure is detected, and turned off when the system has enough free memory. For easier control of schemes activation based on the system situation, this introduces a watermarks-based mechanism. The client can describe the watermark metric (e.g., amount of free memory in the system), watermark check interval, and three watermarks, namely high, mid, and low. If the scheme is deactivated, it only gets the metric and compare that to the three watermarks for every check interval. If the metric is higher than the high watermark, the scheme is deactivated. If the metric is between the mid watermark and the low watermark, the scheme is activated. If the metric is lower than the low watermark, the scheme is deactivated again. This is to allow users fall back to traditional page-granularity mechanisms. Link: https://lkml.kernel.org/r/20211019150731.16699-12-sj@kernel.org Signed-off-by: SeongJae Park <sj@kernel.org> Cc: Amit Shah <amit@kernel.org> Cc: Benjamin Herrenschmidt <benh@kernel.crashing.org> Cc: David Hildenbrand <david@redhat.com> Cc: David Rientjes <rientjes@google.com> Cc: David Woodhouse <dwmw@amazon.com> Cc: Greg Thelen <gthelen@google.com> Cc: Jonathan Cameron <Jonathan.Cameron@huawei.com> Cc: Jonathan Corbet <corbet@lwn.net> Cc: Leonard Foerster <foersleo@amazon.de> Cc: Marco Elver <elver@google.com> Cc: Markus Boehme <markubo@amazon.de> Cc: Shakeel Butt <shakeelb@google.com> Cc: Shuah Khan <shuah@kernel.org> Signed-off-by: Andrew Morton <akpm@linux-foundation.org> Signed-off-by: Linus Torvalds <torvalds@linux-foundation.org>
2021-11-06 04:47:47 +08:00
*/
enum damos_wmark_metric {
DAMOS_WMARK_NONE,
DAMOS_WMARK_FREE_MEM_RATE,
NR_DAMOS_WMARK_METRICS,
mm/damon/schemes: activate schemes based on a watermarks mechanism DAMON-based operation schemes need to be manually turned on and off. In some use cases, however, the condition for turning a scheme on and off would depend on the system's situation. For example, schemes for proactive pages reclamation would need to be turned on when some memory pressure is detected, and turned off when the system has enough free memory. For easier control of schemes activation based on the system situation, this introduces a watermarks-based mechanism. The client can describe the watermark metric (e.g., amount of free memory in the system), watermark check interval, and three watermarks, namely high, mid, and low. If the scheme is deactivated, it only gets the metric and compare that to the three watermarks for every check interval. If the metric is higher than the high watermark, the scheme is deactivated. If the metric is between the mid watermark and the low watermark, the scheme is activated. If the metric is lower than the low watermark, the scheme is deactivated again. This is to allow users fall back to traditional page-granularity mechanisms. Link: https://lkml.kernel.org/r/20211019150731.16699-12-sj@kernel.org Signed-off-by: SeongJae Park <sj@kernel.org> Cc: Amit Shah <amit@kernel.org> Cc: Benjamin Herrenschmidt <benh@kernel.crashing.org> Cc: David Hildenbrand <david@redhat.com> Cc: David Rientjes <rientjes@google.com> Cc: David Woodhouse <dwmw@amazon.com> Cc: Greg Thelen <gthelen@google.com> Cc: Jonathan Cameron <Jonathan.Cameron@huawei.com> Cc: Jonathan Corbet <corbet@lwn.net> Cc: Leonard Foerster <foersleo@amazon.de> Cc: Marco Elver <elver@google.com> Cc: Markus Boehme <markubo@amazon.de> Cc: Shakeel Butt <shakeelb@google.com> Cc: Shuah Khan <shuah@kernel.org> Signed-off-by: Andrew Morton <akpm@linux-foundation.org> Signed-off-by: Linus Torvalds <torvalds@linux-foundation.org>
2021-11-06 04:47:47 +08:00
};
/**
* struct damos_watermarks - Controls when a given scheme should be activated.
* @metric: Metric for the watermarks.
* @interval: Watermarks check time interval in microseconds.
* @high: High watermark.
* @mid: Middle watermark.
* @low: Low watermark.
*
* If &metric is &DAMOS_WMARK_NONE, the scheme is always active. Being active
* means DAMON does monitoring and applying the action of the scheme to
* appropriate memory regions. Else, DAMON checks &metric of the system for at
* least every &interval microseconds and works as below.
*
* If &metric is higher than &high, the scheme is inactivated. If &metric is
* between &mid and &low, the scheme is activated. If &metric is lower than
* &low, the scheme is inactivated.
*/
struct damos_watermarks {
enum damos_wmark_metric metric;
unsigned long interval;
unsigned long high;
unsigned long mid;
unsigned long low;
/* private: */
bool activated;
};
mm/damon/schemes: account scheme actions that successfully applied Patch series "mm/damon/schemes: Extend stats for better online analysis and tuning". To help online access pattern analysis and tuning of DAMON-based Operation Schemes (DAMOS), DAMOS provides simple statistics for each scheme. Introduction of DAMOS time/space quota further made the tuning easier by making the risk management easier. However, that also made understanding of the working schemes a little bit more difficult. For an example, progress of a given scheme can now be throttled by not only the aggressiveness of the target access pattern, but also the time/space quotas. So, when a scheme is showing unexpectedly slow progress, it's difficult to know by what the progress of the scheme is throttled, with currently provided statistics. This patchset extends the statistics to contain some metrics that can be helpful for such online schemes analysis and tuning (patches 1-2), exports those to users (patches 3 and 5), and add documents (patches 4 and 6). This patch (of 6): DAMON-based operation schemes (DAMOS) stats provide only the number and the amount of regions that the action of the scheme has tried to be applied. Because the action could be failed for some reasons, the currently provided information is sometimes not useful or convenient enough for schemes profiling and tuning. To improve this situation, this commit extends the DAMOS stats to provide the number and the amount of regions that the action has successfully applied. Link: https://lkml.kernel.org/r/20211210150016.35349-1-sj@kernel.org Link: https://lkml.kernel.org/r/20211210150016.35349-2-sj@kernel.org Signed-off-by: SeongJae Park <sj@kernel.org> Signed-off-by: Andrew Morton <akpm@linux-foundation.org> Signed-off-by: Linus Torvalds <torvalds@linux-foundation.org>
2022-01-15 06:10:17 +08:00
/**
* struct damos_stat - Statistics on a given scheme.
* @nr_tried: Total number of regions that the scheme is tried to be applied.
* @sz_tried: Total size of regions that the scheme is tried to be applied.
* @nr_applied: Total number of regions that the scheme is applied.
* @sz_applied: Total size of regions that the scheme is applied.
* @qt_exceeds: Total number of times the quota of the scheme has exceeded.
mm/damon/schemes: account scheme actions that successfully applied Patch series "mm/damon/schemes: Extend stats for better online analysis and tuning". To help online access pattern analysis and tuning of DAMON-based Operation Schemes (DAMOS), DAMOS provides simple statistics for each scheme. Introduction of DAMOS time/space quota further made the tuning easier by making the risk management easier. However, that also made understanding of the working schemes a little bit more difficult. For an example, progress of a given scheme can now be throttled by not only the aggressiveness of the target access pattern, but also the time/space quotas. So, when a scheme is showing unexpectedly slow progress, it's difficult to know by what the progress of the scheme is throttled, with currently provided statistics. This patchset extends the statistics to contain some metrics that can be helpful for such online schemes analysis and tuning (patches 1-2), exports those to users (patches 3 and 5), and add documents (patches 4 and 6). This patch (of 6): DAMON-based operation schemes (DAMOS) stats provide only the number and the amount of regions that the action of the scheme has tried to be applied. Because the action could be failed for some reasons, the currently provided information is sometimes not useful or convenient enough for schemes profiling and tuning. To improve this situation, this commit extends the DAMOS stats to provide the number and the amount of regions that the action has successfully applied. Link: https://lkml.kernel.org/r/20211210150016.35349-1-sj@kernel.org Link: https://lkml.kernel.org/r/20211210150016.35349-2-sj@kernel.org Signed-off-by: SeongJae Park <sj@kernel.org> Signed-off-by: Andrew Morton <akpm@linux-foundation.org> Signed-off-by: Linus Torvalds <torvalds@linux-foundation.org>
2022-01-15 06:10:17 +08:00
*/
struct damos_stat {
unsigned long nr_tried;
unsigned long sz_tried;
unsigned long nr_applied;
unsigned long sz_applied;
unsigned long qt_exceeds;
mm/damon/schemes: account scheme actions that successfully applied Patch series "mm/damon/schemes: Extend stats for better online analysis and tuning". To help online access pattern analysis and tuning of DAMON-based Operation Schemes (DAMOS), DAMOS provides simple statistics for each scheme. Introduction of DAMOS time/space quota further made the tuning easier by making the risk management easier. However, that also made understanding of the working schemes a little bit more difficult. For an example, progress of a given scheme can now be throttled by not only the aggressiveness of the target access pattern, but also the time/space quotas. So, when a scheme is showing unexpectedly slow progress, it's difficult to know by what the progress of the scheme is throttled, with currently provided statistics. This patchset extends the statistics to contain some metrics that can be helpful for such online schemes analysis and tuning (patches 1-2), exports those to users (patches 3 and 5), and add documents (patches 4 and 6). This patch (of 6): DAMON-based operation schemes (DAMOS) stats provide only the number and the amount of regions that the action of the scheme has tried to be applied. Because the action could be failed for some reasons, the currently provided information is sometimes not useful or convenient enough for schemes profiling and tuning. To improve this situation, this commit extends the DAMOS stats to provide the number and the amount of regions that the action has successfully applied. Link: https://lkml.kernel.org/r/20211210150016.35349-1-sj@kernel.org Link: https://lkml.kernel.org/r/20211210150016.35349-2-sj@kernel.org Signed-off-by: SeongJae Park <sj@kernel.org> Signed-off-by: Andrew Morton <akpm@linux-foundation.org> Signed-off-by: Linus Torvalds <torvalds@linux-foundation.org>
2022-01-15 06:10:17 +08:00
};
mm/damon/core: implement DAMON-based Operation Schemes (DAMOS) In many cases, users might use DAMON for simple data access aware memory management optimizations such as applying an operation scheme to a memory region of a specific size having a specific access frequency for a specific time. For example, "page out a memory region larger than 100 MiB but having a low access frequency more than 10 minutes", or "Use THP for a memory region larger than 2 MiB having a high access frequency for more than 2 seconds". Most simple form of the solution would be doing offline data access pattern profiling using DAMON and modifying the application source code or system configuration based on the profiling results. Or, developing a daemon constructed with two modules (one for access monitoring and the other for applying memory management actions via mlock(), madvise(), sysctl, etc) is imaginable. To avoid users spending their time for implementation of such simple data access monitoring-based operation schemes, this makes DAMON to handle such schemes directly. With this change, users can simply specify their desired schemes to DAMON. Then, DAMON will automatically apply the schemes to the user-specified target processes. Each of the schemes is composed with conditions for filtering of the target memory regions and desired memory management action for the target. Specifically, the format is:: <min/max size> <min/max access frequency> <min/max age> <action> The filtering conditions are size of memory region, number of accesses to the region monitored by DAMON, and the age of the region. The age of region is incremented periodically but reset when its addresses or access frequency has significantly changed or the action of a scheme was applied. For the action, current implementation supports a few of madvise()-like hints, ``WILLNEED``, ``COLD``, ``PAGEOUT``, ``HUGEPAGE``, and ``NOHUGEPAGE``. Because DAMON supports various address spaces and application of the actions to a monitoring target region is dependent to the type of the target address space, the application code should be implemented by each primitives and registered to the framework. Note that this only implements the framework part. Following commit will implement the action applications for virtual address spaces primitives. Link: https://lkml.kernel.org/r/20211001125604.29660-3-sj@kernel.org Signed-off-by: SeongJae Park <sj@kernel.org> Cc: Amit Shah <amit@kernel.org> Cc: Benjamin Herrenschmidt <benh@kernel.crashing.org> Cc: David Hildenbrand <david@redhat.com> Cc: David Rienjes <rientjes@google.com> Cc: David Woodhouse <dwmw@amazon.com> Cc: Greg Thelen <gthelen@google.com> Cc: Jonathan Cameron <Jonathan.Cameron@huawei.com> Cc: Jonathan Corbet <corbet@lwn.net> Cc: Leonard Foerster <foersleo@amazon.de> Cc: Marco Elver <elver@google.com> Cc: Markus Boehme <markubo@amazon.de> Cc: Shakeel Butt <shakeelb@google.com> Cc: Shuah Khan <shuah@kernel.org> Signed-off-by: Andrew Morton <akpm@linux-foundation.org> Signed-off-by: Linus Torvalds <torvalds@linux-foundation.org>
2021-11-06 04:46:22 +08:00
/**
* struct damos - Represents a Data Access Monitoring-based Operation Scheme.
* @min_sz_region: Minimum size of target regions.
* @max_sz_region: Maximum size of target regions.
* @min_nr_accesses: Minimum ``->nr_accesses`` of target regions.
* @max_nr_accesses: Maximum ``->nr_accesses`` of target regions.
* @min_age_region: Minimum age of target regions.
* @max_age_region: Maximum age of target regions.
* @action: &damo_action to be applied to the target regions.
* @quota: Control the aggressiveness of this scheme.
mm/damon/schemes: activate schemes based on a watermarks mechanism DAMON-based operation schemes need to be manually turned on and off. In some use cases, however, the condition for turning a scheme on and off would depend on the system's situation. For example, schemes for proactive pages reclamation would need to be turned on when some memory pressure is detected, and turned off when the system has enough free memory. For easier control of schemes activation based on the system situation, this introduces a watermarks-based mechanism. The client can describe the watermark metric (e.g., amount of free memory in the system), watermark check interval, and three watermarks, namely high, mid, and low. If the scheme is deactivated, it only gets the metric and compare that to the three watermarks for every check interval. If the metric is higher than the high watermark, the scheme is deactivated. If the metric is between the mid watermark and the low watermark, the scheme is activated. If the metric is lower than the low watermark, the scheme is deactivated again. This is to allow users fall back to traditional page-granularity mechanisms. Link: https://lkml.kernel.org/r/20211019150731.16699-12-sj@kernel.org Signed-off-by: SeongJae Park <sj@kernel.org> Cc: Amit Shah <amit@kernel.org> Cc: Benjamin Herrenschmidt <benh@kernel.crashing.org> Cc: David Hildenbrand <david@redhat.com> Cc: David Rientjes <rientjes@google.com> Cc: David Woodhouse <dwmw@amazon.com> Cc: Greg Thelen <gthelen@google.com> Cc: Jonathan Cameron <Jonathan.Cameron@huawei.com> Cc: Jonathan Corbet <corbet@lwn.net> Cc: Leonard Foerster <foersleo@amazon.de> Cc: Marco Elver <elver@google.com> Cc: Markus Boehme <markubo@amazon.de> Cc: Shakeel Butt <shakeelb@google.com> Cc: Shuah Khan <shuah@kernel.org> Signed-off-by: Andrew Morton <akpm@linux-foundation.org> Signed-off-by: Linus Torvalds <torvalds@linux-foundation.org>
2021-11-06 04:47:47 +08:00
* @wmarks: Watermarks for automated (in)activation of this scheme.
mm/damon/schemes: account scheme actions that successfully applied Patch series "mm/damon/schemes: Extend stats for better online analysis and tuning". To help online access pattern analysis and tuning of DAMON-based Operation Schemes (DAMOS), DAMOS provides simple statistics for each scheme. Introduction of DAMOS time/space quota further made the tuning easier by making the risk management easier. However, that also made understanding of the working schemes a little bit more difficult. For an example, progress of a given scheme can now be throttled by not only the aggressiveness of the target access pattern, but also the time/space quotas. So, when a scheme is showing unexpectedly slow progress, it's difficult to know by what the progress of the scheme is throttled, with currently provided statistics. This patchset extends the statistics to contain some metrics that can be helpful for such online schemes analysis and tuning (patches 1-2), exports those to users (patches 3 and 5), and add documents (patches 4 and 6). This patch (of 6): DAMON-based operation schemes (DAMOS) stats provide only the number and the amount of regions that the action of the scheme has tried to be applied. Because the action could be failed for some reasons, the currently provided information is sometimes not useful or convenient enough for schemes profiling and tuning. To improve this situation, this commit extends the DAMOS stats to provide the number and the amount of regions that the action has successfully applied. Link: https://lkml.kernel.org/r/20211210150016.35349-1-sj@kernel.org Link: https://lkml.kernel.org/r/20211210150016.35349-2-sj@kernel.org Signed-off-by: SeongJae Park <sj@kernel.org> Signed-off-by: Andrew Morton <akpm@linux-foundation.org> Signed-off-by: Linus Torvalds <torvalds@linux-foundation.org>
2022-01-15 06:10:17 +08:00
* @stat: Statistics of this scheme.
mm/damon/core: implement DAMON-based Operation Schemes (DAMOS) In many cases, users might use DAMON for simple data access aware memory management optimizations such as applying an operation scheme to a memory region of a specific size having a specific access frequency for a specific time. For example, "page out a memory region larger than 100 MiB but having a low access frequency more than 10 minutes", or "Use THP for a memory region larger than 2 MiB having a high access frequency for more than 2 seconds". Most simple form of the solution would be doing offline data access pattern profiling using DAMON and modifying the application source code or system configuration based on the profiling results. Or, developing a daemon constructed with two modules (one for access monitoring and the other for applying memory management actions via mlock(), madvise(), sysctl, etc) is imaginable. To avoid users spending their time for implementation of such simple data access monitoring-based operation schemes, this makes DAMON to handle such schemes directly. With this change, users can simply specify their desired schemes to DAMON. Then, DAMON will automatically apply the schemes to the user-specified target processes. Each of the schemes is composed with conditions for filtering of the target memory regions and desired memory management action for the target. Specifically, the format is:: <min/max size> <min/max access frequency> <min/max age> <action> The filtering conditions are size of memory region, number of accesses to the region monitored by DAMON, and the age of the region. The age of region is incremented periodically but reset when its addresses or access frequency has significantly changed or the action of a scheme was applied. For the action, current implementation supports a few of madvise()-like hints, ``WILLNEED``, ``COLD``, ``PAGEOUT``, ``HUGEPAGE``, and ``NOHUGEPAGE``. Because DAMON supports various address spaces and application of the actions to a monitoring target region is dependent to the type of the target address space, the application code should be implemented by each primitives and registered to the framework. Note that this only implements the framework part. Following commit will implement the action applications for virtual address spaces primitives. Link: https://lkml.kernel.org/r/20211001125604.29660-3-sj@kernel.org Signed-off-by: SeongJae Park <sj@kernel.org> Cc: Amit Shah <amit@kernel.org> Cc: Benjamin Herrenschmidt <benh@kernel.crashing.org> Cc: David Hildenbrand <david@redhat.com> Cc: David Rienjes <rientjes@google.com> Cc: David Woodhouse <dwmw@amazon.com> Cc: Greg Thelen <gthelen@google.com> Cc: Jonathan Cameron <Jonathan.Cameron@huawei.com> Cc: Jonathan Corbet <corbet@lwn.net> Cc: Leonard Foerster <foersleo@amazon.de> Cc: Marco Elver <elver@google.com> Cc: Markus Boehme <markubo@amazon.de> Cc: Shakeel Butt <shakeelb@google.com> Cc: Shuah Khan <shuah@kernel.org> Signed-off-by: Andrew Morton <akpm@linux-foundation.org> Signed-off-by: Linus Torvalds <torvalds@linux-foundation.org>
2021-11-06 04:46:22 +08:00
* @list: List head for siblings.
*
* For each aggregation interval, DAMON finds regions which fit in the
* condition (&min_sz_region, &max_sz_region, &min_nr_accesses,
* &max_nr_accesses, &min_age_region, &max_age_region) and applies &action to
* those. To avoid consuming too much CPU time or IO resources for the
* &action, &quota is used.
*
mm/damon/schemes: activate schemes based on a watermarks mechanism DAMON-based operation schemes need to be manually turned on and off. In some use cases, however, the condition for turning a scheme on and off would depend on the system's situation. For example, schemes for proactive pages reclamation would need to be turned on when some memory pressure is detected, and turned off when the system has enough free memory. For easier control of schemes activation based on the system situation, this introduces a watermarks-based mechanism. The client can describe the watermark metric (e.g., amount of free memory in the system), watermark check interval, and three watermarks, namely high, mid, and low. If the scheme is deactivated, it only gets the metric and compare that to the three watermarks for every check interval. If the metric is higher than the high watermark, the scheme is deactivated. If the metric is between the mid watermark and the low watermark, the scheme is activated. If the metric is lower than the low watermark, the scheme is deactivated again. This is to allow users fall back to traditional page-granularity mechanisms. Link: https://lkml.kernel.org/r/20211019150731.16699-12-sj@kernel.org Signed-off-by: SeongJae Park <sj@kernel.org> Cc: Amit Shah <amit@kernel.org> Cc: Benjamin Herrenschmidt <benh@kernel.crashing.org> Cc: David Hildenbrand <david@redhat.com> Cc: David Rientjes <rientjes@google.com> Cc: David Woodhouse <dwmw@amazon.com> Cc: Greg Thelen <gthelen@google.com> Cc: Jonathan Cameron <Jonathan.Cameron@huawei.com> Cc: Jonathan Corbet <corbet@lwn.net> Cc: Leonard Foerster <foersleo@amazon.de> Cc: Marco Elver <elver@google.com> Cc: Markus Boehme <markubo@amazon.de> Cc: Shakeel Butt <shakeelb@google.com> Cc: Shuah Khan <shuah@kernel.org> Signed-off-by: Andrew Morton <akpm@linux-foundation.org> Signed-off-by: Linus Torvalds <torvalds@linux-foundation.org>
2021-11-06 04:47:47 +08:00
* To do the work only when needed, schemes can be activated for specific
* system situations using &wmarks. If all schemes that registered to the
* monitoring context are inactive, DAMON stops monitoring either, and just
* repeatedly checks the watermarks.
*
* If all schemes that registered to a &struct damon_ctx are inactive, DAMON
* stops monitoring and just repeatedly checks the watermarks.
*
* After applying the &action to each region, &stat_count and &stat_sz is
* updated to reflect the number of regions and total size of regions that the
* &action is applied.
mm/damon/core: implement DAMON-based Operation Schemes (DAMOS) In many cases, users might use DAMON for simple data access aware memory management optimizations such as applying an operation scheme to a memory region of a specific size having a specific access frequency for a specific time. For example, "page out a memory region larger than 100 MiB but having a low access frequency more than 10 minutes", or "Use THP for a memory region larger than 2 MiB having a high access frequency for more than 2 seconds". Most simple form of the solution would be doing offline data access pattern profiling using DAMON and modifying the application source code or system configuration based on the profiling results. Or, developing a daemon constructed with two modules (one for access monitoring and the other for applying memory management actions via mlock(), madvise(), sysctl, etc) is imaginable. To avoid users spending their time for implementation of such simple data access monitoring-based operation schemes, this makes DAMON to handle such schemes directly. With this change, users can simply specify their desired schemes to DAMON. Then, DAMON will automatically apply the schemes to the user-specified target processes. Each of the schemes is composed with conditions for filtering of the target memory regions and desired memory management action for the target. Specifically, the format is:: <min/max size> <min/max access frequency> <min/max age> <action> The filtering conditions are size of memory region, number of accesses to the region monitored by DAMON, and the age of the region. The age of region is incremented periodically but reset when its addresses or access frequency has significantly changed or the action of a scheme was applied. For the action, current implementation supports a few of madvise()-like hints, ``WILLNEED``, ``COLD``, ``PAGEOUT``, ``HUGEPAGE``, and ``NOHUGEPAGE``. Because DAMON supports various address spaces and application of the actions to a monitoring target region is dependent to the type of the target address space, the application code should be implemented by each primitives and registered to the framework. Note that this only implements the framework part. Following commit will implement the action applications for virtual address spaces primitives. Link: https://lkml.kernel.org/r/20211001125604.29660-3-sj@kernel.org Signed-off-by: SeongJae Park <sj@kernel.org> Cc: Amit Shah <amit@kernel.org> Cc: Benjamin Herrenschmidt <benh@kernel.crashing.org> Cc: David Hildenbrand <david@redhat.com> Cc: David Rienjes <rientjes@google.com> Cc: David Woodhouse <dwmw@amazon.com> Cc: Greg Thelen <gthelen@google.com> Cc: Jonathan Cameron <Jonathan.Cameron@huawei.com> Cc: Jonathan Corbet <corbet@lwn.net> Cc: Leonard Foerster <foersleo@amazon.de> Cc: Marco Elver <elver@google.com> Cc: Markus Boehme <markubo@amazon.de> Cc: Shakeel Butt <shakeelb@google.com> Cc: Shuah Khan <shuah@kernel.org> Signed-off-by: Andrew Morton <akpm@linux-foundation.org> Signed-off-by: Linus Torvalds <torvalds@linux-foundation.org>
2021-11-06 04:46:22 +08:00
*/
struct damos {
unsigned long min_sz_region;
unsigned long max_sz_region;
unsigned int min_nr_accesses;
unsigned int max_nr_accesses;
unsigned int min_age_region;
unsigned int max_age_region;
enum damos_action action;
struct damos_quota quota;
mm/damon/schemes: activate schemes based on a watermarks mechanism DAMON-based operation schemes need to be manually turned on and off. In some use cases, however, the condition for turning a scheme on and off would depend on the system's situation. For example, schemes for proactive pages reclamation would need to be turned on when some memory pressure is detected, and turned off when the system has enough free memory. For easier control of schemes activation based on the system situation, this introduces a watermarks-based mechanism. The client can describe the watermark metric (e.g., amount of free memory in the system), watermark check interval, and three watermarks, namely high, mid, and low. If the scheme is deactivated, it only gets the metric and compare that to the three watermarks for every check interval. If the metric is higher than the high watermark, the scheme is deactivated. If the metric is between the mid watermark and the low watermark, the scheme is activated. If the metric is lower than the low watermark, the scheme is deactivated again. This is to allow users fall back to traditional page-granularity mechanisms. Link: https://lkml.kernel.org/r/20211019150731.16699-12-sj@kernel.org Signed-off-by: SeongJae Park <sj@kernel.org> Cc: Amit Shah <amit@kernel.org> Cc: Benjamin Herrenschmidt <benh@kernel.crashing.org> Cc: David Hildenbrand <david@redhat.com> Cc: David Rientjes <rientjes@google.com> Cc: David Woodhouse <dwmw@amazon.com> Cc: Greg Thelen <gthelen@google.com> Cc: Jonathan Cameron <Jonathan.Cameron@huawei.com> Cc: Jonathan Corbet <corbet@lwn.net> Cc: Leonard Foerster <foersleo@amazon.de> Cc: Marco Elver <elver@google.com> Cc: Markus Boehme <markubo@amazon.de> Cc: Shakeel Butt <shakeelb@google.com> Cc: Shuah Khan <shuah@kernel.org> Signed-off-by: Andrew Morton <akpm@linux-foundation.org> Signed-off-by: Linus Torvalds <torvalds@linux-foundation.org>
2021-11-06 04:47:47 +08:00
struct damos_watermarks wmarks;
mm/damon/schemes: account scheme actions that successfully applied Patch series "mm/damon/schemes: Extend stats for better online analysis and tuning". To help online access pattern analysis and tuning of DAMON-based Operation Schemes (DAMOS), DAMOS provides simple statistics for each scheme. Introduction of DAMOS time/space quota further made the tuning easier by making the risk management easier. However, that also made understanding of the working schemes a little bit more difficult. For an example, progress of a given scheme can now be throttled by not only the aggressiveness of the target access pattern, but also the time/space quotas. So, when a scheme is showing unexpectedly slow progress, it's difficult to know by what the progress of the scheme is throttled, with currently provided statistics. This patchset extends the statistics to contain some metrics that can be helpful for such online schemes analysis and tuning (patches 1-2), exports those to users (patches 3 and 5), and add documents (patches 4 and 6). This patch (of 6): DAMON-based operation schemes (DAMOS) stats provide only the number and the amount of regions that the action of the scheme has tried to be applied. Because the action could be failed for some reasons, the currently provided information is sometimes not useful or convenient enough for schemes profiling and tuning. To improve this situation, this commit extends the DAMOS stats to provide the number and the amount of regions that the action has successfully applied. Link: https://lkml.kernel.org/r/20211210150016.35349-1-sj@kernel.org Link: https://lkml.kernel.org/r/20211210150016.35349-2-sj@kernel.org Signed-off-by: SeongJae Park <sj@kernel.org> Signed-off-by: Andrew Morton <akpm@linux-foundation.org> Signed-off-by: Linus Torvalds <torvalds@linux-foundation.org>
2022-01-15 06:10:17 +08:00
struct damos_stat stat;
mm/damon/core: implement DAMON-based Operation Schemes (DAMOS) In many cases, users might use DAMON for simple data access aware memory management optimizations such as applying an operation scheme to a memory region of a specific size having a specific access frequency for a specific time. For example, "page out a memory region larger than 100 MiB but having a low access frequency more than 10 minutes", or "Use THP for a memory region larger than 2 MiB having a high access frequency for more than 2 seconds". Most simple form of the solution would be doing offline data access pattern profiling using DAMON and modifying the application source code or system configuration based on the profiling results. Or, developing a daemon constructed with two modules (one for access monitoring and the other for applying memory management actions via mlock(), madvise(), sysctl, etc) is imaginable. To avoid users spending their time for implementation of such simple data access monitoring-based operation schemes, this makes DAMON to handle such schemes directly. With this change, users can simply specify their desired schemes to DAMON. Then, DAMON will automatically apply the schemes to the user-specified target processes. Each of the schemes is composed with conditions for filtering of the target memory regions and desired memory management action for the target. Specifically, the format is:: <min/max size> <min/max access frequency> <min/max age> <action> The filtering conditions are size of memory region, number of accesses to the region monitored by DAMON, and the age of the region. The age of region is incremented periodically but reset when its addresses or access frequency has significantly changed or the action of a scheme was applied. For the action, current implementation supports a few of madvise()-like hints, ``WILLNEED``, ``COLD``, ``PAGEOUT``, ``HUGEPAGE``, and ``NOHUGEPAGE``. Because DAMON supports various address spaces and application of the actions to a monitoring target region is dependent to the type of the target address space, the application code should be implemented by each primitives and registered to the framework. Note that this only implements the framework part. Following commit will implement the action applications for virtual address spaces primitives. Link: https://lkml.kernel.org/r/20211001125604.29660-3-sj@kernel.org Signed-off-by: SeongJae Park <sj@kernel.org> Cc: Amit Shah <amit@kernel.org> Cc: Benjamin Herrenschmidt <benh@kernel.crashing.org> Cc: David Hildenbrand <david@redhat.com> Cc: David Rienjes <rientjes@google.com> Cc: David Woodhouse <dwmw@amazon.com> Cc: Greg Thelen <gthelen@google.com> Cc: Jonathan Cameron <Jonathan.Cameron@huawei.com> Cc: Jonathan Corbet <corbet@lwn.net> Cc: Leonard Foerster <foersleo@amazon.de> Cc: Marco Elver <elver@google.com> Cc: Markus Boehme <markubo@amazon.de> Cc: Shakeel Butt <shakeelb@google.com> Cc: Shuah Khan <shuah@kernel.org> Signed-off-by: Andrew Morton <akpm@linux-foundation.org> Signed-off-by: Linus Torvalds <torvalds@linux-foundation.org>
2021-11-06 04:46:22 +08:00
struct list_head list;
};
/**
* enum damon_ops_id - Identifier for each monitoring operations implementation
*
* @DAMON_OPS_VADDR: Monitoring operations for virtual address spaces
mm/damon/vaddr: register a damon_operations for fixed virtual address ranges monitoring Patch series "support fixed virtual address ranges monitoring". The monitoring operations set for virtual address spaces automatically updates the monitoring target regions to cover entire mappings of the virtual address spaces as much as possible. Some users could have more information about their programs than kernel and therefore have interest in not entire regions but only specific regions. For such cases, the automatic monitoring target regions updates are only unnecessary overhead or distractions. This patchset adds supports for the use case on DAMON's kernel API (DAMON_OPS_FVADDR) and sysfs interface ('fvaddr' keyword for 'operations' sysfs file). This patch (of 3): The monitoring operations set for virtual address spaces automatically updates the monitoring target regions to cover entire mappings of the virtual address spaces as much as possible. Some users could have more information about their programs than kernel and therefore have interest in not entire regions but only specific regions. For such cases, the automatic monitoring target regions updates are only unnecessary overheads or distractions. For such cases, DAMON's API users can simply set the '->init()' and '->update()' of the DAMON context's '->ops' NULL, and set the target monitoring regions when creating the context. But, that would be a dirty hack. Worse yet, the hack is unavailable for DAMON user space interface users. To support the use case in a clean way that can easily exported to the user space, this commit adds another monitoring operations set called 'fvaddr', which is same to 'vaddr' but does not automatically update the monitoring regions. Instead, it will only respect the virtual address regions which have explicitly passed at the initial context creation. Note that this commit leave sysfs interface not supporting the feature yet. The support will be made in a following commit. Link: https://lkml.kernel.org/r/20220426231750.48822-1-sj@kernel.org Link: https://lkml.kernel.org/r/20220426231750.48822-2-sj@kernel.org Signed-off-by: SeongJae Park <sj@kernel.org> Signed-off-by: Andrew Morton <akpm@linux-foundation.org>
2022-05-10 09:20:52 +08:00
* @DAMON_OPS_FVADDR: Monitoring operations for only fixed ranges of virtual
* address spaces
* @DAMON_OPS_PADDR: Monitoring operations for the physical address space
* @NR_DAMON_OPS: Number of monitoring operations implementations
*/
enum damon_ops_id {
DAMON_OPS_VADDR,
mm/damon/vaddr: register a damon_operations for fixed virtual address ranges monitoring Patch series "support fixed virtual address ranges monitoring". The monitoring operations set for virtual address spaces automatically updates the monitoring target regions to cover entire mappings of the virtual address spaces as much as possible. Some users could have more information about their programs than kernel and therefore have interest in not entire regions but only specific regions. For such cases, the automatic monitoring target regions updates are only unnecessary overhead or distractions. This patchset adds supports for the use case on DAMON's kernel API (DAMON_OPS_FVADDR) and sysfs interface ('fvaddr' keyword for 'operations' sysfs file). This patch (of 3): The monitoring operations set for virtual address spaces automatically updates the monitoring target regions to cover entire mappings of the virtual address spaces as much as possible. Some users could have more information about their programs than kernel and therefore have interest in not entire regions but only specific regions. For such cases, the automatic monitoring target regions updates are only unnecessary overheads or distractions. For such cases, DAMON's API users can simply set the '->init()' and '->update()' of the DAMON context's '->ops' NULL, and set the target monitoring regions when creating the context. But, that would be a dirty hack. Worse yet, the hack is unavailable for DAMON user space interface users. To support the use case in a clean way that can easily exported to the user space, this commit adds another monitoring operations set called 'fvaddr', which is same to 'vaddr' but does not automatically update the monitoring regions. Instead, it will only respect the virtual address regions which have explicitly passed at the initial context creation. Note that this commit leave sysfs interface not supporting the feature yet. The support will be made in a following commit. Link: https://lkml.kernel.org/r/20220426231750.48822-1-sj@kernel.org Link: https://lkml.kernel.org/r/20220426231750.48822-2-sj@kernel.org Signed-off-by: SeongJae Park <sj@kernel.org> Signed-off-by: Andrew Morton <akpm@linux-foundation.org>
2022-05-10 09:20:52 +08:00
DAMON_OPS_FVADDR,
DAMON_OPS_PADDR,
NR_DAMON_OPS,
};
mm: introduce Data Access MONitor (DAMON) Patch series "Introduce Data Access MONitor (DAMON)", v34. Introduction ============ DAMON is a data access monitoring framework for the Linux kernel. The core mechanisms of DAMON called 'region based sampling' and 'adaptive regions adjustment' (refer to 'mechanisms.rst' in the 11th patch of this patchset for the detail) make it - accurate (The monitored information is useful for DRAM level memory management. It might not appropriate for Cache-level accuracy, though.), - light-weight (The monitoring overhead is low enough to be applied online while making no impact on the performance of the target workloads.), and - scalable (the upper-bound of the instrumentation overhead is controllable regardless of the size of target workloads.). Using this framework, therefore, several memory management mechanisms such as reclamation and THP can be optimized to aware real data access patterns. Experimental access pattern aware memory management optimization works that incurring high instrumentation overhead will be able to have another try. Though DAMON is for kernel subsystems, it can be easily exposed to the user space by writing a DAMON-wrapper kernel subsystem. Then, user space users who have some special workloads will be able to write personalized tools or applications for deeper understanding and specialized optimizations of their systems. DAMON is also merged in two public Amazon Linux kernel trees that based on v5.4.y[1] and v5.10.y[2]. [1] https://github.com/amazonlinux/linux/tree/amazon-5.4.y/master/mm/damon [2] https://github.com/amazonlinux/linux/tree/amazon-5.10.y/master/mm/damon The userspace tool[1] is available, released under GPLv2, and actively being maintained. I am also planning to implement another basic user interface in perf[2]. Also, the basic test suite for DAMON is available under GPLv2[3]. [1] https://github.com/awslabs/damo [2] https://lore.kernel.org/linux-mm/20210107120729.22328-1-sjpark@amazon.com/ [3] https://github.com/awslabs/damon-tests Long-term Plan -------------- DAMON is a part of a project called Data Access-aware Operating System (DAOS). As the name implies, I want to improve the performance and efficiency of systems using fine-grained data access patterns. The optimizations are for both kernel and user spaces. I will therefore modify or create kernel subsystems, export some of those to user space and implement user space library / tools. Below shows the layers and components for the project. --------------------------------------------------------------------------- Primitives: PTE Accessed bit, PG_idle, rmap, (Intel CMT), ... Framework: DAMON Features: DAMOS, virtual addr, physical addr, ... Applications: DAMON-debugfs, (DARC), ... ^^^^^^^^^^^^^^^^^^^^^^^ KERNEL SPACE ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ Raw Interface: debugfs, (sysfs), (damonfs), tracepoints, (sys_damon), ... vvvvvvvvvvvvvvvvvvvvvvv USER SPACE vvvvvvvvvvvvvvvvvvvvvvvvvvvvvvvv Library: (libdamon), ... Tools: DAMO, (perf), ... --------------------------------------------------------------------------- The components in parentheses or marked as '...' are not implemented yet but in the future plan. IOW, those are the TODO tasks of DAOS project. For more detail, please refer to the plans: https://lore.kernel.org/linux-mm/20201202082731.24828-1-sjpark@amazon.com/ Evaluations =========== We evaluated DAMON's overhead, monitoring quality and usefulness using 24 realistic workloads on my QEMU/KVM based virtual machine running a kernel that v24 DAMON patchset is applied. DAMON is lightweight. It increases system memory usage by 0.39% and slows target workloads down by 1.16%. DAMON is accurate and useful for memory management optimizations. An experimental DAMON-based operation scheme for THP, namely 'ethp', removes 76.15% of THP memory overheads while preserving 51.25% of THP speedup. Another experimental DAMON-based 'proactive reclamation' implementation, 'prcl', reduces 93.38% of residential sets and 23.63% of system memory footprint while incurring only 1.22% runtime overhead in the best case (parsec3/freqmine). NOTE that the experimental THP optimization and proactive reclamation are not for production but only for proof of concepts. Please refer to the official document[1] or "Documentation/admin-guide/mm: Add a document for DAMON" patch in this patchset for detailed evaluation setup and results. [1] https://damonitor.github.io/doc/html/latest-damon/admin-guide/mm/damon/eval.html Real-world User Story ===================== In summary, DAMON has used on production systems and proved its usefulness. DAMON as a profiler ------------------- We analyzed characteristics of a large scale production systems of our customers using DAMON. The systems utilize 70GB DRAM and 36 CPUs. From this, we were able to find interesting things below. There were obviously different access pattern under idle workload and active workload. Under the idle workload, it accessed large memory regions with low frequency, while the active workload accessed small memory regions with high freuqnecy. DAMON found a 7GB memory region that showing obviously high access frequency under the active workload. We believe this is the performance-effective working set and need to be protected. There was a 4KB memory region that showing highest access frequency under not only active but also idle workloads. We think this must be a hottest code section like thing that should never be paged out. For this analysis, DAMON used only 0.3-1% of single CPU time. Because we used recording-based analysis, it consumed about 3-12 MB of disk space per 20 minutes. This is only small amount of disk space, but we can further reduce the disk usage by using non-recording-based DAMON features. I'd like to argue that only DAMON can do such detailed analysis (finding 4KB highest region in 70GB memory) with the light overhead. DAMON as a system optimization tool ----------------------------------- We also found below potential performance problems on the systems and made DAMON-based solutions. The system doesn't want to make the workload suffer from the page reclamation and thus it utilizes enough DRAM but no swap device. However, we found the system is actively reclaiming file-backed pages, because the system has intensive file IO. The file IO turned out to be not performance critical for the workload, but the customer wanted to ensure performance critical file-backed pages like code section to not mistakenly be evicted. Using direct IO should or `mlock()` would be a straightforward solution, but modifying the user space code is not easy for the customer. Alternatively, we could use DAMON-based operation scheme[1]. By using it, we can ask DAMON to track access frequency of each region and make 'process_madvise(MADV_WILLNEED)[2]' call for regions having specific size and access frequency for a time interval. We also found the system is having high number of TLB misses. We tried 'always' THP enabled policy and it greatly reduced TLB misses, but the page reclamation also been more frequent due to the THP internal fragmentation caused memory bloat. We could try another DAMON-based operation scheme that applies 'MADV_HUGEPAGE' to memory regions having >=2MB size and high access frequency, while applying 'MADV_NOHUGEPAGE' to regions having <2MB size and low access frequency. We do not own the systems so we only reported the analysis results and possible optimization solutions to the customers. The customers satisfied about the analysis results and promised to try the optimization guides. [1] https://lore.kernel.org/linux-mm/20201006123931.5847-1-sjpark@amazon.com/ [2] https://lore.kernel.org/linux-api/20200622192900.22757-4-minchan@kernel.org/ Comparison with Idle Page Tracking ================================== Idle Page Tracking allows users to set and read idleness of pages using a bitmap file which represents each page with each bit of the file. One recommended usage of it is working set size detection. Users can do that by 1. find PFN of each page for workloads in interest, 2. set all the pages as idle by doing writes to the bitmap file, 3. wait until the workload accesses its working set, and 4. read the idleness of the pages again and count pages became not idle. NOTE: While Idle Page Tracking is for user space users, DAMON is primarily designed for kernel subsystems though it can easily exposed to the user space. Hence, this section only assumes such user space use of DAMON. For what use cases Idle Page Tracking would be better? ------------------------------------------------------ 1. Flexible usecases other than hotness monitoring. Because Idle Page Tracking allows users to control the primitive (Page idleness) by themselves, Idle Page Tracking users can do anything they want. Meanwhile, DAMON is primarily designed to monitor the hotness of each memory region. For this, DAMON asks users to provide sampling interval and aggregation interval. For the reason, there could be some use case that using Idle Page Tracking is simpler. 2. Physical memory monitoring. Idle Page Tracking receives PFN range as input, so natively supports physical memory monitoring. DAMON is designed to be extensible for multiple address spaces and use cases by implementing and using primitives for the given use case. Therefore, by theory, DAMON has no limitation in the type of target address space as long as primitives for the given address space exists. However, the default primitives introduced by this patchset supports only virtual address spaces. Therefore, for physical memory monitoring, you should implement your own primitives and use it, or simply use Idle Page Tracking. Nonetheless, RFC patchsets[1] for the physical memory address space primitives is already available. It also supports user memory same to Idle Page Tracking. [1] https://lore.kernel.org/linux-mm/20200831104730.28970-1-sjpark@amazon.com/ For what use cases DAMON is better? ----------------------------------- 1. Hotness Monitoring. Idle Page Tracking let users know only if a page frame is accessed or not. For hotness check, the user should write more code and use more memory. DAMON do that by itself. 2. Low Monitoring Overhead DAMON receives user's monitoring request with one step and then provide the results. So, roughly speaking, DAMON require only O(1) user/kernel context switches. In case of Idle Page Tracking, however, because the interface receives contiguous page frames, the number of user/kernel context switches increases as the monitoring target becomes complex and huge. As a result, the context switch overhead could be not negligible. Moreover, DAMON is born to handle with the monitoring overhead. Because the core mechanism is pure logical, Idle Page Tracking users might be able to implement the mechanism on their own, but it would be time consuming and the user/kernel context switching will still more frequent than that of DAMON. Also, the kernel subsystems cannot use the logic in this case. 3. Page granularity working set size detection. Until v22 of this patchset, this was categorized as the thing Idle Page Tracking could do better, because DAMON basically maintains additional metadata for each of the monitoring target regions. So, in the page granularity working set size detection use case, DAMON would incur (number of monitoring target pages * size of metadata) memory overhead. Size of the single metadata item is about 54 bytes, so assuming 4KB pages, about 1.3% of monitoring target pages will be additionally used. All essential metadata for Idle Page Tracking are embedded in 'struct page' and page table entries. Therefore, in this use case, only one counter variable for working set size accounting is required if Idle Page Tracking is used. There are more details to consider, but roughly speaking, this is true in most cases. However, the situation changed from v23. Now DAMON supports arbitrary types of monitoring targets, which don't use the metadata. Using that, DAMON can do the working set size detection with no additional space overhead but less user-kernel context switch. A first draft for the implementation of monitoring primitives for this usage is available in a DAMON development tree[1]. An RFC patchset for it based on this patchset will also be available soon. Since v24, the arbitrary type support is dropped from this patchset because this patchset doesn't introduce real use of the type. You can still get it from the DAMON development tree[2], though. [1] https://github.com/sjp38/linux/tree/damon/pgidle_hack [2] https://github.com/sjp38/linux/tree/damon/master 4. More future usecases While Idle Page Tracking has tight coupling with base primitives (PG_Idle and page table Accessed bits), DAMON is designed to be extensible for many use cases and address spaces. If you need some special address type or want to use special h/w access check primitives, you can write your own primitives for that and configure DAMON to use those. Therefore, if your use case could be changed a lot in future, using DAMON could be better. Can I use both Idle Page Tracking and DAMON? -------------------------------------------- Yes, though using them concurrently for overlapping memory regions could result in interference to each other. Nevertheless, such use case would be rare or makes no sense at all. Even in the case, the noise would bot be really significant. So, you can choose whatever you want depending on the characteristics of your use cases. More Information ================ We prepared a showcase web site[1] that you can get more information. There are - the official documentations[2], - the heatmap format dynamic access pattern of various realistic workloads for heap area[3], mmap()-ed area[4], and stack[5] area, - the dynamic working set size distribution[6] and chronological working set size changes[7], and - the latest performance test results[8]. [1] https://damonitor.github.io/_index [2] https://damonitor.github.io/doc/html/latest-damon [3] https://damonitor.github.io/test/result/visual/latest/rec.heatmap.0.png.html [4] https://damonitor.github.io/test/result/visual/latest/rec.heatmap.1.png.html [5] https://damonitor.github.io/test/result/visual/latest/rec.heatmap.2.png.html [6] https://damonitor.github.io/test/result/visual/latest/rec.wss_sz.png.html [7] https://damonitor.github.io/test/result/visual/latest/rec.wss_time.png.html [8] https://damonitor.github.io/test/result/perf/latest/html/index.html Baseline and Complete Git Trees =============================== The patches are based on the latest -mm tree, specifically v5.14-rc1-mmots-2021-07-15-18-47 of https://github.com/hnaz/linux-mm. You can also clone the complete git tree: $ git clone git://github.com/sjp38/linux -b damon/patches/v34 The web is also available: https://github.com/sjp38/linux/releases/tag/damon/patches/v34 Development Trees ----------------- There are a couple of trees for entire DAMON patchset series and features for future release. - For latest release: https://github.com/sjp38/linux/tree/damon/master - For next release: https://github.com/sjp38/linux/tree/damon/next Long-term Support Trees ----------------------- For people who want to test DAMON but using LTS kernels, there are another couple of trees based on two latest LTS kernels respectively and containing the 'damon/master' backports. - For v5.4.y: https://github.com/sjp38/linux/tree/damon/for-v5.4.y - For v5.10.y: https://github.com/sjp38/linux/tree/damon/for-v5.10.y Amazon Linux Kernel Trees ------------------------- DAMON is also merged in two public Amazon Linux kernel trees that based on v5.4.y[1] and v5.10.y[2]. [1] https://github.com/amazonlinux/linux/tree/amazon-5.4.y/master/mm/damon [2] https://github.com/amazonlinux/linux/tree/amazon-5.10.y/master/mm/damon Git Tree for Diff of Patches ============================ For easy review of diff between different versions of each patch, I prepared a git tree containing all versions of the DAMON patchset series: https://github.com/sjp38/damon-patches You can clone it and use 'diff' for easy review of changes between different versions of the patchset. For example: $ git clone https://github.com/sjp38/damon-patches && cd damon-patches $ diff -u damon/v33 damon/v34 Sequence Of Patches =================== First three patches implement the core logics of DAMON. The 1st patch introduces basic sampling based hotness monitoring for arbitrary types of targets. Following two patches implement the core mechanisms for control of overhead and accuracy, namely regions based sampling (patch 2) and adaptive regions adjustment (patch 3). Now the essential parts of DAMON is complete, but it cannot work unless someone provides monitoring primitives for a specific use case. The following two patches make it just work for virtual address spaces monitoring. The 4th patch makes 'PG_idle' can be used by DAMON and the 5th patch implements the virtual memory address space specific monitoring primitives using page table Accessed bits and the 'PG_idle' page flag. Now DAMON just works for virtual address space monitoring via the kernel space api. To let the user space users can use DAMON, following four patches add interfaces for them. The 6th patch adds a tracepoint for monitoring results. The 7th patch implements a DAMON application kernel module, namely damon-dbgfs, that simply wraps DAMON and exposes DAMON interface to the user space via the debugfs interface. The 8th patch further exports pid of monitoring thread (kdamond) to user space for easier cpu usage accounting, and the 9th patch makes the debugfs interface to support multiple contexts. Three patches for maintainability follows. The 10th patch adds documentations for both the user space and the kernel space. The 11th patch provides unit tests (based on the kunit) while the 12th patch adds user space tests (based on the kselftest). Finally, the last patch (13th) updates the MAINTAINERS file. This patch (of 13): DAMON is a data access monitoring framework for the Linux kernel. The core mechanisms of DAMON make it - accurate (the monitoring output is useful enough for DRAM level performance-centric memory management; It might be inappropriate for CPU cache levels, though), - light-weight (the monitoring overhead is normally low enough to be applied online), and - scalable (the upper-bound of the overhead is in constant range regardless of the size of target workloads). Using this framework, hence, we can easily write efficient kernel space data access monitoring applications. For example, the kernel's memory management mechanisms can make advanced decisions using this. Experimental data access aware optimization works that incurring high access monitoring overhead could again be implemented on top of this. Due to its simple and flexible interface, providing user space interface would be also easy. Then, user space users who have some special workloads can write personalized applications for better understanding and optimizations of their workloads and systems. === Nevertheless, this commit is defining and implementing only basic access check part without the overhead-accuracy handling core logic. The basic access check is as below. The output of DAMON says what memory regions are how frequently accessed for a given duration. The resolution of the access frequency is controlled by setting ``sampling interval`` and ``aggregation interval``. In detail, DAMON checks access to each page per ``sampling interval`` and aggregates the results. In other words, counts the number of the accesses to each region. After each ``aggregation interval`` passes, DAMON calls callback functions that previously registered by users so that users can read the aggregated results and then clears the results. This can be described in below simple pseudo-code:: init() while monitoring_on: for page in monitoring_target: if accessed(page): nr_accesses[page] += 1 if time() % aggregation_interval == 0: for callback in user_registered_callbacks: callback(monitoring_target, nr_accesses) for page in monitoring_target: nr_accesses[page] = 0 if time() % update_interval == 0: update() sleep(sampling interval) The target regions constructed at the beginning of the monitoring and updated after each ``regions_update_interval``, because the target regions could be dynamically changed (e.g., mmap() or memory hotplug). The monitoring overhead of this mechanism will arbitrarily increase as the size of the target workload grows. The basic monitoring primitives for actual access check and dynamic target regions construction aren't in the core part of DAMON. Instead, it allows users to implement their own primitives that are optimized for their use case and configure DAMON to use those. In other words, users cannot use current version of DAMON without some additional works. Following commits will implement the core mechanisms for the overhead-accuracy control and default primitives implementations. Link: https://lkml.kernel.org/r/20210716081449.22187-1-sj38.park@gmail.com Link: https://lkml.kernel.org/r/20210716081449.22187-2-sj38.park@gmail.com Signed-off-by: SeongJae Park <sjpark@amazon.de> Reviewed-by: Leonard Foerster <foersleo@amazon.de> Reviewed-by: Fernand Sieber <sieberf@amazon.com> Acked-by: Shakeel Butt <shakeelb@google.com> Cc: Jonathan Cameron <Jonathan.Cameron@huawei.com> Cc: Alexander Shishkin <alexander.shishkin@linux.intel.com> Cc: Amit Shah <amit@kernel.org> Cc: Benjamin Herrenschmidt <benh@kernel.crashing.org> Cc: Jonathan Corbet <corbet@lwn.net> Cc: David Hildenbrand <david@redhat.com> Cc: David Woodhouse <dwmw@amazon.com> Cc: Marco Elver <elver@google.com> Cc: Fan Du <fan.du@intel.com> Cc: Greg Kroah-Hartman <greg@kroah.com> Cc: Greg Thelen <gthelen@google.com> Cc: Joe Perches <joe@perches.com> Cc: Mel Gorman <mgorman@suse.de> Cc: Maximilian Heyne <mheyne@amazon.de> Cc: Minchan Kim <minchan@kernel.org> Cc: Ingo Molnar <mingo@redhat.com> Cc: Namhyung Kim <namhyung@kernel.org> Cc: Peter Zijlstra <peterz@infradead.org> Cc: Rik van Riel <riel@surriel.com> Cc: David Rientjes <rientjes@google.com> Cc: Steven Rostedt (VMware) <rostedt@goodmis.org> Cc: Shuah Khan <shuah@kernel.org> Cc: Vlastimil Babka <vbabka@suse.cz> Cc: Vladimir Davydov <vdavydov.dev@gmail.com> Cc: Brendan Higgins <brendanhiggins@google.com> Cc: Markus Boehme <markubo@amazon.de> Signed-off-by: Andrew Morton <akpm@linux-foundation.org> Signed-off-by: Linus Torvalds <torvalds@linux-foundation.org>
2021-09-08 10:56:28 +08:00
struct damon_ctx;
/**
mm/damon: rename damon_primitives to damon_operations Patch series "Allow DAMON user code independent of monitoring primitives". In-kernel DAMON user code is required to configure the monitoring context (struct damon_ctx) with proper monitoring primitives (struct damon_primitive). This makes the user code dependent to all supporting monitoring primitives. For example, DAMON debugfs interface depends on both DAMON_VADDR and DAMON_PADDR, though some users have interest in only one use case. As more monitoring primitives are introduced, the problem will be bigger. To minimize such unnecessary dependency, this patchset makes monitoring primitives can be registered by the implemnting code and later dynamically searched and selected by the user code. In addition to that, this patchset renames monitoring primitives to monitoring operations, which is more easy to intuitively understand what it means and how it would be structed. This patch (of 8): DAMON has a set of callback functions called monitoring primitives and let it can be configured with various implementations for easy extension for different address spaces and usages. However, the word 'primitive' is not so explicit. Meanwhile, many other structs resembles similar purpose calls themselves 'operations'. To make the code easier to be understood, this commit renames 'damon_primitives' to 'damon_operations' before it is too late to rename. Link: https://lkml.kernel.org/r/20220215184603.1479-1-sj@kernel.org Link: https://lkml.kernel.org/r/20220215184603.1479-2-sj@kernel.org Signed-off-by: SeongJae Park <sj@kernel.org> Cc: Xin Hao <xhao@linux.alibaba.com> Cc: David Rientjes <rientjes@google.com> Signed-off-by: Andrew Morton <akpm@linux-foundation.org> Signed-off-by: Linus Torvalds <torvalds@linux-foundation.org>
2022-03-23 05:48:46 +08:00
* struct damon_operations - Monitoring operations for given use cases.
mm: introduce Data Access MONitor (DAMON) Patch series "Introduce Data Access MONitor (DAMON)", v34. Introduction ============ DAMON is a data access monitoring framework for the Linux kernel. The core mechanisms of DAMON called 'region based sampling' and 'adaptive regions adjustment' (refer to 'mechanisms.rst' in the 11th patch of this patchset for the detail) make it - accurate (The monitored information is useful for DRAM level memory management. It might not appropriate for Cache-level accuracy, though.), - light-weight (The monitoring overhead is low enough to be applied online while making no impact on the performance of the target workloads.), and - scalable (the upper-bound of the instrumentation overhead is controllable regardless of the size of target workloads.). Using this framework, therefore, several memory management mechanisms such as reclamation and THP can be optimized to aware real data access patterns. Experimental access pattern aware memory management optimization works that incurring high instrumentation overhead will be able to have another try. Though DAMON is for kernel subsystems, it can be easily exposed to the user space by writing a DAMON-wrapper kernel subsystem. Then, user space users who have some special workloads will be able to write personalized tools or applications for deeper understanding and specialized optimizations of their systems. DAMON is also merged in two public Amazon Linux kernel trees that based on v5.4.y[1] and v5.10.y[2]. [1] https://github.com/amazonlinux/linux/tree/amazon-5.4.y/master/mm/damon [2] https://github.com/amazonlinux/linux/tree/amazon-5.10.y/master/mm/damon The userspace tool[1] is available, released under GPLv2, and actively being maintained. I am also planning to implement another basic user interface in perf[2]. Also, the basic test suite for DAMON is available under GPLv2[3]. [1] https://github.com/awslabs/damo [2] https://lore.kernel.org/linux-mm/20210107120729.22328-1-sjpark@amazon.com/ [3] https://github.com/awslabs/damon-tests Long-term Plan -------------- DAMON is a part of a project called Data Access-aware Operating System (DAOS). As the name implies, I want to improve the performance and efficiency of systems using fine-grained data access patterns. The optimizations are for both kernel and user spaces. I will therefore modify or create kernel subsystems, export some of those to user space and implement user space library / tools. Below shows the layers and components for the project. --------------------------------------------------------------------------- Primitives: PTE Accessed bit, PG_idle, rmap, (Intel CMT), ... Framework: DAMON Features: DAMOS, virtual addr, physical addr, ... Applications: DAMON-debugfs, (DARC), ... ^^^^^^^^^^^^^^^^^^^^^^^ KERNEL SPACE ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ Raw Interface: debugfs, (sysfs), (damonfs), tracepoints, (sys_damon), ... vvvvvvvvvvvvvvvvvvvvvvv USER SPACE vvvvvvvvvvvvvvvvvvvvvvvvvvvvvvvv Library: (libdamon), ... Tools: DAMO, (perf), ... --------------------------------------------------------------------------- The components in parentheses or marked as '...' are not implemented yet but in the future plan. IOW, those are the TODO tasks of DAOS project. For more detail, please refer to the plans: https://lore.kernel.org/linux-mm/20201202082731.24828-1-sjpark@amazon.com/ Evaluations =========== We evaluated DAMON's overhead, monitoring quality and usefulness using 24 realistic workloads on my QEMU/KVM based virtual machine running a kernel that v24 DAMON patchset is applied. DAMON is lightweight. It increases system memory usage by 0.39% and slows target workloads down by 1.16%. DAMON is accurate and useful for memory management optimizations. An experimental DAMON-based operation scheme for THP, namely 'ethp', removes 76.15% of THP memory overheads while preserving 51.25% of THP speedup. Another experimental DAMON-based 'proactive reclamation' implementation, 'prcl', reduces 93.38% of residential sets and 23.63% of system memory footprint while incurring only 1.22% runtime overhead in the best case (parsec3/freqmine). NOTE that the experimental THP optimization and proactive reclamation are not for production but only for proof of concepts. Please refer to the official document[1] or "Documentation/admin-guide/mm: Add a document for DAMON" patch in this patchset for detailed evaluation setup and results. [1] https://damonitor.github.io/doc/html/latest-damon/admin-guide/mm/damon/eval.html Real-world User Story ===================== In summary, DAMON has used on production systems and proved its usefulness. DAMON as a profiler ------------------- We analyzed characteristics of a large scale production systems of our customers using DAMON. The systems utilize 70GB DRAM and 36 CPUs. From this, we were able to find interesting things below. There were obviously different access pattern under idle workload and active workload. Under the idle workload, it accessed large memory regions with low frequency, while the active workload accessed small memory regions with high freuqnecy. DAMON found a 7GB memory region that showing obviously high access frequency under the active workload. We believe this is the performance-effective working set and need to be protected. There was a 4KB memory region that showing highest access frequency under not only active but also idle workloads. We think this must be a hottest code section like thing that should never be paged out. For this analysis, DAMON used only 0.3-1% of single CPU time. Because we used recording-based analysis, it consumed about 3-12 MB of disk space per 20 minutes. This is only small amount of disk space, but we can further reduce the disk usage by using non-recording-based DAMON features. I'd like to argue that only DAMON can do such detailed analysis (finding 4KB highest region in 70GB memory) with the light overhead. DAMON as a system optimization tool ----------------------------------- We also found below potential performance problems on the systems and made DAMON-based solutions. The system doesn't want to make the workload suffer from the page reclamation and thus it utilizes enough DRAM but no swap device. However, we found the system is actively reclaiming file-backed pages, because the system has intensive file IO. The file IO turned out to be not performance critical for the workload, but the customer wanted to ensure performance critical file-backed pages like code section to not mistakenly be evicted. Using direct IO should or `mlock()` would be a straightforward solution, but modifying the user space code is not easy for the customer. Alternatively, we could use DAMON-based operation scheme[1]. By using it, we can ask DAMON to track access frequency of each region and make 'process_madvise(MADV_WILLNEED)[2]' call for regions having specific size and access frequency for a time interval. We also found the system is having high number of TLB misses. We tried 'always' THP enabled policy and it greatly reduced TLB misses, but the page reclamation also been more frequent due to the THP internal fragmentation caused memory bloat. We could try another DAMON-based operation scheme that applies 'MADV_HUGEPAGE' to memory regions having >=2MB size and high access frequency, while applying 'MADV_NOHUGEPAGE' to regions having <2MB size and low access frequency. We do not own the systems so we only reported the analysis results and possible optimization solutions to the customers. The customers satisfied about the analysis results and promised to try the optimization guides. [1] https://lore.kernel.org/linux-mm/20201006123931.5847-1-sjpark@amazon.com/ [2] https://lore.kernel.org/linux-api/20200622192900.22757-4-minchan@kernel.org/ Comparison with Idle Page Tracking ================================== Idle Page Tracking allows users to set and read idleness of pages using a bitmap file which represents each page with each bit of the file. One recommended usage of it is working set size detection. Users can do that by 1. find PFN of each page for workloads in interest, 2. set all the pages as idle by doing writes to the bitmap file, 3. wait until the workload accesses its working set, and 4. read the idleness of the pages again and count pages became not idle. NOTE: While Idle Page Tracking is for user space users, DAMON is primarily designed for kernel subsystems though it can easily exposed to the user space. Hence, this section only assumes such user space use of DAMON. For what use cases Idle Page Tracking would be better? ------------------------------------------------------ 1. Flexible usecases other than hotness monitoring. Because Idle Page Tracking allows users to control the primitive (Page idleness) by themselves, Idle Page Tracking users can do anything they want. Meanwhile, DAMON is primarily designed to monitor the hotness of each memory region. For this, DAMON asks users to provide sampling interval and aggregation interval. For the reason, there could be some use case that using Idle Page Tracking is simpler. 2. Physical memory monitoring. Idle Page Tracking receives PFN range as input, so natively supports physical memory monitoring. DAMON is designed to be extensible for multiple address spaces and use cases by implementing and using primitives for the given use case. Therefore, by theory, DAMON has no limitation in the type of target address space as long as primitives for the given address space exists. However, the default primitives introduced by this patchset supports only virtual address spaces. Therefore, for physical memory monitoring, you should implement your own primitives and use it, or simply use Idle Page Tracking. Nonetheless, RFC patchsets[1] for the physical memory address space primitives is already available. It also supports user memory same to Idle Page Tracking. [1] https://lore.kernel.org/linux-mm/20200831104730.28970-1-sjpark@amazon.com/ For what use cases DAMON is better? ----------------------------------- 1. Hotness Monitoring. Idle Page Tracking let users know only if a page frame is accessed or not. For hotness check, the user should write more code and use more memory. DAMON do that by itself. 2. Low Monitoring Overhead DAMON receives user's monitoring request with one step and then provide the results. So, roughly speaking, DAMON require only O(1) user/kernel context switches. In case of Idle Page Tracking, however, because the interface receives contiguous page frames, the number of user/kernel context switches increases as the monitoring target becomes complex and huge. As a result, the context switch overhead could be not negligible. Moreover, DAMON is born to handle with the monitoring overhead. Because the core mechanism is pure logical, Idle Page Tracking users might be able to implement the mechanism on their own, but it would be time consuming and the user/kernel context switching will still more frequent than that of DAMON. Also, the kernel subsystems cannot use the logic in this case. 3. Page granularity working set size detection. Until v22 of this patchset, this was categorized as the thing Idle Page Tracking could do better, because DAMON basically maintains additional metadata for each of the monitoring target regions. So, in the page granularity working set size detection use case, DAMON would incur (number of monitoring target pages * size of metadata) memory overhead. Size of the single metadata item is about 54 bytes, so assuming 4KB pages, about 1.3% of monitoring target pages will be additionally used. All essential metadata for Idle Page Tracking are embedded in 'struct page' and page table entries. Therefore, in this use case, only one counter variable for working set size accounting is required if Idle Page Tracking is used. There are more details to consider, but roughly speaking, this is true in most cases. However, the situation changed from v23. Now DAMON supports arbitrary types of monitoring targets, which don't use the metadata. Using that, DAMON can do the working set size detection with no additional space overhead but less user-kernel context switch. A first draft for the implementation of monitoring primitives for this usage is available in a DAMON development tree[1]. An RFC patchset for it based on this patchset will also be available soon. Since v24, the arbitrary type support is dropped from this patchset because this patchset doesn't introduce real use of the type. You can still get it from the DAMON development tree[2], though. [1] https://github.com/sjp38/linux/tree/damon/pgidle_hack [2] https://github.com/sjp38/linux/tree/damon/master 4. More future usecases While Idle Page Tracking has tight coupling with base primitives (PG_Idle and page table Accessed bits), DAMON is designed to be extensible for many use cases and address spaces. If you need some special address type or want to use special h/w access check primitives, you can write your own primitives for that and configure DAMON to use those. Therefore, if your use case could be changed a lot in future, using DAMON could be better. Can I use both Idle Page Tracking and DAMON? -------------------------------------------- Yes, though using them concurrently for overlapping memory regions could result in interference to each other. Nevertheless, such use case would be rare or makes no sense at all. Even in the case, the noise would bot be really significant. So, you can choose whatever you want depending on the characteristics of your use cases. More Information ================ We prepared a showcase web site[1] that you can get more information. There are - the official documentations[2], - the heatmap format dynamic access pattern of various realistic workloads for heap area[3], mmap()-ed area[4], and stack[5] area, - the dynamic working set size distribution[6] and chronological working set size changes[7], and - the latest performance test results[8]. [1] https://damonitor.github.io/_index [2] https://damonitor.github.io/doc/html/latest-damon [3] https://damonitor.github.io/test/result/visual/latest/rec.heatmap.0.png.html [4] https://damonitor.github.io/test/result/visual/latest/rec.heatmap.1.png.html [5] https://damonitor.github.io/test/result/visual/latest/rec.heatmap.2.png.html [6] https://damonitor.github.io/test/result/visual/latest/rec.wss_sz.png.html [7] https://damonitor.github.io/test/result/visual/latest/rec.wss_time.png.html [8] https://damonitor.github.io/test/result/perf/latest/html/index.html Baseline and Complete Git Trees =============================== The patches are based on the latest -mm tree, specifically v5.14-rc1-mmots-2021-07-15-18-47 of https://github.com/hnaz/linux-mm. You can also clone the complete git tree: $ git clone git://github.com/sjp38/linux -b damon/patches/v34 The web is also available: https://github.com/sjp38/linux/releases/tag/damon/patches/v34 Development Trees ----------------- There are a couple of trees for entire DAMON patchset series and features for future release. - For latest release: https://github.com/sjp38/linux/tree/damon/master - For next release: https://github.com/sjp38/linux/tree/damon/next Long-term Support Trees ----------------------- For people who want to test DAMON but using LTS kernels, there are another couple of trees based on two latest LTS kernels respectively and containing the 'damon/master' backports. - For v5.4.y: https://github.com/sjp38/linux/tree/damon/for-v5.4.y - For v5.10.y: https://github.com/sjp38/linux/tree/damon/for-v5.10.y Amazon Linux Kernel Trees ------------------------- DAMON is also merged in two public Amazon Linux kernel trees that based on v5.4.y[1] and v5.10.y[2]. [1] https://github.com/amazonlinux/linux/tree/amazon-5.4.y/master/mm/damon [2] https://github.com/amazonlinux/linux/tree/amazon-5.10.y/master/mm/damon Git Tree for Diff of Patches ============================ For easy review of diff between different versions of each patch, I prepared a git tree containing all versions of the DAMON patchset series: https://github.com/sjp38/damon-patches You can clone it and use 'diff' for easy review of changes between different versions of the patchset. For example: $ git clone https://github.com/sjp38/damon-patches && cd damon-patches $ diff -u damon/v33 damon/v34 Sequence Of Patches =================== First three patches implement the core logics of DAMON. The 1st patch introduces basic sampling based hotness monitoring for arbitrary types of targets. Following two patches implement the core mechanisms for control of overhead and accuracy, namely regions based sampling (patch 2) and adaptive regions adjustment (patch 3). Now the essential parts of DAMON is complete, but it cannot work unless someone provides monitoring primitives for a specific use case. The following two patches make it just work for virtual address spaces monitoring. The 4th patch makes 'PG_idle' can be used by DAMON and the 5th patch implements the virtual memory address space specific monitoring primitives using page table Accessed bits and the 'PG_idle' page flag. Now DAMON just works for virtual address space monitoring via the kernel space api. To let the user space users can use DAMON, following four patches add interfaces for them. The 6th patch adds a tracepoint for monitoring results. The 7th patch implements a DAMON application kernel module, namely damon-dbgfs, that simply wraps DAMON and exposes DAMON interface to the user space via the debugfs interface. The 8th patch further exports pid of monitoring thread (kdamond) to user space for easier cpu usage accounting, and the 9th patch makes the debugfs interface to support multiple contexts. Three patches for maintainability follows. The 10th patch adds documentations for both the user space and the kernel space. The 11th patch provides unit tests (based on the kunit) while the 12th patch adds user space tests (based on the kselftest). Finally, the last patch (13th) updates the MAINTAINERS file. This patch (of 13): DAMON is a data access monitoring framework for the Linux kernel. The core mechanisms of DAMON make it - accurate (the monitoring output is useful enough for DRAM level performance-centric memory management; It might be inappropriate for CPU cache levels, though), - light-weight (the monitoring overhead is normally low enough to be applied online), and - scalable (the upper-bound of the overhead is in constant range regardless of the size of target workloads). Using this framework, hence, we can easily write efficient kernel space data access monitoring applications. For example, the kernel's memory management mechanisms can make advanced decisions using this. Experimental data access aware optimization works that incurring high access monitoring overhead could again be implemented on top of this. Due to its simple and flexible interface, providing user space interface would be also easy. Then, user space users who have some special workloads can write personalized applications for better understanding and optimizations of their workloads and systems. === Nevertheless, this commit is defining and implementing only basic access check part without the overhead-accuracy handling core logic. The basic access check is as below. The output of DAMON says what memory regions are how frequently accessed for a given duration. The resolution of the access frequency is controlled by setting ``sampling interval`` and ``aggregation interval``. In detail, DAMON checks access to each page per ``sampling interval`` and aggregates the results. In other words, counts the number of the accesses to each region. After each ``aggregation interval`` passes, DAMON calls callback functions that previously registered by users so that users can read the aggregated results and then clears the results. This can be described in below simple pseudo-code:: init() while monitoring_on: for page in monitoring_target: if accessed(page): nr_accesses[page] += 1 if time() % aggregation_interval == 0: for callback in user_registered_callbacks: callback(monitoring_target, nr_accesses) for page in monitoring_target: nr_accesses[page] = 0 if time() % update_interval == 0: update() sleep(sampling interval) The target regions constructed at the beginning of the monitoring and updated after each ``regions_update_interval``, because the target regions could be dynamically changed (e.g., mmap() or memory hotplug). The monitoring overhead of this mechanism will arbitrarily increase as the size of the target workload grows. The basic monitoring primitives for actual access check and dynamic target regions construction aren't in the core part of DAMON. Instead, it allows users to implement their own primitives that are optimized for their use case and configure DAMON to use those. In other words, users cannot use current version of DAMON without some additional works. Following commits will implement the core mechanisms for the overhead-accuracy control and default primitives implementations. Link: https://lkml.kernel.org/r/20210716081449.22187-1-sj38.park@gmail.com Link: https://lkml.kernel.org/r/20210716081449.22187-2-sj38.park@gmail.com Signed-off-by: SeongJae Park <sjpark@amazon.de> Reviewed-by: Leonard Foerster <foersleo@amazon.de> Reviewed-by: Fernand Sieber <sieberf@amazon.com> Acked-by: Shakeel Butt <shakeelb@google.com> Cc: Jonathan Cameron <Jonathan.Cameron@huawei.com> Cc: Alexander Shishkin <alexander.shishkin@linux.intel.com> Cc: Amit Shah <amit@kernel.org> Cc: Benjamin Herrenschmidt <benh@kernel.crashing.org> Cc: Jonathan Corbet <corbet@lwn.net> Cc: David Hildenbrand <david@redhat.com> Cc: David Woodhouse <dwmw@amazon.com> Cc: Marco Elver <elver@google.com> Cc: Fan Du <fan.du@intel.com> Cc: Greg Kroah-Hartman <greg@kroah.com> Cc: Greg Thelen <gthelen@google.com> Cc: Joe Perches <joe@perches.com> Cc: Mel Gorman <mgorman@suse.de> Cc: Maximilian Heyne <mheyne@amazon.de> Cc: Minchan Kim <minchan@kernel.org> Cc: Ingo Molnar <mingo@redhat.com> Cc: Namhyung Kim <namhyung@kernel.org> Cc: Peter Zijlstra <peterz@infradead.org> Cc: Rik van Riel <riel@surriel.com> Cc: David Rientjes <rientjes@google.com> Cc: Steven Rostedt (VMware) <rostedt@goodmis.org> Cc: Shuah Khan <shuah@kernel.org> Cc: Vlastimil Babka <vbabka@suse.cz> Cc: Vladimir Davydov <vdavydov.dev@gmail.com> Cc: Brendan Higgins <brendanhiggins@google.com> Cc: Markus Boehme <markubo@amazon.de> Signed-off-by: Andrew Morton <akpm@linux-foundation.org> Signed-off-by: Linus Torvalds <torvalds@linux-foundation.org>
2021-09-08 10:56:28 +08:00
*
* @id: Identifier of this operations set.
mm/damon: rename damon_primitives to damon_operations Patch series "Allow DAMON user code independent of monitoring primitives". In-kernel DAMON user code is required to configure the monitoring context (struct damon_ctx) with proper monitoring primitives (struct damon_primitive). This makes the user code dependent to all supporting monitoring primitives. For example, DAMON debugfs interface depends on both DAMON_VADDR and DAMON_PADDR, though some users have interest in only one use case. As more monitoring primitives are introduced, the problem will be bigger. To minimize such unnecessary dependency, this patchset makes monitoring primitives can be registered by the implemnting code and later dynamically searched and selected by the user code. In addition to that, this patchset renames monitoring primitives to monitoring operations, which is more easy to intuitively understand what it means and how it would be structed. This patch (of 8): DAMON has a set of callback functions called monitoring primitives and let it can be configured with various implementations for easy extension for different address spaces and usages. However, the word 'primitive' is not so explicit. Meanwhile, many other structs resembles similar purpose calls themselves 'operations'. To make the code easier to be understood, this commit renames 'damon_primitives' to 'damon_operations' before it is too late to rename. Link: https://lkml.kernel.org/r/20220215184603.1479-1-sj@kernel.org Link: https://lkml.kernel.org/r/20220215184603.1479-2-sj@kernel.org Signed-off-by: SeongJae Park <sj@kernel.org> Cc: Xin Hao <xhao@linux.alibaba.com> Cc: David Rientjes <rientjes@google.com> Signed-off-by: Andrew Morton <akpm@linux-foundation.org> Signed-off-by: Linus Torvalds <torvalds@linux-foundation.org>
2022-03-23 05:48:46 +08:00
* @init: Initialize operations-related data structures.
* @update: Update operations-related data structures.
mm: introduce Data Access MONitor (DAMON) Patch series "Introduce Data Access MONitor (DAMON)", v34. Introduction ============ DAMON is a data access monitoring framework for the Linux kernel. The core mechanisms of DAMON called 'region based sampling' and 'adaptive regions adjustment' (refer to 'mechanisms.rst' in the 11th patch of this patchset for the detail) make it - accurate (The monitored information is useful for DRAM level memory management. It might not appropriate for Cache-level accuracy, though.), - light-weight (The monitoring overhead is low enough to be applied online while making no impact on the performance of the target workloads.), and - scalable (the upper-bound of the instrumentation overhead is controllable regardless of the size of target workloads.). Using this framework, therefore, several memory management mechanisms such as reclamation and THP can be optimized to aware real data access patterns. Experimental access pattern aware memory management optimization works that incurring high instrumentation overhead will be able to have another try. Though DAMON is for kernel subsystems, it can be easily exposed to the user space by writing a DAMON-wrapper kernel subsystem. Then, user space users who have some special workloads will be able to write personalized tools or applications for deeper understanding and specialized optimizations of their systems. DAMON is also merged in two public Amazon Linux kernel trees that based on v5.4.y[1] and v5.10.y[2]. [1] https://github.com/amazonlinux/linux/tree/amazon-5.4.y/master/mm/damon [2] https://github.com/amazonlinux/linux/tree/amazon-5.10.y/master/mm/damon The userspace tool[1] is available, released under GPLv2, and actively being maintained. I am also planning to implement another basic user interface in perf[2]. Also, the basic test suite for DAMON is available under GPLv2[3]. [1] https://github.com/awslabs/damo [2] https://lore.kernel.org/linux-mm/20210107120729.22328-1-sjpark@amazon.com/ [3] https://github.com/awslabs/damon-tests Long-term Plan -------------- DAMON is a part of a project called Data Access-aware Operating System (DAOS). As the name implies, I want to improve the performance and efficiency of systems using fine-grained data access patterns. The optimizations are for both kernel and user spaces. I will therefore modify or create kernel subsystems, export some of those to user space and implement user space library / tools. Below shows the layers and components for the project. --------------------------------------------------------------------------- Primitives: PTE Accessed bit, PG_idle, rmap, (Intel CMT), ... Framework: DAMON Features: DAMOS, virtual addr, physical addr, ... Applications: DAMON-debugfs, (DARC), ... ^^^^^^^^^^^^^^^^^^^^^^^ KERNEL SPACE ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ Raw Interface: debugfs, (sysfs), (damonfs), tracepoints, (sys_damon), ... vvvvvvvvvvvvvvvvvvvvvvv USER SPACE vvvvvvvvvvvvvvvvvvvvvvvvvvvvvvvv Library: (libdamon), ... Tools: DAMO, (perf), ... --------------------------------------------------------------------------- The components in parentheses or marked as '...' are not implemented yet but in the future plan. IOW, those are the TODO tasks of DAOS project. For more detail, please refer to the plans: https://lore.kernel.org/linux-mm/20201202082731.24828-1-sjpark@amazon.com/ Evaluations =========== We evaluated DAMON's overhead, monitoring quality and usefulness using 24 realistic workloads on my QEMU/KVM based virtual machine running a kernel that v24 DAMON patchset is applied. DAMON is lightweight. It increases system memory usage by 0.39% and slows target workloads down by 1.16%. DAMON is accurate and useful for memory management optimizations. An experimental DAMON-based operation scheme for THP, namely 'ethp', removes 76.15% of THP memory overheads while preserving 51.25% of THP speedup. Another experimental DAMON-based 'proactive reclamation' implementation, 'prcl', reduces 93.38% of residential sets and 23.63% of system memory footprint while incurring only 1.22% runtime overhead in the best case (parsec3/freqmine). NOTE that the experimental THP optimization and proactive reclamation are not for production but only for proof of concepts. Please refer to the official document[1] or "Documentation/admin-guide/mm: Add a document for DAMON" patch in this patchset for detailed evaluation setup and results. [1] https://damonitor.github.io/doc/html/latest-damon/admin-guide/mm/damon/eval.html Real-world User Story ===================== In summary, DAMON has used on production systems and proved its usefulness. DAMON as a profiler ------------------- We analyzed characteristics of a large scale production systems of our customers using DAMON. The systems utilize 70GB DRAM and 36 CPUs. From this, we were able to find interesting things below. There were obviously different access pattern under idle workload and active workload. Under the idle workload, it accessed large memory regions with low frequency, while the active workload accessed small memory regions with high freuqnecy. DAMON found a 7GB memory region that showing obviously high access frequency under the active workload. We believe this is the performance-effective working set and need to be protected. There was a 4KB memory region that showing highest access frequency under not only active but also idle workloads. We think this must be a hottest code section like thing that should never be paged out. For this analysis, DAMON used only 0.3-1% of single CPU time. Because we used recording-based analysis, it consumed about 3-12 MB of disk space per 20 minutes. This is only small amount of disk space, but we can further reduce the disk usage by using non-recording-based DAMON features. I'd like to argue that only DAMON can do such detailed analysis (finding 4KB highest region in 70GB memory) with the light overhead. DAMON as a system optimization tool ----------------------------------- We also found below potential performance problems on the systems and made DAMON-based solutions. The system doesn't want to make the workload suffer from the page reclamation and thus it utilizes enough DRAM but no swap device. However, we found the system is actively reclaiming file-backed pages, because the system has intensive file IO. The file IO turned out to be not performance critical for the workload, but the customer wanted to ensure performance critical file-backed pages like code section to not mistakenly be evicted. Using direct IO should or `mlock()` would be a straightforward solution, but modifying the user space code is not easy for the customer. Alternatively, we could use DAMON-based operation scheme[1]. By using it, we can ask DAMON to track access frequency of each region and make 'process_madvise(MADV_WILLNEED)[2]' call for regions having specific size and access frequency for a time interval. We also found the system is having high number of TLB misses. We tried 'always' THP enabled policy and it greatly reduced TLB misses, but the page reclamation also been more frequent due to the THP internal fragmentation caused memory bloat. We could try another DAMON-based operation scheme that applies 'MADV_HUGEPAGE' to memory regions having >=2MB size and high access frequency, while applying 'MADV_NOHUGEPAGE' to regions having <2MB size and low access frequency. We do not own the systems so we only reported the analysis results and possible optimization solutions to the customers. The customers satisfied about the analysis results and promised to try the optimization guides. [1] https://lore.kernel.org/linux-mm/20201006123931.5847-1-sjpark@amazon.com/ [2] https://lore.kernel.org/linux-api/20200622192900.22757-4-minchan@kernel.org/ Comparison with Idle Page Tracking ================================== Idle Page Tracking allows users to set and read idleness of pages using a bitmap file which represents each page with each bit of the file. One recommended usage of it is working set size detection. Users can do that by 1. find PFN of each page for workloads in interest, 2. set all the pages as idle by doing writes to the bitmap file, 3. wait until the workload accesses its working set, and 4. read the idleness of the pages again and count pages became not idle. NOTE: While Idle Page Tracking is for user space users, DAMON is primarily designed for kernel subsystems though it can easily exposed to the user space. Hence, this section only assumes such user space use of DAMON. For what use cases Idle Page Tracking would be better? ------------------------------------------------------ 1. Flexible usecases other than hotness monitoring. Because Idle Page Tracking allows users to control the primitive (Page idleness) by themselves, Idle Page Tracking users can do anything they want. Meanwhile, DAMON is primarily designed to monitor the hotness of each memory region. For this, DAMON asks users to provide sampling interval and aggregation interval. For the reason, there could be some use case that using Idle Page Tracking is simpler. 2. Physical memory monitoring. Idle Page Tracking receives PFN range as input, so natively supports physical memory monitoring. DAMON is designed to be extensible for multiple address spaces and use cases by implementing and using primitives for the given use case. Therefore, by theory, DAMON has no limitation in the type of target address space as long as primitives for the given address space exists. However, the default primitives introduced by this patchset supports only virtual address spaces. Therefore, for physical memory monitoring, you should implement your own primitives and use it, or simply use Idle Page Tracking. Nonetheless, RFC patchsets[1] for the physical memory address space primitives is already available. It also supports user memory same to Idle Page Tracking. [1] https://lore.kernel.org/linux-mm/20200831104730.28970-1-sjpark@amazon.com/ For what use cases DAMON is better? ----------------------------------- 1. Hotness Monitoring. Idle Page Tracking let users know only if a page frame is accessed or not. For hotness check, the user should write more code and use more memory. DAMON do that by itself. 2. Low Monitoring Overhead DAMON receives user's monitoring request with one step and then provide the results. So, roughly speaking, DAMON require only O(1) user/kernel context switches. In case of Idle Page Tracking, however, because the interface receives contiguous page frames, the number of user/kernel context switches increases as the monitoring target becomes complex and huge. As a result, the context switch overhead could be not negligible. Moreover, DAMON is born to handle with the monitoring overhead. Because the core mechanism is pure logical, Idle Page Tracking users might be able to implement the mechanism on their own, but it would be time consuming and the user/kernel context switching will still more frequent than that of DAMON. Also, the kernel subsystems cannot use the logic in this case. 3. Page granularity working set size detection. Until v22 of this patchset, this was categorized as the thing Idle Page Tracking could do better, because DAMON basically maintains additional metadata for each of the monitoring target regions. So, in the page granularity working set size detection use case, DAMON would incur (number of monitoring target pages * size of metadata) memory overhead. Size of the single metadata item is about 54 bytes, so assuming 4KB pages, about 1.3% of monitoring target pages will be additionally used. All essential metadata for Idle Page Tracking are embedded in 'struct page' and page table entries. Therefore, in this use case, only one counter variable for working set size accounting is required if Idle Page Tracking is used. There are more details to consider, but roughly speaking, this is true in most cases. However, the situation changed from v23. Now DAMON supports arbitrary types of monitoring targets, which don't use the metadata. Using that, DAMON can do the working set size detection with no additional space overhead but less user-kernel context switch. A first draft for the implementation of monitoring primitives for this usage is available in a DAMON development tree[1]. An RFC patchset for it based on this patchset will also be available soon. Since v24, the arbitrary type support is dropped from this patchset because this patchset doesn't introduce real use of the type. You can still get it from the DAMON development tree[2], though. [1] https://github.com/sjp38/linux/tree/damon/pgidle_hack [2] https://github.com/sjp38/linux/tree/damon/master 4. More future usecases While Idle Page Tracking has tight coupling with base primitives (PG_Idle and page table Accessed bits), DAMON is designed to be extensible for many use cases and address spaces. If you need some special address type or want to use special h/w access check primitives, you can write your own primitives for that and configure DAMON to use those. Therefore, if your use case could be changed a lot in future, using DAMON could be better. Can I use both Idle Page Tracking and DAMON? -------------------------------------------- Yes, though using them concurrently for overlapping memory regions could result in interference to each other. Nevertheless, such use case would be rare or makes no sense at all. Even in the case, the noise would bot be really significant. So, you can choose whatever you want depending on the characteristics of your use cases. More Information ================ We prepared a showcase web site[1] that you can get more information. There are - the official documentations[2], - the heatmap format dynamic access pattern of various realistic workloads for heap area[3], mmap()-ed area[4], and stack[5] area, - the dynamic working set size distribution[6] and chronological working set size changes[7], and - the latest performance test results[8]. [1] https://damonitor.github.io/_index [2] https://damonitor.github.io/doc/html/latest-damon [3] https://damonitor.github.io/test/result/visual/latest/rec.heatmap.0.png.html [4] https://damonitor.github.io/test/result/visual/latest/rec.heatmap.1.png.html [5] https://damonitor.github.io/test/result/visual/latest/rec.heatmap.2.png.html [6] https://damonitor.github.io/test/result/visual/latest/rec.wss_sz.png.html [7] https://damonitor.github.io/test/result/visual/latest/rec.wss_time.png.html [8] https://damonitor.github.io/test/result/perf/latest/html/index.html Baseline and Complete Git Trees =============================== The patches are based on the latest -mm tree, specifically v5.14-rc1-mmots-2021-07-15-18-47 of https://github.com/hnaz/linux-mm. You can also clone the complete git tree: $ git clone git://github.com/sjp38/linux -b damon/patches/v34 The web is also available: https://github.com/sjp38/linux/releases/tag/damon/patches/v34 Development Trees ----------------- There are a couple of trees for entire DAMON patchset series and features for future release. - For latest release: https://github.com/sjp38/linux/tree/damon/master - For next release: https://github.com/sjp38/linux/tree/damon/next Long-term Support Trees ----------------------- For people who want to test DAMON but using LTS kernels, there are another couple of trees based on two latest LTS kernels respectively and containing the 'damon/master' backports. - For v5.4.y: https://github.com/sjp38/linux/tree/damon/for-v5.4.y - For v5.10.y: https://github.com/sjp38/linux/tree/damon/for-v5.10.y Amazon Linux Kernel Trees ------------------------- DAMON is also merged in two public Amazon Linux kernel trees that based on v5.4.y[1] and v5.10.y[2]. [1] https://github.com/amazonlinux/linux/tree/amazon-5.4.y/master/mm/damon [2] https://github.com/amazonlinux/linux/tree/amazon-5.10.y/master/mm/damon Git Tree for Diff of Patches ============================ For easy review of diff between different versions of each patch, I prepared a git tree containing all versions of the DAMON patchset series: https://github.com/sjp38/damon-patches You can clone it and use 'diff' for easy review of changes between different versions of the patchset. For example: $ git clone https://github.com/sjp38/damon-patches && cd damon-patches $ diff -u damon/v33 damon/v34 Sequence Of Patches =================== First three patches implement the core logics of DAMON. The 1st patch introduces basic sampling based hotness monitoring for arbitrary types of targets. Following two patches implement the core mechanisms for control of overhead and accuracy, namely regions based sampling (patch 2) and adaptive regions adjustment (patch 3). Now the essential parts of DAMON is complete, but it cannot work unless someone provides monitoring primitives for a specific use case. The following two patches make it just work for virtual address spaces monitoring. The 4th patch makes 'PG_idle' can be used by DAMON and the 5th patch implements the virtual memory address space specific monitoring primitives using page table Accessed bits and the 'PG_idle' page flag. Now DAMON just works for virtual address space monitoring via the kernel space api. To let the user space users can use DAMON, following four patches add interfaces for them. The 6th patch adds a tracepoint for monitoring results. The 7th patch implements a DAMON application kernel module, namely damon-dbgfs, that simply wraps DAMON and exposes DAMON interface to the user space via the debugfs interface. The 8th patch further exports pid of monitoring thread (kdamond) to user space for easier cpu usage accounting, and the 9th patch makes the debugfs interface to support multiple contexts. Three patches for maintainability follows. The 10th patch adds documentations for both the user space and the kernel space. The 11th patch provides unit tests (based on the kunit) while the 12th patch adds user space tests (based on the kselftest). Finally, the last patch (13th) updates the MAINTAINERS file. This patch (of 13): DAMON is a data access monitoring framework for the Linux kernel. The core mechanisms of DAMON make it - accurate (the monitoring output is useful enough for DRAM level performance-centric memory management; It might be inappropriate for CPU cache levels, though), - light-weight (the monitoring overhead is normally low enough to be applied online), and - scalable (the upper-bound of the overhead is in constant range regardless of the size of target workloads). Using this framework, hence, we can easily write efficient kernel space data access monitoring applications. For example, the kernel's memory management mechanisms can make advanced decisions using this. Experimental data access aware optimization works that incurring high access monitoring overhead could again be implemented on top of this. Due to its simple and flexible interface, providing user space interface would be also easy. Then, user space users who have some special workloads can write personalized applications for better understanding and optimizations of their workloads and systems. === Nevertheless, this commit is defining and implementing only basic access check part without the overhead-accuracy handling core logic. The basic access check is as below. The output of DAMON says what memory regions are how frequently accessed for a given duration. The resolution of the access frequency is controlled by setting ``sampling interval`` and ``aggregation interval``. In detail, DAMON checks access to each page per ``sampling interval`` and aggregates the results. In other words, counts the number of the accesses to each region. After each ``aggregation interval`` passes, DAMON calls callback functions that previously registered by users so that users can read the aggregated results and then clears the results. This can be described in below simple pseudo-code:: init() while monitoring_on: for page in monitoring_target: if accessed(page): nr_accesses[page] += 1 if time() % aggregation_interval == 0: for callback in user_registered_callbacks: callback(monitoring_target, nr_accesses) for page in monitoring_target: nr_accesses[page] = 0 if time() % update_interval == 0: update() sleep(sampling interval) The target regions constructed at the beginning of the monitoring and updated after each ``regions_update_interval``, because the target regions could be dynamically changed (e.g., mmap() or memory hotplug). The monitoring overhead of this mechanism will arbitrarily increase as the size of the target workload grows. The basic monitoring primitives for actual access check and dynamic target regions construction aren't in the core part of DAMON. Instead, it allows users to implement their own primitives that are optimized for their use case and configure DAMON to use those. In other words, users cannot use current version of DAMON without some additional works. Following commits will implement the core mechanisms for the overhead-accuracy control and default primitives implementations. Link: https://lkml.kernel.org/r/20210716081449.22187-1-sj38.park@gmail.com Link: https://lkml.kernel.org/r/20210716081449.22187-2-sj38.park@gmail.com Signed-off-by: SeongJae Park <sjpark@amazon.de> Reviewed-by: Leonard Foerster <foersleo@amazon.de> Reviewed-by: Fernand Sieber <sieberf@amazon.com> Acked-by: Shakeel Butt <shakeelb@google.com> Cc: Jonathan Cameron <Jonathan.Cameron@huawei.com> Cc: Alexander Shishkin <alexander.shishkin@linux.intel.com> Cc: Amit Shah <amit@kernel.org> Cc: Benjamin Herrenschmidt <benh@kernel.crashing.org> Cc: Jonathan Corbet <corbet@lwn.net> Cc: David Hildenbrand <david@redhat.com> Cc: David Woodhouse <dwmw@amazon.com> Cc: Marco Elver <elver@google.com> Cc: Fan Du <fan.du@intel.com> Cc: Greg Kroah-Hartman <greg@kroah.com> Cc: Greg Thelen <gthelen@google.com> Cc: Joe Perches <joe@perches.com> Cc: Mel Gorman <mgorman@suse.de> Cc: Maximilian Heyne <mheyne@amazon.de> Cc: Minchan Kim <minchan@kernel.org> Cc: Ingo Molnar <mingo@redhat.com> Cc: Namhyung Kim <namhyung@kernel.org> Cc: Peter Zijlstra <peterz@infradead.org> Cc: Rik van Riel <riel@surriel.com> Cc: David Rientjes <rientjes@google.com> Cc: Steven Rostedt (VMware) <rostedt@goodmis.org> Cc: Shuah Khan <shuah@kernel.org> Cc: Vlastimil Babka <vbabka@suse.cz> Cc: Vladimir Davydov <vdavydov.dev@gmail.com> Cc: Brendan Higgins <brendanhiggins@google.com> Cc: Markus Boehme <markubo@amazon.de> Signed-off-by: Andrew Morton <akpm@linux-foundation.org> Signed-off-by: Linus Torvalds <torvalds@linux-foundation.org>
2021-09-08 10:56:28 +08:00
* @prepare_access_checks: Prepare next access check of target regions.
* @check_accesses: Check the accesses to target regions.
* @reset_aggregated: Reset aggregated accesses monitoring results.
2021-11-06 04:47:33 +08:00
* @get_scheme_score: Get the score of a region for a scheme.
mm/damon/core: implement DAMON-based Operation Schemes (DAMOS) In many cases, users might use DAMON for simple data access aware memory management optimizations such as applying an operation scheme to a memory region of a specific size having a specific access frequency for a specific time. For example, "page out a memory region larger than 100 MiB but having a low access frequency more than 10 minutes", or "Use THP for a memory region larger than 2 MiB having a high access frequency for more than 2 seconds". Most simple form of the solution would be doing offline data access pattern profiling using DAMON and modifying the application source code or system configuration based on the profiling results. Or, developing a daemon constructed with two modules (one for access monitoring and the other for applying memory management actions via mlock(), madvise(), sysctl, etc) is imaginable. To avoid users spending their time for implementation of such simple data access monitoring-based operation schemes, this makes DAMON to handle such schemes directly. With this change, users can simply specify their desired schemes to DAMON. Then, DAMON will automatically apply the schemes to the user-specified target processes. Each of the schemes is composed with conditions for filtering of the target memory regions and desired memory management action for the target. Specifically, the format is:: <min/max size> <min/max access frequency> <min/max age> <action> The filtering conditions are size of memory region, number of accesses to the region monitored by DAMON, and the age of the region. The age of region is incremented periodically but reset when its addresses or access frequency has significantly changed or the action of a scheme was applied. For the action, current implementation supports a few of madvise()-like hints, ``WILLNEED``, ``COLD``, ``PAGEOUT``, ``HUGEPAGE``, and ``NOHUGEPAGE``. Because DAMON supports various address spaces and application of the actions to a monitoring target region is dependent to the type of the target address space, the application code should be implemented by each primitives and registered to the framework. Note that this only implements the framework part. Following commit will implement the action applications for virtual address spaces primitives. Link: https://lkml.kernel.org/r/20211001125604.29660-3-sj@kernel.org Signed-off-by: SeongJae Park <sj@kernel.org> Cc: Amit Shah <amit@kernel.org> Cc: Benjamin Herrenschmidt <benh@kernel.crashing.org> Cc: David Hildenbrand <david@redhat.com> Cc: David Rienjes <rientjes@google.com> Cc: David Woodhouse <dwmw@amazon.com> Cc: Greg Thelen <gthelen@google.com> Cc: Jonathan Cameron <Jonathan.Cameron@huawei.com> Cc: Jonathan Corbet <corbet@lwn.net> Cc: Leonard Foerster <foersleo@amazon.de> Cc: Marco Elver <elver@google.com> Cc: Markus Boehme <markubo@amazon.de> Cc: Shakeel Butt <shakeelb@google.com> Cc: Shuah Khan <shuah@kernel.org> Signed-off-by: Andrew Morton <akpm@linux-foundation.org> Signed-off-by: Linus Torvalds <torvalds@linux-foundation.org>
2021-11-06 04:46:22 +08:00
* @apply_scheme: Apply a DAMON-based operation scheme.
mm: introduce Data Access MONitor (DAMON) Patch series "Introduce Data Access MONitor (DAMON)", v34. Introduction ============ DAMON is a data access monitoring framework for the Linux kernel. The core mechanisms of DAMON called 'region based sampling' and 'adaptive regions adjustment' (refer to 'mechanisms.rst' in the 11th patch of this patchset for the detail) make it - accurate (The monitored information is useful for DRAM level memory management. It might not appropriate for Cache-level accuracy, though.), - light-weight (The monitoring overhead is low enough to be applied online while making no impact on the performance of the target workloads.), and - scalable (the upper-bound of the instrumentation overhead is controllable regardless of the size of target workloads.). Using this framework, therefore, several memory management mechanisms such as reclamation and THP can be optimized to aware real data access patterns. Experimental access pattern aware memory management optimization works that incurring high instrumentation overhead will be able to have another try. Though DAMON is for kernel subsystems, it can be easily exposed to the user space by writing a DAMON-wrapper kernel subsystem. Then, user space users who have some special workloads will be able to write personalized tools or applications for deeper understanding and specialized optimizations of their systems. DAMON is also merged in two public Amazon Linux kernel trees that based on v5.4.y[1] and v5.10.y[2]. [1] https://github.com/amazonlinux/linux/tree/amazon-5.4.y/master/mm/damon [2] https://github.com/amazonlinux/linux/tree/amazon-5.10.y/master/mm/damon The userspace tool[1] is available, released under GPLv2, and actively being maintained. I am also planning to implement another basic user interface in perf[2]. Also, the basic test suite for DAMON is available under GPLv2[3]. [1] https://github.com/awslabs/damo [2] https://lore.kernel.org/linux-mm/20210107120729.22328-1-sjpark@amazon.com/ [3] https://github.com/awslabs/damon-tests Long-term Plan -------------- DAMON is a part of a project called Data Access-aware Operating System (DAOS). As the name implies, I want to improve the performance and efficiency of systems using fine-grained data access patterns. The optimizations are for both kernel and user spaces. I will therefore modify or create kernel subsystems, export some of those to user space and implement user space library / tools. Below shows the layers and components for the project. --------------------------------------------------------------------------- Primitives: PTE Accessed bit, PG_idle, rmap, (Intel CMT), ... Framework: DAMON Features: DAMOS, virtual addr, physical addr, ... Applications: DAMON-debugfs, (DARC), ... ^^^^^^^^^^^^^^^^^^^^^^^ KERNEL SPACE ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ Raw Interface: debugfs, (sysfs), (damonfs), tracepoints, (sys_damon), ... vvvvvvvvvvvvvvvvvvvvvvv USER SPACE vvvvvvvvvvvvvvvvvvvvvvvvvvvvvvvv Library: (libdamon), ... Tools: DAMO, (perf), ... --------------------------------------------------------------------------- The components in parentheses or marked as '...' are not implemented yet but in the future plan. IOW, those are the TODO tasks of DAOS project. For more detail, please refer to the plans: https://lore.kernel.org/linux-mm/20201202082731.24828-1-sjpark@amazon.com/ Evaluations =========== We evaluated DAMON's overhead, monitoring quality and usefulness using 24 realistic workloads on my QEMU/KVM based virtual machine running a kernel that v24 DAMON patchset is applied. DAMON is lightweight. It increases system memory usage by 0.39% and slows target workloads down by 1.16%. DAMON is accurate and useful for memory management optimizations. An experimental DAMON-based operation scheme for THP, namely 'ethp', removes 76.15% of THP memory overheads while preserving 51.25% of THP speedup. Another experimental DAMON-based 'proactive reclamation' implementation, 'prcl', reduces 93.38% of residential sets and 23.63% of system memory footprint while incurring only 1.22% runtime overhead in the best case (parsec3/freqmine). NOTE that the experimental THP optimization and proactive reclamation are not for production but only for proof of concepts. Please refer to the official document[1] or "Documentation/admin-guide/mm: Add a document for DAMON" patch in this patchset for detailed evaluation setup and results. [1] https://damonitor.github.io/doc/html/latest-damon/admin-guide/mm/damon/eval.html Real-world User Story ===================== In summary, DAMON has used on production systems and proved its usefulness. DAMON as a profiler ------------------- We analyzed characteristics of a large scale production systems of our customers using DAMON. The systems utilize 70GB DRAM and 36 CPUs. From this, we were able to find interesting things below. There were obviously different access pattern under idle workload and active workload. Under the idle workload, it accessed large memory regions with low frequency, while the active workload accessed small memory regions with high freuqnecy. DAMON found a 7GB memory region that showing obviously high access frequency under the active workload. We believe this is the performance-effective working set and need to be protected. There was a 4KB memory region that showing highest access frequency under not only active but also idle workloads. We think this must be a hottest code section like thing that should never be paged out. For this analysis, DAMON used only 0.3-1% of single CPU time. Because we used recording-based analysis, it consumed about 3-12 MB of disk space per 20 minutes. This is only small amount of disk space, but we can further reduce the disk usage by using non-recording-based DAMON features. I'd like to argue that only DAMON can do such detailed analysis (finding 4KB highest region in 70GB memory) with the light overhead. DAMON as a system optimization tool ----------------------------------- We also found below potential performance problems on the systems and made DAMON-based solutions. The system doesn't want to make the workload suffer from the page reclamation and thus it utilizes enough DRAM but no swap device. However, we found the system is actively reclaiming file-backed pages, because the system has intensive file IO. The file IO turned out to be not performance critical for the workload, but the customer wanted to ensure performance critical file-backed pages like code section to not mistakenly be evicted. Using direct IO should or `mlock()` would be a straightforward solution, but modifying the user space code is not easy for the customer. Alternatively, we could use DAMON-based operation scheme[1]. By using it, we can ask DAMON to track access frequency of each region and make 'process_madvise(MADV_WILLNEED)[2]' call for regions having specific size and access frequency for a time interval. We also found the system is having high number of TLB misses. We tried 'always' THP enabled policy and it greatly reduced TLB misses, but the page reclamation also been more frequent due to the THP internal fragmentation caused memory bloat. We could try another DAMON-based operation scheme that applies 'MADV_HUGEPAGE' to memory regions having >=2MB size and high access frequency, while applying 'MADV_NOHUGEPAGE' to regions having <2MB size and low access frequency. We do not own the systems so we only reported the analysis results and possible optimization solutions to the customers. The customers satisfied about the analysis results and promised to try the optimization guides. [1] https://lore.kernel.org/linux-mm/20201006123931.5847-1-sjpark@amazon.com/ [2] https://lore.kernel.org/linux-api/20200622192900.22757-4-minchan@kernel.org/ Comparison with Idle Page Tracking ================================== Idle Page Tracking allows users to set and read idleness of pages using a bitmap file which represents each page with each bit of the file. One recommended usage of it is working set size detection. Users can do that by 1. find PFN of each page for workloads in interest, 2. set all the pages as idle by doing writes to the bitmap file, 3. wait until the workload accesses its working set, and 4. read the idleness of the pages again and count pages became not idle. NOTE: While Idle Page Tracking is for user space users, DAMON is primarily designed for kernel subsystems though it can easily exposed to the user space. Hence, this section only assumes such user space use of DAMON. For what use cases Idle Page Tracking would be better? ------------------------------------------------------ 1. Flexible usecases other than hotness monitoring. Because Idle Page Tracking allows users to control the primitive (Page idleness) by themselves, Idle Page Tracking users can do anything they want. Meanwhile, DAMON is primarily designed to monitor the hotness of each memory region. For this, DAMON asks users to provide sampling interval and aggregation interval. For the reason, there could be some use case that using Idle Page Tracking is simpler. 2. Physical memory monitoring. Idle Page Tracking receives PFN range as input, so natively supports physical memory monitoring. DAMON is designed to be extensible for multiple address spaces and use cases by implementing and using primitives for the given use case. Therefore, by theory, DAMON has no limitation in the type of target address space as long as primitives for the given address space exists. However, the default primitives introduced by this patchset supports only virtual address spaces. Therefore, for physical memory monitoring, you should implement your own primitives and use it, or simply use Idle Page Tracking. Nonetheless, RFC patchsets[1] for the physical memory address space primitives is already available. It also supports user memory same to Idle Page Tracking. [1] https://lore.kernel.org/linux-mm/20200831104730.28970-1-sjpark@amazon.com/ For what use cases DAMON is better? ----------------------------------- 1. Hotness Monitoring. Idle Page Tracking let users know only if a page frame is accessed or not. For hotness check, the user should write more code and use more memory. DAMON do that by itself. 2. Low Monitoring Overhead DAMON receives user's monitoring request with one step and then provide the results. So, roughly speaking, DAMON require only O(1) user/kernel context switches. In case of Idle Page Tracking, however, because the interface receives contiguous page frames, the number of user/kernel context switches increases as the monitoring target becomes complex and huge. As a result, the context switch overhead could be not negligible. Moreover, DAMON is born to handle with the monitoring overhead. Because the core mechanism is pure logical, Idle Page Tracking users might be able to implement the mechanism on their own, but it would be time consuming and the user/kernel context switching will still more frequent than that of DAMON. Also, the kernel subsystems cannot use the logic in this case. 3. Page granularity working set size detection. Until v22 of this patchset, this was categorized as the thing Idle Page Tracking could do better, because DAMON basically maintains additional metadata for each of the monitoring target regions. So, in the page granularity working set size detection use case, DAMON would incur (number of monitoring target pages * size of metadata) memory overhead. Size of the single metadata item is about 54 bytes, so assuming 4KB pages, about 1.3% of monitoring target pages will be additionally used. All essential metadata for Idle Page Tracking are embedded in 'struct page' and page table entries. Therefore, in this use case, only one counter variable for working set size accounting is required if Idle Page Tracking is used. There are more details to consider, but roughly speaking, this is true in most cases. However, the situation changed from v23. Now DAMON supports arbitrary types of monitoring targets, which don't use the metadata. Using that, DAMON can do the working set size detection with no additional space overhead but less user-kernel context switch. A first draft for the implementation of monitoring primitives for this usage is available in a DAMON development tree[1]. An RFC patchset for it based on this patchset will also be available soon. Since v24, the arbitrary type support is dropped from this patchset because this patchset doesn't introduce real use of the type. You can still get it from the DAMON development tree[2], though. [1] https://github.com/sjp38/linux/tree/damon/pgidle_hack [2] https://github.com/sjp38/linux/tree/damon/master 4. More future usecases While Idle Page Tracking has tight coupling with base primitives (PG_Idle and page table Accessed bits), DAMON is designed to be extensible for many use cases and address spaces. If you need some special address type or want to use special h/w access check primitives, you can write your own primitives for that and configure DAMON to use those. Therefore, if your use case could be changed a lot in future, using DAMON could be better. Can I use both Idle Page Tracking and DAMON? -------------------------------------------- Yes, though using them concurrently for overlapping memory regions could result in interference to each other. Nevertheless, such use case would be rare or makes no sense at all. Even in the case, the noise would bot be really significant. So, you can choose whatever you want depending on the characteristics of your use cases. More Information ================ We prepared a showcase web site[1] that you can get more information. There are - the official documentations[2], - the heatmap format dynamic access pattern of various realistic workloads for heap area[3], mmap()-ed area[4], and stack[5] area, - the dynamic working set size distribution[6] and chronological working set size changes[7], and - the latest performance test results[8]. [1] https://damonitor.github.io/_index [2] https://damonitor.github.io/doc/html/latest-damon [3] https://damonitor.github.io/test/result/visual/latest/rec.heatmap.0.png.html [4] https://damonitor.github.io/test/result/visual/latest/rec.heatmap.1.png.html [5] https://damonitor.github.io/test/result/visual/latest/rec.heatmap.2.png.html [6] https://damonitor.github.io/test/result/visual/latest/rec.wss_sz.png.html [7] https://damonitor.github.io/test/result/visual/latest/rec.wss_time.png.html [8] https://damonitor.github.io/test/result/perf/latest/html/index.html Baseline and Complete Git Trees =============================== The patches are based on the latest -mm tree, specifically v5.14-rc1-mmots-2021-07-15-18-47 of https://github.com/hnaz/linux-mm. You can also clone the complete git tree: $ git clone git://github.com/sjp38/linux -b damon/patches/v34 The web is also available: https://github.com/sjp38/linux/releases/tag/damon/patches/v34 Development Trees ----------------- There are a couple of trees for entire DAMON patchset series and features for future release. - For latest release: https://github.com/sjp38/linux/tree/damon/master - For next release: https://github.com/sjp38/linux/tree/damon/next Long-term Support Trees ----------------------- For people who want to test DAMON but using LTS kernels, there are another couple of trees based on two latest LTS kernels respectively and containing the 'damon/master' backports. - For v5.4.y: https://github.com/sjp38/linux/tree/damon/for-v5.4.y - For v5.10.y: https://github.com/sjp38/linux/tree/damon/for-v5.10.y Amazon Linux Kernel Trees ------------------------- DAMON is also merged in two public Amazon Linux kernel trees that based on v5.4.y[1] and v5.10.y[2]. [1] https://github.com/amazonlinux/linux/tree/amazon-5.4.y/master/mm/damon [2] https://github.com/amazonlinux/linux/tree/amazon-5.10.y/master/mm/damon Git Tree for Diff of Patches ============================ For easy review of diff between different versions of each patch, I prepared a git tree containing all versions of the DAMON patchset series: https://github.com/sjp38/damon-patches You can clone it and use 'diff' for easy review of changes between different versions of the patchset. For example: $ git clone https://github.com/sjp38/damon-patches && cd damon-patches $ diff -u damon/v33 damon/v34 Sequence Of Patches =================== First three patches implement the core logics of DAMON. The 1st patch introduces basic sampling based hotness monitoring for arbitrary types of targets. Following two patches implement the core mechanisms for control of overhead and accuracy, namely regions based sampling (patch 2) and adaptive regions adjustment (patch 3). Now the essential parts of DAMON is complete, but it cannot work unless someone provides monitoring primitives for a specific use case. The following two patches make it just work for virtual address spaces monitoring. The 4th patch makes 'PG_idle' can be used by DAMON and the 5th patch implements the virtual memory address space specific monitoring primitives using page table Accessed bits and the 'PG_idle' page flag. Now DAMON just works for virtual address space monitoring via the kernel space api. To let the user space users can use DAMON, following four patches add interfaces for them. The 6th patch adds a tracepoint for monitoring results. The 7th patch implements a DAMON application kernel module, namely damon-dbgfs, that simply wraps DAMON and exposes DAMON interface to the user space via the debugfs interface. The 8th patch further exports pid of monitoring thread (kdamond) to user space for easier cpu usage accounting, and the 9th patch makes the debugfs interface to support multiple contexts. Three patches for maintainability follows. The 10th patch adds documentations for both the user space and the kernel space. The 11th patch provides unit tests (based on the kunit) while the 12th patch adds user space tests (based on the kselftest). Finally, the last patch (13th) updates the MAINTAINERS file. This patch (of 13): DAMON is a data access monitoring framework for the Linux kernel. The core mechanisms of DAMON make it - accurate (the monitoring output is useful enough for DRAM level performance-centric memory management; It might be inappropriate for CPU cache levels, though), - light-weight (the monitoring overhead is normally low enough to be applied online), and - scalable (the upper-bound of the overhead is in constant range regardless of the size of target workloads). Using this framework, hence, we can easily write efficient kernel space data access monitoring applications. For example, the kernel's memory management mechanisms can make advanced decisions using this. Experimental data access aware optimization works that incurring high access monitoring overhead could again be implemented on top of this. Due to its simple and flexible interface, providing user space interface would be also easy. Then, user space users who have some special workloads can write personalized applications for better understanding and optimizations of their workloads and systems. === Nevertheless, this commit is defining and implementing only basic access check part without the overhead-accuracy handling core logic. The basic access check is as below. The output of DAMON says what memory regions are how frequently accessed for a given duration. The resolution of the access frequency is controlled by setting ``sampling interval`` and ``aggregation interval``. In detail, DAMON checks access to each page per ``sampling interval`` and aggregates the results. In other words, counts the number of the accesses to each region. After each ``aggregation interval`` passes, DAMON calls callback functions that previously registered by users so that users can read the aggregated results and then clears the results. This can be described in below simple pseudo-code:: init() while monitoring_on: for page in monitoring_target: if accessed(page): nr_accesses[page] += 1 if time() % aggregation_interval == 0: for callback in user_registered_callbacks: callback(monitoring_target, nr_accesses) for page in monitoring_target: nr_accesses[page] = 0 if time() % update_interval == 0: update() sleep(sampling interval) The target regions constructed at the beginning of the monitoring and updated after each ``regions_update_interval``, because the target regions could be dynamically changed (e.g., mmap() or memory hotplug). The monitoring overhead of this mechanism will arbitrarily increase as the size of the target workload grows. The basic monitoring primitives for actual access check and dynamic target regions construction aren't in the core part of DAMON. Instead, it allows users to implement their own primitives that are optimized for their use case and configure DAMON to use those. In other words, users cannot use current version of DAMON without some additional works. Following commits will implement the core mechanisms for the overhead-accuracy control and default primitives implementations. Link: https://lkml.kernel.org/r/20210716081449.22187-1-sj38.park@gmail.com Link: https://lkml.kernel.org/r/20210716081449.22187-2-sj38.park@gmail.com Signed-off-by: SeongJae Park <sjpark@amazon.de> Reviewed-by: Leonard Foerster <foersleo@amazon.de> Reviewed-by: Fernand Sieber <sieberf@amazon.com> Acked-by: Shakeel Butt <shakeelb@google.com> Cc: Jonathan Cameron <Jonathan.Cameron@huawei.com> Cc: Alexander Shishkin <alexander.shishkin@linux.intel.com> Cc: Amit Shah <amit@kernel.org> Cc: Benjamin Herrenschmidt <benh@kernel.crashing.org> Cc: Jonathan Corbet <corbet@lwn.net> Cc: David Hildenbrand <david@redhat.com> Cc: David Woodhouse <dwmw@amazon.com> Cc: Marco Elver <elver@google.com> Cc: Fan Du <fan.du@intel.com> Cc: Greg Kroah-Hartman <greg@kroah.com> Cc: Greg Thelen <gthelen@google.com> Cc: Joe Perches <joe@perches.com> Cc: Mel Gorman <mgorman@suse.de> Cc: Maximilian Heyne <mheyne@amazon.de> Cc: Minchan Kim <minchan@kernel.org> Cc: Ingo Molnar <mingo@redhat.com> Cc: Namhyung Kim <namhyung@kernel.org> Cc: Peter Zijlstra <peterz@infradead.org> Cc: Rik van Riel <riel@surriel.com> Cc: David Rientjes <rientjes@google.com> Cc: Steven Rostedt (VMware) <rostedt@goodmis.org> Cc: Shuah Khan <shuah@kernel.org> Cc: Vlastimil Babka <vbabka@suse.cz> Cc: Vladimir Davydov <vdavydov.dev@gmail.com> Cc: Brendan Higgins <brendanhiggins@google.com> Cc: Markus Boehme <markubo@amazon.de> Signed-off-by: Andrew Morton <akpm@linux-foundation.org> Signed-off-by: Linus Torvalds <torvalds@linux-foundation.org>
2021-09-08 10:56:28 +08:00
* @target_valid: Determine if the target is valid.
* @cleanup: Clean up the context.
*
* DAMON can be extended for various address spaces and usages. For this,
mm/damon: rename damon_primitives to damon_operations Patch series "Allow DAMON user code independent of monitoring primitives". In-kernel DAMON user code is required to configure the monitoring context (struct damon_ctx) with proper monitoring primitives (struct damon_primitive). This makes the user code dependent to all supporting monitoring primitives. For example, DAMON debugfs interface depends on both DAMON_VADDR and DAMON_PADDR, though some users have interest in only one use case. As more monitoring primitives are introduced, the problem will be bigger. To minimize such unnecessary dependency, this patchset makes monitoring primitives can be registered by the implemnting code and later dynamically searched and selected by the user code. In addition to that, this patchset renames monitoring primitives to monitoring operations, which is more easy to intuitively understand what it means and how it would be structed. This patch (of 8): DAMON has a set of callback functions called monitoring primitives and let it can be configured with various implementations for easy extension for different address spaces and usages. However, the word 'primitive' is not so explicit. Meanwhile, many other structs resembles similar purpose calls themselves 'operations'. To make the code easier to be understood, this commit renames 'damon_primitives' to 'damon_operations' before it is too late to rename. Link: https://lkml.kernel.org/r/20220215184603.1479-1-sj@kernel.org Link: https://lkml.kernel.org/r/20220215184603.1479-2-sj@kernel.org Signed-off-by: SeongJae Park <sj@kernel.org> Cc: Xin Hao <xhao@linux.alibaba.com> Cc: David Rientjes <rientjes@google.com> Signed-off-by: Andrew Morton <akpm@linux-foundation.org> Signed-off-by: Linus Torvalds <torvalds@linux-foundation.org>
2022-03-23 05:48:46 +08:00
* users should register the low level operations for their target address
* space and usecase via the &damon_ctx.ops. Then, the monitoring thread
mm: introduce Data Access MONitor (DAMON) Patch series "Introduce Data Access MONitor (DAMON)", v34. Introduction ============ DAMON is a data access monitoring framework for the Linux kernel. The core mechanisms of DAMON called 'region based sampling' and 'adaptive regions adjustment' (refer to 'mechanisms.rst' in the 11th patch of this patchset for the detail) make it - accurate (The monitored information is useful for DRAM level memory management. It might not appropriate for Cache-level accuracy, though.), - light-weight (The monitoring overhead is low enough to be applied online while making no impact on the performance of the target workloads.), and - scalable (the upper-bound of the instrumentation overhead is controllable regardless of the size of target workloads.). Using this framework, therefore, several memory management mechanisms such as reclamation and THP can be optimized to aware real data access patterns. Experimental access pattern aware memory management optimization works that incurring high instrumentation overhead will be able to have another try. Though DAMON is for kernel subsystems, it can be easily exposed to the user space by writing a DAMON-wrapper kernel subsystem. Then, user space users who have some special workloads will be able to write personalized tools or applications for deeper understanding and specialized optimizations of their systems. DAMON is also merged in two public Amazon Linux kernel trees that based on v5.4.y[1] and v5.10.y[2]. [1] https://github.com/amazonlinux/linux/tree/amazon-5.4.y/master/mm/damon [2] https://github.com/amazonlinux/linux/tree/amazon-5.10.y/master/mm/damon The userspace tool[1] is available, released under GPLv2, and actively being maintained. I am also planning to implement another basic user interface in perf[2]. Also, the basic test suite for DAMON is available under GPLv2[3]. [1] https://github.com/awslabs/damo [2] https://lore.kernel.org/linux-mm/20210107120729.22328-1-sjpark@amazon.com/ [3] https://github.com/awslabs/damon-tests Long-term Plan -------------- DAMON is a part of a project called Data Access-aware Operating System (DAOS). As the name implies, I want to improve the performance and efficiency of systems using fine-grained data access patterns. The optimizations are for both kernel and user spaces. I will therefore modify or create kernel subsystems, export some of those to user space and implement user space library / tools. Below shows the layers and components for the project. --------------------------------------------------------------------------- Primitives: PTE Accessed bit, PG_idle, rmap, (Intel CMT), ... Framework: DAMON Features: DAMOS, virtual addr, physical addr, ... Applications: DAMON-debugfs, (DARC), ... ^^^^^^^^^^^^^^^^^^^^^^^ KERNEL SPACE ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ Raw Interface: debugfs, (sysfs), (damonfs), tracepoints, (sys_damon), ... vvvvvvvvvvvvvvvvvvvvvvv USER SPACE vvvvvvvvvvvvvvvvvvvvvvvvvvvvvvvv Library: (libdamon), ... Tools: DAMO, (perf), ... --------------------------------------------------------------------------- The components in parentheses or marked as '...' are not implemented yet but in the future plan. IOW, those are the TODO tasks of DAOS project. For more detail, please refer to the plans: https://lore.kernel.org/linux-mm/20201202082731.24828-1-sjpark@amazon.com/ Evaluations =========== We evaluated DAMON's overhead, monitoring quality and usefulness using 24 realistic workloads on my QEMU/KVM based virtual machine running a kernel that v24 DAMON patchset is applied. DAMON is lightweight. It increases system memory usage by 0.39% and slows target workloads down by 1.16%. DAMON is accurate and useful for memory management optimizations. An experimental DAMON-based operation scheme for THP, namely 'ethp', removes 76.15% of THP memory overheads while preserving 51.25% of THP speedup. Another experimental DAMON-based 'proactive reclamation' implementation, 'prcl', reduces 93.38% of residential sets and 23.63% of system memory footprint while incurring only 1.22% runtime overhead in the best case (parsec3/freqmine). NOTE that the experimental THP optimization and proactive reclamation are not for production but only for proof of concepts. Please refer to the official document[1] or "Documentation/admin-guide/mm: Add a document for DAMON" patch in this patchset for detailed evaluation setup and results. [1] https://damonitor.github.io/doc/html/latest-damon/admin-guide/mm/damon/eval.html Real-world User Story ===================== In summary, DAMON has used on production systems and proved its usefulness. DAMON as a profiler ------------------- We analyzed characteristics of a large scale production systems of our customers using DAMON. The systems utilize 70GB DRAM and 36 CPUs. From this, we were able to find interesting things below. There were obviously different access pattern under idle workload and active workload. Under the idle workload, it accessed large memory regions with low frequency, while the active workload accessed small memory regions with high freuqnecy. DAMON found a 7GB memory region that showing obviously high access frequency under the active workload. We believe this is the performance-effective working set and need to be protected. There was a 4KB memory region that showing highest access frequency under not only active but also idle workloads. We think this must be a hottest code section like thing that should never be paged out. For this analysis, DAMON used only 0.3-1% of single CPU time. Because we used recording-based analysis, it consumed about 3-12 MB of disk space per 20 minutes. This is only small amount of disk space, but we can further reduce the disk usage by using non-recording-based DAMON features. I'd like to argue that only DAMON can do such detailed analysis (finding 4KB highest region in 70GB memory) with the light overhead. DAMON as a system optimization tool ----------------------------------- We also found below potential performance problems on the systems and made DAMON-based solutions. The system doesn't want to make the workload suffer from the page reclamation and thus it utilizes enough DRAM but no swap device. However, we found the system is actively reclaiming file-backed pages, because the system has intensive file IO. The file IO turned out to be not performance critical for the workload, but the customer wanted to ensure performance critical file-backed pages like code section to not mistakenly be evicted. Using direct IO should or `mlock()` would be a straightforward solution, but modifying the user space code is not easy for the customer. Alternatively, we could use DAMON-based operation scheme[1]. By using it, we can ask DAMON to track access frequency of each region and make 'process_madvise(MADV_WILLNEED)[2]' call for regions having specific size and access frequency for a time interval. We also found the system is having high number of TLB misses. We tried 'always' THP enabled policy and it greatly reduced TLB misses, but the page reclamation also been more frequent due to the THP internal fragmentation caused memory bloat. We could try another DAMON-based operation scheme that applies 'MADV_HUGEPAGE' to memory regions having >=2MB size and high access frequency, while applying 'MADV_NOHUGEPAGE' to regions having <2MB size and low access frequency. We do not own the systems so we only reported the analysis results and possible optimization solutions to the customers. The customers satisfied about the analysis results and promised to try the optimization guides. [1] https://lore.kernel.org/linux-mm/20201006123931.5847-1-sjpark@amazon.com/ [2] https://lore.kernel.org/linux-api/20200622192900.22757-4-minchan@kernel.org/ Comparison with Idle Page Tracking ================================== Idle Page Tracking allows users to set and read idleness of pages using a bitmap file which represents each page with each bit of the file. One recommended usage of it is working set size detection. Users can do that by 1. find PFN of each page for workloads in interest, 2. set all the pages as idle by doing writes to the bitmap file, 3. wait until the workload accesses its working set, and 4. read the idleness of the pages again and count pages became not idle. NOTE: While Idle Page Tracking is for user space users, DAMON is primarily designed for kernel subsystems though it can easily exposed to the user space. Hence, this section only assumes such user space use of DAMON. For what use cases Idle Page Tracking would be better? ------------------------------------------------------ 1. Flexible usecases other than hotness monitoring. Because Idle Page Tracking allows users to control the primitive (Page idleness) by themselves, Idle Page Tracking users can do anything they want. Meanwhile, DAMON is primarily designed to monitor the hotness of each memory region. For this, DAMON asks users to provide sampling interval and aggregation interval. For the reason, there could be some use case that using Idle Page Tracking is simpler. 2. Physical memory monitoring. Idle Page Tracking receives PFN range as input, so natively supports physical memory monitoring. DAMON is designed to be extensible for multiple address spaces and use cases by implementing and using primitives for the given use case. Therefore, by theory, DAMON has no limitation in the type of target address space as long as primitives for the given address space exists. However, the default primitives introduced by this patchset supports only virtual address spaces. Therefore, for physical memory monitoring, you should implement your own primitives and use it, or simply use Idle Page Tracking. Nonetheless, RFC patchsets[1] for the physical memory address space primitives is already available. It also supports user memory same to Idle Page Tracking. [1] https://lore.kernel.org/linux-mm/20200831104730.28970-1-sjpark@amazon.com/ For what use cases DAMON is better? ----------------------------------- 1. Hotness Monitoring. Idle Page Tracking let users know only if a page frame is accessed or not. For hotness check, the user should write more code and use more memory. DAMON do that by itself. 2. Low Monitoring Overhead DAMON receives user's monitoring request with one step and then provide the results. So, roughly speaking, DAMON require only O(1) user/kernel context switches. In case of Idle Page Tracking, however, because the interface receives contiguous page frames, the number of user/kernel context switches increases as the monitoring target becomes complex and huge. As a result, the context switch overhead could be not negligible. Moreover, DAMON is born to handle with the monitoring overhead. Because the core mechanism is pure logical, Idle Page Tracking users might be able to implement the mechanism on their own, but it would be time consuming and the user/kernel context switching will still more frequent than that of DAMON. Also, the kernel subsystems cannot use the logic in this case. 3. Page granularity working set size detection. Until v22 of this patchset, this was categorized as the thing Idle Page Tracking could do better, because DAMON basically maintains additional metadata for each of the monitoring target regions. So, in the page granularity working set size detection use case, DAMON would incur (number of monitoring target pages * size of metadata) memory overhead. Size of the single metadata item is about 54 bytes, so assuming 4KB pages, about 1.3% of monitoring target pages will be additionally used. All essential metadata for Idle Page Tracking are embedded in 'struct page' and page table entries. Therefore, in this use case, only one counter variable for working set size accounting is required if Idle Page Tracking is used. There are more details to consider, but roughly speaking, this is true in most cases. However, the situation changed from v23. Now DAMON supports arbitrary types of monitoring targets, which don't use the metadata. Using that, DAMON can do the working set size detection with no additional space overhead but less user-kernel context switch. A first draft for the implementation of monitoring primitives for this usage is available in a DAMON development tree[1]. An RFC patchset for it based on this patchset will also be available soon. Since v24, the arbitrary type support is dropped from this patchset because this patchset doesn't introduce real use of the type. You can still get it from the DAMON development tree[2], though. [1] https://github.com/sjp38/linux/tree/damon/pgidle_hack [2] https://github.com/sjp38/linux/tree/damon/master 4. More future usecases While Idle Page Tracking has tight coupling with base primitives (PG_Idle and page table Accessed bits), DAMON is designed to be extensible for many use cases and address spaces. If you need some special address type or want to use special h/w access check primitives, you can write your own primitives for that and configure DAMON to use those. Therefore, if your use case could be changed a lot in future, using DAMON could be better. Can I use both Idle Page Tracking and DAMON? -------------------------------------------- Yes, though using them concurrently for overlapping memory regions could result in interference to each other. Nevertheless, such use case would be rare or makes no sense at all. Even in the case, the noise would bot be really significant. So, you can choose whatever you want depending on the characteristics of your use cases. More Information ================ We prepared a showcase web site[1] that you can get more information. There are - the official documentations[2], - the heatmap format dynamic access pattern of various realistic workloads for heap area[3], mmap()-ed area[4], and stack[5] area, - the dynamic working set size distribution[6] and chronological working set size changes[7], and - the latest performance test results[8]. [1] https://damonitor.github.io/_index [2] https://damonitor.github.io/doc/html/latest-damon [3] https://damonitor.github.io/test/result/visual/latest/rec.heatmap.0.png.html [4] https://damonitor.github.io/test/result/visual/latest/rec.heatmap.1.png.html [5] https://damonitor.github.io/test/result/visual/latest/rec.heatmap.2.png.html [6] https://damonitor.github.io/test/result/visual/latest/rec.wss_sz.png.html [7] https://damonitor.github.io/test/result/visual/latest/rec.wss_time.png.html [8] https://damonitor.github.io/test/result/perf/latest/html/index.html Baseline and Complete Git Trees =============================== The patches are based on the latest -mm tree, specifically v5.14-rc1-mmots-2021-07-15-18-47 of https://github.com/hnaz/linux-mm. You can also clone the complete git tree: $ git clone git://github.com/sjp38/linux -b damon/patches/v34 The web is also available: https://github.com/sjp38/linux/releases/tag/damon/patches/v34 Development Trees ----------------- There are a couple of trees for entire DAMON patchset series and features for future release. - For latest release: https://github.com/sjp38/linux/tree/damon/master - For next release: https://github.com/sjp38/linux/tree/damon/next Long-term Support Trees ----------------------- For people who want to test DAMON but using LTS kernels, there are another couple of trees based on two latest LTS kernels respectively and containing the 'damon/master' backports. - For v5.4.y: https://github.com/sjp38/linux/tree/damon/for-v5.4.y - For v5.10.y: https://github.com/sjp38/linux/tree/damon/for-v5.10.y Amazon Linux Kernel Trees ------------------------- DAMON is also merged in two public Amazon Linux kernel trees that based on v5.4.y[1] and v5.10.y[2]. [1] https://github.com/amazonlinux/linux/tree/amazon-5.4.y/master/mm/damon [2] https://github.com/amazonlinux/linux/tree/amazon-5.10.y/master/mm/damon Git Tree for Diff of Patches ============================ For easy review of diff between different versions of each patch, I prepared a git tree containing all versions of the DAMON patchset series: https://github.com/sjp38/damon-patches You can clone it and use 'diff' for easy review of changes between different versions of the patchset. For example: $ git clone https://github.com/sjp38/damon-patches && cd damon-patches $ diff -u damon/v33 damon/v34 Sequence Of Patches =================== First three patches implement the core logics of DAMON. The 1st patch introduces basic sampling based hotness monitoring for arbitrary types of targets. Following two patches implement the core mechanisms for control of overhead and accuracy, namely regions based sampling (patch 2) and adaptive regions adjustment (patch 3). Now the essential parts of DAMON is complete, but it cannot work unless someone provides monitoring primitives for a specific use case. The following two patches make it just work for virtual address spaces monitoring. The 4th patch makes 'PG_idle' can be used by DAMON and the 5th patch implements the virtual memory address space specific monitoring primitives using page table Accessed bits and the 'PG_idle' page flag. Now DAMON just works for virtual address space monitoring via the kernel space api. To let the user space users can use DAMON, following four patches add interfaces for them. The 6th patch adds a tracepoint for monitoring results. The 7th patch implements a DAMON application kernel module, namely damon-dbgfs, that simply wraps DAMON and exposes DAMON interface to the user space via the debugfs interface. The 8th patch further exports pid of monitoring thread (kdamond) to user space for easier cpu usage accounting, and the 9th patch makes the debugfs interface to support multiple contexts. Three patches for maintainability follows. The 10th patch adds documentations for both the user space and the kernel space. The 11th patch provides unit tests (based on the kunit) while the 12th patch adds user space tests (based on the kselftest). Finally, the last patch (13th) updates the MAINTAINERS file. This patch (of 13): DAMON is a data access monitoring framework for the Linux kernel. The core mechanisms of DAMON make it - accurate (the monitoring output is useful enough for DRAM level performance-centric memory management; It might be inappropriate for CPU cache levels, though), - light-weight (the monitoring overhead is normally low enough to be applied online), and - scalable (the upper-bound of the overhead is in constant range regardless of the size of target workloads). Using this framework, hence, we can easily write efficient kernel space data access monitoring applications. For example, the kernel's memory management mechanisms can make advanced decisions using this. Experimental data access aware optimization works that incurring high access monitoring overhead could again be implemented on top of this. Due to its simple and flexible interface, providing user space interface would be also easy. Then, user space users who have some special workloads can write personalized applications for better understanding and optimizations of their workloads and systems. === Nevertheless, this commit is defining and implementing only basic access check part without the overhead-accuracy handling core logic. The basic access check is as below. The output of DAMON says what memory regions are how frequently accessed for a given duration. The resolution of the access frequency is controlled by setting ``sampling interval`` and ``aggregation interval``. In detail, DAMON checks access to each page per ``sampling interval`` and aggregates the results. In other words, counts the number of the accesses to each region. After each ``aggregation interval`` passes, DAMON calls callback functions that previously registered by users so that users can read the aggregated results and then clears the results. This can be described in below simple pseudo-code:: init() while monitoring_on: for page in monitoring_target: if accessed(page): nr_accesses[page] += 1 if time() % aggregation_interval == 0: for callback in user_registered_callbacks: callback(monitoring_target, nr_accesses) for page in monitoring_target: nr_accesses[page] = 0 if time() % update_interval == 0: update() sleep(sampling interval) The target regions constructed at the beginning of the monitoring and updated after each ``regions_update_interval``, because the target regions could be dynamically changed (e.g., mmap() or memory hotplug). The monitoring overhead of this mechanism will arbitrarily increase as the size of the target workload grows. The basic monitoring primitives for actual access check and dynamic target regions construction aren't in the core part of DAMON. Instead, it allows users to implement their own primitives that are optimized for their use case and configure DAMON to use those. In other words, users cannot use current version of DAMON without some additional works. Following commits will implement the core mechanisms for the overhead-accuracy control and default primitives implementations. Link: https://lkml.kernel.org/r/20210716081449.22187-1-sj38.park@gmail.com Link: https://lkml.kernel.org/r/20210716081449.22187-2-sj38.park@gmail.com Signed-off-by: SeongJae Park <sjpark@amazon.de> Reviewed-by: Leonard Foerster <foersleo@amazon.de> Reviewed-by: Fernand Sieber <sieberf@amazon.com> Acked-by: Shakeel Butt <shakeelb@google.com> Cc: Jonathan Cameron <Jonathan.Cameron@huawei.com> Cc: Alexander Shishkin <alexander.shishkin@linux.intel.com> Cc: Amit Shah <amit@kernel.org> Cc: Benjamin Herrenschmidt <benh@kernel.crashing.org> Cc: Jonathan Corbet <corbet@lwn.net> Cc: David Hildenbrand <david@redhat.com> Cc: David Woodhouse <dwmw@amazon.com> Cc: Marco Elver <elver@google.com> Cc: Fan Du <fan.du@intel.com> Cc: Greg Kroah-Hartman <greg@kroah.com> Cc: Greg Thelen <gthelen@google.com> Cc: Joe Perches <joe@perches.com> Cc: Mel Gorman <mgorman@suse.de> Cc: Maximilian Heyne <mheyne@amazon.de> Cc: Minchan Kim <minchan@kernel.org> Cc: Ingo Molnar <mingo@redhat.com> Cc: Namhyung Kim <namhyung@kernel.org> Cc: Peter Zijlstra <peterz@infradead.org> Cc: Rik van Riel <riel@surriel.com> Cc: David Rientjes <rientjes@google.com> Cc: Steven Rostedt (VMware) <rostedt@goodmis.org> Cc: Shuah Khan <shuah@kernel.org> Cc: Vlastimil Babka <vbabka@suse.cz> Cc: Vladimir Davydov <vdavydov.dev@gmail.com> Cc: Brendan Higgins <brendanhiggins@google.com> Cc: Markus Boehme <markubo@amazon.de> Signed-off-by: Andrew Morton <akpm@linux-foundation.org> Signed-off-by: Linus Torvalds <torvalds@linux-foundation.org>
2021-09-08 10:56:28 +08:00
* (&damon_ctx.kdamond) calls @init and @prepare_access_checks before starting
mm/damon: rename damon_primitives to damon_operations Patch series "Allow DAMON user code independent of monitoring primitives". In-kernel DAMON user code is required to configure the monitoring context (struct damon_ctx) with proper monitoring primitives (struct damon_primitive). This makes the user code dependent to all supporting monitoring primitives. For example, DAMON debugfs interface depends on both DAMON_VADDR and DAMON_PADDR, though some users have interest in only one use case. As more monitoring primitives are introduced, the problem will be bigger. To minimize such unnecessary dependency, this patchset makes monitoring primitives can be registered by the implemnting code and later dynamically searched and selected by the user code. In addition to that, this patchset renames monitoring primitives to monitoring operations, which is more easy to intuitively understand what it means and how it would be structed. This patch (of 8): DAMON has a set of callback functions called monitoring primitives and let it can be configured with various implementations for easy extension for different address spaces and usages. However, the word 'primitive' is not so explicit. Meanwhile, many other structs resembles similar purpose calls themselves 'operations'. To make the code easier to be understood, this commit renames 'damon_primitives' to 'damon_operations' before it is too late to rename. Link: https://lkml.kernel.org/r/20220215184603.1479-1-sj@kernel.org Link: https://lkml.kernel.org/r/20220215184603.1479-2-sj@kernel.org Signed-off-by: SeongJae Park <sj@kernel.org> Cc: Xin Hao <xhao@linux.alibaba.com> Cc: David Rientjes <rientjes@google.com> Signed-off-by: Andrew Morton <akpm@linux-foundation.org> Signed-off-by: Linus Torvalds <torvalds@linux-foundation.org>
2022-03-23 05:48:46 +08:00
* the monitoring, @update after each &damon_ctx.ops_update_interval, and
mm: introduce Data Access MONitor (DAMON) Patch series "Introduce Data Access MONitor (DAMON)", v34. Introduction ============ DAMON is a data access monitoring framework for the Linux kernel. The core mechanisms of DAMON called 'region based sampling' and 'adaptive regions adjustment' (refer to 'mechanisms.rst' in the 11th patch of this patchset for the detail) make it - accurate (The monitored information is useful for DRAM level memory management. It might not appropriate for Cache-level accuracy, though.), - light-weight (The monitoring overhead is low enough to be applied online while making no impact on the performance of the target workloads.), and - scalable (the upper-bound of the instrumentation overhead is controllable regardless of the size of target workloads.). Using this framework, therefore, several memory management mechanisms such as reclamation and THP can be optimized to aware real data access patterns. Experimental access pattern aware memory management optimization works that incurring high instrumentation overhead will be able to have another try. Though DAMON is for kernel subsystems, it can be easily exposed to the user space by writing a DAMON-wrapper kernel subsystem. Then, user space users who have some special workloads will be able to write personalized tools or applications for deeper understanding and specialized optimizations of their systems. DAMON is also merged in two public Amazon Linux kernel trees that based on v5.4.y[1] and v5.10.y[2]. [1] https://github.com/amazonlinux/linux/tree/amazon-5.4.y/master/mm/damon [2] https://github.com/amazonlinux/linux/tree/amazon-5.10.y/master/mm/damon The userspace tool[1] is available, released under GPLv2, and actively being maintained. I am also planning to implement another basic user interface in perf[2]. Also, the basic test suite for DAMON is available under GPLv2[3]. [1] https://github.com/awslabs/damo [2] https://lore.kernel.org/linux-mm/20210107120729.22328-1-sjpark@amazon.com/ [3] https://github.com/awslabs/damon-tests Long-term Plan -------------- DAMON is a part of a project called Data Access-aware Operating System (DAOS). As the name implies, I want to improve the performance and efficiency of systems using fine-grained data access patterns. The optimizations are for both kernel and user spaces. I will therefore modify or create kernel subsystems, export some of those to user space and implement user space library / tools. Below shows the layers and components for the project. --------------------------------------------------------------------------- Primitives: PTE Accessed bit, PG_idle, rmap, (Intel CMT), ... Framework: DAMON Features: DAMOS, virtual addr, physical addr, ... Applications: DAMON-debugfs, (DARC), ... ^^^^^^^^^^^^^^^^^^^^^^^ KERNEL SPACE ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ Raw Interface: debugfs, (sysfs), (damonfs), tracepoints, (sys_damon), ... vvvvvvvvvvvvvvvvvvvvvvv USER SPACE vvvvvvvvvvvvvvvvvvvvvvvvvvvvvvvv Library: (libdamon), ... Tools: DAMO, (perf), ... --------------------------------------------------------------------------- The components in parentheses or marked as '...' are not implemented yet but in the future plan. IOW, those are the TODO tasks of DAOS project. For more detail, please refer to the plans: https://lore.kernel.org/linux-mm/20201202082731.24828-1-sjpark@amazon.com/ Evaluations =========== We evaluated DAMON's overhead, monitoring quality and usefulness using 24 realistic workloads on my QEMU/KVM based virtual machine running a kernel that v24 DAMON patchset is applied. DAMON is lightweight. It increases system memory usage by 0.39% and slows target workloads down by 1.16%. DAMON is accurate and useful for memory management optimizations. An experimental DAMON-based operation scheme for THP, namely 'ethp', removes 76.15% of THP memory overheads while preserving 51.25% of THP speedup. Another experimental DAMON-based 'proactive reclamation' implementation, 'prcl', reduces 93.38% of residential sets and 23.63% of system memory footprint while incurring only 1.22% runtime overhead in the best case (parsec3/freqmine). NOTE that the experimental THP optimization and proactive reclamation are not for production but only for proof of concepts. Please refer to the official document[1] or "Documentation/admin-guide/mm: Add a document for DAMON" patch in this patchset for detailed evaluation setup and results. [1] https://damonitor.github.io/doc/html/latest-damon/admin-guide/mm/damon/eval.html Real-world User Story ===================== In summary, DAMON has used on production systems and proved its usefulness. DAMON as a profiler ------------------- We analyzed characteristics of a large scale production systems of our customers using DAMON. The systems utilize 70GB DRAM and 36 CPUs. From this, we were able to find interesting things below. There were obviously different access pattern under idle workload and active workload. Under the idle workload, it accessed large memory regions with low frequency, while the active workload accessed small memory regions with high freuqnecy. DAMON found a 7GB memory region that showing obviously high access frequency under the active workload. We believe this is the performance-effective working set and need to be protected. There was a 4KB memory region that showing highest access frequency under not only active but also idle workloads. We think this must be a hottest code section like thing that should never be paged out. For this analysis, DAMON used only 0.3-1% of single CPU time. Because we used recording-based analysis, it consumed about 3-12 MB of disk space per 20 minutes. This is only small amount of disk space, but we can further reduce the disk usage by using non-recording-based DAMON features. I'd like to argue that only DAMON can do such detailed analysis (finding 4KB highest region in 70GB memory) with the light overhead. DAMON as a system optimization tool ----------------------------------- We also found below potential performance problems on the systems and made DAMON-based solutions. The system doesn't want to make the workload suffer from the page reclamation and thus it utilizes enough DRAM but no swap device. However, we found the system is actively reclaiming file-backed pages, because the system has intensive file IO. The file IO turned out to be not performance critical for the workload, but the customer wanted to ensure performance critical file-backed pages like code section to not mistakenly be evicted. Using direct IO should or `mlock()` would be a straightforward solution, but modifying the user space code is not easy for the customer. Alternatively, we could use DAMON-based operation scheme[1]. By using it, we can ask DAMON to track access frequency of each region and make 'process_madvise(MADV_WILLNEED)[2]' call for regions having specific size and access frequency for a time interval. We also found the system is having high number of TLB misses. We tried 'always' THP enabled policy and it greatly reduced TLB misses, but the page reclamation also been more frequent due to the THP internal fragmentation caused memory bloat. We could try another DAMON-based operation scheme that applies 'MADV_HUGEPAGE' to memory regions having >=2MB size and high access frequency, while applying 'MADV_NOHUGEPAGE' to regions having <2MB size and low access frequency. We do not own the systems so we only reported the analysis results and possible optimization solutions to the customers. The customers satisfied about the analysis results and promised to try the optimization guides. [1] https://lore.kernel.org/linux-mm/20201006123931.5847-1-sjpark@amazon.com/ [2] https://lore.kernel.org/linux-api/20200622192900.22757-4-minchan@kernel.org/ Comparison with Idle Page Tracking ================================== Idle Page Tracking allows users to set and read idleness of pages using a bitmap file which represents each page with each bit of the file. One recommended usage of it is working set size detection. Users can do that by 1. find PFN of each page for workloads in interest, 2. set all the pages as idle by doing writes to the bitmap file, 3. wait until the workload accesses its working set, and 4. read the idleness of the pages again and count pages became not idle. NOTE: While Idle Page Tracking is for user space users, DAMON is primarily designed for kernel subsystems though it can easily exposed to the user space. Hence, this section only assumes such user space use of DAMON. For what use cases Idle Page Tracking would be better? ------------------------------------------------------ 1. Flexible usecases other than hotness monitoring. Because Idle Page Tracking allows users to control the primitive (Page idleness) by themselves, Idle Page Tracking users can do anything they want. Meanwhile, DAMON is primarily designed to monitor the hotness of each memory region. For this, DAMON asks users to provide sampling interval and aggregation interval. For the reason, there could be some use case that using Idle Page Tracking is simpler. 2. Physical memory monitoring. Idle Page Tracking receives PFN range as input, so natively supports physical memory monitoring. DAMON is designed to be extensible for multiple address spaces and use cases by implementing and using primitives for the given use case. Therefore, by theory, DAMON has no limitation in the type of target address space as long as primitives for the given address space exists. However, the default primitives introduced by this patchset supports only virtual address spaces. Therefore, for physical memory monitoring, you should implement your own primitives and use it, or simply use Idle Page Tracking. Nonetheless, RFC patchsets[1] for the physical memory address space primitives is already available. It also supports user memory same to Idle Page Tracking. [1] https://lore.kernel.org/linux-mm/20200831104730.28970-1-sjpark@amazon.com/ For what use cases DAMON is better? ----------------------------------- 1. Hotness Monitoring. Idle Page Tracking let users know only if a page frame is accessed or not. For hotness check, the user should write more code and use more memory. DAMON do that by itself. 2. Low Monitoring Overhead DAMON receives user's monitoring request with one step and then provide the results. So, roughly speaking, DAMON require only O(1) user/kernel context switches. In case of Idle Page Tracking, however, because the interface receives contiguous page frames, the number of user/kernel context switches increases as the monitoring target becomes complex and huge. As a result, the context switch overhead could be not negligible. Moreover, DAMON is born to handle with the monitoring overhead. Because the core mechanism is pure logical, Idle Page Tracking users might be able to implement the mechanism on their own, but it would be time consuming and the user/kernel context switching will still more frequent than that of DAMON. Also, the kernel subsystems cannot use the logic in this case. 3. Page granularity working set size detection. Until v22 of this patchset, this was categorized as the thing Idle Page Tracking could do better, because DAMON basically maintains additional metadata for each of the monitoring target regions. So, in the page granularity working set size detection use case, DAMON would incur (number of monitoring target pages * size of metadata) memory overhead. Size of the single metadata item is about 54 bytes, so assuming 4KB pages, about 1.3% of monitoring target pages will be additionally used. All essential metadata for Idle Page Tracking are embedded in 'struct page' and page table entries. Therefore, in this use case, only one counter variable for working set size accounting is required if Idle Page Tracking is used. There are more details to consider, but roughly speaking, this is true in most cases. However, the situation changed from v23. Now DAMON supports arbitrary types of monitoring targets, which don't use the metadata. Using that, DAMON can do the working set size detection with no additional space overhead but less user-kernel context switch. A first draft for the implementation of monitoring primitives for this usage is available in a DAMON development tree[1]. An RFC patchset for it based on this patchset will also be available soon. Since v24, the arbitrary type support is dropped from this patchset because this patchset doesn't introduce real use of the type. You can still get it from the DAMON development tree[2], though. [1] https://github.com/sjp38/linux/tree/damon/pgidle_hack [2] https://github.com/sjp38/linux/tree/damon/master 4. More future usecases While Idle Page Tracking has tight coupling with base primitives (PG_Idle and page table Accessed bits), DAMON is designed to be extensible for many use cases and address spaces. If you need some special address type or want to use special h/w access check primitives, you can write your own primitives for that and configure DAMON to use those. Therefore, if your use case could be changed a lot in future, using DAMON could be better. Can I use both Idle Page Tracking and DAMON? -------------------------------------------- Yes, though using them concurrently for overlapping memory regions could result in interference to each other. Nevertheless, such use case would be rare or makes no sense at all. Even in the case, the noise would bot be really significant. So, you can choose whatever you want depending on the characteristics of your use cases. More Information ================ We prepared a showcase web site[1] that you can get more information. There are - the official documentations[2], - the heatmap format dynamic access pattern of various realistic workloads for heap area[3], mmap()-ed area[4], and stack[5] area, - the dynamic working set size distribution[6] and chronological working set size changes[7], and - the latest performance test results[8]. [1] https://damonitor.github.io/_index [2] https://damonitor.github.io/doc/html/latest-damon [3] https://damonitor.github.io/test/result/visual/latest/rec.heatmap.0.png.html [4] https://damonitor.github.io/test/result/visual/latest/rec.heatmap.1.png.html [5] https://damonitor.github.io/test/result/visual/latest/rec.heatmap.2.png.html [6] https://damonitor.github.io/test/result/visual/latest/rec.wss_sz.png.html [7] https://damonitor.github.io/test/result/visual/latest/rec.wss_time.png.html [8] https://damonitor.github.io/test/result/perf/latest/html/index.html Baseline and Complete Git Trees =============================== The patches are based on the latest -mm tree, specifically v5.14-rc1-mmots-2021-07-15-18-47 of https://github.com/hnaz/linux-mm. You can also clone the complete git tree: $ git clone git://github.com/sjp38/linux -b damon/patches/v34 The web is also available: https://github.com/sjp38/linux/releases/tag/damon/patches/v34 Development Trees ----------------- There are a couple of trees for entire DAMON patchset series and features for future release. - For latest release: https://github.com/sjp38/linux/tree/damon/master - For next release: https://github.com/sjp38/linux/tree/damon/next Long-term Support Trees ----------------------- For people who want to test DAMON but using LTS kernels, there are another couple of trees based on two latest LTS kernels respectively and containing the 'damon/master' backports. - For v5.4.y: https://github.com/sjp38/linux/tree/damon/for-v5.4.y - For v5.10.y: https://github.com/sjp38/linux/tree/damon/for-v5.10.y Amazon Linux Kernel Trees ------------------------- DAMON is also merged in two public Amazon Linux kernel trees that based on v5.4.y[1] and v5.10.y[2]. [1] https://github.com/amazonlinux/linux/tree/amazon-5.4.y/master/mm/damon [2] https://github.com/amazonlinux/linux/tree/amazon-5.10.y/master/mm/damon Git Tree for Diff of Patches ============================ For easy review of diff between different versions of each patch, I prepared a git tree containing all versions of the DAMON patchset series: https://github.com/sjp38/damon-patches You can clone it and use 'diff' for easy review of changes between different versions of the patchset. For example: $ git clone https://github.com/sjp38/damon-patches && cd damon-patches $ diff -u damon/v33 damon/v34 Sequence Of Patches =================== First three patches implement the core logics of DAMON. The 1st patch introduces basic sampling based hotness monitoring for arbitrary types of targets. Following two patches implement the core mechanisms for control of overhead and accuracy, namely regions based sampling (patch 2) and adaptive regions adjustment (patch 3). Now the essential parts of DAMON is complete, but it cannot work unless someone provides monitoring primitives for a specific use case. The following two patches make it just work for virtual address spaces monitoring. The 4th patch makes 'PG_idle' can be used by DAMON and the 5th patch implements the virtual memory address space specific monitoring primitives using page table Accessed bits and the 'PG_idle' page flag. Now DAMON just works for virtual address space monitoring via the kernel space api. To let the user space users can use DAMON, following four patches add interfaces for them. The 6th patch adds a tracepoint for monitoring results. The 7th patch implements a DAMON application kernel module, namely damon-dbgfs, that simply wraps DAMON and exposes DAMON interface to the user space via the debugfs interface. The 8th patch further exports pid of monitoring thread (kdamond) to user space for easier cpu usage accounting, and the 9th patch makes the debugfs interface to support multiple contexts. Three patches for maintainability follows. The 10th patch adds documentations for both the user space and the kernel space. The 11th patch provides unit tests (based on the kunit) while the 12th patch adds user space tests (based on the kselftest). Finally, the last patch (13th) updates the MAINTAINERS file. This patch (of 13): DAMON is a data access monitoring framework for the Linux kernel. The core mechanisms of DAMON make it - accurate (the monitoring output is useful enough for DRAM level performance-centric memory management; It might be inappropriate for CPU cache levels, though), - light-weight (the monitoring overhead is normally low enough to be applied online), and - scalable (the upper-bound of the overhead is in constant range regardless of the size of target workloads). Using this framework, hence, we can easily write efficient kernel space data access monitoring applications. For example, the kernel's memory management mechanisms can make advanced decisions using this. Experimental data access aware optimization works that incurring high access monitoring overhead could again be implemented on top of this. Due to its simple and flexible interface, providing user space interface would be also easy. Then, user space users who have some special workloads can write personalized applications for better understanding and optimizations of their workloads and systems. === Nevertheless, this commit is defining and implementing only basic access check part without the overhead-accuracy handling core logic. The basic access check is as below. The output of DAMON says what memory regions are how frequently accessed for a given duration. The resolution of the access frequency is controlled by setting ``sampling interval`` and ``aggregation interval``. In detail, DAMON checks access to each page per ``sampling interval`` and aggregates the results. In other words, counts the number of the accesses to each region. After each ``aggregation interval`` passes, DAMON calls callback functions that previously registered by users so that users can read the aggregated results and then clears the results. This can be described in below simple pseudo-code:: init() while monitoring_on: for page in monitoring_target: if accessed(page): nr_accesses[page] += 1 if time() % aggregation_interval == 0: for callback in user_registered_callbacks: callback(monitoring_target, nr_accesses) for page in monitoring_target: nr_accesses[page] = 0 if time() % update_interval == 0: update() sleep(sampling interval) The target regions constructed at the beginning of the monitoring and updated after each ``regions_update_interval``, because the target regions could be dynamically changed (e.g., mmap() or memory hotplug). The monitoring overhead of this mechanism will arbitrarily increase as the size of the target workload grows. The basic monitoring primitives for actual access check and dynamic target regions construction aren't in the core part of DAMON. Instead, it allows users to implement their own primitives that are optimized for their use case and configure DAMON to use those. In other words, users cannot use current version of DAMON without some additional works. Following commits will implement the core mechanisms for the overhead-accuracy control and default primitives implementations. Link: https://lkml.kernel.org/r/20210716081449.22187-1-sj38.park@gmail.com Link: https://lkml.kernel.org/r/20210716081449.22187-2-sj38.park@gmail.com Signed-off-by: SeongJae Park <sjpark@amazon.de> Reviewed-by: Leonard Foerster <foersleo@amazon.de> Reviewed-by: Fernand Sieber <sieberf@amazon.com> Acked-by: Shakeel Butt <shakeelb@google.com> Cc: Jonathan Cameron <Jonathan.Cameron@huawei.com> Cc: Alexander Shishkin <alexander.shishkin@linux.intel.com> Cc: Amit Shah <amit@kernel.org> Cc: Benjamin Herrenschmidt <benh@kernel.crashing.org> Cc: Jonathan Corbet <corbet@lwn.net> Cc: David Hildenbrand <david@redhat.com> Cc: David Woodhouse <dwmw@amazon.com> Cc: Marco Elver <elver@google.com> Cc: Fan Du <fan.du@intel.com> Cc: Greg Kroah-Hartman <greg@kroah.com> Cc: Greg Thelen <gthelen@google.com> Cc: Joe Perches <joe@perches.com> Cc: Mel Gorman <mgorman@suse.de> Cc: Maximilian Heyne <mheyne@amazon.de> Cc: Minchan Kim <minchan@kernel.org> Cc: Ingo Molnar <mingo@redhat.com> Cc: Namhyung Kim <namhyung@kernel.org> Cc: Peter Zijlstra <peterz@infradead.org> Cc: Rik van Riel <riel@surriel.com> Cc: David Rientjes <rientjes@google.com> Cc: Steven Rostedt (VMware) <rostedt@goodmis.org> Cc: Shuah Khan <shuah@kernel.org> Cc: Vlastimil Babka <vbabka@suse.cz> Cc: Vladimir Davydov <vdavydov.dev@gmail.com> Cc: Brendan Higgins <brendanhiggins@google.com> Cc: Markus Boehme <markubo@amazon.de> Signed-off-by: Andrew Morton <akpm@linux-foundation.org> Signed-off-by: Linus Torvalds <torvalds@linux-foundation.org>
2021-09-08 10:56:28 +08:00
* @check_accesses, @target_valid and @prepare_access_checks after each
* &damon_ctx.sample_interval. Finally, @reset_aggregated is called after each
* &damon_ctx.aggr_interval.
*
* Each &struct damon_operations instance having valid @id can be registered
* via damon_register_ops() and selected by damon_select_ops() later.
mm/damon: rename damon_primitives to damon_operations Patch series "Allow DAMON user code independent of monitoring primitives". In-kernel DAMON user code is required to configure the monitoring context (struct damon_ctx) with proper monitoring primitives (struct damon_primitive). This makes the user code dependent to all supporting monitoring primitives. For example, DAMON debugfs interface depends on both DAMON_VADDR and DAMON_PADDR, though some users have interest in only one use case. As more monitoring primitives are introduced, the problem will be bigger. To minimize such unnecessary dependency, this patchset makes monitoring primitives can be registered by the implemnting code and later dynamically searched and selected by the user code. In addition to that, this patchset renames monitoring primitives to monitoring operations, which is more easy to intuitively understand what it means and how it would be structed. This patch (of 8): DAMON has a set of callback functions called monitoring primitives and let it can be configured with various implementations for easy extension for different address spaces and usages. However, the word 'primitive' is not so explicit. Meanwhile, many other structs resembles similar purpose calls themselves 'operations'. To make the code easier to be understood, this commit renames 'damon_primitives' to 'damon_operations' before it is too late to rename. Link: https://lkml.kernel.org/r/20220215184603.1479-1-sj@kernel.org Link: https://lkml.kernel.org/r/20220215184603.1479-2-sj@kernel.org Signed-off-by: SeongJae Park <sj@kernel.org> Cc: Xin Hao <xhao@linux.alibaba.com> Cc: David Rientjes <rientjes@google.com> Signed-off-by: Andrew Morton <akpm@linux-foundation.org> Signed-off-by: Linus Torvalds <torvalds@linux-foundation.org>
2022-03-23 05:48:46 +08:00
* @init should initialize operations-related data structures. For example,
mm: introduce Data Access MONitor (DAMON) Patch series "Introduce Data Access MONitor (DAMON)", v34. Introduction ============ DAMON is a data access monitoring framework for the Linux kernel. The core mechanisms of DAMON called 'region based sampling' and 'adaptive regions adjustment' (refer to 'mechanisms.rst' in the 11th patch of this patchset for the detail) make it - accurate (The monitored information is useful for DRAM level memory management. It might not appropriate for Cache-level accuracy, though.), - light-weight (The monitoring overhead is low enough to be applied online while making no impact on the performance of the target workloads.), and - scalable (the upper-bound of the instrumentation overhead is controllable regardless of the size of target workloads.). Using this framework, therefore, several memory management mechanisms such as reclamation and THP can be optimized to aware real data access patterns. Experimental access pattern aware memory management optimization works that incurring high instrumentation overhead will be able to have another try. Though DAMON is for kernel subsystems, it can be easily exposed to the user space by writing a DAMON-wrapper kernel subsystem. Then, user space users who have some special workloads will be able to write personalized tools or applications for deeper understanding and specialized optimizations of their systems. DAMON is also merged in two public Amazon Linux kernel trees that based on v5.4.y[1] and v5.10.y[2]. [1] https://github.com/amazonlinux/linux/tree/amazon-5.4.y/master/mm/damon [2] https://github.com/amazonlinux/linux/tree/amazon-5.10.y/master/mm/damon The userspace tool[1] is available, released under GPLv2, and actively being maintained. I am also planning to implement another basic user interface in perf[2]. Also, the basic test suite for DAMON is available under GPLv2[3]. [1] https://github.com/awslabs/damo [2] https://lore.kernel.org/linux-mm/20210107120729.22328-1-sjpark@amazon.com/ [3] https://github.com/awslabs/damon-tests Long-term Plan -------------- DAMON is a part of a project called Data Access-aware Operating System (DAOS). As the name implies, I want to improve the performance and efficiency of systems using fine-grained data access patterns. The optimizations are for both kernel and user spaces. I will therefore modify or create kernel subsystems, export some of those to user space and implement user space library / tools. Below shows the layers and components for the project. --------------------------------------------------------------------------- Primitives: PTE Accessed bit, PG_idle, rmap, (Intel CMT), ... Framework: DAMON Features: DAMOS, virtual addr, physical addr, ... Applications: DAMON-debugfs, (DARC), ... ^^^^^^^^^^^^^^^^^^^^^^^ KERNEL SPACE ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ Raw Interface: debugfs, (sysfs), (damonfs), tracepoints, (sys_damon), ... vvvvvvvvvvvvvvvvvvvvvvv USER SPACE vvvvvvvvvvvvvvvvvvvvvvvvvvvvvvvv Library: (libdamon), ... Tools: DAMO, (perf), ... --------------------------------------------------------------------------- The components in parentheses or marked as '...' are not implemented yet but in the future plan. IOW, those are the TODO tasks of DAOS project. For more detail, please refer to the plans: https://lore.kernel.org/linux-mm/20201202082731.24828-1-sjpark@amazon.com/ Evaluations =========== We evaluated DAMON's overhead, monitoring quality and usefulness using 24 realistic workloads on my QEMU/KVM based virtual machine running a kernel that v24 DAMON patchset is applied. DAMON is lightweight. It increases system memory usage by 0.39% and slows target workloads down by 1.16%. DAMON is accurate and useful for memory management optimizations. An experimental DAMON-based operation scheme for THP, namely 'ethp', removes 76.15% of THP memory overheads while preserving 51.25% of THP speedup. Another experimental DAMON-based 'proactive reclamation' implementation, 'prcl', reduces 93.38% of residential sets and 23.63% of system memory footprint while incurring only 1.22% runtime overhead in the best case (parsec3/freqmine). NOTE that the experimental THP optimization and proactive reclamation are not for production but only for proof of concepts. Please refer to the official document[1] or "Documentation/admin-guide/mm: Add a document for DAMON" patch in this patchset for detailed evaluation setup and results. [1] https://damonitor.github.io/doc/html/latest-damon/admin-guide/mm/damon/eval.html Real-world User Story ===================== In summary, DAMON has used on production systems and proved its usefulness. DAMON as a profiler ------------------- We analyzed characteristics of a large scale production systems of our customers using DAMON. The systems utilize 70GB DRAM and 36 CPUs. From this, we were able to find interesting things below. There were obviously different access pattern under idle workload and active workload. Under the idle workload, it accessed large memory regions with low frequency, while the active workload accessed small memory regions with high freuqnecy. DAMON found a 7GB memory region that showing obviously high access frequency under the active workload. We believe this is the performance-effective working set and need to be protected. There was a 4KB memory region that showing highest access frequency under not only active but also idle workloads. We think this must be a hottest code section like thing that should never be paged out. For this analysis, DAMON used only 0.3-1% of single CPU time. Because we used recording-based analysis, it consumed about 3-12 MB of disk space per 20 minutes. This is only small amount of disk space, but we can further reduce the disk usage by using non-recording-based DAMON features. I'd like to argue that only DAMON can do such detailed analysis (finding 4KB highest region in 70GB memory) with the light overhead. DAMON as a system optimization tool ----------------------------------- We also found below potential performance problems on the systems and made DAMON-based solutions. The system doesn't want to make the workload suffer from the page reclamation and thus it utilizes enough DRAM but no swap device. However, we found the system is actively reclaiming file-backed pages, because the system has intensive file IO. The file IO turned out to be not performance critical for the workload, but the customer wanted to ensure performance critical file-backed pages like code section to not mistakenly be evicted. Using direct IO should or `mlock()` would be a straightforward solution, but modifying the user space code is not easy for the customer. Alternatively, we could use DAMON-based operation scheme[1]. By using it, we can ask DAMON to track access frequency of each region and make 'process_madvise(MADV_WILLNEED)[2]' call for regions having specific size and access frequency for a time interval. We also found the system is having high number of TLB misses. We tried 'always' THP enabled policy and it greatly reduced TLB misses, but the page reclamation also been more frequent due to the THP internal fragmentation caused memory bloat. We could try another DAMON-based operation scheme that applies 'MADV_HUGEPAGE' to memory regions having >=2MB size and high access frequency, while applying 'MADV_NOHUGEPAGE' to regions having <2MB size and low access frequency. We do not own the systems so we only reported the analysis results and possible optimization solutions to the customers. The customers satisfied about the analysis results and promised to try the optimization guides. [1] https://lore.kernel.org/linux-mm/20201006123931.5847-1-sjpark@amazon.com/ [2] https://lore.kernel.org/linux-api/20200622192900.22757-4-minchan@kernel.org/ Comparison with Idle Page Tracking ================================== Idle Page Tracking allows users to set and read idleness of pages using a bitmap file which represents each page with each bit of the file. One recommended usage of it is working set size detection. Users can do that by 1. find PFN of each page for workloads in interest, 2. set all the pages as idle by doing writes to the bitmap file, 3. wait until the workload accesses its working set, and 4. read the idleness of the pages again and count pages became not idle. NOTE: While Idle Page Tracking is for user space users, DAMON is primarily designed for kernel subsystems though it can easily exposed to the user space. Hence, this section only assumes such user space use of DAMON. For what use cases Idle Page Tracking would be better? ------------------------------------------------------ 1. Flexible usecases other than hotness monitoring. Because Idle Page Tracking allows users to control the primitive (Page idleness) by themselves, Idle Page Tracking users can do anything they want. Meanwhile, DAMON is primarily designed to monitor the hotness of each memory region. For this, DAMON asks users to provide sampling interval and aggregation interval. For the reason, there could be some use case that using Idle Page Tracking is simpler. 2. Physical memory monitoring. Idle Page Tracking receives PFN range as input, so natively supports physical memory monitoring. DAMON is designed to be extensible for multiple address spaces and use cases by implementing and using primitives for the given use case. Therefore, by theory, DAMON has no limitation in the type of target address space as long as primitives for the given address space exists. However, the default primitives introduced by this patchset supports only virtual address spaces. Therefore, for physical memory monitoring, you should implement your own primitives and use it, or simply use Idle Page Tracking. Nonetheless, RFC patchsets[1] for the physical memory address space primitives is already available. It also supports user memory same to Idle Page Tracking. [1] https://lore.kernel.org/linux-mm/20200831104730.28970-1-sjpark@amazon.com/ For what use cases DAMON is better? ----------------------------------- 1. Hotness Monitoring. Idle Page Tracking let users know only if a page frame is accessed or not. For hotness check, the user should write more code and use more memory. DAMON do that by itself. 2. Low Monitoring Overhead DAMON receives user's monitoring request with one step and then provide the results. So, roughly speaking, DAMON require only O(1) user/kernel context switches. In case of Idle Page Tracking, however, because the interface receives contiguous page frames, the number of user/kernel context switches increases as the monitoring target becomes complex and huge. As a result, the context switch overhead could be not negligible. Moreover, DAMON is born to handle with the monitoring overhead. Because the core mechanism is pure logical, Idle Page Tracking users might be able to implement the mechanism on their own, but it would be time consuming and the user/kernel context switching will still more frequent than that of DAMON. Also, the kernel subsystems cannot use the logic in this case. 3. Page granularity working set size detection. Until v22 of this patchset, this was categorized as the thing Idle Page Tracking could do better, because DAMON basically maintains additional metadata for each of the monitoring target regions. So, in the page granularity working set size detection use case, DAMON would incur (number of monitoring target pages * size of metadata) memory overhead. Size of the single metadata item is about 54 bytes, so assuming 4KB pages, about 1.3% of monitoring target pages will be additionally used. All essential metadata for Idle Page Tracking are embedded in 'struct page' and page table entries. Therefore, in this use case, only one counter variable for working set size accounting is required if Idle Page Tracking is used. There are more details to consider, but roughly speaking, this is true in most cases. However, the situation changed from v23. Now DAMON supports arbitrary types of monitoring targets, which don't use the metadata. Using that, DAMON can do the working set size detection with no additional space overhead but less user-kernel context switch. A first draft for the implementation of monitoring primitives for this usage is available in a DAMON development tree[1]. An RFC patchset for it based on this patchset will also be available soon. Since v24, the arbitrary type support is dropped from this patchset because this patchset doesn't introduce real use of the type. You can still get it from the DAMON development tree[2], though. [1] https://github.com/sjp38/linux/tree/damon/pgidle_hack [2] https://github.com/sjp38/linux/tree/damon/master 4. More future usecases While Idle Page Tracking has tight coupling with base primitives (PG_Idle and page table Accessed bits), DAMON is designed to be extensible for many use cases and address spaces. If you need some special address type or want to use special h/w access check primitives, you can write your own primitives for that and configure DAMON to use those. Therefore, if your use case could be changed a lot in future, using DAMON could be better. Can I use both Idle Page Tracking and DAMON? -------------------------------------------- Yes, though using them concurrently for overlapping memory regions could result in interference to each other. Nevertheless, such use case would be rare or makes no sense at all. Even in the case, the noise would bot be really significant. So, you can choose whatever you want depending on the characteristics of your use cases. More Information ================ We prepared a showcase web site[1] that you can get more information. There are - the official documentations[2], - the heatmap format dynamic access pattern of various realistic workloads for heap area[3], mmap()-ed area[4], and stack[5] area, - the dynamic working set size distribution[6] and chronological working set size changes[7], and - the latest performance test results[8]. [1] https://damonitor.github.io/_index [2] https://damonitor.github.io/doc/html/latest-damon [3] https://damonitor.github.io/test/result/visual/latest/rec.heatmap.0.png.html [4] https://damonitor.github.io/test/result/visual/latest/rec.heatmap.1.png.html [5] https://damonitor.github.io/test/result/visual/latest/rec.heatmap.2.png.html [6] https://damonitor.github.io/test/result/visual/latest/rec.wss_sz.png.html [7] https://damonitor.github.io/test/result/visual/latest/rec.wss_time.png.html [8] https://damonitor.github.io/test/result/perf/latest/html/index.html Baseline and Complete Git Trees =============================== The patches are based on the latest -mm tree, specifically v5.14-rc1-mmots-2021-07-15-18-47 of https://github.com/hnaz/linux-mm. You can also clone the complete git tree: $ git clone git://github.com/sjp38/linux -b damon/patches/v34 The web is also available: https://github.com/sjp38/linux/releases/tag/damon/patches/v34 Development Trees ----------------- There are a couple of trees for entire DAMON patchset series and features for future release. - For latest release: https://github.com/sjp38/linux/tree/damon/master - For next release: https://github.com/sjp38/linux/tree/damon/next Long-term Support Trees ----------------------- For people who want to test DAMON but using LTS kernels, there are another couple of trees based on two latest LTS kernels respectively and containing the 'damon/master' backports. - For v5.4.y: https://github.com/sjp38/linux/tree/damon/for-v5.4.y - For v5.10.y: https://github.com/sjp38/linux/tree/damon/for-v5.10.y Amazon Linux Kernel Trees ------------------------- DAMON is also merged in two public Amazon Linux kernel trees that based on v5.4.y[1] and v5.10.y[2]. [1] https://github.com/amazonlinux/linux/tree/amazon-5.4.y/master/mm/damon [2] https://github.com/amazonlinux/linux/tree/amazon-5.10.y/master/mm/damon Git Tree for Diff of Patches ============================ For easy review of diff between different versions of each patch, I prepared a git tree containing all versions of the DAMON patchset series: https://github.com/sjp38/damon-patches You can clone it and use 'diff' for easy review of changes between different versions of the patchset. For example: $ git clone https://github.com/sjp38/damon-patches && cd damon-patches $ diff -u damon/v33 damon/v34 Sequence Of Patches =================== First three patches implement the core logics of DAMON. The 1st patch introduces basic sampling based hotness monitoring for arbitrary types of targets. Following two patches implement the core mechanisms for control of overhead and accuracy, namely regions based sampling (patch 2) and adaptive regions adjustment (patch 3). Now the essential parts of DAMON is complete, but it cannot work unless someone provides monitoring primitives for a specific use case. The following two patches make it just work for virtual address spaces monitoring. The 4th patch makes 'PG_idle' can be used by DAMON and the 5th patch implements the virtual memory address space specific monitoring primitives using page table Accessed bits and the 'PG_idle' page flag. Now DAMON just works for virtual address space monitoring via the kernel space api. To let the user space users can use DAMON, following four patches add interfaces for them. The 6th patch adds a tracepoint for monitoring results. The 7th patch implements a DAMON application kernel module, namely damon-dbgfs, that simply wraps DAMON and exposes DAMON interface to the user space via the debugfs interface. The 8th patch further exports pid of monitoring thread (kdamond) to user space for easier cpu usage accounting, and the 9th patch makes the debugfs interface to support multiple contexts. Three patches for maintainability follows. The 10th patch adds documentations for both the user space and the kernel space. The 11th patch provides unit tests (based on the kunit) while the 12th patch adds user space tests (based on the kselftest). Finally, the last patch (13th) updates the MAINTAINERS file. This patch (of 13): DAMON is a data access monitoring framework for the Linux kernel. The core mechanisms of DAMON make it - accurate (the monitoring output is useful enough for DRAM level performance-centric memory management; It might be inappropriate for CPU cache levels, though), - light-weight (the monitoring overhead is normally low enough to be applied online), and - scalable (the upper-bound of the overhead is in constant range regardless of the size of target workloads). Using this framework, hence, we can easily write efficient kernel space data access monitoring applications. For example, the kernel's memory management mechanisms can make advanced decisions using this. Experimental data access aware optimization works that incurring high access monitoring overhead could again be implemented on top of this. Due to its simple and flexible interface, providing user space interface would be also easy. Then, user space users who have some special workloads can write personalized applications for better understanding and optimizations of their workloads and systems. === Nevertheless, this commit is defining and implementing only basic access check part without the overhead-accuracy handling core logic. The basic access check is as below. The output of DAMON says what memory regions are how frequently accessed for a given duration. The resolution of the access frequency is controlled by setting ``sampling interval`` and ``aggregation interval``. In detail, DAMON checks access to each page per ``sampling interval`` and aggregates the results. In other words, counts the number of the accesses to each region. After each ``aggregation interval`` passes, DAMON calls callback functions that previously registered by users so that users can read the aggregated results and then clears the results. This can be described in below simple pseudo-code:: init() while monitoring_on: for page in monitoring_target: if accessed(page): nr_accesses[page] += 1 if time() % aggregation_interval == 0: for callback in user_registered_callbacks: callback(monitoring_target, nr_accesses) for page in monitoring_target: nr_accesses[page] = 0 if time() % update_interval == 0: update() sleep(sampling interval) The target regions constructed at the beginning of the monitoring and updated after each ``regions_update_interval``, because the target regions could be dynamically changed (e.g., mmap() or memory hotplug). The monitoring overhead of this mechanism will arbitrarily increase as the size of the target workload grows. The basic monitoring primitives for actual access check and dynamic target regions construction aren't in the core part of DAMON. Instead, it allows users to implement their own primitives that are optimized for their use case and configure DAMON to use those. In other words, users cannot use current version of DAMON without some additional works. Following commits will implement the core mechanisms for the overhead-accuracy control and default primitives implementations. Link: https://lkml.kernel.org/r/20210716081449.22187-1-sj38.park@gmail.com Link: https://lkml.kernel.org/r/20210716081449.22187-2-sj38.park@gmail.com Signed-off-by: SeongJae Park <sjpark@amazon.de> Reviewed-by: Leonard Foerster <foersleo@amazon.de> Reviewed-by: Fernand Sieber <sieberf@amazon.com> Acked-by: Shakeel Butt <shakeelb@google.com> Cc: Jonathan Cameron <Jonathan.Cameron@huawei.com> Cc: Alexander Shishkin <alexander.shishkin@linux.intel.com> Cc: Amit Shah <amit@kernel.org> Cc: Benjamin Herrenschmidt <benh@kernel.crashing.org> Cc: Jonathan Corbet <corbet@lwn.net> Cc: David Hildenbrand <david@redhat.com> Cc: David Woodhouse <dwmw@amazon.com> Cc: Marco Elver <elver@google.com> Cc: Fan Du <fan.du@intel.com> Cc: Greg Kroah-Hartman <greg@kroah.com> Cc: Greg Thelen <gthelen@google.com> Cc: Joe Perches <joe@perches.com> Cc: Mel Gorman <mgorman@suse.de> Cc: Maximilian Heyne <mheyne@amazon.de> Cc: Minchan Kim <minchan@kernel.org> Cc: Ingo Molnar <mingo@redhat.com> Cc: Namhyung Kim <namhyung@kernel.org> Cc: Peter Zijlstra <peterz@infradead.org> Cc: Rik van Riel <riel@surriel.com> Cc: David Rientjes <rientjes@google.com> Cc: Steven Rostedt (VMware) <rostedt@goodmis.org> Cc: Shuah Khan <shuah@kernel.org> Cc: Vlastimil Babka <vbabka@suse.cz> Cc: Vladimir Davydov <vdavydov.dev@gmail.com> Cc: Brendan Higgins <brendanhiggins@google.com> Cc: Markus Boehme <markubo@amazon.de> Signed-off-by: Andrew Morton <akpm@linux-foundation.org> Signed-off-by: Linus Torvalds <torvalds@linux-foundation.org>
2021-09-08 10:56:28 +08:00
* this could be used to construct proper monitoring target regions and link
mm/damon/core: implement region-based sampling To avoid the unbounded increase of the overhead, DAMON groups adjacent pages that are assumed to have the same access frequencies into a region. As long as the assumption (pages in a region have the same access frequencies) is kept, only one page in the region is required to be checked. Thus, for each ``sampling interval``, 1. the 'prepare_access_checks' primitive picks one page in each region, 2. waits for one ``sampling interval``, 3. checks whether the page is accessed meanwhile, and 4. increases the access count of the region if so. Therefore, the monitoring overhead is controllable by adjusting the number of regions. DAMON allows both the underlying primitives and user callbacks to adjust regions for the trade-off. In other words, this commit makes DAMON to use not only time-based sampling but also space-based sampling. This scheme, however, cannot preserve the quality of the output if the assumption is not guaranteed. Next commit will address this problem. Link: https://lkml.kernel.org/r/20210716081449.22187-3-sj38.park@gmail.com Signed-off-by: SeongJae Park <sjpark@amazon.de> Reviewed-by: Leonard Foerster <foersleo@amazon.de> Reviewed-by: Fernand Sieber <sieberf@amazon.com> Acked-by: Shakeel Butt <shakeelb@google.com> Cc: Alexander Shishkin <alexander.shishkin@linux.intel.com> Cc: Amit Shah <amit@kernel.org> Cc: Benjamin Herrenschmidt <benh@kernel.crashing.org> Cc: Brendan Higgins <brendanhiggins@google.com> Cc: David Hildenbrand <david@redhat.com> Cc: David Rientjes <rientjes@google.com> Cc: David Woodhouse <dwmw@amazon.com> Cc: Fan Du <fan.du@intel.com> Cc: Greg Kroah-Hartman <greg@kroah.com> Cc: Greg Thelen <gthelen@google.com> Cc: Ingo Molnar <mingo@redhat.com> Cc: Joe Perches <joe@perches.com> Cc: Jonathan Cameron <Jonathan.Cameron@huawei.com> Cc: Jonathan Corbet <corbet@lwn.net> Cc: Marco Elver <elver@google.com> Cc: Markus Boehme <markubo@amazon.de> Cc: Maximilian Heyne <mheyne@amazon.de> Cc: Mel Gorman <mgorman@suse.de> Cc: Minchan Kim <minchan@kernel.org> Cc: Namhyung Kim <namhyung@kernel.org> Cc: Peter Zijlstra <peterz@infradead.org> Cc: Rik van Riel <riel@surriel.com> Cc: Shuah Khan <shuah@kernel.org> Cc: Steven Rostedt (VMware) <rostedt@goodmis.org> Cc: Vladimir Davydov <vdavydov.dev@gmail.com> Cc: Vlastimil Babka <vbabka@suse.cz> Signed-off-by: Andrew Morton <akpm@linux-foundation.org> Signed-off-by: Linus Torvalds <torvalds@linux-foundation.org>
2021-09-08 10:56:32 +08:00
* those to @damon_ctx.adaptive_targets.
mm/damon: rename damon_primitives to damon_operations Patch series "Allow DAMON user code independent of monitoring primitives". In-kernel DAMON user code is required to configure the monitoring context (struct damon_ctx) with proper monitoring primitives (struct damon_primitive). This makes the user code dependent to all supporting monitoring primitives. For example, DAMON debugfs interface depends on both DAMON_VADDR and DAMON_PADDR, though some users have interest in only one use case. As more monitoring primitives are introduced, the problem will be bigger. To minimize such unnecessary dependency, this patchset makes monitoring primitives can be registered by the implemnting code and later dynamically searched and selected by the user code. In addition to that, this patchset renames monitoring primitives to monitoring operations, which is more easy to intuitively understand what it means and how it would be structed. This patch (of 8): DAMON has a set of callback functions called monitoring primitives and let it can be configured with various implementations for easy extension for different address spaces and usages. However, the word 'primitive' is not so explicit. Meanwhile, many other structs resembles similar purpose calls themselves 'operations'. To make the code easier to be understood, this commit renames 'damon_primitives' to 'damon_operations' before it is too late to rename. Link: https://lkml.kernel.org/r/20220215184603.1479-1-sj@kernel.org Link: https://lkml.kernel.org/r/20220215184603.1479-2-sj@kernel.org Signed-off-by: SeongJae Park <sj@kernel.org> Cc: Xin Hao <xhao@linux.alibaba.com> Cc: David Rientjes <rientjes@google.com> Signed-off-by: Andrew Morton <akpm@linux-foundation.org> Signed-off-by: Linus Torvalds <torvalds@linux-foundation.org>
2022-03-23 05:48:46 +08:00
* @update should update the operations-related data structures. For example,
mm: introduce Data Access MONitor (DAMON) Patch series "Introduce Data Access MONitor (DAMON)", v34. Introduction ============ DAMON is a data access monitoring framework for the Linux kernel. The core mechanisms of DAMON called 'region based sampling' and 'adaptive regions adjustment' (refer to 'mechanisms.rst' in the 11th patch of this patchset for the detail) make it - accurate (The monitored information is useful for DRAM level memory management. It might not appropriate for Cache-level accuracy, though.), - light-weight (The monitoring overhead is low enough to be applied online while making no impact on the performance of the target workloads.), and - scalable (the upper-bound of the instrumentation overhead is controllable regardless of the size of target workloads.). Using this framework, therefore, several memory management mechanisms such as reclamation and THP can be optimized to aware real data access patterns. Experimental access pattern aware memory management optimization works that incurring high instrumentation overhead will be able to have another try. Though DAMON is for kernel subsystems, it can be easily exposed to the user space by writing a DAMON-wrapper kernel subsystem. Then, user space users who have some special workloads will be able to write personalized tools or applications for deeper understanding and specialized optimizations of their systems. DAMON is also merged in two public Amazon Linux kernel trees that based on v5.4.y[1] and v5.10.y[2]. [1] https://github.com/amazonlinux/linux/tree/amazon-5.4.y/master/mm/damon [2] https://github.com/amazonlinux/linux/tree/amazon-5.10.y/master/mm/damon The userspace tool[1] is available, released under GPLv2, and actively being maintained. I am also planning to implement another basic user interface in perf[2]. Also, the basic test suite for DAMON is available under GPLv2[3]. [1] https://github.com/awslabs/damo [2] https://lore.kernel.org/linux-mm/20210107120729.22328-1-sjpark@amazon.com/ [3] https://github.com/awslabs/damon-tests Long-term Plan -------------- DAMON is a part of a project called Data Access-aware Operating System (DAOS). As the name implies, I want to improve the performance and efficiency of systems using fine-grained data access patterns. The optimizations are for both kernel and user spaces. I will therefore modify or create kernel subsystems, export some of those to user space and implement user space library / tools. Below shows the layers and components for the project. --------------------------------------------------------------------------- Primitives: PTE Accessed bit, PG_idle, rmap, (Intel CMT), ... Framework: DAMON Features: DAMOS, virtual addr, physical addr, ... Applications: DAMON-debugfs, (DARC), ... ^^^^^^^^^^^^^^^^^^^^^^^ KERNEL SPACE ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ Raw Interface: debugfs, (sysfs), (damonfs), tracepoints, (sys_damon), ... vvvvvvvvvvvvvvvvvvvvvvv USER SPACE vvvvvvvvvvvvvvvvvvvvvvvvvvvvvvvv Library: (libdamon), ... Tools: DAMO, (perf), ... --------------------------------------------------------------------------- The components in parentheses or marked as '...' are not implemented yet but in the future plan. IOW, those are the TODO tasks of DAOS project. For more detail, please refer to the plans: https://lore.kernel.org/linux-mm/20201202082731.24828-1-sjpark@amazon.com/ Evaluations =========== We evaluated DAMON's overhead, monitoring quality and usefulness using 24 realistic workloads on my QEMU/KVM based virtual machine running a kernel that v24 DAMON patchset is applied. DAMON is lightweight. It increases system memory usage by 0.39% and slows target workloads down by 1.16%. DAMON is accurate and useful for memory management optimizations. An experimental DAMON-based operation scheme for THP, namely 'ethp', removes 76.15% of THP memory overheads while preserving 51.25% of THP speedup. Another experimental DAMON-based 'proactive reclamation' implementation, 'prcl', reduces 93.38% of residential sets and 23.63% of system memory footprint while incurring only 1.22% runtime overhead in the best case (parsec3/freqmine). NOTE that the experimental THP optimization and proactive reclamation are not for production but only for proof of concepts. Please refer to the official document[1] or "Documentation/admin-guide/mm: Add a document for DAMON" patch in this patchset for detailed evaluation setup and results. [1] https://damonitor.github.io/doc/html/latest-damon/admin-guide/mm/damon/eval.html Real-world User Story ===================== In summary, DAMON has used on production systems and proved its usefulness. DAMON as a profiler ------------------- We analyzed characteristics of a large scale production systems of our customers using DAMON. The systems utilize 70GB DRAM and 36 CPUs. From this, we were able to find interesting things below. There were obviously different access pattern under idle workload and active workload. Under the idle workload, it accessed large memory regions with low frequency, while the active workload accessed small memory regions with high freuqnecy. DAMON found a 7GB memory region that showing obviously high access frequency under the active workload. We believe this is the performance-effective working set and need to be protected. There was a 4KB memory region that showing highest access frequency under not only active but also idle workloads. We think this must be a hottest code section like thing that should never be paged out. For this analysis, DAMON used only 0.3-1% of single CPU time. Because we used recording-based analysis, it consumed about 3-12 MB of disk space per 20 minutes. This is only small amount of disk space, but we can further reduce the disk usage by using non-recording-based DAMON features. I'd like to argue that only DAMON can do such detailed analysis (finding 4KB highest region in 70GB memory) with the light overhead. DAMON as a system optimization tool ----------------------------------- We also found below potential performance problems on the systems and made DAMON-based solutions. The system doesn't want to make the workload suffer from the page reclamation and thus it utilizes enough DRAM but no swap device. However, we found the system is actively reclaiming file-backed pages, because the system has intensive file IO. The file IO turned out to be not performance critical for the workload, but the customer wanted to ensure performance critical file-backed pages like code section to not mistakenly be evicted. Using direct IO should or `mlock()` would be a straightforward solution, but modifying the user space code is not easy for the customer. Alternatively, we could use DAMON-based operation scheme[1]. By using it, we can ask DAMON to track access frequency of each region and make 'process_madvise(MADV_WILLNEED)[2]' call for regions having specific size and access frequency for a time interval. We also found the system is having high number of TLB misses. We tried 'always' THP enabled policy and it greatly reduced TLB misses, but the page reclamation also been more frequent due to the THP internal fragmentation caused memory bloat. We could try another DAMON-based operation scheme that applies 'MADV_HUGEPAGE' to memory regions having >=2MB size and high access frequency, while applying 'MADV_NOHUGEPAGE' to regions having <2MB size and low access frequency. We do not own the systems so we only reported the analysis results and possible optimization solutions to the customers. The customers satisfied about the analysis results and promised to try the optimization guides. [1] https://lore.kernel.org/linux-mm/20201006123931.5847-1-sjpark@amazon.com/ [2] https://lore.kernel.org/linux-api/20200622192900.22757-4-minchan@kernel.org/ Comparison with Idle Page Tracking ================================== Idle Page Tracking allows users to set and read idleness of pages using a bitmap file which represents each page with each bit of the file. One recommended usage of it is working set size detection. Users can do that by 1. find PFN of each page for workloads in interest, 2. set all the pages as idle by doing writes to the bitmap file, 3. wait until the workload accesses its working set, and 4. read the idleness of the pages again and count pages became not idle. NOTE: While Idle Page Tracking is for user space users, DAMON is primarily designed for kernel subsystems though it can easily exposed to the user space. Hence, this section only assumes such user space use of DAMON. For what use cases Idle Page Tracking would be better? ------------------------------------------------------ 1. Flexible usecases other than hotness monitoring. Because Idle Page Tracking allows users to control the primitive (Page idleness) by themselves, Idle Page Tracking users can do anything they want. Meanwhile, DAMON is primarily designed to monitor the hotness of each memory region. For this, DAMON asks users to provide sampling interval and aggregation interval. For the reason, there could be some use case that using Idle Page Tracking is simpler. 2. Physical memory monitoring. Idle Page Tracking receives PFN range as input, so natively supports physical memory monitoring. DAMON is designed to be extensible for multiple address spaces and use cases by implementing and using primitives for the given use case. Therefore, by theory, DAMON has no limitation in the type of target address space as long as primitives for the given address space exists. However, the default primitives introduced by this patchset supports only virtual address spaces. Therefore, for physical memory monitoring, you should implement your own primitives and use it, or simply use Idle Page Tracking. Nonetheless, RFC patchsets[1] for the physical memory address space primitives is already available. It also supports user memory same to Idle Page Tracking. [1] https://lore.kernel.org/linux-mm/20200831104730.28970-1-sjpark@amazon.com/ For what use cases DAMON is better? ----------------------------------- 1. Hotness Monitoring. Idle Page Tracking let users know only if a page frame is accessed or not. For hotness check, the user should write more code and use more memory. DAMON do that by itself. 2. Low Monitoring Overhead DAMON receives user's monitoring request with one step and then provide the results. So, roughly speaking, DAMON require only O(1) user/kernel context switches. In case of Idle Page Tracking, however, because the interface receives contiguous page frames, the number of user/kernel context switches increases as the monitoring target becomes complex and huge. As a result, the context switch overhead could be not negligible. Moreover, DAMON is born to handle with the monitoring overhead. Because the core mechanism is pure logical, Idle Page Tracking users might be able to implement the mechanism on their own, but it would be time consuming and the user/kernel context switching will still more frequent than that of DAMON. Also, the kernel subsystems cannot use the logic in this case. 3. Page granularity working set size detection. Until v22 of this patchset, this was categorized as the thing Idle Page Tracking could do better, because DAMON basically maintains additional metadata for each of the monitoring target regions. So, in the page granularity working set size detection use case, DAMON would incur (number of monitoring target pages * size of metadata) memory overhead. Size of the single metadata item is about 54 bytes, so assuming 4KB pages, about 1.3% of monitoring target pages will be additionally used. All essential metadata for Idle Page Tracking are embedded in 'struct page' and page table entries. Therefore, in this use case, only one counter variable for working set size accounting is required if Idle Page Tracking is used. There are more details to consider, but roughly speaking, this is true in most cases. However, the situation changed from v23. Now DAMON supports arbitrary types of monitoring targets, which don't use the metadata. Using that, DAMON can do the working set size detection with no additional space overhead but less user-kernel context switch. A first draft for the implementation of monitoring primitives for this usage is available in a DAMON development tree[1]. An RFC patchset for it based on this patchset will also be available soon. Since v24, the arbitrary type support is dropped from this patchset because this patchset doesn't introduce real use of the type. You can still get it from the DAMON development tree[2], though. [1] https://github.com/sjp38/linux/tree/damon/pgidle_hack [2] https://github.com/sjp38/linux/tree/damon/master 4. More future usecases While Idle Page Tracking has tight coupling with base primitives (PG_Idle and page table Accessed bits), DAMON is designed to be extensible for many use cases and address spaces. If you need some special address type or want to use special h/w access check primitives, you can write your own primitives for that and configure DAMON to use those. Therefore, if your use case could be changed a lot in future, using DAMON could be better. Can I use both Idle Page Tracking and DAMON? -------------------------------------------- Yes, though using them concurrently for overlapping memory regions could result in interference to each other. Nevertheless, such use case would be rare or makes no sense at all. Even in the case, the noise would bot be really significant. So, you can choose whatever you want depending on the characteristics of your use cases. More Information ================ We prepared a showcase web site[1] that you can get more information. There are - the official documentations[2], - the heatmap format dynamic access pattern of various realistic workloads for heap area[3], mmap()-ed area[4], and stack[5] area, - the dynamic working set size distribution[6] and chronological working set size changes[7], and - the latest performance test results[8]. [1] https://damonitor.github.io/_index [2] https://damonitor.github.io/doc/html/latest-damon [3] https://damonitor.github.io/test/result/visual/latest/rec.heatmap.0.png.html [4] https://damonitor.github.io/test/result/visual/latest/rec.heatmap.1.png.html [5] https://damonitor.github.io/test/result/visual/latest/rec.heatmap.2.png.html [6] https://damonitor.github.io/test/result/visual/latest/rec.wss_sz.png.html [7] https://damonitor.github.io/test/result/visual/latest/rec.wss_time.png.html [8] https://damonitor.github.io/test/result/perf/latest/html/index.html Baseline and Complete Git Trees =============================== The patches are based on the latest -mm tree, specifically v5.14-rc1-mmots-2021-07-15-18-47 of https://github.com/hnaz/linux-mm. You can also clone the complete git tree: $ git clone git://github.com/sjp38/linux -b damon/patches/v34 The web is also available: https://github.com/sjp38/linux/releases/tag/damon/patches/v34 Development Trees ----------------- There are a couple of trees for entire DAMON patchset series and features for future release. - For latest release: https://github.com/sjp38/linux/tree/damon/master - For next release: https://github.com/sjp38/linux/tree/damon/next Long-term Support Trees ----------------------- For people who want to test DAMON but using LTS kernels, there are another couple of trees based on two latest LTS kernels respectively and containing the 'damon/master' backports. - For v5.4.y: https://github.com/sjp38/linux/tree/damon/for-v5.4.y - For v5.10.y: https://github.com/sjp38/linux/tree/damon/for-v5.10.y Amazon Linux Kernel Trees ------------------------- DAMON is also merged in two public Amazon Linux kernel trees that based on v5.4.y[1] and v5.10.y[2]. [1] https://github.com/amazonlinux/linux/tree/amazon-5.4.y/master/mm/damon [2] https://github.com/amazonlinux/linux/tree/amazon-5.10.y/master/mm/damon Git Tree for Diff of Patches ============================ For easy review of diff between different versions of each patch, I prepared a git tree containing all versions of the DAMON patchset series: https://github.com/sjp38/damon-patches You can clone it and use 'diff' for easy review of changes between different versions of the patchset. For example: $ git clone https://github.com/sjp38/damon-patches && cd damon-patches $ diff -u damon/v33 damon/v34 Sequence Of Patches =================== First three patches implement the core logics of DAMON. The 1st patch introduces basic sampling based hotness monitoring for arbitrary types of targets. Following two patches implement the core mechanisms for control of overhead and accuracy, namely regions based sampling (patch 2) and adaptive regions adjustment (patch 3). Now the essential parts of DAMON is complete, but it cannot work unless someone provides monitoring primitives for a specific use case. The following two patches make it just work for virtual address spaces monitoring. The 4th patch makes 'PG_idle' can be used by DAMON and the 5th patch implements the virtual memory address space specific monitoring primitives using page table Accessed bits and the 'PG_idle' page flag. Now DAMON just works for virtual address space monitoring via the kernel space api. To let the user space users can use DAMON, following four patches add interfaces for them. The 6th patch adds a tracepoint for monitoring results. The 7th patch implements a DAMON application kernel module, namely damon-dbgfs, that simply wraps DAMON and exposes DAMON interface to the user space via the debugfs interface. The 8th patch further exports pid of monitoring thread (kdamond) to user space for easier cpu usage accounting, and the 9th patch makes the debugfs interface to support multiple contexts. Three patches for maintainability follows. The 10th patch adds documentations for both the user space and the kernel space. The 11th patch provides unit tests (based on the kunit) while the 12th patch adds user space tests (based on the kselftest). Finally, the last patch (13th) updates the MAINTAINERS file. This patch (of 13): DAMON is a data access monitoring framework for the Linux kernel. The core mechanisms of DAMON make it - accurate (the monitoring output is useful enough for DRAM level performance-centric memory management; It might be inappropriate for CPU cache levels, though), - light-weight (the monitoring overhead is normally low enough to be applied online), and - scalable (the upper-bound of the overhead is in constant range regardless of the size of target workloads). Using this framework, hence, we can easily write efficient kernel space data access monitoring applications. For example, the kernel's memory management mechanisms can make advanced decisions using this. Experimental data access aware optimization works that incurring high access monitoring overhead could again be implemented on top of this. Due to its simple and flexible interface, providing user space interface would be also easy. Then, user space users who have some special workloads can write personalized applications for better understanding and optimizations of their workloads and systems. === Nevertheless, this commit is defining and implementing only basic access check part without the overhead-accuracy handling core logic. The basic access check is as below. The output of DAMON says what memory regions are how frequently accessed for a given duration. The resolution of the access frequency is controlled by setting ``sampling interval`` and ``aggregation interval``. In detail, DAMON checks access to each page per ``sampling interval`` and aggregates the results. In other words, counts the number of the accesses to each region. After each ``aggregation interval`` passes, DAMON calls callback functions that previously registered by users so that users can read the aggregated results and then clears the results. This can be described in below simple pseudo-code:: init() while monitoring_on: for page in monitoring_target: if accessed(page): nr_accesses[page] += 1 if time() % aggregation_interval == 0: for callback in user_registered_callbacks: callback(monitoring_target, nr_accesses) for page in monitoring_target: nr_accesses[page] = 0 if time() % update_interval == 0: update() sleep(sampling interval) The target regions constructed at the beginning of the monitoring and updated after each ``regions_update_interval``, because the target regions could be dynamically changed (e.g., mmap() or memory hotplug). The monitoring overhead of this mechanism will arbitrarily increase as the size of the target workload grows. The basic monitoring primitives for actual access check and dynamic target regions construction aren't in the core part of DAMON. Instead, it allows users to implement their own primitives that are optimized for their use case and configure DAMON to use those. In other words, users cannot use current version of DAMON without some additional works. Following commits will implement the core mechanisms for the overhead-accuracy control and default primitives implementations. Link: https://lkml.kernel.org/r/20210716081449.22187-1-sj38.park@gmail.com Link: https://lkml.kernel.org/r/20210716081449.22187-2-sj38.park@gmail.com Signed-off-by: SeongJae Park <sjpark@amazon.de> Reviewed-by: Leonard Foerster <foersleo@amazon.de> Reviewed-by: Fernand Sieber <sieberf@amazon.com> Acked-by: Shakeel Butt <shakeelb@google.com> Cc: Jonathan Cameron <Jonathan.Cameron@huawei.com> Cc: Alexander Shishkin <alexander.shishkin@linux.intel.com> Cc: Amit Shah <amit@kernel.org> Cc: Benjamin Herrenschmidt <benh@kernel.crashing.org> Cc: Jonathan Corbet <corbet@lwn.net> Cc: David Hildenbrand <david@redhat.com> Cc: David Woodhouse <dwmw@amazon.com> Cc: Marco Elver <elver@google.com> Cc: Fan Du <fan.du@intel.com> Cc: Greg Kroah-Hartman <greg@kroah.com> Cc: Greg Thelen <gthelen@google.com> Cc: Joe Perches <joe@perches.com> Cc: Mel Gorman <mgorman@suse.de> Cc: Maximilian Heyne <mheyne@amazon.de> Cc: Minchan Kim <minchan@kernel.org> Cc: Ingo Molnar <mingo@redhat.com> Cc: Namhyung Kim <namhyung@kernel.org> Cc: Peter Zijlstra <peterz@infradead.org> Cc: Rik van Riel <riel@surriel.com> Cc: David Rientjes <rientjes@google.com> Cc: Steven Rostedt (VMware) <rostedt@goodmis.org> Cc: Shuah Khan <shuah@kernel.org> Cc: Vlastimil Babka <vbabka@suse.cz> Cc: Vladimir Davydov <vdavydov.dev@gmail.com> Cc: Brendan Higgins <brendanhiggins@google.com> Cc: Markus Boehme <markubo@amazon.de> Signed-off-by: Andrew Morton <akpm@linux-foundation.org> Signed-off-by: Linus Torvalds <torvalds@linux-foundation.org>
2021-09-08 10:56:28 +08:00
* this could be used to update monitoring target regions for current status.
* @prepare_access_checks should manipulate the monitoring regions to be
* prepared for the next access check.
* @check_accesses should check the accesses to each region that made after the
* last preparation and update the number of observed accesses of each region.
mm/damon: adaptively adjust regions Even somehow the initial monitoring target regions are well constructed to fulfill the assumption (pages in same region have similar access frequencies), the data access pattern can be dynamically changed. This will result in low monitoring quality. To keep the assumption as much as possible, DAMON adaptively merges and splits each region based on their access frequency. For each ``aggregation interval``, it compares the access frequencies of adjacent regions and merges those if the frequency difference is small. Then, after it reports and clears the aggregated access frequency of each region, it splits each region into two or three regions if the total number of regions will not exceed the user-specified maximum number of regions after the split. In this way, DAMON provides its best-effort quality and minimal overhead while keeping the upper-bound overhead that users set. Link: https://lkml.kernel.org/r/20210716081449.22187-4-sj38.park@gmail.com Signed-off-by: SeongJae Park <sjpark@amazon.de> Reviewed-by: Leonard Foerster <foersleo@amazon.de> Reviewed-by: Fernand Sieber <sieberf@amazon.com> Acked-by: Shakeel Butt <shakeelb@google.com> Cc: Alexander Shishkin <alexander.shishkin@linux.intel.com> Cc: Amit Shah <amit@kernel.org> Cc: Benjamin Herrenschmidt <benh@kernel.crashing.org> Cc: Brendan Higgins <brendanhiggins@google.com> Cc: David Hildenbrand <david@redhat.com> Cc: David Rientjes <rientjes@google.com> Cc: David Woodhouse <dwmw@amazon.com> Cc: Fan Du <fan.du@intel.com> Cc: Greg Kroah-Hartman <greg@kroah.com> Cc: Greg Thelen <gthelen@google.com> Cc: Ingo Molnar <mingo@redhat.com> Cc: Joe Perches <joe@perches.com> Cc: Jonathan Cameron <Jonathan.Cameron@huawei.com> Cc: Jonathan Corbet <corbet@lwn.net> Cc: Marco Elver <elver@google.com> Cc: Markus Boehme <markubo@amazon.de> Cc: Maximilian Heyne <mheyne@amazon.de> Cc: Mel Gorman <mgorman@suse.de> Cc: Minchan Kim <minchan@kernel.org> Cc: Namhyung Kim <namhyung@kernel.org> Cc: Peter Zijlstra <peterz@infradead.org> Cc: Rik van Riel <riel@surriel.com> Cc: Shuah Khan <shuah@kernel.org> Cc: Steven Rostedt (VMware) <rostedt@goodmis.org> Cc: Vladimir Davydov <vdavydov.dev@gmail.com> Cc: Vlastimil Babka <vbabka@suse.cz> Signed-off-by: Andrew Morton <akpm@linux-foundation.org> Signed-off-by: Linus Torvalds <torvalds@linux-foundation.org>
2021-09-08 10:56:36 +08:00
* It should also return max number of observed accesses that made as a result
* of its update. The value will be used for regions adjustment threshold.
mm: introduce Data Access MONitor (DAMON) Patch series "Introduce Data Access MONitor (DAMON)", v34. Introduction ============ DAMON is a data access monitoring framework for the Linux kernel. The core mechanisms of DAMON called 'region based sampling' and 'adaptive regions adjustment' (refer to 'mechanisms.rst' in the 11th patch of this patchset for the detail) make it - accurate (The monitored information is useful for DRAM level memory management. It might not appropriate for Cache-level accuracy, though.), - light-weight (The monitoring overhead is low enough to be applied online while making no impact on the performance of the target workloads.), and - scalable (the upper-bound of the instrumentation overhead is controllable regardless of the size of target workloads.). Using this framework, therefore, several memory management mechanisms such as reclamation and THP can be optimized to aware real data access patterns. Experimental access pattern aware memory management optimization works that incurring high instrumentation overhead will be able to have another try. Though DAMON is for kernel subsystems, it can be easily exposed to the user space by writing a DAMON-wrapper kernel subsystem. Then, user space users who have some special workloads will be able to write personalized tools or applications for deeper understanding and specialized optimizations of their systems. DAMON is also merged in two public Amazon Linux kernel trees that based on v5.4.y[1] and v5.10.y[2]. [1] https://github.com/amazonlinux/linux/tree/amazon-5.4.y/master/mm/damon [2] https://github.com/amazonlinux/linux/tree/amazon-5.10.y/master/mm/damon The userspace tool[1] is available, released under GPLv2, and actively being maintained. I am also planning to implement another basic user interface in perf[2]. Also, the basic test suite for DAMON is available under GPLv2[3]. [1] https://github.com/awslabs/damo [2] https://lore.kernel.org/linux-mm/20210107120729.22328-1-sjpark@amazon.com/ [3] https://github.com/awslabs/damon-tests Long-term Plan -------------- DAMON is a part of a project called Data Access-aware Operating System (DAOS). As the name implies, I want to improve the performance and efficiency of systems using fine-grained data access patterns. The optimizations are for both kernel and user spaces. I will therefore modify or create kernel subsystems, export some of those to user space and implement user space library / tools. Below shows the layers and components for the project. --------------------------------------------------------------------------- Primitives: PTE Accessed bit, PG_idle, rmap, (Intel CMT), ... Framework: DAMON Features: DAMOS, virtual addr, physical addr, ... Applications: DAMON-debugfs, (DARC), ... ^^^^^^^^^^^^^^^^^^^^^^^ KERNEL SPACE ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ Raw Interface: debugfs, (sysfs), (damonfs), tracepoints, (sys_damon), ... vvvvvvvvvvvvvvvvvvvvvvv USER SPACE vvvvvvvvvvvvvvvvvvvvvvvvvvvvvvvv Library: (libdamon), ... Tools: DAMO, (perf), ... --------------------------------------------------------------------------- The components in parentheses or marked as '...' are not implemented yet but in the future plan. IOW, those are the TODO tasks of DAOS project. For more detail, please refer to the plans: https://lore.kernel.org/linux-mm/20201202082731.24828-1-sjpark@amazon.com/ Evaluations =========== We evaluated DAMON's overhead, monitoring quality and usefulness using 24 realistic workloads on my QEMU/KVM based virtual machine running a kernel that v24 DAMON patchset is applied. DAMON is lightweight. It increases system memory usage by 0.39% and slows target workloads down by 1.16%. DAMON is accurate and useful for memory management optimizations. An experimental DAMON-based operation scheme for THP, namely 'ethp', removes 76.15% of THP memory overheads while preserving 51.25% of THP speedup. Another experimental DAMON-based 'proactive reclamation' implementation, 'prcl', reduces 93.38% of residential sets and 23.63% of system memory footprint while incurring only 1.22% runtime overhead in the best case (parsec3/freqmine). NOTE that the experimental THP optimization and proactive reclamation are not for production but only for proof of concepts. Please refer to the official document[1] or "Documentation/admin-guide/mm: Add a document for DAMON" patch in this patchset for detailed evaluation setup and results. [1] https://damonitor.github.io/doc/html/latest-damon/admin-guide/mm/damon/eval.html Real-world User Story ===================== In summary, DAMON has used on production systems and proved its usefulness. DAMON as a profiler ------------------- We analyzed characteristics of a large scale production systems of our customers using DAMON. The systems utilize 70GB DRAM and 36 CPUs. From this, we were able to find interesting things below. There were obviously different access pattern under idle workload and active workload. Under the idle workload, it accessed large memory regions with low frequency, while the active workload accessed small memory regions with high freuqnecy. DAMON found a 7GB memory region that showing obviously high access frequency under the active workload. We believe this is the performance-effective working set and need to be protected. There was a 4KB memory region that showing highest access frequency under not only active but also idle workloads. We think this must be a hottest code section like thing that should never be paged out. For this analysis, DAMON used only 0.3-1% of single CPU time. Because we used recording-based analysis, it consumed about 3-12 MB of disk space per 20 minutes. This is only small amount of disk space, but we can further reduce the disk usage by using non-recording-based DAMON features. I'd like to argue that only DAMON can do such detailed analysis (finding 4KB highest region in 70GB memory) with the light overhead. DAMON as a system optimization tool ----------------------------------- We also found below potential performance problems on the systems and made DAMON-based solutions. The system doesn't want to make the workload suffer from the page reclamation and thus it utilizes enough DRAM but no swap device. However, we found the system is actively reclaiming file-backed pages, because the system has intensive file IO. The file IO turned out to be not performance critical for the workload, but the customer wanted to ensure performance critical file-backed pages like code section to not mistakenly be evicted. Using direct IO should or `mlock()` would be a straightforward solution, but modifying the user space code is not easy for the customer. Alternatively, we could use DAMON-based operation scheme[1]. By using it, we can ask DAMON to track access frequency of each region and make 'process_madvise(MADV_WILLNEED)[2]' call for regions having specific size and access frequency for a time interval. We also found the system is having high number of TLB misses. We tried 'always' THP enabled policy and it greatly reduced TLB misses, but the page reclamation also been more frequent due to the THP internal fragmentation caused memory bloat. We could try another DAMON-based operation scheme that applies 'MADV_HUGEPAGE' to memory regions having >=2MB size and high access frequency, while applying 'MADV_NOHUGEPAGE' to regions having <2MB size and low access frequency. We do not own the systems so we only reported the analysis results and possible optimization solutions to the customers. The customers satisfied about the analysis results and promised to try the optimization guides. [1] https://lore.kernel.org/linux-mm/20201006123931.5847-1-sjpark@amazon.com/ [2] https://lore.kernel.org/linux-api/20200622192900.22757-4-minchan@kernel.org/ Comparison with Idle Page Tracking ================================== Idle Page Tracking allows users to set and read idleness of pages using a bitmap file which represents each page with each bit of the file. One recommended usage of it is working set size detection. Users can do that by 1. find PFN of each page for workloads in interest, 2. set all the pages as idle by doing writes to the bitmap file, 3. wait until the workload accesses its working set, and 4. read the idleness of the pages again and count pages became not idle. NOTE: While Idle Page Tracking is for user space users, DAMON is primarily designed for kernel subsystems though it can easily exposed to the user space. Hence, this section only assumes such user space use of DAMON. For what use cases Idle Page Tracking would be better? ------------------------------------------------------ 1. Flexible usecases other than hotness monitoring. Because Idle Page Tracking allows users to control the primitive (Page idleness) by themselves, Idle Page Tracking users can do anything they want. Meanwhile, DAMON is primarily designed to monitor the hotness of each memory region. For this, DAMON asks users to provide sampling interval and aggregation interval. For the reason, there could be some use case that using Idle Page Tracking is simpler. 2. Physical memory monitoring. Idle Page Tracking receives PFN range as input, so natively supports physical memory monitoring. DAMON is designed to be extensible for multiple address spaces and use cases by implementing and using primitives for the given use case. Therefore, by theory, DAMON has no limitation in the type of target address space as long as primitives for the given address space exists. However, the default primitives introduced by this patchset supports only virtual address spaces. Therefore, for physical memory monitoring, you should implement your own primitives and use it, or simply use Idle Page Tracking. Nonetheless, RFC patchsets[1] for the physical memory address space primitives is already available. It also supports user memory same to Idle Page Tracking. [1] https://lore.kernel.org/linux-mm/20200831104730.28970-1-sjpark@amazon.com/ For what use cases DAMON is better? ----------------------------------- 1. Hotness Monitoring. Idle Page Tracking let users know only if a page frame is accessed or not. For hotness check, the user should write more code and use more memory. DAMON do that by itself. 2. Low Monitoring Overhead DAMON receives user's monitoring request with one step and then provide the results. So, roughly speaking, DAMON require only O(1) user/kernel context switches. In case of Idle Page Tracking, however, because the interface receives contiguous page frames, the number of user/kernel context switches increases as the monitoring target becomes complex and huge. As a result, the context switch overhead could be not negligible. Moreover, DAMON is born to handle with the monitoring overhead. Because the core mechanism is pure logical, Idle Page Tracking users might be able to implement the mechanism on their own, but it would be time consuming and the user/kernel context switching will still more frequent than that of DAMON. Also, the kernel subsystems cannot use the logic in this case. 3. Page granularity working set size detection. Until v22 of this patchset, this was categorized as the thing Idle Page Tracking could do better, because DAMON basically maintains additional metadata for each of the monitoring target regions. So, in the page granularity working set size detection use case, DAMON would incur (number of monitoring target pages * size of metadata) memory overhead. Size of the single metadata item is about 54 bytes, so assuming 4KB pages, about 1.3% of monitoring target pages will be additionally used. All essential metadata for Idle Page Tracking are embedded in 'struct page' and page table entries. Therefore, in this use case, only one counter variable for working set size accounting is required if Idle Page Tracking is used. There are more details to consider, but roughly speaking, this is true in most cases. However, the situation changed from v23. Now DAMON supports arbitrary types of monitoring targets, which don't use the metadata. Using that, DAMON can do the working set size detection with no additional space overhead but less user-kernel context switch. A first draft for the implementation of monitoring primitives for this usage is available in a DAMON development tree[1]. An RFC patchset for it based on this patchset will also be available soon. Since v24, the arbitrary type support is dropped from this patchset because this patchset doesn't introduce real use of the type. You can still get it from the DAMON development tree[2], though. [1] https://github.com/sjp38/linux/tree/damon/pgidle_hack [2] https://github.com/sjp38/linux/tree/damon/master 4. More future usecases While Idle Page Tracking has tight coupling with base primitives (PG_Idle and page table Accessed bits), DAMON is designed to be extensible for many use cases and address spaces. If you need some special address type or want to use special h/w access check primitives, you can write your own primitives for that and configure DAMON to use those. Therefore, if your use case could be changed a lot in future, using DAMON could be better. Can I use both Idle Page Tracking and DAMON? -------------------------------------------- Yes, though using them concurrently for overlapping memory regions could result in interference to each other. Nevertheless, such use case would be rare or makes no sense at all. Even in the case, the noise would bot be really significant. So, you can choose whatever you want depending on the characteristics of your use cases. More Information ================ We prepared a showcase web site[1] that you can get more information. There are - the official documentations[2], - the heatmap format dynamic access pattern of various realistic workloads for heap area[3], mmap()-ed area[4], and stack[5] area, - the dynamic working set size distribution[6] and chronological working set size changes[7], and - the latest performance test results[8]. [1] https://damonitor.github.io/_index [2] https://damonitor.github.io/doc/html/latest-damon [3] https://damonitor.github.io/test/result/visual/latest/rec.heatmap.0.png.html [4] https://damonitor.github.io/test/result/visual/latest/rec.heatmap.1.png.html [5] https://damonitor.github.io/test/result/visual/latest/rec.heatmap.2.png.html [6] https://damonitor.github.io/test/result/visual/latest/rec.wss_sz.png.html [7] https://damonitor.github.io/test/result/visual/latest/rec.wss_time.png.html [8] https://damonitor.github.io/test/result/perf/latest/html/index.html Baseline and Complete Git Trees =============================== The patches are based on the latest -mm tree, specifically v5.14-rc1-mmots-2021-07-15-18-47 of https://github.com/hnaz/linux-mm. You can also clone the complete git tree: $ git clone git://github.com/sjp38/linux -b damon/patches/v34 The web is also available: https://github.com/sjp38/linux/releases/tag/damon/patches/v34 Development Trees ----------------- There are a couple of trees for entire DAMON patchset series and features for future release. - For latest release: https://github.com/sjp38/linux/tree/damon/master - For next release: https://github.com/sjp38/linux/tree/damon/next Long-term Support Trees ----------------------- For people who want to test DAMON but using LTS kernels, there are another couple of trees based on two latest LTS kernels respectively and containing the 'damon/master' backports. - For v5.4.y: https://github.com/sjp38/linux/tree/damon/for-v5.4.y - For v5.10.y: https://github.com/sjp38/linux/tree/damon/for-v5.10.y Amazon Linux Kernel Trees ------------------------- DAMON is also merged in two public Amazon Linux kernel trees that based on v5.4.y[1] and v5.10.y[2]. [1] https://github.com/amazonlinux/linux/tree/amazon-5.4.y/master/mm/damon [2] https://github.com/amazonlinux/linux/tree/amazon-5.10.y/master/mm/damon Git Tree for Diff of Patches ============================ For easy review of diff between different versions of each patch, I prepared a git tree containing all versions of the DAMON patchset series: https://github.com/sjp38/damon-patches You can clone it and use 'diff' for easy review of changes between different versions of the patchset. For example: $ git clone https://github.com/sjp38/damon-patches && cd damon-patches $ diff -u damon/v33 damon/v34 Sequence Of Patches =================== First three patches implement the core logics of DAMON. The 1st patch introduces basic sampling based hotness monitoring for arbitrary types of targets. Following two patches implement the core mechanisms for control of overhead and accuracy, namely regions based sampling (patch 2) and adaptive regions adjustment (patch 3). Now the essential parts of DAMON is complete, but it cannot work unless someone provides monitoring primitives for a specific use case. The following two patches make it just work for virtual address spaces monitoring. The 4th patch makes 'PG_idle' can be used by DAMON and the 5th patch implements the virtual memory address space specific monitoring primitives using page table Accessed bits and the 'PG_idle' page flag. Now DAMON just works for virtual address space monitoring via the kernel space api. To let the user space users can use DAMON, following four patches add interfaces for them. The 6th patch adds a tracepoint for monitoring results. The 7th patch implements a DAMON application kernel module, namely damon-dbgfs, that simply wraps DAMON and exposes DAMON interface to the user space via the debugfs interface. The 8th patch further exports pid of monitoring thread (kdamond) to user space for easier cpu usage accounting, and the 9th patch makes the debugfs interface to support multiple contexts. Three patches for maintainability follows. The 10th patch adds documentations for both the user space and the kernel space. The 11th patch provides unit tests (based on the kunit) while the 12th patch adds user space tests (based on the kselftest). Finally, the last patch (13th) updates the MAINTAINERS file. This patch (of 13): DAMON is a data access monitoring framework for the Linux kernel. The core mechanisms of DAMON make it - accurate (the monitoring output is useful enough for DRAM level performance-centric memory management; It might be inappropriate for CPU cache levels, though), - light-weight (the monitoring overhead is normally low enough to be applied online), and - scalable (the upper-bound of the overhead is in constant range regardless of the size of target workloads). Using this framework, hence, we can easily write efficient kernel space data access monitoring applications. For example, the kernel's memory management mechanisms can make advanced decisions using this. Experimental data access aware optimization works that incurring high access monitoring overhead could again be implemented on top of this. Due to its simple and flexible interface, providing user space interface would be also easy. Then, user space users who have some special workloads can write personalized applications for better understanding and optimizations of their workloads and systems. === Nevertheless, this commit is defining and implementing only basic access check part without the overhead-accuracy handling core logic. The basic access check is as below. The output of DAMON says what memory regions are how frequently accessed for a given duration. The resolution of the access frequency is controlled by setting ``sampling interval`` and ``aggregation interval``. In detail, DAMON checks access to each page per ``sampling interval`` and aggregates the results. In other words, counts the number of the accesses to each region. After each ``aggregation interval`` passes, DAMON calls callback functions that previously registered by users so that users can read the aggregated results and then clears the results. This can be described in below simple pseudo-code:: init() while monitoring_on: for page in monitoring_target: if accessed(page): nr_accesses[page] += 1 if time() % aggregation_interval == 0: for callback in user_registered_callbacks: callback(monitoring_target, nr_accesses) for page in monitoring_target: nr_accesses[page] = 0 if time() % update_interval == 0: update() sleep(sampling interval) The target regions constructed at the beginning of the monitoring and updated after each ``regions_update_interval``, because the target regions could be dynamically changed (e.g., mmap() or memory hotplug). The monitoring overhead of this mechanism will arbitrarily increase as the size of the target workload grows. The basic monitoring primitives for actual access check and dynamic target regions construction aren't in the core part of DAMON. Instead, it allows users to implement their own primitives that are optimized for their use case and configure DAMON to use those. In other words, users cannot use current version of DAMON without some additional works. Following commits will implement the core mechanisms for the overhead-accuracy control and default primitives implementations. Link: https://lkml.kernel.org/r/20210716081449.22187-1-sj38.park@gmail.com Link: https://lkml.kernel.org/r/20210716081449.22187-2-sj38.park@gmail.com Signed-off-by: SeongJae Park <sjpark@amazon.de> Reviewed-by: Leonard Foerster <foersleo@amazon.de> Reviewed-by: Fernand Sieber <sieberf@amazon.com> Acked-by: Shakeel Butt <shakeelb@google.com> Cc: Jonathan Cameron <Jonathan.Cameron@huawei.com> Cc: Alexander Shishkin <alexander.shishkin@linux.intel.com> Cc: Amit Shah <amit@kernel.org> Cc: Benjamin Herrenschmidt <benh@kernel.crashing.org> Cc: Jonathan Corbet <corbet@lwn.net> Cc: David Hildenbrand <david@redhat.com> Cc: David Woodhouse <dwmw@amazon.com> Cc: Marco Elver <elver@google.com> Cc: Fan Du <fan.du@intel.com> Cc: Greg Kroah-Hartman <greg@kroah.com> Cc: Greg Thelen <gthelen@google.com> Cc: Joe Perches <joe@perches.com> Cc: Mel Gorman <mgorman@suse.de> Cc: Maximilian Heyne <mheyne@amazon.de> Cc: Minchan Kim <minchan@kernel.org> Cc: Ingo Molnar <mingo@redhat.com> Cc: Namhyung Kim <namhyung@kernel.org> Cc: Peter Zijlstra <peterz@infradead.org> Cc: Rik van Riel <riel@surriel.com> Cc: David Rientjes <rientjes@google.com> Cc: Steven Rostedt (VMware) <rostedt@goodmis.org> Cc: Shuah Khan <shuah@kernel.org> Cc: Vlastimil Babka <vbabka@suse.cz> Cc: Vladimir Davydov <vdavydov.dev@gmail.com> Cc: Brendan Higgins <brendanhiggins@google.com> Cc: Markus Boehme <markubo@amazon.de> Signed-off-by: Andrew Morton <akpm@linux-foundation.org> Signed-off-by: Linus Torvalds <torvalds@linux-foundation.org>
2021-09-08 10:56:28 +08:00
* @reset_aggregated should reset the access monitoring results that aggregated
* by @check_accesses.
2021-11-06 04:47:33 +08:00
* @get_scheme_score should return the priority score of a region for a scheme
* as an integer in [0, &DAMOS_MAX_SCORE].
mm/damon/core: implement DAMON-based Operation Schemes (DAMOS) In many cases, users might use DAMON for simple data access aware memory management optimizations such as applying an operation scheme to a memory region of a specific size having a specific access frequency for a specific time. For example, "page out a memory region larger than 100 MiB but having a low access frequency more than 10 minutes", or "Use THP for a memory region larger than 2 MiB having a high access frequency for more than 2 seconds". Most simple form of the solution would be doing offline data access pattern profiling using DAMON and modifying the application source code or system configuration based on the profiling results. Or, developing a daemon constructed with two modules (one for access monitoring and the other for applying memory management actions via mlock(), madvise(), sysctl, etc) is imaginable. To avoid users spending their time for implementation of such simple data access monitoring-based operation schemes, this makes DAMON to handle such schemes directly. With this change, users can simply specify their desired schemes to DAMON. Then, DAMON will automatically apply the schemes to the user-specified target processes. Each of the schemes is composed with conditions for filtering of the target memory regions and desired memory management action for the target. Specifically, the format is:: <min/max size> <min/max access frequency> <min/max age> <action> The filtering conditions are size of memory region, number of accesses to the region monitored by DAMON, and the age of the region. The age of region is incremented periodically but reset when its addresses or access frequency has significantly changed or the action of a scheme was applied. For the action, current implementation supports a few of madvise()-like hints, ``WILLNEED``, ``COLD``, ``PAGEOUT``, ``HUGEPAGE``, and ``NOHUGEPAGE``. Because DAMON supports various address spaces and application of the actions to a monitoring target region is dependent to the type of the target address space, the application code should be implemented by each primitives and registered to the framework. Note that this only implements the framework part. Following commit will implement the action applications for virtual address spaces primitives. Link: https://lkml.kernel.org/r/20211001125604.29660-3-sj@kernel.org Signed-off-by: SeongJae Park <sj@kernel.org> Cc: Amit Shah <amit@kernel.org> Cc: Benjamin Herrenschmidt <benh@kernel.crashing.org> Cc: David Hildenbrand <david@redhat.com> Cc: David Rienjes <rientjes@google.com> Cc: David Woodhouse <dwmw@amazon.com> Cc: Greg Thelen <gthelen@google.com> Cc: Jonathan Cameron <Jonathan.Cameron@huawei.com> Cc: Jonathan Corbet <corbet@lwn.net> Cc: Leonard Foerster <foersleo@amazon.de> Cc: Marco Elver <elver@google.com> Cc: Markus Boehme <markubo@amazon.de> Cc: Shakeel Butt <shakeelb@google.com> Cc: Shuah Khan <shuah@kernel.org> Signed-off-by: Andrew Morton <akpm@linux-foundation.org> Signed-off-by: Linus Torvalds <torvalds@linux-foundation.org>
2021-11-06 04:46:22 +08:00
* @apply_scheme is called from @kdamond when a region for user provided
* DAMON-based operation scheme is found. It should apply the scheme's action
mm/damon/schemes: account scheme actions that successfully applied Patch series "mm/damon/schemes: Extend stats for better online analysis and tuning". To help online access pattern analysis and tuning of DAMON-based Operation Schemes (DAMOS), DAMOS provides simple statistics for each scheme. Introduction of DAMOS time/space quota further made the tuning easier by making the risk management easier. However, that also made understanding of the working schemes a little bit more difficult. For an example, progress of a given scheme can now be throttled by not only the aggressiveness of the target access pattern, but also the time/space quotas. So, when a scheme is showing unexpectedly slow progress, it's difficult to know by what the progress of the scheme is throttled, with currently provided statistics. This patchset extends the statistics to contain some metrics that can be helpful for such online schemes analysis and tuning (patches 1-2), exports those to users (patches 3 and 5), and add documents (patches 4 and 6). This patch (of 6): DAMON-based operation schemes (DAMOS) stats provide only the number and the amount of regions that the action of the scheme has tried to be applied. Because the action could be failed for some reasons, the currently provided information is sometimes not useful or convenient enough for schemes profiling and tuning. To improve this situation, this commit extends the DAMOS stats to provide the number and the amount of regions that the action has successfully applied. Link: https://lkml.kernel.org/r/20211210150016.35349-1-sj@kernel.org Link: https://lkml.kernel.org/r/20211210150016.35349-2-sj@kernel.org Signed-off-by: SeongJae Park <sj@kernel.org> Signed-off-by: Andrew Morton <akpm@linux-foundation.org> Signed-off-by: Linus Torvalds <torvalds@linux-foundation.org>
2022-01-15 06:10:17 +08:00
* to the region and return bytes of the region that the action is successfully
* applied.
mm: introduce Data Access MONitor (DAMON) Patch series "Introduce Data Access MONitor (DAMON)", v34. Introduction ============ DAMON is a data access monitoring framework for the Linux kernel. The core mechanisms of DAMON called 'region based sampling' and 'adaptive regions adjustment' (refer to 'mechanisms.rst' in the 11th patch of this patchset for the detail) make it - accurate (The monitored information is useful for DRAM level memory management. It might not appropriate for Cache-level accuracy, though.), - light-weight (The monitoring overhead is low enough to be applied online while making no impact on the performance of the target workloads.), and - scalable (the upper-bound of the instrumentation overhead is controllable regardless of the size of target workloads.). Using this framework, therefore, several memory management mechanisms such as reclamation and THP can be optimized to aware real data access patterns. Experimental access pattern aware memory management optimization works that incurring high instrumentation overhead will be able to have another try. Though DAMON is for kernel subsystems, it can be easily exposed to the user space by writing a DAMON-wrapper kernel subsystem. Then, user space users who have some special workloads will be able to write personalized tools or applications for deeper understanding and specialized optimizations of their systems. DAMON is also merged in two public Amazon Linux kernel trees that based on v5.4.y[1] and v5.10.y[2]. [1] https://github.com/amazonlinux/linux/tree/amazon-5.4.y/master/mm/damon [2] https://github.com/amazonlinux/linux/tree/amazon-5.10.y/master/mm/damon The userspace tool[1] is available, released under GPLv2, and actively being maintained. I am also planning to implement another basic user interface in perf[2]. Also, the basic test suite for DAMON is available under GPLv2[3]. [1] https://github.com/awslabs/damo [2] https://lore.kernel.org/linux-mm/20210107120729.22328-1-sjpark@amazon.com/ [3] https://github.com/awslabs/damon-tests Long-term Plan -------------- DAMON is a part of a project called Data Access-aware Operating System (DAOS). As the name implies, I want to improve the performance and efficiency of systems using fine-grained data access patterns. The optimizations are for both kernel and user spaces. I will therefore modify or create kernel subsystems, export some of those to user space and implement user space library / tools. Below shows the layers and components for the project. --------------------------------------------------------------------------- Primitives: PTE Accessed bit, PG_idle, rmap, (Intel CMT), ... Framework: DAMON Features: DAMOS, virtual addr, physical addr, ... Applications: DAMON-debugfs, (DARC), ... ^^^^^^^^^^^^^^^^^^^^^^^ KERNEL SPACE ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ Raw Interface: debugfs, (sysfs), (damonfs), tracepoints, (sys_damon), ... vvvvvvvvvvvvvvvvvvvvvvv USER SPACE vvvvvvvvvvvvvvvvvvvvvvvvvvvvvvvv Library: (libdamon), ... Tools: DAMO, (perf), ... --------------------------------------------------------------------------- The components in parentheses or marked as '...' are not implemented yet but in the future plan. IOW, those are the TODO tasks of DAOS project. For more detail, please refer to the plans: https://lore.kernel.org/linux-mm/20201202082731.24828-1-sjpark@amazon.com/ Evaluations =========== We evaluated DAMON's overhead, monitoring quality and usefulness using 24 realistic workloads on my QEMU/KVM based virtual machine running a kernel that v24 DAMON patchset is applied. DAMON is lightweight. It increases system memory usage by 0.39% and slows target workloads down by 1.16%. DAMON is accurate and useful for memory management optimizations. An experimental DAMON-based operation scheme for THP, namely 'ethp', removes 76.15% of THP memory overheads while preserving 51.25% of THP speedup. Another experimental DAMON-based 'proactive reclamation' implementation, 'prcl', reduces 93.38% of residential sets and 23.63% of system memory footprint while incurring only 1.22% runtime overhead in the best case (parsec3/freqmine). NOTE that the experimental THP optimization and proactive reclamation are not for production but only for proof of concepts. Please refer to the official document[1] or "Documentation/admin-guide/mm: Add a document for DAMON" patch in this patchset for detailed evaluation setup and results. [1] https://damonitor.github.io/doc/html/latest-damon/admin-guide/mm/damon/eval.html Real-world User Story ===================== In summary, DAMON has used on production systems and proved its usefulness. DAMON as a profiler ------------------- We analyzed characteristics of a large scale production systems of our customers using DAMON. The systems utilize 70GB DRAM and 36 CPUs. From this, we were able to find interesting things below. There were obviously different access pattern under idle workload and active workload. Under the idle workload, it accessed large memory regions with low frequency, while the active workload accessed small memory regions with high freuqnecy. DAMON found a 7GB memory region that showing obviously high access frequency under the active workload. We believe this is the performance-effective working set and need to be protected. There was a 4KB memory region that showing highest access frequency under not only active but also idle workloads. We think this must be a hottest code section like thing that should never be paged out. For this analysis, DAMON used only 0.3-1% of single CPU time. Because we used recording-based analysis, it consumed about 3-12 MB of disk space per 20 minutes. This is only small amount of disk space, but we can further reduce the disk usage by using non-recording-based DAMON features. I'd like to argue that only DAMON can do such detailed analysis (finding 4KB highest region in 70GB memory) with the light overhead. DAMON as a system optimization tool ----------------------------------- We also found below potential performance problems on the systems and made DAMON-based solutions. The system doesn't want to make the workload suffer from the page reclamation and thus it utilizes enough DRAM but no swap device. However, we found the system is actively reclaiming file-backed pages, because the system has intensive file IO. The file IO turned out to be not performance critical for the workload, but the customer wanted to ensure performance critical file-backed pages like code section to not mistakenly be evicted. Using direct IO should or `mlock()` would be a straightforward solution, but modifying the user space code is not easy for the customer. Alternatively, we could use DAMON-based operation scheme[1]. By using it, we can ask DAMON to track access frequency of each region and make 'process_madvise(MADV_WILLNEED)[2]' call for regions having specific size and access frequency for a time interval. We also found the system is having high number of TLB misses. We tried 'always' THP enabled policy and it greatly reduced TLB misses, but the page reclamation also been more frequent due to the THP internal fragmentation caused memory bloat. We could try another DAMON-based operation scheme that applies 'MADV_HUGEPAGE' to memory regions having >=2MB size and high access frequency, while applying 'MADV_NOHUGEPAGE' to regions having <2MB size and low access frequency. We do not own the systems so we only reported the analysis results and possible optimization solutions to the customers. The customers satisfied about the analysis results and promised to try the optimization guides. [1] https://lore.kernel.org/linux-mm/20201006123931.5847-1-sjpark@amazon.com/ [2] https://lore.kernel.org/linux-api/20200622192900.22757-4-minchan@kernel.org/ Comparison with Idle Page Tracking ================================== Idle Page Tracking allows users to set and read idleness of pages using a bitmap file which represents each page with each bit of the file. One recommended usage of it is working set size detection. Users can do that by 1. find PFN of each page for workloads in interest, 2. set all the pages as idle by doing writes to the bitmap file, 3. wait until the workload accesses its working set, and 4. read the idleness of the pages again and count pages became not idle. NOTE: While Idle Page Tracking is for user space users, DAMON is primarily designed for kernel subsystems though it can easily exposed to the user space. Hence, this section only assumes such user space use of DAMON. For what use cases Idle Page Tracking would be better? ------------------------------------------------------ 1. Flexible usecases other than hotness monitoring. Because Idle Page Tracking allows users to control the primitive (Page idleness) by themselves, Idle Page Tracking users can do anything they want. Meanwhile, DAMON is primarily designed to monitor the hotness of each memory region. For this, DAMON asks users to provide sampling interval and aggregation interval. For the reason, there could be some use case that using Idle Page Tracking is simpler. 2. Physical memory monitoring. Idle Page Tracking receives PFN range as input, so natively supports physical memory monitoring. DAMON is designed to be extensible for multiple address spaces and use cases by implementing and using primitives for the given use case. Therefore, by theory, DAMON has no limitation in the type of target address space as long as primitives for the given address space exists. However, the default primitives introduced by this patchset supports only virtual address spaces. Therefore, for physical memory monitoring, you should implement your own primitives and use it, or simply use Idle Page Tracking. Nonetheless, RFC patchsets[1] for the physical memory address space primitives is already available. It also supports user memory same to Idle Page Tracking. [1] https://lore.kernel.org/linux-mm/20200831104730.28970-1-sjpark@amazon.com/ For what use cases DAMON is better? ----------------------------------- 1. Hotness Monitoring. Idle Page Tracking let users know only if a page frame is accessed or not. For hotness check, the user should write more code and use more memory. DAMON do that by itself. 2. Low Monitoring Overhead DAMON receives user's monitoring request with one step and then provide the results. So, roughly speaking, DAMON require only O(1) user/kernel context switches. In case of Idle Page Tracking, however, because the interface receives contiguous page frames, the number of user/kernel context switches increases as the monitoring target becomes complex and huge. As a result, the context switch overhead could be not negligible. Moreover, DAMON is born to handle with the monitoring overhead. Because the core mechanism is pure logical, Idle Page Tracking users might be able to implement the mechanism on their own, but it would be time consuming and the user/kernel context switching will still more frequent than that of DAMON. Also, the kernel subsystems cannot use the logic in this case. 3. Page granularity working set size detection. Until v22 of this patchset, this was categorized as the thing Idle Page Tracking could do better, because DAMON basically maintains additional metadata for each of the monitoring target regions. So, in the page granularity working set size detection use case, DAMON would incur (number of monitoring target pages * size of metadata) memory overhead. Size of the single metadata item is about 54 bytes, so assuming 4KB pages, about 1.3% of monitoring target pages will be additionally used. All essential metadata for Idle Page Tracking are embedded in 'struct page' and page table entries. Therefore, in this use case, only one counter variable for working set size accounting is required if Idle Page Tracking is used. There are more details to consider, but roughly speaking, this is true in most cases. However, the situation changed from v23. Now DAMON supports arbitrary types of monitoring targets, which don't use the metadata. Using that, DAMON can do the working set size detection with no additional space overhead but less user-kernel context switch. A first draft for the implementation of monitoring primitives for this usage is available in a DAMON development tree[1]. An RFC patchset for it based on this patchset will also be available soon. Since v24, the arbitrary type support is dropped from this patchset because this patchset doesn't introduce real use of the type. You can still get it from the DAMON development tree[2], though. [1] https://github.com/sjp38/linux/tree/damon/pgidle_hack [2] https://github.com/sjp38/linux/tree/damon/master 4. More future usecases While Idle Page Tracking has tight coupling with base primitives (PG_Idle and page table Accessed bits), DAMON is designed to be extensible for many use cases and address spaces. If you need some special address type or want to use special h/w access check primitives, you can write your own primitives for that and configure DAMON to use those. Therefore, if your use case could be changed a lot in future, using DAMON could be better. Can I use both Idle Page Tracking and DAMON? -------------------------------------------- Yes, though using them concurrently for overlapping memory regions could result in interference to each other. Nevertheless, such use case would be rare or makes no sense at all. Even in the case, the noise would bot be really significant. So, you can choose whatever you want depending on the characteristics of your use cases. More Information ================ We prepared a showcase web site[1] that you can get more information. There are - the official documentations[2], - the heatmap format dynamic access pattern of various realistic workloads for heap area[3], mmap()-ed area[4], and stack[5] area, - the dynamic working set size distribution[6] and chronological working set size changes[7], and - the latest performance test results[8]. [1] https://damonitor.github.io/_index [2] https://damonitor.github.io/doc/html/latest-damon [3] https://damonitor.github.io/test/result/visual/latest/rec.heatmap.0.png.html [4] https://damonitor.github.io/test/result/visual/latest/rec.heatmap.1.png.html [5] https://damonitor.github.io/test/result/visual/latest/rec.heatmap.2.png.html [6] https://damonitor.github.io/test/result/visual/latest/rec.wss_sz.png.html [7] https://damonitor.github.io/test/result/visual/latest/rec.wss_time.png.html [8] https://damonitor.github.io/test/result/perf/latest/html/index.html Baseline and Complete Git Trees =============================== The patches are based on the latest -mm tree, specifically v5.14-rc1-mmots-2021-07-15-18-47 of https://github.com/hnaz/linux-mm. You can also clone the complete git tree: $ git clone git://github.com/sjp38/linux -b damon/patches/v34 The web is also available: https://github.com/sjp38/linux/releases/tag/damon/patches/v34 Development Trees ----------------- There are a couple of trees for entire DAMON patchset series and features for future release. - For latest release: https://github.com/sjp38/linux/tree/damon/master - For next release: https://github.com/sjp38/linux/tree/damon/next Long-term Support Trees ----------------------- For people who want to test DAMON but using LTS kernels, there are another couple of trees based on two latest LTS kernels respectively and containing the 'damon/master' backports. - For v5.4.y: https://github.com/sjp38/linux/tree/damon/for-v5.4.y - For v5.10.y: https://github.com/sjp38/linux/tree/damon/for-v5.10.y Amazon Linux Kernel Trees ------------------------- DAMON is also merged in two public Amazon Linux kernel trees that based on v5.4.y[1] and v5.10.y[2]. [1] https://github.com/amazonlinux/linux/tree/amazon-5.4.y/master/mm/damon [2] https://github.com/amazonlinux/linux/tree/amazon-5.10.y/master/mm/damon Git Tree for Diff of Patches ============================ For easy review of diff between different versions of each patch, I prepared a git tree containing all versions of the DAMON patchset series: https://github.com/sjp38/damon-patches You can clone it and use 'diff' for easy review of changes between different versions of the patchset. For example: $ git clone https://github.com/sjp38/damon-patches && cd damon-patches $ diff -u damon/v33 damon/v34 Sequence Of Patches =================== First three patches implement the core logics of DAMON. The 1st patch introduces basic sampling based hotness monitoring for arbitrary types of targets. Following two patches implement the core mechanisms for control of overhead and accuracy, namely regions based sampling (patch 2) and adaptive regions adjustment (patch 3). Now the essential parts of DAMON is complete, but it cannot work unless someone provides monitoring primitives for a specific use case. The following two patches make it just work for virtual address spaces monitoring. The 4th patch makes 'PG_idle' can be used by DAMON and the 5th patch implements the virtual memory address space specific monitoring primitives using page table Accessed bits and the 'PG_idle' page flag. Now DAMON just works for virtual address space monitoring via the kernel space api. To let the user space users can use DAMON, following four patches add interfaces for them. The 6th patch adds a tracepoint for monitoring results. The 7th patch implements a DAMON application kernel module, namely damon-dbgfs, that simply wraps DAMON and exposes DAMON interface to the user space via the debugfs interface. The 8th patch further exports pid of monitoring thread (kdamond) to user space for easier cpu usage accounting, and the 9th patch makes the debugfs interface to support multiple contexts. Three patches for maintainability follows. The 10th patch adds documentations for both the user space and the kernel space. The 11th patch provides unit tests (based on the kunit) while the 12th patch adds user space tests (based on the kselftest). Finally, the last patch (13th) updates the MAINTAINERS file. This patch (of 13): DAMON is a data access monitoring framework for the Linux kernel. The core mechanisms of DAMON make it - accurate (the monitoring output is useful enough for DRAM level performance-centric memory management; It might be inappropriate for CPU cache levels, though), - light-weight (the monitoring overhead is normally low enough to be applied online), and - scalable (the upper-bound of the overhead is in constant range regardless of the size of target workloads). Using this framework, hence, we can easily write efficient kernel space data access monitoring applications. For example, the kernel's memory management mechanisms can make advanced decisions using this. Experimental data access aware optimization works that incurring high access monitoring overhead could again be implemented on top of this. Due to its simple and flexible interface, providing user space interface would be also easy. Then, user space users who have some special workloads can write personalized applications for better understanding and optimizations of their workloads and systems. === Nevertheless, this commit is defining and implementing only basic access check part without the overhead-accuracy handling core logic. The basic access check is as below. The output of DAMON says what memory regions are how frequently accessed for a given duration. The resolution of the access frequency is controlled by setting ``sampling interval`` and ``aggregation interval``. In detail, DAMON checks access to each page per ``sampling interval`` and aggregates the results. In other words, counts the number of the accesses to each region. After each ``aggregation interval`` passes, DAMON calls callback functions that previously registered by users so that users can read the aggregated results and then clears the results. This can be described in below simple pseudo-code:: init() while monitoring_on: for page in monitoring_target: if accessed(page): nr_accesses[page] += 1 if time() % aggregation_interval == 0: for callback in user_registered_callbacks: callback(monitoring_target, nr_accesses) for page in monitoring_target: nr_accesses[page] = 0 if time() % update_interval == 0: update() sleep(sampling interval) The target regions constructed at the beginning of the monitoring and updated after each ``regions_update_interval``, because the target regions could be dynamically changed (e.g., mmap() or memory hotplug). The monitoring overhead of this mechanism will arbitrarily increase as the size of the target workload grows. The basic monitoring primitives for actual access check and dynamic target regions construction aren't in the core part of DAMON. Instead, it allows users to implement their own primitives that are optimized for their use case and configure DAMON to use those. In other words, users cannot use current version of DAMON without some additional works. Following commits will implement the core mechanisms for the overhead-accuracy control and default primitives implementations. Link: https://lkml.kernel.org/r/20210716081449.22187-1-sj38.park@gmail.com Link: https://lkml.kernel.org/r/20210716081449.22187-2-sj38.park@gmail.com Signed-off-by: SeongJae Park <sjpark@amazon.de> Reviewed-by: Leonard Foerster <foersleo@amazon.de> Reviewed-by: Fernand Sieber <sieberf@amazon.com> Acked-by: Shakeel Butt <shakeelb@google.com> Cc: Jonathan Cameron <Jonathan.Cameron@huawei.com> Cc: Alexander Shishkin <alexander.shishkin@linux.intel.com> Cc: Amit Shah <amit@kernel.org> Cc: Benjamin Herrenschmidt <benh@kernel.crashing.org> Cc: Jonathan Corbet <corbet@lwn.net> Cc: David Hildenbrand <david@redhat.com> Cc: David Woodhouse <dwmw@amazon.com> Cc: Marco Elver <elver@google.com> Cc: Fan Du <fan.du@intel.com> Cc: Greg Kroah-Hartman <greg@kroah.com> Cc: Greg Thelen <gthelen@google.com> Cc: Joe Perches <joe@perches.com> Cc: Mel Gorman <mgorman@suse.de> Cc: Maximilian Heyne <mheyne@amazon.de> Cc: Minchan Kim <minchan@kernel.org> Cc: Ingo Molnar <mingo@redhat.com> Cc: Namhyung Kim <namhyung@kernel.org> Cc: Peter Zijlstra <peterz@infradead.org> Cc: Rik van Riel <riel@surriel.com> Cc: David Rientjes <rientjes@google.com> Cc: Steven Rostedt (VMware) <rostedt@goodmis.org> Cc: Shuah Khan <shuah@kernel.org> Cc: Vlastimil Babka <vbabka@suse.cz> Cc: Vladimir Davydov <vdavydov.dev@gmail.com> Cc: Brendan Higgins <brendanhiggins@google.com> Cc: Markus Boehme <markubo@amazon.de> Signed-off-by: Andrew Morton <akpm@linux-foundation.org> Signed-off-by: Linus Torvalds <torvalds@linux-foundation.org>
2021-09-08 10:56:28 +08:00
* @target_valid should check whether the target is still valid for the
* monitoring.
* @cleanup is called from @kdamond just before its termination.
*/
mm/damon: rename damon_primitives to damon_operations Patch series "Allow DAMON user code independent of monitoring primitives". In-kernel DAMON user code is required to configure the monitoring context (struct damon_ctx) with proper monitoring primitives (struct damon_primitive). This makes the user code dependent to all supporting monitoring primitives. For example, DAMON debugfs interface depends on both DAMON_VADDR and DAMON_PADDR, though some users have interest in only one use case. As more monitoring primitives are introduced, the problem will be bigger. To minimize such unnecessary dependency, this patchset makes monitoring primitives can be registered by the implemnting code and later dynamically searched and selected by the user code. In addition to that, this patchset renames monitoring primitives to monitoring operations, which is more easy to intuitively understand what it means and how it would be structed. This patch (of 8): DAMON has a set of callback functions called monitoring primitives and let it can be configured with various implementations for easy extension for different address spaces and usages. However, the word 'primitive' is not so explicit. Meanwhile, many other structs resembles similar purpose calls themselves 'operations'. To make the code easier to be understood, this commit renames 'damon_primitives' to 'damon_operations' before it is too late to rename. Link: https://lkml.kernel.org/r/20220215184603.1479-1-sj@kernel.org Link: https://lkml.kernel.org/r/20220215184603.1479-2-sj@kernel.org Signed-off-by: SeongJae Park <sj@kernel.org> Cc: Xin Hao <xhao@linux.alibaba.com> Cc: David Rientjes <rientjes@google.com> Signed-off-by: Andrew Morton <akpm@linux-foundation.org> Signed-off-by: Linus Torvalds <torvalds@linux-foundation.org>
2022-03-23 05:48:46 +08:00
struct damon_operations {
enum damon_ops_id id;
mm: introduce Data Access MONitor (DAMON) Patch series "Introduce Data Access MONitor (DAMON)", v34. Introduction ============ DAMON is a data access monitoring framework for the Linux kernel. The core mechanisms of DAMON called 'region based sampling' and 'adaptive regions adjustment' (refer to 'mechanisms.rst' in the 11th patch of this patchset for the detail) make it - accurate (The monitored information is useful for DRAM level memory management. It might not appropriate for Cache-level accuracy, though.), - light-weight (The monitoring overhead is low enough to be applied online while making no impact on the performance of the target workloads.), and - scalable (the upper-bound of the instrumentation overhead is controllable regardless of the size of target workloads.). Using this framework, therefore, several memory management mechanisms such as reclamation and THP can be optimized to aware real data access patterns. Experimental access pattern aware memory management optimization works that incurring high instrumentation overhead will be able to have another try. Though DAMON is for kernel subsystems, it can be easily exposed to the user space by writing a DAMON-wrapper kernel subsystem. Then, user space users who have some special workloads will be able to write personalized tools or applications for deeper understanding and specialized optimizations of their systems. DAMON is also merged in two public Amazon Linux kernel trees that based on v5.4.y[1] and v5.10.y[2]. [1] https://github.com/amazonlinux/linux/tree/amazon-5.4.y/master/mm/damon [2] https://github.com/amazonlinux/linux/tree/amazon-5.10.y/master/mm/damon The userspace tool[1] is available, released under GPLv2, and actively being maintained. I am also planning to implement another basic user interface in perf[2]. Also, the basic test suite for DAMON is available under GPLv2[3]. [1] https://github.com/awslabs/damo [2] https://lore.kernel.org/linux-mm/20210107120729.22328-1-sjpark@amazon.com/ [3] https://github.com/awslabs/damon-tests Long-term Plan -------------- DAMON is a part of a project called Data Access-aware Operating System (DAOS). As the name implies, I want to improve the performance and efficiency of systems using fine-grained data access patterns. The optimizations are for both kernel and user spaces. I will therefore modify or create kernel subsystems, export some of those to user space and implement user space library / tools. Below shows the layers and components for the project. --------------------------------------------------------------------------- Primitives: PTE Accessed bit, PG_idle, rmap, (Intel CMT), ... Framework: DAMON Features: DAMOS, virtual addr, physical addr, ... Applications: DAMON-debugfs, (DARC), ... ^^^^^^^^^^^^^^^^^^^^^^^ KERNEL SPACE ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ Raw Interface: debugfs, (sysfs), (damonfs), tracepoints, (sys_damon), ... vvvvvvvvvvvvvvvvvvvvvvv USER SPACE vvvvvvvvvvvvvvvvvvvvvvvvvvvvvvvv Library: (libdamon), ... Tools: DAMO, (perf), ... --------------------------------------------------------------------------- The components in parentheses or marked as '...' are not implemented yet but in the future plan. IOW, those are the TODO tasks of DAOS project. For more detail, please refer to the plans: https://lore.kernel.org/linux-mm/20201202082731.24828-1-sjpark@amazon.com/ Evaluations =========== We evaluated DAMON's overhead, monitoring quality and usefulness using 24 realistic workloads on my QEMU/KVM based virtual machine running a kernel that v24 DAMON patchset is applied. DAMON is lightweight. It increases system memory usage by 0.39% and slows target workloads down by 1.16%. DAMON is accurate and useful for memory management optimizations. An experimental DAMON-based operation scheme for THP, namely 'ethp', removes 76.15% of THP memory overheads while preserving 51.25% of THP speedup. Another experimental DAMON-based 'proactive reclamation' implementation, 'prcl', reduces 93.38% of residential sets and 23.63% of system memory footprint while incurring only 1.22% runtime overhead in the best case (parsec3/freqmine). NOTE that the experimental THP optimization and proactive reclamation are not for production but only for proof of concepts. Please refer to the official document[1] or "Documentation/admin-guide/mm: Add a document for DAMON" patch in this patchset for detailed evaluation setup and results. [1] https://damonitor.github.io/doc/html/latest-damon/admin-guide/mm/damon/eval.html Real-world User Story ===================== In summary, DAMON has used on production systems and proved its usefulness. DAMON as a profiler ------------------- We analyzed characteristics of a large scale production systems of our customers using DAMON. The systems utilize 70GB DRAM and 36 CPUs. From this, we were able to find interesting things below. There were obviously different access pattern under idle workload and active workload. Under the idle workload, it accessed large memory regions with low frequency, while the active workload accessed small memory regions with high freuqnecy. DAMON found a 7GB memory region that showing obviously high access frequency under the active workload. We believe this is the performance-effective working set and need to be protected. There was a 4KB memory region that showing highest access frequency under not only active but also idle workloads. We think this must be a hottest code section like thing that should never be paged out. For this analysis, DAMON used only 0.3-1% of single CPU time. Because we used recording-based analysis, it consumed about 3-12 MB of disk space per 20 minutes. This is only small amount of disk space, but we can further reduce the disk usage by using non-recording-based DAMON features. I'd like to argue that only DAMON can do such detailed analysis (finding 4KB highest region in 70GB memory) with the light overhead. DAMON as a system optimization tool ----------------------------------- We also found below potential performance problems on the systems and made DAMON-based solutions. The system doesn't want to make the workload suffer from the page reclamation and thus it utilizes enough DRAM but no swap device. However, we found the system is actively reclaiming file-backed pages, because the system has intensive file IO. The file IO turned out to be not performance critical for the workload, but the customer wanted to ensure performance critical file-backed pages like code section to not mistakenly be evicted. Using direct IO should or `mlock()` would be a straightforward solution, but modifying the user space code is not easy for the customer. Alternatively, we could use DAMON-based operation scheme[1]. By using it, we can ask DAMON to track access frequency of each region and make 'process_madvise(MADV_WILLNEED)[2]' call for regions having specific size and access frequency for a time interval. We also found the system is having high number of TLB misses. We tried 'always' THP enabled policy and it greatly reduced TLB misses, but the page reclamation also been more frequent due to the THP internal fragmentation caused memory bloat. We could try another DAMON-based operation scheme that applies 'MADV_HUGEPAGE' to memory regions having >=2MB size and high access frequency, while applying 'MADV_NOHUGEPAGE' to regions having <2MB size and low access frequency. We do not own the systems so we only reported the analysis results and possible optimization solutions to the customers. The customers satisfied about the analysis results and promised to try the optimization guides. [1] https://lore.kernel.org/linux-mm/20201006123931.5847-1-sjpark@amazon.com/ [2] https://lore.kernel.org/linux-api/20200622192900.22757-4-minchan@kernel.org/ Comparison with Idle Page Tracking ================================== Idle Page Tracking allows users to set and read idleness of pages using a bitmap file which represents each page with each bit of the file. One recommended usage of it is working set size detection. Users can do that by 1. find PFN of each page for workloads in interest, 2. set all the pages as idle by doing writes to the bitmap file, 3. wait until the workload accesses its working set, and 4. read the idleness of the pages again and count pages became not idle. NOTE: While Idle Page Tracking is for user space users, DAMON is primarily designed for kernel subsystems though it can easily exposed to the user space. Hence, this section only assumes such user space use of DAMON. For what use cases Idle Page Tracking would be better? ------------------------------------------------------ 1. Flexible usecases other than hotness monitoring. Because Idle Page Tracking allows users to control the primitive (Page idleness) by themselves, Idle Page Tracking users can do anything they want. Meanwhile, DAMON is primarily designed to monitor the hotness of each memory region. For this, DAMON asks users to provide sampling interval and aggregation interval. For the reason, there could be some use case that using Idle Page Tracking is simpler. 2. Physical memory monitoring. Idle Page Tracking receives PFN range as input, so natively supports physical memory monitoring. DAMON is designed to be extensible for multiple address spaces and use cases by implementing and using primitives for the given use case. Therefore, by theory, DAMON has no limitation in the type of target address space as long as primitives for the given address space exists. However, the default primitives introduced by this patchset supports only virtual address spaces. Therefore, for physical memory monitoring, you should implement your own primitives and use it, or simply use Idle Page Tracking. Nonetheless, RFC patchsets[1] for the physical memory address space primitives is already available. It also supports user memory same to Idle Page Tracking. [1] https://lore.kernel.org/linux-mm/20200831104730.28970-1-sjpark@amazon.com/ For what use cases DAMON is better? ----------------------------------- 1. Hotness Monitoring. Idle Page Tracking let users know only if a page frame is accessed or not. For hotness check, the user should write more code and use more memory. DAMON do that by itself. 2. Low Monitoring Overhead DAMON receives user's monitoring request with one step and then provide the results. So, roughly speaking, DAMON require only O(1) user/kernel context switches. In case of Idle Page Tracking, however, because the interface receives contiguous page frames, the number of user/kernel context switches increases as the monitoring target becomes complex and huge. As a result, the context switch overhead could be not negligible. Moreover, DAMON is born to handle with the monitoring overhead. Because the core mechanism is pure logical, Idle Page Tracking users might be able to implement the mechanism on their own, but it would be time consuming and the user/kernel context switching will still more frequent than that of DAMON. Also, the kernel subsystems cannot use the logic in this case. 3. Page granularity working set size detection. Until v22 of this patchset, this was categorized as the thing Idle Page Tracking could do better, because DAMON basically maintains additional metadata for each of the monitoring target regions. So, in the page granularity working set size detection use case, DAMON would incur (number of monitoring target pages * size of metadata) memory overhead. Size of the single metadata item is about 54 bytes, so assuming 4KB pages, about 1.3% of monitoring target pages will be additionally used. All essential metadata for Idle Page Tracking are embedded in 'struct page' and page table entries. Therefore, in this use case, only one counter variable for working set size accounting is required if Idle Page Tracking is used. There are more details to consider, but roughly speaking, this is true in most cases. However, the situation changed from v23. Now DAMON supports arbitrary types of monitoring targets, which don't use the metadata. Using that, DAMON can do the working set size detection with no additional space overhead but less user-kernel context switch. A first draft for the implementation of monitoring primitives for this usage is available in a DAMON development tree[1]. An RFC patchset for it based on this patchset will also be available soon. Since v24, the arbitrary type support is dropped from this patchset because this patchset doesn't introduce real use of the type. You can still get it from the DAMON development tree[2], though. [1] https://github.com/sjp38/linux/tree/damon/pgidle_hack [2] https://github.com/sjp38/linux/tree/damon/master 4. More future usecases While Idle Page Tracking has tight coupling with base primitives (PG_Idle and page table Accessed bits), DAMON is designed to be extensible for many use cases and address spaces. If you need some special address type or want to use special h/w access check primitives, you can write your own primitives for that and configure DAMON to use those. Therefore, if your use case could be changed a lot in future, using DAMON could be better. Can I use both Idle Page Tracking and DAMON? -------------------------------------------- Yes, though using them concurrently for overlapping memory regions could result in interference to each other. Nevertheless, such use case would be rare or makes no sense at all. Even in the case, the noise would bot be really significant. So, you can choose whatever you want depending on the characteristics of your use cases. More Information ================ We prepared a showcase web site[1] that you can get more information. There are - the official documentations[2], - the heatmap format dynamic access pattern of various realistic workloads for heap area[3], mmap()-ed area[4], and stack[5] area, - the dynamic working set size distribution[6] and chronological working set size changes[7], and - the latest performance test results[8]. [1] https://damonitor.github.io/_index [2] https://damonitor.github.io/doc/html/latest-damon [3] https://damonitor.github.io/test/result/visual/latest/rec.heatmap.0.png.html [4] https://damonitor.github.io/test/result/visual/latest/rec.heatmap.1.png.html [5] https://damonitor.github.io/test/result/visual/latest/rec.heatmap.2.png.html [6] https://damonitor.github.io/test/result/visual/latest/rec.wss_sz.png.html [7] https://damonitor.github.io/test/result/visual/latest/rec.wss_time.png.html [8] https://damonitor.github.io/test/result/perf/latest/html/index.html Baseline and Complete Git Trees =============================== The patches are based on the latest -mm tree, specifically v5.14-rc1-mmots-2021-07-15-18-47 of https://github.com/hnaz/linux-mm. You can also clone the complete git tree: $ git clone git://github.com/sjp38/linux -b damon/patches/v34 The web is also available: https://github.com/sjp38/linux/releases/tag/damon/patches/v34 Development Trees ----------------- There are a couple of trees for entire DAMON patchset series and features for future release. - For latest release: https://github.com/sjp38/linux/tree/damon/master - For next release: https://github.com/sjp38/linux/tree/damon/next Long-term Support Trees ----------------------- For people who want to test DAMON but using LTS kernels, there are another couple of trees based on two latest LTS kernels respectively and containing the 'damon/master' backports. - For v5.4.y: https://github.com/sjp38/linux/tree/damon/for-v5.4.y - For v5.10.y: https://github.com/sjp38/linux/tree/damon/for-v5.10.y Amazon Linux Kernel Trees ------------------------- DAMON is also merged in two public Amazon Linux kernel trees that based on v5.4.y[1] and v5.10.y[2]. [1] https://github.com/amazonlinux/linux/tree/amazon-5.4.y/master/mm/damon [2] https://github.com/amazonlinux/linux/tree/amazon-5.10.y/master/mm/damon Git Tree for Diff of Patches ============================ For easy review of diff between different versions of each patch, I prepared a git tree containing all versions of the DAMON patchset series: https://github.com/sjp38/damon-patches You can clone it and use 'diff' for easy review of changes between different versions of the patchset. For example: $ git clone https://github.com/sjp38/damon-patches && cd damon-patches $ diff -u damon/v33 damon/v34 Sequence Of Patches =================== First three patches implement the core logics of DAMON. The 1st patch introduces basic sampling based hotness monitoring for arbitrary types of targets. Following two patches implement the core mechanisms for control of overhead and accuracy, namely regions based sampling (patch 2) and adaptive regions adjustment (patch 3). Now the essential parts of DAMON is complete, but it cannot work unless someone provides monitoring primitives for a specific use case. The following two patches make it just work for virtual address spaces monitoring. The 4th patch makes 'PG_idle' can be used by DAMON and the 5th patch implements the virtual memory address space specific monitoring primitives using page table Accessed bits and the 'PG_idle' page flag. Now DAMON just works for virtual address space monitoring via the kernel space api. To let the user space users can use DAMON, following four patches add interfaces for them. The 6th patch adds a tracepoint for monitoring results. The 7th patch implements a DAMON application kernel module, namely damon-dbgfs, that simply wraps DAMON and exposes DAMON interface to the user space via the debugfs interface. The 8th patch further exports pid of monitoring thread (kdamond) to user space for easier cpu usage accounting, and the 9th patch makes the debugfs interface to support multiple contexts. Three patches for maintainability follows. The 10th patch adds documentations for both the user space and the kernel space. The 11th patch provides unit tests (based on the kunit) while the 12th patch adds user space tests (based on the kselftest). Finally, the last patch (13th) updates the MAINTAINERS file. This patch (of 13): DAMON is a data access monitoring framework for the Linux kernel. The core mechanisms of DAMON make it - accurate (the monitoring output is useful enough for DRAM level performance-centric memory management; It might be inappropriate for CPU cache levels, though), - light-weight (the monitoring overhead is normally low enough to be applied online), and - scalable (the upper-bound of the overhead is in constant range regardless of the size of target workloads). Using this framework, hence, we can easily write efficient kernel space data access monitoring applications. For example, the kernel's memory management mechanisms can make advanced decisions using this. Experimental data access aware optimization works that incurring high access monitoring overhead could again be implemented on top of this. Due to its simple and flexible interface, providing user space interface would be also easy. Then, user space users who have some special workloads can write personalized applications for better understanding and optimizations of their workloads and systems. === Nevertheless, this commit is defining and implementing only basic access check part without the overhead-accuracy handling core logic. The basic access check is as below. The output of DAMON says what memory regions are how frequently accessed for a given duration. The resolution of the access frequency is controlled by setting ``sampling interval`` and ``aggregation interval``. In detail, DAMON checks access to each page per ``sampling interval`` and aggregates the results. In other words, counts the number of the accesses to each region. After each ``aggregation interval`` passes, DAMON calls callback functions that previously registered by users so that users can read the aggregated results and then clears the results. This can be described in below simple pseudo-code:: init() while monitoring_on: for page in monitoring_target: if accessed(page): nr_accesses[page] += 1 if time() % aggregation_interval == 0: for callback in user_registered_callbacks: callback(monitoring_target, nr_accesses) for page in monitoring_target: nr_accesses[page] = 0 if time() % update_interval == 0: update() sleep(sampling interval) The target regions constructed at the beginning of the monitoring and updated after each ``regions_update_interval``, because the target regions could be dynamically changed (e.g., mmap() or memory hotplug). The monitoring overhead of this mechanism will arbitrarily increase as the size of the target workload grows. The basic monitoring primitives for actual access check and dynamic target regions construction aren't in the core part of DAMON. Instead, it allows users to implement their own primitives that are optimized for their use case and configure DAMON to use those. In other words, users cannot use current version of DAMON without some additional works. Following commits will implement the core mechanisms for the overhead-accuracy control and default primitives implementations. Link: https://lkml.kernel.org/r/20210716081449.22187-1-sj38.park@gmail.com Link: https://lkml.kernel.org/r/20210716081449.22187-2-sj38.park@gmail.com Signed-off-by: SeongJae Park <sjpark@amazon.de> Reviewed-by: Leonard Foerster <foersleo@amazon.de> Reviewed-by: Fernand Sieber <sieberf@amazon.com> Acked-by: Shakeel Butt <shakeelb@google.com> Cc: Jonathan Cameron <Jonathan.Cameron@huawei.com> Cc: Alexander Shishkin <alexander.shishkin@linux.intel.com> Cc: Amit Shah <amit@kernel.org> Cc: Benjamin Herrenschmidt <benh@kernel.crashing.org> Cc: Jonathan Corbet <corbet@lwn.net> Cc: David Hildenbrand <david@redhat.com> Cc: David Woodhouse <dwmw@amazon.com> Cc: Marco Elver <elver@google.com> Cc: Fan Du <fan.du@intel.com> Cc: Greg Kroah-Hartman <greg@kroah.com> Cc: Greg Thelen <gthelen@google.com> Cc: Joe Perches <joe@perches.com> Cc: Mel Gorman <mgorman@suse.de> Cc: Maximilian Heyne <mheyne@amazon.de> Cc: Minchan Kim <minchan@kernel.org> Cc: Ingo Molnar <mingo@redhat.com> Cc: Namhyung Kim <namhyung@kernel.org> Cc: Peter Zijlstra <peterz@infradead.org> Cc: Rik van Riel <riel@surriel.com> Cc: David Rientjes <rientjes@google.com> Cc: Steven Rostedt (VMware) <rostedt@goodmis.org> Cc: Shuah Khan <shuah@kernel.org> Cc: Vlastimil Babka <vbabka@suse.cz> Cc: Vladimir Davydov <vdavydov.dev@gmail.com> Cc: Brendan Higgins <brendanhiggins@google.com> Cc: Markus Boehme <markubo@amazon.de> Signed-off-by: Andrew Morton <akpm@linux-foundation.org> Signed-off-by: Linus Torvalds <torvalds@linux-foundation.org>
2021-09-08 10:56:28 +08:00
void (*init)(struct damon_ctx *context);
void (*update)(struct damon_ctx *context);
void (*prepare_access_checks)(struct damon_ctx *context);
mm/damon: adaptively adjust regions Even somehow the initial monitoring target regions are well constructed to fulfill the assumption (pages in same region have similar access frequencies), the data access pattern can be dynamically changed. This will result in low monitoring quality. To keep the assumption as much as possible, DAMON adaptively merges and splits each region based on their access frequency. For each ``aggregation interval``, it compares the access frequencies of adjacent regions and merges those if the frequency difference is small. Then, after it reports and clears the aggregated access frequency of each region, it splits each region into two or three regions if the total number of regions will not exceed the user-specified maximum number of regions after the split. In this way, DAMON provides its best-effort quality and minimal overhead while keeping the upper-bound overhead that users set. Link: https://lkml.kernel.org/r/20210716081449.22187-4-sj38.park@gmail.com Signed-off-by: SeongJae Park <sjpark@amazon.de> Reviewed-by: Leonard Foerster <foersleo@amazon.de> Reviewed-by: Fernand Sieber <sieberf@amazon.com> Acked-by: Shakeel Butt <shakeelb@google.com> Cc: Alexander Shishkin <alexander.shishkin@linux.intel.com> Cc: Amit Shah <amit@kernel.org> Cc: Benjamin Herrenschmidt <benh@kernel.crashing.org> Cc: Brendan Higgins <brendanhiggins@google.com> Cc: David Hildenbrand <david@redhat.com> Cc: David Rientjes <rientjes@google.com> Cc: David Woodhouse <dwmw@amazon.com> Cc: Fan Du <fan.du@intel.com> Cc: Greg Kroah-Hartman <greg@kroah.com> Cc: Greg Thelen <gthelen@google.com> Cc: Ingo Molnar <mingo@redhat.com> Cc: Joe Perches <joe@perches.com> Cc: Jonathan Cameron <Jonathan.Cameron@huawei.com> Cc: Jonathan Corbet <corbet@lwn.net> Cc: Marco Elver <elver@google.com> Cc: Markus Boehme <markubo@amazon.de> Cc: Maximilian Heyne <mheyne@amazon.de> Cc: Mel Gorman <mgorman@suse.de> Cc: Minchan Kim <minchan@kernel.org> Cc: Namhyung Kim <namhyung@kernel.org> Cc: Peter Zijlstra <peterz@infradead.org> Cc: Rik van Riel <riel@surriel.com> Cc: Shuah Khan <shuah@kernel.org> Cc: Steven Rostedt (VMware) <rostedt@goodmis.org> Cc: Vladimir Davydov <vdavydov.dev@gmail.com> Cc: Vlastimil Babka <vbabka@suse.cz> Signed-off-by: Andrew Morton <akpm@linux-foundation.org> Signed-off-by: Linus Torvalds <torvalds@linux-foundation.org>
2021-09-08 10:56:36 +08:00
unsigned int (*check_accesses)(struct damon_ctx *context);
mm: introduce Data Access MONitor (DAMON) Patch series "Introduce Data Access MONitor (DAMON)", v34. Introduction ============ DAMON is a data access monitoring framework for the Linux kernel. The core mechanisms of DAMON called 'region based sampling' and 'adaptive regions adjustment' (refer to 'mechanisms.rst' in the 11th patch of this patchset for the detail) make it - accurate (The monitored information is useful for DRAM level memory management. It might not appropriate for Cache-level accuracy, though.), - light-weight (The monitoring overhead is low enough to be applied online while making no impact on the performance of the target workloads.), and - scalable (the upper-bound of the instrumentation overhead is controllable regardless of the size of target workloads.). Using this framework, therefore, several memory management mechanisms such as reclamation and THP can be optimized to aware real data access patterns. Experimental access pattern aware memory management optimization works that incurring high instrumentation overhead will be able to have another try. Though DAMON is for kernel subsystems, it can be easily exposed to the user space by writing a DAMON-wrapper kernel subsystem. Then, user space users who have some special workloads will be able to write personalized tools or applications for deeper understanding and specialized optimizations of their systems. DAMON is also merged in two public Amazon Linux kernel trees that based on v5.4.y[1] and v5.10.y[2]. [1] https://github.com/amazonlinux/linux/tree/amazon-5.4.y/master/mm/damon [2] https://github.com/amazonlinux/linux/tree/amazon-5.10.y/master/mm/damon The userspace tool[1] is available, released under GPLv2, and actively being maintained. I am also planning to implement another basic user interface in perf[2]. Also, the basic test suite for DAMON is available under GPLv2[3]. [1] https://github.com/awslabs/damo [2] https://lore.kernel.org/linux-mm/20210107120729.22328-1-sjpark@amazon.com/ [3] https://github.com/awslabs/damon-tests Long-term Plan -------------- DAMON is a part of a project called Data Access-aware Operating System (DAOS). As the name implies, I want to improve the performance and efficiency of systems using fine-grained data access patterns. The optimizations are for both kernel and user spaces. I will therefore modify or create kernel subsystems, export some of those to user space and implement user space library / tools. Below shows the layers and components for the project. --------------------------------------------------------------------------- Primitives: PTE Accessed bit, PG_idle, rmap, (Intel CMT), ... Framework: DAMON Features: DAMOS, virtual addr, physical addr, ... Applications: DAMON-debugfs, (DARC), ... ^^^^^^^^^^^^^^^^^^^^^^^ KERNEL SPACE ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ Raw Interface: debugfs, (sysfs), (damonfs), tracepoints, (sys_damon), ... vvvvvvvvvvvvvvvvvvvvvvv USER SPACE vvvvvvvvvvvvvvvvvvvvvvvvvvvvvvvv Library: (libdamon), ... Tools: DAMO, (perf), ... --------------------------------------------------------------------------- The components in parentheses or marked as '...' are not implemented yet but in the future plan. IOW, those are the TODO tasks of DAOS project. For more detail, please refer to the plans: https://lore.kernel.org/linux-mm/20201202082731.24828-1-sjpark@amazon.com/ Evaluations =========== We evaluated DAMON's overhead, monitoring quality and usefulness using 24 realistic workloads on my QEMU/KVM based virtual machine running a kernel that v24 DAMON patchset is applied. DAMON is lightweight. It increases system memory usage by 0.39% and slows target workloads down by 1.16%. DAMON is accurate and useful for memory management optimizations. An experimental DAMON-based operation scheme for THP, namely 'ethp', removes 76.15% of THP memory overheads while preserving 51.25% of THP speedup. Another experimental DAMON-based 'proactive reclamation' implementation, 'prcl', reduces 93.38% of residential sets and 23.63% of system memory footprint while incurring only 1.22% runtime overhead in the best case (parsec3/freqmine). NOTE that the experimental THP optimization and proactive reclamation are not for production but only for proof of concepts. Please refer to the official document[1] or "Documentation/admin-guide/mm: Add a document for DAMON" patch in this patchset for detailed evaluation setup and results. [1] https://damonitor.github.io/doc/html/latest-damon/admin-guide/mm/damon/eval.html Real-world User Story ===================== In summary, DAMON has used on production systems and proved its usefulness. DAMON as a profiler ------------------- We analyzed characteristics of a large scale production systems of our customers using DAMON. The systems utilize 70GB DRAM and 36 CPUs. From this, we were able to find interesting things below. There were obviously different access pattern under idle workload and active workload. Under the idle workload, it accessed large memory regions with low frequency, while the active workload accessed small memory regions with high freuqnecy. DAMON found a 7GB memory region that showing obviously high access frequency under the active workload. We believe this is the performance-effective working set and need to be protected. There was a 4KB memory region that showing highest access frequency under not only active but also idle workloads. We think this must be a hottest code section like thing that should never be paged out. For this analysis, DAMON used only 0.3-1% of single CPU time. Because we used recording-based analysis, it consumed about 3-12 MB of disk space per 20 minutes. This is only small amount of disk space, but we can further reduce the disk usage by using non-recording-based DAMON features. I'd like to argue that only DAMON can do such detailed analysis (finding 4KB highest region in 70GB memory) with the light overhead. DAMON as a system optimization tool ----------------------------------- We also found below potential performance problems on the systems and made DAMON-based solutions. The system doesn't want to make the workload suffer from the page reclamation and thus it utilizes enough DRAM but no swap device. However, we found the system is actively reclaiming file-backed pages, because the system has intensive file IO. The file IO turned out to be not performance critical for the workload, but the customer wanted to ensure performance critical file-backed pages like code section to not mistakenly be evicted. Using direct IO should or `mlock()` would be a straightforward solution, but modifying the user space code is not easy for the customer. Alternatively, we could use DAMON-based operation scheme[1]. By using it, we can ask DAMON to track access frequency of each region and make 'process_madvise(MADV_WILLNEED)[2]' call for regions having specific size and access frequency for a time interval. We also found the system is having high number of TLB misses. We tried 'always' THP enabled policy and it greatly reduced TLB misses, but the page reclamation also been more frequent due to the THP internal fragmentation caused memory bloat. We could try another DAMON-based operation scheme that applies 'MADV_HUGEPAGE' to memory regions having >=2MB size and high access frequency, while applying 'MADV_NOHUGEPAGE' to regions having <2MB size and low access frequency. We do not own the systems so we only reported the analysis results and possible optimization solutions to the customers. The customers satisfied about the analysis results and promised to try the optimization guides. [1] https://lore.kernel.org/linux-mm/20201006123931.5847-1-sjpark@amazon.com/ [2] https://lore.kernel.org/linux-api/20200622192900.22757-4-minchan@kernel.org/ Comparison with Idle Page Tracking ================================== Idle Page Tracking allows users to set and read idleness of pages using a bitmap file which represents each page with each bit of the file. One recommended usage of it is working set size detection. Users can do that by 1. find PFN of each page for workloads in interest, 2. set all the pages as idle by doing writes to the bitmap file, 3. wait until the workload accesses its working set, and 4. read the idleness of the pages again and count pages became not idle. NOTE: While Idle Page Tracking is for user space users, DAMON is primarily designed for kernel subsystems though it can easily exposed to the user space. Hence, this section only assumes such user space use of DAMON. For what use cases Idle Page Tracking would be better? ------------------------------------------------------ 1. Flexible usecases other than hotness monitoring. Because Idle Page Tracking allows users to control the primitive (Page idleness) by themselves, Idle Page Tracking users can do anything they want. Meanwhile, DAMON is primarily designed to monitor the hotness of each memory region. For this, DAMON asks users to provide sampling interval and aggregation interval. For the reason, there could be some use case that using Idle Page Tracking is simpler. 2. Physical memory monitoring. Idle Page Tracking receives PFN range as input, so natively supports physical memory monitoring. DAMON is designed to be extensible for multiple address spaces and use cases by implementing and using primitives for the given use case. Therefore, by theory, DAMON has no limitation in the type of target address space as long as primitives for the given address space exists. However, the default primitives introduced by this patchset supports only virtual address spaces. Therefore, for physical memory monitoring, you should implement your own primitives and use it, or simply use Idle Page Tracking. Nonetheless, RFC patchsets[1] for the physical memory address space primitives is already available. It also supports user memory same to Idle Page Tracking. [1] https://lore.kernel.org/linux-mm/20200831104730.28970-1-sjpark@amazon.com/ For what use cases DAMON is better? ----------------------------------- 1. Hotness Monitoring. Idle Page Tracking let users know only if a page frame is accessed or not. For hotness check, the user should write more code and use more memory. DAMON do that by itself. 2. Low Monitoring Overhead DAMON receives user's monitoring request with one step and then provide the results. So, roughly speaking, DAMON require only O(1) user/kernel context switches. In case of Idle Page Tracking, however, because the interface receives contiguous page frames, the number of user/kernel context switches increases as the monitoring target becomes complex and huge. As a result, the context switch overhead could be not negligible. Moreover, DAMON is born to handle with the monitoring overhead. Because the core mechanism is pure logical, Idle Page Tracking users might be able to implement the mechanism on their own, but it would be time consuming and the user/kernel context switching will still more frequent than that of DAMON. Also, the kernel subsystems cannot use the logic in this case. 3. Page granularity working set size detection. Until v22 of this patchset, this was categorized as the thing Idle Page Tracking could do better, because DAMON basically maintains additional metadata for each of the monitoring target regions. So, in the page granularity working set size detection use case, DAMON would incur (number of monitoring target pages * size of metadata) memory overhead. Size of the single metadata item is about 54 bytes, so assuming 4KB pages, about 1.3% of monitoring target pages will be additionally used. All essential metadata for Idle Page Tracking are embedded in 'struct page' and page table entries. Therefore, in this use case, only one counter variable for working set size accounting is required if Idle Page Tracking is used. There are more details to consider, but roughly speaking, this is true in most cases. However, the situation changed from v23. Now DAMON supports arbitrary types of monitoring targets, which don't use the metadata. Using that, DAMON can do the working set size detection with no additional space overhead but less user-kernel context switch. A first draft for the implementation of monitoring primitives for this usage is available in a DAMON development tree[1]. An RFC patchset for it based on this patchset will also be available soon. Since v24, the arbitrary type support is dropped from this patchset because this patchset doesn't introduce real use of the type. You can still get it from the DAMON development tree[2], though. [1] https://github.com/sjp38/linux/tree/damon/pgidle_hack [2] https://github.com/sjp38/linux/tree/damon/master 4. More future usecases While Idle Page Tracking has tight coupling with base primitives (PG_Idle and page table Accessed bits), DAMON is designed to be extensible for many use cases and address spaces. If you need some special address type or want to use special h/w access check primitives, you can write your own primitives for that and configure DAMON to use those. Therefore, if your use case could be changed a lot in future, using DAMON could be better. Can I use both Idle Page Tracking and DAMON? -------------------------------------------- Yes, though using them concurrently for overlapping memory regions could result in interference to each other. Nevertheless, such use case would be rare or makes no sense at all. Even in the case, the noise would bot be really significant. So, you can choose whatever you want depending on the characteristics of your use cases. More Information ================ We prepared a showcase web site[1] that you can get more information. There are - the official documentations[2], - the heatmap format dynamic access pattern of various realistic workloads for heap area[3], mmap()-ed area[4], and stack[5] area, - the dynamic working set size distribution[6] and chronological working set size changes[7], and - the latest performance test results[8]. [1] https://damonitor.github.io/_index [2] https://damonitor.github.io/doc/html/latest-damon [3] https://damonitor.github.io/test/result/visual/latest/rec.heatmap.0.png.html [4] https://damonitor.github.io/test/result/visual/latest/rec.heatmap.1.png.html [5] https://damonitor.github.io/test/result/visual/latest/rec.heatmap.2.png.html [6] https://damonitor.github.io/test/result/visual/latest/rec.wss_sz.png.html [7] https://damonitor.github.io/test/result/visual/latest/rec.wss_time.png.html [8] https://damonitor.github.io/test/result/perf/latest/html/index.html Baseline and Complete Git Trees =============================== The patches are based on the latest -mm tree, specifically v5.14-rc1-mmots-2021-07-15-18-47 of https://github.com/hnaz/linux-mm. You can also clone the complete git tree: $ git clone git://github.com/sjp38/linux -b damon/patches/v34 The web is also available: https://github.com/sjp38/linux/releases/tag/damon/patches/v34 Development Trees ----------------- There are a couple of trees for entire DAMON patchset series and features for future release. - For latest release: https://github.com/sjp38/linux/tree/damon/master - For next release: https://github.com/sjp38/linux/tree/damon/next Long-term Support Trees ----------------------- For people who want to test DAMON but using LTS kernels, there are another couple of trees based on two latest LTS kernels respectively and containing the 'damon/master' backports. - For v5.4.y: https://github.com/sjp38/linux/tree/damon/for-v5.4.y - For v5.10.y: https://github.com/sjp38/linux/tree/damon/for-v5.10.y Amazon Linux Kernel Trees ------------------------- DAMON is also merged in two public Amazon Linux kernel trees that based on v5.4.y[1] and v5.10.y[2]. [1] https://github.com/amazonlinux/linux/tree/amazon-5.4.y/master/mm/damon [2] https://github.com/amazonlinux/linux/tree/amazon-5.10.y/master/mm/damon Git Tree for Diff of Patches ============================ For easy review of diff between different versions of each patch, I prepared a git tree containing all versions of the DAMON patchset series: https://github.com/sjp38/damon-patches You can clone it and use 'diff' for easy review of changes between different versions of the patchset. For example: $ git clone https://github.com/sjp38/damon-patches && cd damon-patches $ diff -u damon/v33 damon/v34 Sequence Of Patches =================== First three patches implement the core logics of DAMON. The 1st patch introduces basic sampling based hotness monitoring for arbitrary types of targets. Following two patches implement the core mechanisms for control of overhead and accuracy, namely regions based sampling (patch 2) and adaptive regions adjustment (patch 3). Now the essential parts of DAMON is complete, but it cannot work unless someone provides monitoring primitives for a specific use case. The following two patches make it just work for virtual address spaces monitoring. The 4th patch makes 'PG_idle' can be used by DAMON and the 5th patch implements the virtual memory address space specific monitoring primitives using page table Accessed bits and the 'PG_idle' page flag. Now DAMON just works for virtual address space monitoring via the kernel space api. To let the user space users can use DAMON, following four patches add interfaces for them. The 6th patch adds a tracepoint for monitoring results. The 7th patch implements a DAMON application kernel module, namely damon-dbgfs, that simply wraps DAMON and exposes DAMON interface to the user space via the debugfs interface. The 8th patch further exports pid of monitoring thread (kdamond) to user space for easier cpu usage accounting, and the 9th patch makes the debugfs interface to support multiple contexts. Three patches for maintainability follows. The 10th patch adds documentations for both the user space and the kernel space. The 11th patch provides unit tests (based on the kunit) while the 12th patch adds user space tests (based on the kselftest). Finally, the last patch (13th) updates the MAINTAINERS file. This patch (of 13): DAMON is a data access monitoring framework for the Linux kernel. The core mechanisms of DAMON make it - accurate (the monitoring output is useful enough for DRAM level performance-centric memory management; It might be inappropriate for CPU cache levels, though), - light-weight (the monitoring overhead is normally low enough to be applied online), and - scalable (the upper-bound of the overhead is in constant range regardless of the size of target workloads). Using this framework, hence, we can easily write efficient kernel space data access monitoring applications. For example, the kernel's memory management mechanisms can make advanced decisions using this. Experimental data access aware optimization works that incurring high access monitoring overhead could again be implemented on top of this. Due to its simple and flexible interface, providing user space interface would be also easy. Then, user space users who have some special workloads can write personalized applications for better understanding and optimizations of their workloads and systems. === Nevertheless, this commit is defining and implementing only basic access check part without the overhead-accuracy handling core logic. The basic access check is as below. The output of DAMON says what memory regions are how frequently accessed for a given duration. The resolution of the access frequency is controlled by setting ``sampling interval`` and ``aggregation interval``. In detail, DAMON checks access to each page per ``sampling interval`` and aggregates the results. In other words, counts the number of the accesses to each region. After each ``aggregation interval`` passes, DAMON calls callback functions that previously registered by users so that users can read the aggregated results and then clears the results. This can be described in below simple pseudo-code:: init() while monitoring_on: for page in monitoring_target: if accessed(page): nr_accesses[page] += 1 if time() % aggregation_interval == 0: for callback in user_registered_callbacks: callback(monitoring_target, nr_accesses) for page in monitoring_target: nr_accesses[page] = 0 if time() % update_interval == 0: update() sleep(sampling interval) The target regions constructed at the beginning of the monitoring and updated after each ``regions_update_interval``, because the target regions could be dynamically changed (e.g., mmap() or memory hotplug). The monitoring overhead of this mechanism will arbitrarily increase as the size of the target workload grows. The basic monitoring primitives for actual access check and dynamic target regions construction aren't in the core part of DAMON. Instead, it allows users to implement their own primitives that are optimized for their use case and configure DAMON to use those. In other words, users cannot use current version of DAMON without some additional works. Following commits will implement the core mechanisms for the overhead-accuracy control and default primitives implementations. Link: https://lkml.kernel.org/r/20210716081449.22187-1-sj38.park@gmail.com Link: https://lkml.kernel.org/r/20210716081449.22187-2-sj38.park@gmail.com Signed-off-by: SeongJae Park <sjpark@amazon.de> Reviewed-by: Leonard Foerster <foersleo@amazon.de> Reviewed-by: Fernand Sieber <sieberf@amazon.com> Acked-by: Shakeel Butt <shakeelb@google.com> Cc: Jonathan Cameron <Jonathan.Cameron@huawei.com> Cc: Alexander Shishkin <alexander.shishkin@linux.intel.com> Cc: Amit Shah <amit@kernel.org> Cc: Benjamin Herrenschmidt <benh@kernel.crashing.org> Cc: Jonathan Corbet <corbet@lwn.net> Cc: David Hildenbrand <david@redhat.com> Cc: David Woodhouse <dwmw@amazon.com> Cc: Marco Elver <elver@google.com> Cc: Fan Du <fan.du@intel.com> Cc: Greg Kroah-Hartman <greg@kroah.com> Cc: Greg Thelen <gthelen@google.com> Cc: Joe Perches <joe@perches.com> Cc: Mel Gorman <mgorman@suse.de> Cc: Maximilian Heyne <mheyne@amazon.de> Cc: Minchan Kim <minchan@kernel.org> Cc: Ingo Molnar <mingo@redhat.com> Cc: Namhyung Kim <namhyung@kernel.org> Cc: Peter Zijlstra <peterz@infradead.org> Cc: Rik van Riel <riel@surriel.com> Cc: David Rientjes <rientjes@google.com> Cc: Steven Rostedt (VMware) <rostedt@goodmis.org> Cc: Shuah Khan <shuah@kernel.org> Cc: Vlastimil Babka <vbabka@suse.cz> Cc: Vladimir Davydov <vdavydov.dev@gmail.com> Cc: Brendan Higgins <brendanhiggins@google.com> Cc: Markus Boehme <markubo@amazon.de> Signed-off-by: Andrew Morton <akpm@linux-foundation.org> Signed-off-by: Linus Torvalds <torvalds@linux-foundation.org>
2021-09-08 10:56:28 +08:00
void (*reset_aggregated)(struct damon_ctx *context);
2021-11-06 04:47:33 +08:00
int (*get_scheme_score)(struct damon_ctx *context,
struct damon_target *t, struct damon_region *r,
struct damos *scheme);
mm/damon/schemes: account scheme actions that successfully applied Patch series "mm/damon/schemes: Extend stats for better online analysis and tuning". To help online access pattern analysis and tuning of DAMON-based Operation Schemes (DAMOS), DAMOS provides simple statistics for each scheme. Introduction of DAMOS time/space quota further made the tuning easier by making the risk management easier. However, that also made understanding of the working schemes a little bit more difficult. For an example, progress of a given scheme can now be throttled by not only the aggressiveness of the target access pattern, but also the time/space quotas. So, when a scheme is showing unexpectedly slow progress, it's difficult to know by what the progress of the scheme is throttled, with currently provided statistics. This patchset extends the statistics to contain some metrics that can be helpful for such online schemes analysis and tuning (patches 1-2), exports those to users (patches 3 and 5), and add documents (patches 4 and 6). This patch (of 6): DAMON-based operation schemes (DAMOS) stats provide only the number and the amount of regions that the action of the scheme has tried to be applied. Because the action could be failed for some reasons, the currently provided information is sometimes not useful or convenient enough for schemes profiling and tuning. To improve this situation, this commit extends the DAMOS stats to provide the number and the amount of regions that the action has successfully applied. Link: https://lkml.kernel.org/r/20211210150016.35349-1-sj@kernel.org Link: https://lkml.kernel.org/r/20211210150016.35349-2-sj@kernel.org Signed-off-by: SeongJae Park <sj@kernel.org> Signed-off-by: Andrew Morton <akpm@linux-foundation.org> Signed-off-by: Linus Torvalds <torvalds@linux-foundation.org>
2022-01-15 06:10:17 +08:00
unsigned long (*apply_scheme)(struct damon_ctx *context,
struct damon_target *t, struct damon_region *r,
struct damos *scheme);
mm: introduce Data Access MONitor (DAMON) Patch series "Introduce Data Access MONitor (DAMON)", v34. Introduction ============ DAMON is a data access monitoring framework for the Linux kernel. The core mechanisms of DAMON called 'region based sampling' and 'adaptive regions adjustment' (refer to 'mechanisms.rst' in the 11th patch of this patchset for the detail) make it - accurate (The monitored information is useful for DRAM level memory management. It might not appropriate for Cache-level accuracy, though.), - light-weight (The monitoring overhead is low enough to be applied online while making no impact on the performance of the target workloads.), and - scalable (the upper-bound of the instrumentation overhead is controllable regardless of the size of target workloads.). Using this framework, therefore, several memory management mechanisms such as reclamation and THP can be optimized to aware real data access patterns. Experimental access pattern aware memory management optimization works that incurring high instrumentation overhead will be able to have another try. Though DAMON is for kernel subsystems, it can be easily exposed to the user space by writing a DAMON-wrapper kernel subsystem. Then, user space users who have some special workloads will be able to write personalized tools or applications for deeper understanding and specialized optimizations of their systems. DAMON is also merged in two public Amazon Linux kernel trees that based on v5.4.y[1] and v5.10.y[2]. [1] https://github.com/amazonlinux/linux/tree/amazon-5.4.y/master/mm/damon [2] https://github.com/amazonlinux/linux/tree/amazon-5.10.y/master/mm/damon The userspace tool[1] is available, released under GPLv2, and actively being maintained. I am also planning to implement another basic user interface in perf[2]. Also, the basic test suite for DAMON is available under GPLv2[3]. [1] https://github.com/awslabs/damo [2] https://lore.kernel.org/linux-mm/20210107120729.22328-1-sjpark@amazon.com/ [3] https://github.com/awslabs/damon-tests Long-term Plan -------------- DAMON is a part of a project called Data Access-aware Operating System (DAOS). As the name implies, I want to improve the performance and efficiency of systems using fine-grained data access patterns. The optimizations are for both kernel and user spaces. I will therefore modify or create kernel subsystems, export some of those to user space and implement user space library / tools. Below shows the layers and components for the project. --------------------------------------------------------------------------- Primitives: PTE Accessed bit, PG_idle, rmap, (Intel CMT), ... Framework: DAMON Features: DAMOS, virtual addr, physical addr, ... Applications: DAMON-debugfs, (DARC), ... ^^^^^^^^^^^^^^^^^^^^^^^ KERNEL SPACE ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ Raw Interface: debugfs, (sysfs), (damonfs), tracepoints, (sys_damon), ... vvvvvvvvvvvvvvvvvvvvvvv USER SPACE vvvvvvvvvvvvvvvvvvvvvvvvvvvvvvvv Library: (libdamon), ... Tools: DAMO, (perf), ... --------------------------------------------------------------------------- The components in parentheses or marked as '...' are not implemented yet but in the future plan. IOW, those are the TODO tasks of DAOS project. For more detail, please refer to the plans: https://lore.kernel.org/linux-mm/20201202082731.24828-1-sjpark@amazon.com/ Evaluations =========== We evaluated DAMON's overhead, monitoring quality and usefulness using 24 realistic workloads on my QEMU/KVM based virtual machine running a kernel that v24 DAMON patchset is applied. DAMON is lightweight. It increases system memory usage by 0.39% and slows target workloads down by 1.16%. DAMON is accurate and useful for memory management optimizations. An experimental DAMON-based operation scheme for THP, namely 'ethp', removes 76.15% of THP memory overheads while preserving 51.25% of THP speedup. Another experimental DAMON-based 'proactive reclamation' implementation, 'prcl', reduces 93.38% of residential sets and 23.63% of system memory footprint while incurring only 1.22% runtime overhead in the best case (parsec3/freqmine). NOTE that the experimental THP optimization and proactive reclamation are not for production but only for proof of concepts. Please refer to the official document[1] or "Documentation/admin-guide/mm: Add a document for DAMON" patch in this patchset for detailed evaluation setup and results. [1] https://damonitor.github.io/doc/html/latest-damon/admin-guide/mm/damon/eval.html Real-world User Story ===================== In summary, DAMON has used on production systems and proved its usefulness. DAMON as a profiler ------------------- We analyzed characteristics of a large scale production systems of our customers using DAMON. The systems utilize 70GB DRAM and 36 CPUs. From this, we were able to find interesting things below. There were obviously different access pattern under idle workload and active workload. Under the idle workload, it accessed large memory regions with low frequency, while the active workload accessed small memory regions with high freuqnecy. DAMON found a 7GB memory region that showing obviously high access frequency under the active workload. We believe this is the performance-effective working set and need to be protected. There was a 4KB memory region that showing highest access frequency under not only active but also idle workloads. We think this must be a hottest code section like thing that should never be paged out. For this analysis, DAMON used only 0.3-1% of single CPU time. Because we used recording-based analysis, it consumed about 3-12 MB of disk space per 20 minutes. This is only small amount of disk space, but we can further reduce the disk usage by using non-recording-based DAMON features. I'd like to argue that only DAMON can do such detailed analysis (finding 4KB highest region in 70GB memory) with the light overhead. DAMON as a system optimization tool ----------------------------------- We also found below potential performance problems on the systems and made DAMON-based solutions. The system doesn't want to make the workload suffer from the page reclamation and thus it utilizes enough DRAM but no swap device. However, we found the system is actively reclaiming file-backed pages, because the system has intensive file IO. The file IO turned out to be not performance critical for the workload, but the customer wanted to ensure performance critical file-backed pages like code section to not mistakenly be evicted. Using direct IO should or `mlock()` would be a straightforward solution, but modifying the user space code is not easy for the customer. Alternatively, we could use DAMON-based operation scheme[1]. By using it, we can ask DAMON to track access frequency of each region and make 'process_madvise(MADV_WILLNEED)[2]' call for regions having specific size and access frequency for a time interval. We also found the system is having high number of TLB misses. We tried 'always' THP enabled policy and it greatly reduced TLB misses, but the page reclamation also been more frequent due to the THP internal fragmentation caused memory bloat. We could try another DAMON-based operation scheme that applies 'MADV_HUGEPAGE' to memory regions having >=2MB size and high access frequency, while applying 'MADV_NOHUGEPAGE' to regions having <2MB size and low access frequency. We do not own the systems so we only reported the analysis results and possible optimization solutions to the customers. The customers satisfied about the analysis results and promised to try the optimization guides. [1] https://lore.kernel.org/linux-mm/20201006123931.5847-1-sjpark@amazon.com/ [2] https://lore.kernel.org/linux-api/20200622192900.22757-4-minchan@kernel.org/ Comparison with Idle Page Tracking ================================== Idle Page Tracking allows users to set and read idleness of pages using a bitmap file which represents each page with each bit of the file. One recommended usage of it is working set size detection. Users can do that by 1. find PFN of each page for workloads in interest, 2. set all the pages as idle by doing writes to the bitmap file, 3. wait until the workload accesses its working set, and 4. read the idleness of the pages again and count pages became not idle. NOTE: While Idle Page Tracking is for user space users, DAMON is primarily designed for kernel subsystems though it can easily exposed to the user space. Hence, this section only assumes such user space use of DAMON. For what use cases Idle Page Tracking would be better? ------------------------------------------------------ 1. Flexible usecases other than hotness monitoring. Because Idle Page Tracking allows users to control the primitive (Page idleness) by themselves, Idle Page Tracking users can do anything they want. Meanwhile, DAMON is primarily designed to monitor the hotness of each memory region. For this, DAMON asks users to provide sampling interval and aggregation interval. For the reason, there could be some use case that using Idle Page Tracking is simpler. 2. Physical memory monitoring. Idle Page Tracking receives PFN range as input, so natively supports physical memory monitoring. DAMON is designed to be extensible for multiple address spaces and use cases by implementing and using primitives for the given use case. Therefore, by theory, DAMON has no limitation in the type of target address space as long as primitives for the given address space exists. However, the default primitives introduced by this patchset supports only virtual address spaces. Therefore, for physical memory monitoring, you should implement your own primitives and use it, or simply use Idle Page Tracking. Nonetheless, RFC patchsets[1] for the physical memory address space primitives is already available. It also supports user memory same to Idle Page Tracking. [1] https://lore.kernel.org/linux-mm/20200831104730.28970-1-sjpark@amazon.com/ For what use cases DAMON is better? ----------------------------------- 1. Hotness Monitoring. Idle Page Tracking let users know only if a page frame is accessed or not. For hotness check, the user should write more code and use more memory. DAMON do that by itself. 2. Low Monitoring Overhead DAMON receives user's monitoring request with one step and then provide the results. So, roughly speaking, DAMON require only O(1) user/kernel context switches. In case of Idle Page Tracking, however, because the interface receives contiguous page frames, the number of user/kernel context switches increases as the monitoring target becomes complex and huge. As a result, the context switch overhead could be not negligible. Moreover, DAMON is born to handle with the monitoring overhead. Because the core mechanism is pure logical, Idle Page Tracking users might be able to implement the mechanism on their own, but it would be time consuming and the user/kernel context switching will still more frequent than that of DAMON. Also, the kernel subsystems cannot use the logic in this case. 3. Page granularity working set size detection. Until v22 of this patchset, this was categorized as the thing Idle Page Tracking could do better, because DAMON basically maintains additional metadata for each of the monitoring target regions. So, in the page granularity working set size detection use case, DAMON would incur (number of monitoring target pages * size of metadata) memory overhead. Size of the single metadata item is about 54 bytes, so assuming 4KB pages, about 1.3% of monitoring target pages will be additionally used. All essential metadata for Idle Page Tracking are embedded in 'struct page' and page table entries. Therefore, in this use case, only one counter variable for working set size accounting is required if Idle Page Tracking is used. There are more details to consider, but roughly speaking, this is true in most cases. However, the situation changed from v23. Now DAMON supports arbitrary types of monitoring targets, which don't use the metadata. Using that, DAMON can do the working set size detection with no additional space overhead but less user-kernel context switch. A first draft for the implementation of monitoring primitives for this usage is available in a DAMON development tree[1]. An RFC patchset for it based on this patchset will also be available soon. Since v24, the arbitrary type support is dropped from this patchset because this patchset doesn't introduce real use of the type. You can still get it from the DAMON development tree[2], though. [1] https://github.com/sjp38/linux/tree/damon/pgidle_hack [2] https://github.com/sjp38/linux/tree/damon/master 4. More future usecases While Idle Page Tracking has tight coupling with base primitives (PG_Idle and page table Accessed bits), DAMON is designed to be extensible for many use cases and address spaces. If you need some special address type or want to use special h/w access check primitives, you can write your own primitives for that and configure DAMON to use those. Therefore, if your use case could be changed a lot in future, using DAMON could be better. Can I use both Idle Page Tracking and DAMON? -------------------------------------------- Yes, though using them concurrently for overlapping memory regions could result in interference to each other. Nevertheless, such use case would be rare or makes no sense at all. Even in the case, the noise would bot be really significant. So, you can choose whatever you want depending on the characteristics of your use cases. More Information ================ We prepared a showcase web site[1] that you can get more information. There are - the official documentations[2], - the heatmap format dynamic access pattern of various realistic workloads for heap area[3], mmap()-ed area[4], and stack[5] area, - the dynamic working set size distribution[6] and chronological working set size changes[7], and - the latest performance test results[8]. [1] https://damonitor.github.io/_index [2] https://damonitor.github.io/doc/html/latest-damon [3] https://damonitor.github.io/test/result/visual/latest/rec.heatmap.0.png.html [4] https://damonitor.github.io/test/result/visual/latest/rec.heatmap.1.png.html [5] https://damonitor.github.io/test/result/visual/latest/rec.heatmap.2.png.html [6] https://damonitor.github.io/test/result/visual/latest/rec.wss_sz.png.html [7] https://damonitor.github.io/test/result/visual/latest/rec.wss_time.png.html [8] https://damonitor.github.io/test/result/perf/latest/html/index.html Baseline and Complete Git Trees =============================== The patches are based on the latest -mm tree, specifically v5.14-rc1-mmots-2021-07-15-18-47 of https://github.com/hnaz/linux-mm. You can also clone the complete git tree: $ git clone git://github.com/sjp38/linux -b damon/patches/v34 The web is also available: https://github.com/sjp38/linux/releases/tag/damon/patches/v34 Development Trees ----------------- There are a couple of trees for entire DAMON patchset series and features for future release. - For latest release: https://github.com/sjp38/linux/tree/damon/master - For next release: https://github.com/sjp38/linux/tree/damon/next Long-term Support Trees ----------------------- For people who want to test DAMON but using LTS kernels, there are another couple of trees based on two latest LTS kernels respectively and containing the 'damon/master' backports. - For v5.4.y: https://github.com/sjp38/linux/tree/damon/for-v5.4.y - For v5.10.y: https://github.com/sjp38/linux/tree/damon/for-v5.10.y Amazon Linux Kernel Trees ------------------------- DAMON is also merged in two public Amazon Linux kernel trees that based on v5.4.y[1] and v5.10.y[2]. [1] https://github.com/amazonlinux/linux/tree/amazon-5.4.y/master/mm/damon [2] https://github.com/amazonlinux/linux/tree/amazon-5.10.y/master/mm/damon Git Tree for Diff of Patches ============================ For easy review of diff between different versions of each patch, I prepared a git tree containing all versions of the DAMON patchset series: https://github.com/sjp38/damon-patches You can clone it and use 'diff' for easy review of changes between different versions of the patchset. For example: $ git clone https://github.com/sjp38/damon-patches && cd damon-patches $ diff -u damon/v33 damon/v34 Sequence Of Patches =================== First three patches implement the core logics of DAMON. The 1st patch introduces basic sampling based hotness monitoring for arbitrary types of targets. Following two patches implement the core mechanisms for control of overhead and accuracy, namely regions based sampling (patch 2) and adaptive regions adjustment (patch 3). Now the essential parts of DAMON is complete, but it cannot work unless someone provides monitoring primitives for a specific use case. The following two patches make it just work for virtual address spaces monitoring. The 4th patch makes 'PG_idle' can be used by DAMON and the 5th patch implements the virtual memory address space specific monitoring primitives using page table Accessed bits and the 'PG_idle' page flag. Now DAMON just works for virtual address space monitoring via the kernel space api. To let the user space users can use DAMON, following four patches add interfaces for them. The 6th patch adds a tracepoint for monitoring results. The 7th patch implements a DAMON application kernel module, namely damon-dbgfs, that simply wraps DAMON and exposes DAMON interface to the user space via the debugfs interface. The 8th patch further exports pid of monitoring thread (kdamond) to user space for easier cpu usage accounting, and the 9th patch makes the debugfs interface to support multiple contexts. Three patches for maintainability follows. The 10th patch adds documentations for both the user space and the kernel space. The 11th patch provides unit tests (based on the kunit) while the 12th patch adds user space tests (based on the kselftest). Finally, the last patch (13th) updates the MAINTAINERS file. This patch (of 13): DAMON is a data access monitoring framework for the Linux kernel. The core mechanisms of DAMON make it - accurate (the monitoring output is useful enough for DRAM level performance-centric memory management; It might be inappropriate for CPU cache levels, though), - light-weight (the monitoring overhead is normally low enough to be applied online), and - scalable (the upper-bound of the overhead is in constant range regardless of the size of target workloads). Using this framework, hence, we can easily write efficient kernel space data access monitoring applications. For example, the kernel's memory management mechanisms can make advanced decisions using this. Experimental data access aware optimization works that incurring high access monitoring overhead could again be implemented on top of this. Due to its simple and flexible interface, providing user space interface would be also easy. Then, user space users who have some special workloads can write personalized applications for better understanding and optimizations of their workloads and systems. === Nevertheless, this commit is defining and implementing only basic access check part without the overhead-accuracy handling core logic. The basic access check is as below. The output of DAMON says what memory regions are how frequently accessed for a given duration. The resolution of the access frequency is controlled by setting ``sampling interval`` and ``aggregation interval``. In detail, DAMON checks access to each page per ``sampling interval`` and aggregates the results. In other words, counts the number of the accesses to each region. After each ``aggregation interval`` passes, DAMON calls callback functions that previously registered by users so that users can read the aggregated results and then clears the results. This can be described in below simple pseudo-code:: init() while monitoring_on: for page in monitoring_target: if accessed(page): nr_accesses[page] += 1 if time() % aggregation_interval == 0: for callback in user_registered_callbacks: callback(monitoring_target, nr_accesses) for page in monitoring_target: nr_accesses[page] = 0 if time() % update_interval == 0: update() sleep(sampling interval) The target regions constructed at the beginning of the monitoring and updated after each ``regions_update_interval``, because the target regions could be dynamically changed (e.g., mmap() or memory hotplug). The monitoring overhead of this mechanism will arbitrarily increase as the size of the target workload grows. The basic monitoring primitives for actual access check and dynamic target regions construction aren't in the core part of DAMON. Instead, it allows users to implement their own primitives that are optimized for their use case and configure DAMON to use those. In other words, users cannot use current version of DAMON without some additional works. Following commits will implement the core mechanisms for the overhead-accuracy control and default primitives implementations. Link: https://lkml.kernel.org/r/20210716081449.22187-1-sj38.park@gmail.com Link: https://lkml.kernel.org/r/20210716081449.22187-2-sj38.park@gmail.com Signed-off-by: SeongJae Park <sjpark@amazon.de> Reviewed-by: Leonard Foerster <foersleo@amazon.de> Reviewed-by: Fernand Sieber <sieberf@amazon.com> Acked-by: Shakeel Butt <shakeelb@google.com> Cc: Jonathan Cameron <Jonathan.Cameron@huawei.com> Cc: Alexander Shishkin <alexander.shishkin@linux.intel.com> Cc: Amit Shah <amit@kernel.org> Cc: Benjamin Herrenschmidt <benh@kernel.crashing.org> Cc: Jonathan Corbet <corbet@lwn.net> Cc: David Hildenbrand <david@redhat.com> Cc: David Woodhouse <dwmw@amazon.com> Cc: Marco Elver <elver@google.com> Cc: Fan Du <fan.du@intel.com> Cc: Greg Kroah-Hartman <greg@kroah.com> Cc: Greg Thelen <gthelen@google.com> Cc: Joe Perches <joe@perches.com> Cc: Mel Gorman <mgorman@suse.de> Cc: Maximilian Heyne <mheyne@amazon.de> Cc: Minchan Kim <minchan@kernel.org> Cc: Ingo Molnar <mingo@redhat.com> Cc: Namhyung Kim <namhyung@kernel.org> Cc: Peter Zijlstra <peterz@infradead.org> Cc: Rik van Riel <riel@surriel.com> Cc: David Rientjes <rientjes@google.com> Cc: Steven Rostedt (VMware) <rostedt@goodmis.org> Cc: Shuah Khan <shuah@kernel.org> Cc: Vlastimil Babka <vbabka@suse.cz> Cc: Vladimir Davydov <vdavydov.dev@gmail.com> Cc: Brendan Higgins <brendanhiggins@google.com> Cc: Markus Boehme <markubo@amazon.de> Signed-off-by: Andrew Morton <akpm@linux-foundation.org> Signed-off-by: Linus Torvalds <torvalds@linux-foundation.org>
2021-09-08 10:56:28 +08:00
bool (*target_valid)(void *target);
void (*cleanup)(struct damon_ctx *context);
};
/**
* struct damon_callback - Monitoring events notification callbacks.
mm: introduce Data Access MONitor (DAMON) Patch series "Introduce Data Access MONitor (DAMON)", v34. Introduction ============ DAMON is a data access monitoring framework for the Linux kernel. The core mechanisms of DAMON called 'region based sampling' and 'adaptive regions adjustment' (refer to 'mechanisms.rst' in the 11th patch of this patchset for the detail) make it - accurate (The monitored information is useful for DRAM level memory management. It might not appropriate for Cache-level accuracy, though.), - light-weight (The monitoring overhead is low enough to be applied online while making no impact on the performance of the target workloads.), and - scalable (the upper-bound of the instrumentation overhead is controllable regardless of the size of target workloads.). Using this framework, therefore, several memory management mechanisms such as reclamation and THP can be optimized to aware real data access patterns. Experimental access pattern aware memory management optimization works that incurring high instrumentation overhead will be able to have another try. Though DAMON is for kernel subsystems, it can be easily exposed to the user space by writing a DAMON-wrapper kernel subsystem. Then, user space users who have some special workloads will be able to write personalized tools or applications for deeper understanding and specialized optimizations of their systems. DAMON is also merged in two public Amazon Linux kernel trees that based on v5.4.y[1] and v5.10.y[2]. [1] https://github.com/amazonlinux/linux/tree/amazon-5.4.y/master/mm/damon [2] https://github.com/amazonlinux/linux/tree/amazon-5.10.y/master/mm/damon The userspace tool[1] is available, released under GPLv2, and actively being maintained. I am also planning to implement another basic user interface in perf[2]. Also, the basic test suite for DAMON is available under GPLv2[3]. [1] https://github.com/awslabs/damo [2] https://lore.kernel.org/linux-mm/20210107120729.22328-1-sjpark@amazon.com/ [3] https://github.com/awslabs/damon-tests Long-term Plan -------------- DAMON is a part of a project called Data Access-aware Operating System (DAOS). As the name implies, I want to improve the performance and efficiency of systems using fine-grained data access patterns. The optimizations are for both kernel and user spaces. I will therefore modify or create kernel subsystems, export some of those to user space and implement user space library / tools. Below shows the layers and components for the project. --------------------------------------------------------------------------- Primitives: PTE Accessed bit, PG_idle, rmap, (Intel CMT), ... Framework: DAMON Features: DAMOS, virtual addr, physical addr, ... Applications: DAMON-debugfs, (DARC), ... ^^^^^^^^^^^^^^^^^^^^^^^ KERNEL SPACE ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ Raw Interface: debugfs, (sysfs), (damonfs), tracepoints, (sys_damon), ... vvvvvvvvvvvvvvvvvvvvvvv USER SPACE vvvvvvvvvvvvvvvvvvvvvvvvvvvvvvvv Library: (libdamon), ... Tools: DAMO, (perf), ... --------------------------------------------------------------------------- The components in parentheses or marked as '...' are not implemented yet but in the future plan. IOW, those are the TODO tasks of DAOS project. For more detail, please refer to the plans: https://lore.kernel.org/linux-mm/20201202082731.24828-1-sjpark@amazon.com/ Evaluations =========== We evaluated DAMON's overhead, monitoring quality and usefulness using 24 realistic workloads on my QEMU/KVM based virtual machine running a kernel that v24 DAMON patchset is applied. DAMON is lightweight. It increases system memory usage by 0.39% and slows target workloads down by 1.16%. DAMON is accurate and useful for memory management optimizations. An experimental DAMON-based operation scheme for THP, namely 'ethp', removes 76.15% of THP memory overheads while preserving 51.25% of THP speedup. Another experimental DAMON-based 'proactive reclamation' implementation, 'prcl', reduces 93.38% of residential sets and 23.63% of system memory footprint while incurring only 1.22% runtime overhead in the best case (parsec3/freqmine). NOTE that the experimental THP optimization and proactive reclamation are not for production but only for proof of concepts. Please refer to the official document[1] or "Documentation/admin-guide/mm: Add a document for DAMON" patch in this patchset for detailed evaluation setup and results. [1] https://damonitor.github.io/doc/html/latest-damon/admin-guide/mm/damon/eval.html Real-world User Story ===================== In summary, DAMON has used on production systems and proved its usefulness. DAMON as a profiler ------------------- We analyzed characteristics of a large scale production systems of our customers using DAMON. The systems utilize 70GB DRAM and 36 CPUs. From this, we were able to find interesting things below. There were obviously different access pattern under idle workload and active workload. Under the idle workload, it accessed large memory regions with low frequency, while the active workload accessed small memory regions with high freuqnecy. DAMON found a 7GB memory region that showing obviously high access frequency under the active workload. We believe this is the performance-effective working set and need to be protected. There was a 4KB memory region that showing highest access frequency under not only active but also idle workloads. We think this must be a hottest code section like thing that should never be paged out. For this analysis, DAMON used only 0.3-1% of single CPU time. Because we used recording-based analysis, it consumed about 3-12 MB of disk space per 20 minutes. This is only small amount of disk space, but we can further reduce the disk usage by using non-recording-based DAMON features. I'd like to argue that only DAMON can do such detailed analysis (finding 4KB highest region in 70GB memory) with the light overhead. DAMON as a system optimization tool ----------------------------------- We also found below potential performance problems on the systems and made DAMON-based solutions. The system doesn't want to make the workload suffer from the page reclamation and thus it utilizes enough DRAM but no swap device. However, we found the system is actively reclaiming file-backed pages, because the system has intensive file IO. The file IO turned out to be not performance critical for the workload, but the customer wanted to ensure performance critical file-backed pages like code section to not mistakenly be evicted. Using direct IO should or `mlock()` would be a straightforward solution, but modifying the user space code is not easy for the customer. Alternatively, we could use DAMON-based operation scheme[1]. By using it, we can ask DAMON to track access frequency of each region and make 'process_madvise(MADV_WILLNEED)[2]' call for regions having specific size and access frequency for a time interval. We also found the system is having high number of TLB misses. We tried 'always' THP enabled policy and it greatly reduced TLB misses, but the page reclamation also been more frequent due to the THP internal fragmentation caused memory bloat. We could try another DAMON-based operation scheme that applies 'MADV_HUGEPAGE' to memory regions having >=2MB size and high access frequency, while applying 'MADV_NOHUGEPAGE' to regions having <2MB size and low access frequency. We do not own the systems so we only reported the analysis results and possible optimization solutions to the customers. The customers satisfied about the analysis results and promised to try the optimization guides. [1] https://lore.kernel.org/linux-mm/20201006123931.5847-1-sjpark@amazon.com/ [2] https://lore.kernel.org/linux-api/20200622192900.22757-4-minchan@kernel.org/ Comparison with Idle Page Tracking ================================== Idle Page Tracking allows users to set and read idleness of pages using a bitmap file which represents each page with each bit of the file. One recommended usage of it is working set size detection. Users can do that by 1. find PFN of each page for workloads in interest, 2. set all the pages as idle by doing writes to the bitmap file, 3. wait until the workload accesses its working set, and 4. read the idleness of the pages again and count pages became not idle. NOTE: While Idle Page Tracking is for user space users, DAMON is primarily designed for kernel subsystems though it can easily exposed to the user space. Hence, this section only assumes such user space use of DAMON. For what use cases Idle Page Tracking would be better? ------------------------------------------------------ 1. Flexible usecases other than hotness monitoring. Because Idle Page Tracking allows users to control the primitive (Page idleness) by themselves, Idle Page Tracking users can do anything they want. Meanwhile, DAMON is primarily designed to monitor the hotness of each memory region. For this, DAMON asks users to provide sampling interval and aggregation interval. For the reason, there could be some use case that using Idle Page Tracking is simpler. 2. Physical memory monitoring. Idle Page Tracking receives PFN range as input, so natively supports physical memory monitoring. DAMON is designed to be extensible for multiple address spaces and use cases by implementing and using primitives for the given use case. Therefore, by theory, DAMON has no limitation in the type of target address space as long as primitives for the given address space exists. However, the default primitives introduced by this patchset supports only virtual address spaces. Therefore, for physical memory monitoring, you should implement your own primitives and use it, or simply use Idle Page Tracking. Nonetheless, RFC patchsets[1] for the physical memory address space primitives is already available. It also supports user memory same to Idle Page Tracking. [1] https://lore.kernel.org/linux-mm/20200831104730.28970-1-sjpark@amazon.com/ For what use cases DAMON is better? ----------------------------------- 1. Hotness Monitoring. Idle Page Tracking let users know only if a page frame is accessed or not. For hotness check, the user should write more code and use more memory. DAMON do that by itself. 2. Low Monitoring Overhead DAMON receives user's monitoring request with one step and then provide the results. So, roughly speaking, DAMON require only O(1) user/kernel context switches. In case of Idle Page Tracking, however, because the interface receives contiguous page frames, the number of user/kernel context switches increases as the monitoring target becomes complex and huge. As a result, the context switch overhead could be not negligible. Moreover, DAMON is born to handle with the monitoring overhead. Because the core mechanism is pure logical, Idle Page Tracking users might be able to implement the mechanism on their own, but it would be time consuming and the user/kernel context switching will still more frequent than that of DAMON. Also, the kernel subsystems cannot use the logic in this case. 3. Page granularity working set size detection. Until v22 of this patchset, this was categorized as the thing Idle Page Tracking could do better, because DAMON basically maintains additional metadata for each of the monitoring target regions. So, in the page granularity working set size detection use case, DAMON would incur (number of monitoring target pages * size of metadata) memory overhead. Size of the single metadata item is about 54 bytes, so assuming 4KB pages, about 1.3% of monitoring target pages will be additionally used. All essential metadata for Idle Page Tracking are embedded in 'struct page' and page table entries. Therefore, in this use case, only one counter variable for working set size accounting is required if Idle Page Tracking is used. There are more details to consider, but roughly speaking, this is true in most cases. However, the situation changed from v23. Now DAMON supports arbitrary types of monitoring targets, which don't use the metadata. Using that, DAMON can do the working set size detection with no additional space overhead but less user-kernel context switch. A first draft for the implementation of monitoring primitives for this usage is available in a DAMON development tree[1]. An RFC patchset for it based on this patchset will also be available soon. Since v24, the arbitrary type support is dropped from this patchset because this patchset doesn't introduce real use of the type. You can still get it from the DAMON development tree[2], though. [1] https://github.com/sjp38/linux/tree/damon/pgidle_hack [2] https://github.com/sjp38/linux/tree/damon/master 4. More future usecases While Idle Page Tracking has tight coupling with base primitives (PG_Idle and page table Accessed bits), DAMON is designed to be extensible for many use cases and address spaces. If you need some special address type or want to use special h/w access check primitives, you can write your own primitives for that and configure DAMON to use those. Therefore, if your use case could be changed a lot in future, using DAMON could be better. Can I use both Idle Page Tracking and DAMON? -------------------------------------------- Yes, though using them concurrently for overlapping memory regions could result in interference to each other. Nevertheless, such use case would be rare or makes no sense at all. Even in the case, the noise would bot be really significant. So, you can choose whatever you want depending on the characteristics of your use cases. More Information ================ We prepared a showcase web site[1] that you can get more information. There are - the official documentations[2], - the heatmap format dynamic access pattern of various realistic workloads for heap area[3], mmap()-ed area[4], and stack[5] area, - the dynamic working set size distribution[6] and chronological working set size changes[7], and - the latest performance test results[8]. [1] https://damonitor.github.io/_index [2] https://damonitor.github.io/doc/html/latest-damon [3] https://damonitor.github.io/test/result/visual/latest/rec.heatmap.0.png.html [4] https://damonitor.github.io/test/result/visual/latest/rec.heatmap.1.png.html [5] https://damonitor.github.io/test/result/visual/latest/rec.heatmap.2.png.html [6] https://damonitor.github.io/test/result/visual/latest/rec.wss_sz.png.html [7] https://damonitor.github.io/test/result/visual/latest/rec.wss_time.png.html [8] https://damonitor.github.io/test/result/perf/latest/html/index.html Baseline and Complete Git Trees =============================== The patches are based on the latest -mm tree, specifically v5.14-rc1-mmots-2021-07-15-18-47 of https://github.com/hnaz/linux-mm. You can also clone the complete git tree: $ git clone git://github.com/sjp38/linux -b damon/patches/v34 The web is also available: https://github.com/sjp38/linux/releases/tag/damon/patches/v34 Development Trees ----------------- There are a couple of trees for entire DAMON patchset series and features for future release. - For latest release: https://github.com/sjp38/linux/tree/damon/master - For next release: https://github.com/sjp38/linux/tree/damon/next Long-term Support Trees ----------------------- For people who want to test DAMON but using LTS kernels, there are another couple of trees based on two latest LTS kernels respectively and containing the 'damon/master' backports. - For v5.4.y: https://github.com/sjp38/linux/tree/damon/for-v5.4.y - For v5.10.y: https://github.com/sjp38/linux/tree/damon/for-v5.10.y Amazon Linux Kernel Trees ------------------------- DAMON is also merged in two public Amazon Linux kernel trees that based on v5.4.y[1] and v5.10.y[2]. [1] https://github.com/amazonlinux/linux/tree/amazon-5.4.y/master/mm/damon [2] https://github.com/amazonlinux/linux/tree/amazon-5.10.y/master/mm/damon Git Tree for Diff of Patches ============================ For easy review of diff between different versions of each patch, I prepared a git tree containing all versions of the DAMON patchset series: https://github.com/sjp38/damon-patches You can clone it and use 'diff' for easy review of changes between different versions of the patchset. For example: $ git clone https://github.com/sjp38/damon-patches && cd damon-patches $ diff -u damon/v33 damon/v34 Sequence Of Patches =================== First three patches implement the core logics of DAMON. The 1st patch introduces basic sampling based hotness monitoring for arbitrary types of targets. Following two patches implement the core mechanisms for control of overhead and accuracy, namely regions based sampling (patch 2) and adaptive regions adjustment (patch 3). Now the essential parts of DAMON is complete, but it cannot work unless someone provides monitoring primitives for a specific use case. The following two patches make it just work for virtual address spaces monitoring. The 4th patch makes 'PG_idle' can be used by DAMON and the 5th patch implements the virtual memory address space specific monitoring primitives using page table Accessed bits and the 'PG_idle' page flag. Now DAMON just works for virtual address space monitoring via the kernel space api. To let the user space users can use DAMON, following four patches add interfaces for them. The 6th patch adds a tracepoint for monitoring results. The 7th patch implements a DAMON application kernel module, namely damon-dbgfs, that simply wraps DAMON and exposes DAMON interface to the user space via the debugfs interface. The 8th patch further exports pid of monitoring thread (kdamond) to user space for easier cpu usage accounting, and the 9th patch makes the debugfs interface to support multiple contexts. Three patches for maintainability follows. The 10th patch adds documentations for both the user space and the kernel space. The 11th patch provides unit tests (based on the kunit) while the 12th patch adds user space tests (based on the kselftest). Finally, the last patch (13th) updates the MAINTAINERS file. This patch (of 13): DAMON is a data access monitoring framework for the Linux kernel. The core mechanisms of DAMON make it - accurate (the monitoring output is useful enough for DRAM level performance-centric memory management; It might be inappropriate for CPU cache levels, though), - light-weight (the monitoring overhead is normally low enough to be applied online), and - scalable (the upper-bound of the overhead is in constant range regardless of the size of target workloads). Using this framework, hence, we can easily write efficient kernel space data access monitoring applications. For example, the kernel's memory management mechanisms can make advanced decisions using this. Experimental data access aware optimization works that incurring high access monitoring overhead could again be implemented on top of this. Due to its simple and flexible interface, providing user space interface would be also easy. Then, user space users who have some special workloads can write personalized applications for better understanding and optimizations of their workloads and systems. === Nevertheless, this commit is defining and implementing only basic access check part without the overhead-accuracy handling core logic. The basic access check is as below. The output of DAMON says what memory regions are how frequently accessed for a given duration. The resolution of the access frequency is controlled by setting ``sampling interval`` and ``aggregation interval``. In detail, DAMON checks access to each page per ``sampling interval`` and aggregates the results. In other words, counts the number of the accesses to each region. After each ``aggregation interval`` passes, DAMON calls callback functions that previously registered by users so that users can read the aggregated results and then clears the results. This can be described in below simple pseudo-code:: init() while monitoring_on: for page in monitoring_target: if accessed(page): nr_accesses[page] += 1 if time() % aggregation_interval == 0: for callback in user_registered_callbacks: callback(monitoring_target, nr_accesses) for page in monitoring_target: nr_accesses[page] = 0 if time() % update_interval == 0: update() sleep(sampling interval) The target regions constructed at the beginning of the monitoring and updated after each ``regions_update_interval``, because the target regions could be dynamically changed (e.g., mmap() or memory hotplug). The monitoring overhead of this mechanism will arbitrarily increase as the size of the target workload grows. The basic monitoring primitives for actual access check and dynamic target regions construction aren't in the core part of DAMON. Instead, it allows users to implement their own primitives that are optimized for their use case and configure DAMON to use those. In other words, users cannot use current version of DAMON without some additional works. Following commits will implement the core mechanisms for the overhead-accuracy control and default primitives implementations. Link: https://lkml.kernel.org/r/20210716081449.22187-1-sj38.park@gmail.com Link: https://lkml.kernel.org/r/20210716081449.22187-2-sj38.park@gmail.com Signed-off-by: SeongJae Park <sjpark@amazon.de> Reviewed-by: Leonard Foerster <foersleo@amazon.de> Reviewed-by: Fernand Sieber <sieberf@amazon.com> Acked-by: Shakeel Butt <shakeelb@google.com> Cc: Jonathan Cameron <Jonathan.Cameron@huawei.com> Cc: Alexander Shishkin <alexander.shishkin@linux.intel.com> Cc: Amit Shah <amit@kernel.org> Cc: Benjamin Herrenschmidt <benh@kernel.crashing.org> Cc: Jonathan Corbet <corbet@lwn.net> Cc: David Hildenbrand <david@redhat.com> Cc: David Woodhouse <dwmw@amazon.com> Cc: Marco Elver <elver@google.com> Cc: Fan Du <fan.du@intel.com> Cc: Greg Kroah-Hartman <greg@kroah.com> Cc: Greg Thelen <gthelen@google.com> Cc: Joe Perches <joe@perches.com> Cc: Mel Gorman <mgorman@suse.de> Cc: Maximilian Heyne <mheyne@amazon.de> Cc: Minchan Kim <minchan@kernel.org> Cc: Ingo Molnar <mingo@redhat.com> Cc: Namhyung Kim <namhyung@kernel.org> Cc: Peter Zijlstra <peterz@infradead.org> Cc: Rik van Riel <riel@surriel.com> Cc: David Rientjes <rientjes@google.com> Cc: Steven Rostedt (VMware) <rostedt@goodmis.org> Cc: Shuah Khan <shuah@kernel.org> Cc: Vlastimil Babka <vbabka@suse.cz> Cc: Vladimir Davydov <vdavydov.dev@gmail.com> Cc: Brendan Higgins <brendanhiggins@google.com> Cc: Markus Boehme <markubo@amazon.de> Signed-off-by: Andrew Morton <akpm@linux-foundation.org> Signed-off-by: Linus Torvalds <torvalds@linux-foundation.org>
2021-09-08 10:56:28 +08:00
*
* @before_start: Called before starting the monitoring.
mm/damon/core: add a new callback for watermarks checks Patch series "mm/damon: Support online tuning". Effects of DAMON and DAMON-based Operation Schemes highly depends on the configurations. Wrong configurations could even result in unexpected efficiency degradations. For finding a best configuration, repeating incremental configuration changes and results measurements, in other words, online tuning, could be helpful. Nevertheless, DAMON kernel API supports only restrictive online tuning. Worse yet, the sysfs-based DAMON user interface doesn't support online tuning at all. DAMON_RECLAIM also doesn't support online tuning. This patchset makes the DAMON kernel API, DAMON sysfs interface, and DAMON_RECLAIM supports online tuning. Sequence of patches ------------------- First two patches enhance DAMON online tuning for kernel API users. Specifically, patch 1 let kernel API users to be able to do DAMON online tuning without a restriction, and patch 2 makes error handling easier. Following seven patches (patches 3-9) refactor code for better readability and easier reuse of code fragments that will be useful for online tuning support. Patch 10 introduces DAMON callback based user request handling structure for DAMON sysfs interface, and patch 11 enables DAMON online tuning via DAMON sysfs interface. Documentation patch (patch 12) for usage of it follows. Patch 13 enables online tuning of DAMON_RECLAIM and finally patch 14 documents the DAMON_RECLAIM online tuning usage. This patch (of 14): For updating input parameters for running DAMON contexts, DAMON kernel API users can use the contexts' callbacks, as it is the safe place for context internal data accesses. When the context has DAMON-based operation schemes and all schemes are deactivated due to their watermarks, however, DAMON does nothing but only watermarks checks. As a result, no callbacks will be called back, and therefore the kernel API users cannot update the input parameters including monitoring attributes, DAMON-based operation schemes, and watermarks. To let users easily update such DAMON input parameters in such a case, this commit adds a new callback, 'after_wmarks_check()'. It will be called after each watermarks check. Users can do the online input parameters update in the callback even under the schemes deactivated case. Link: https://lkml.kernel.org/r/20220429160606.127307-2-sj@kernel.org Signed-off-by: SeongJae Park <sj@kernel.org> Signed-off-by: Andrew Morton <akpm@linux-foundation.org>
2022-05-10 09:20:54 +08:00
* @after_wmarks_check: Called after each schemes' watermarks check.
mm: introduce Data Access MONitor (DAMON) Patch series "Introduce Data Access MONitor (DAMON)", v34. Introduction ============ DAMON is a data access monitoring framework for the Linux kernel. The core mechanisms of DAMON called 'region based sampling' and 'adaptive regions adjustment' (refer to 'mechanisms.rst' in the 11th patch of this patchset for the detail) make it - accurate (The monitored information is useful for DRAM level memory management. It might not appropriate for Cache-level accuracy, though.), - light-weight (The monitoring overhead is low enough to be applied online while making no impact on the performance of the target workloads.), and - scalable (the upper-bound of the instrumentation overhead is controllable regardless of the size of target workloads.). Using this framework, therefore, several memory management mechanisms such as reclamation and THP can be optimized to aware real data access patterns. Experimental access pattern aware memory management optimization works that incurring high instrumentation overhead will be able to have another try. Though DAMON is for kernel subsystems, it can be easily exposed to the user space by writing a DAMON-wrapper kernel subsystem. Then, user space users who have some special workloads will be able to write personalized tools or applications for deeper understanding and specialized optimizations of their systems. DAMON is also merged in two public Amazon Linux kernel trees that based on v5.4.y[1] and v5.10.y[2]. [1] https://github.com/amazonlinux/linux/tree/amazon-5.4.y/master/mm/damon [2] https://github.com/amazonlinux/linux/tree/amazon-5.10.y/master/mm/damon The userspace tool[1] is available, released under GPLv2, and actively being maintained. I am also planning to implement another basic user interface in perf[2]. Also, the basic test suite for DAMON is available under GPLv2[3]. [1] https://github.com/awslabs/damo [2] https://lore.kernel.org/linux-mm/20210107120729.22328-1-sjpark@amazon.com/ [3] https://github.com/awslabs/damon-tests Long-term Plan -------------- DAMON is a part of a project called Data Access-aware Operating System (DAOS). As the name implies, I want to improve the performance and efficiency of systems using fine-grained data access patterns. The optimizations are for both kernel and user spaces. I will therefore modify or create kernel subsystems, export some of those to user space and implement user space library / tools. Below shows the layers and components for the project. --------------------------------------------------------------------------- Primitives: PTE Accessed bit, PG_idle, rmap, (Intel CMT), ... Framework: DAMON Features: DAMOS, virtual addr, physical addr, ... Applications: DAMON-debugfs, (DARC), ... ^^^^^^^^^^^^^^^^^^^^^^^ KERNEL SPACE ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ Raw Interface: debugfs, (sysfs), (damonfs), tracepoints, (sys_damon), ... vvvvvvvvvvvvvvvvvvvvvvv USER SPACE vvvvvvvvvvvvvvvvvvvvvvvvvvvvvvvv Library: (libdamon), ... Tools: DAMO, (perf), ... --------------------------------------------------------------------------- The components in parentheses or marked as '...' are not implemented yet but in the future plan. IOW, those are the TODO tasks of DAOS project. For more detail, please refer to the plans: https://lore.kernel.org/linux-mm/20201202082731.24828-1-sjpark@amazon.com/ Evaluations =========== We evaluated DAMON's overhead, monitoring quality and usefulness using 24 realistic workloads on my QEMU/KVM based virtual machine running a kernel that v24 DAMON patchset is applied. DAMON is lightweight. It increases system memory usage by 0.39% and slows target workloads down by 1.16%. DAMON is accurate and useful for memory management optimizations. An experimental DAMON-based operation scheme for THP, namely 'ethp', removes 76.15% of THP memory overheads while preserving 51.25% of THP speedup. Another experimental DAMON-based 'proactive reclamation' implementation, 'prcl', reduces 93.38% of residential sets and 23.63% of system memory footprint while incurring only 1.22% runtime overhead in the best case (parsec3/freqmine). NOTE that the experimental THP optimization and proactive reclamation are not for production but only for proof of concepts. Please refer to the official document[1] or "Documentation/admin-guide/mm: Add a document for DAMON" patch in this patchset for detailed evaluation setup and results. [1] https://damonitor.github.io/doc/html/latest-damon/admin-guide/mm/damon/eval.html Real-world User Story ===================== In summary, DAMON has used on production systems and proved its usefulness. DAMON as a profiler ------------------- We analyzed characteristics of a large scale production systems of our customers using DAMON. The systems utilize 70GB DRAM and 36 CPUs. From this, we were able to find interesting things below. There were obviously different access pattern under idle workload and active workload. Under the idle workload, it accessed large memory regions with low frequency, while the active workload accessed small memory regions with high freuqnecy. DAMON found a 7GB memory region that showing obviously high access frequency under the active workload. We believe this is the performance-effective working set and need to be protected. There was a 4KB memory region that showing highest access frequency under not only active but also idle workloads. We think this must be a hottest code section like thing that should never be paged out. For this analysis, DAMON used only 0.3-1% of single CPU time. Because we used recording-based analysis, it consumed about 3-12 MB of disk space per 20 minutes. This is only small amount of disk space, but we can further reduce the disk usage by using non-recording-based DAMON features. I'd like to argue that only DAMON can do such detailed analysis (finding 4KB highest region in 70GB memory) with the light overhead. DAMON as a system optimization tool ----------------------------------- We also found below potential performance problems on the systems and made DAMON-based solutions. The system doesn't want to make the workload suffer from the page reclamation and thus it utilizes enough DRAM but no swap device. However, we found the system is actively reclaiming file-backed pages, because the system has intensive file IO. The file IO turned out to be not performance critical for the workload, but the customer wanted to ensure performance critical file-backed pages like code section to not mistakenly be evicted. Using direct IO should or `mlock()` would be a straightforward solution, but modifying the user space code is not easy for the customer. Alternatively, we could use DAMON-based operation scheme[1]. By using it, we can ask DAMON to track access frequency of each region and make 'process_madvise(MADV_WILLNEED)[2]' call for regions having specific size and access frequency for a time interval. We also found the system is having high number of TLB misses. We tried 'always' THP enabled policy and it greatly reduced TLB misses, but the page reclamation also been more frequent due to the THP internal fragmentation caused memory bloat. We could try another DAMON-based operation scheme that applies 'MADV_HUGEPAGE' to memory regions having >=2MB size and high access frequency, while applying 'MADV_NOHUGEPAGE' to regions having <2MB size and low access frequency. We do not own the systems so we only reported the analysis results and possible optimization solutions to the customers. The customers satisfied about the analysis results and promised to try the optimization guides. [1] https://lore.kernel.org/linux-mm/20201006123931.5847-1-sjpark@amazon.com/ [2] https://lore.kernel.org/linux-api/20200622192900.22757-4-minchan@kernel.org/ Comparison with Idle Page Tracking ================================== Idle Page Tracking allows users to set and read idleness of pages using a bitmap file which represents each page with each bit of the file. One recommended usage of it is working set size detection. Users can do that by 1. find PFN of each page for workloads in interest, 2. set all the pages as idle by doing writes to the bitmap file, 3. wait until the workload accesses its working set, and 4. read the idleness of the pages again and count pages became not idle. NOTE: While Idle Page Tracking is for user space users, DAMON is primarily designed for kernel subsystems though it can easily exposed to the user space. Hence, this section only assumes such user space use of DAMON. For what use cases Idle Page Tracking would be better? ------------------------------------------------------ 1. Flexible usecases other than hotness monitoring. Because Idle Page Tracking allows users to control the primitive (Page idleness) by themselves, Idle Page Tracking users can do anything they want. Meanwhile, DAMON is primarily designed to monitor the hotness of each memory region. For this, DAMON asks users to provide sampling interval and aggregation interval. For the reason, there could be some use case that using Idle Page Tracking is simpler. 2. Physical memory monitoring. Idle Page Tracking receives PFN range as input, so natively supports physical memory monitoring. DAMON is designed to be extensible for multiple address spaces and use cases by implementing and using primitives for the given use case. Therefore, by theory, DAMON has no limitation in the type of target address space as long as primitives for the given address space exists. However, the default primitives introduced by this patchset supports only virtual address spaces. Therefore, for physical memory monitoring, you should implement your own primitives and use it, or simply use Idle Page Tracking. Nonetheless, RFC patchsets[1] for the physical memory address space primitives is already available. It also supports user memory same to Idle Page Tracking. [1] https://lore.kernel.org/linux-mm/20200831104730.28970-1-sjpark@amazon.com/ For what use cases DAMON is better? ----------------------------------- 1. Hotness Monitoring. Idle Page Tracking let users know only if a page frame is accessed or not. For hotness check, the user should write more code and use more memory. DAMON do that by itself. 2. Low Monitoring Overhead DAMON receives user's monitoring request with one step and then provide the results. So, roughly speaking, DAMON require only O(1) user/kernel context switches. In case of Idle Page Tracking, however, because the interface receives contiguous page frames, the number of user/kernel context switches increases as the monitoring target becomes complex and huge. As a result, the context switch overhead could be not negligible. Moreover, DAMON is born to handle with the monitoring overhead. Because the core mechanism is pure logical, Idle Page Tracking users might be able to implement the mechanism on their own, but it would be time consuming and the user/kernel context switching will still more frequent than that of DAMON. Also, the kernel subsystems cannot use the logic in this case. 3. Page granularity working set size detection. Until v22 of this patchset, this was categorized as the thing Idle Page Tracking could do better, because DAMON basically maintains additional metadata for each of the monitoring target regions. So, in the page granularity working set size detection use case, DAMON would incur (number of monitoring target pages * size of metadata) memory overhead. Size of the single metadata item is about 54 bytes, so assuming 4KB pages, about 1.3% of monitoring target pages will be additionally used. All essential metadata for Idle Page Tracking are embedded in 'struct page' and page table entries. Therefore, in this use case, only one counter variable for working set size accounting is required if Idle Page Tracking is used. There are more details to consider, but roughly speaking, this is true in most cases. However, the situation changed from v23. Now DAMON supports arbitrary types of monitoring targets, which don't use the metadata. Using that, DAMON can do the working set size detection with no additional space overhead but less user-kernel context switch. A first draft for the implementation of monitoring primitives for this usage is available in a DAMON development tree[1]. An RFC patchset for it based on this patchset will also be available soon. Since v24, the arbitrary type support is dropped from this patchset because this patchset doesn't introduce real use of the type. You can still get it from the DAMON development tree[2], though. [1] https://github.com/sjp38/linux/tree/damon/pgidle_hack [2] https://github.com/sjp38/linux/tree/damon/master 4. More future usecases While Idle Page Tracking has tight coupling with base primitives (PG_Idle and page table Accessed bits), DAMON is designed to be extensible for many use cases and address spaces. If you need some special address type or want to use special h/w access check primitives, you can write your own primitives for that and configure DAMON to use those. Therefore, if your use case could be changed a lot in future, using DAMON could be better. Can I use both Idle Page Tracking and DAMON? -------------------------------------------- Yes, though using them concurrently for overlapping memory regions could result in interference to each other. Nevertheless, such use case would be rare or makes no sense at all. Even in the case, the noise would bot be really significant. So, you can choose whatever you want depending on the characteristics of your use cases. More Information ================ We prepared a showcase web site[1] that you can get more information. There are - the official documentations[2], - the heatmap format dynamic access pattern of various realistic workloads for heap area[3], mmap()-ed area[4], and stack[5] area, - the dynamic working set size distribution[6] and chronological working set size changes[7], and - the latest performance test results[8]. [1] https://damonitor.github.io/_index [2] https://damonitor.github.io/doc/html/latest-damon [3] https://damonitor.github.io/test/result/visual/latest/rec.heatmap.0.png.html [4] https://damonitor.github.io/test/result/visual/latest/rec.heatmap.1.png.html [5] https://damonitor.github.io/test/result/visual/latest/rec.heatmap.2.png.html [6] https://damonitor.github.io/test/result/visual/latest/rec.wss_sz.png.html [7] https://damonitor.github.io/test/result/visual/latest/rec.wss_time.png.html [8] https://damonitor.github.io/test/result/perf/latest/html/index.html Baseline and Complete Git Trees =============================== The patches are based on the latest -mm tree, specifically v5.14-rc1-mmots-2021-07-15-18-47 of https://github.com/hnaz/linux-mm. You can also clone the complete git tree: $ git clone git://github.com/sjp38/linux -b damon/patches/v34 The web is also available: https://github.com/sjp38/linux/releases/tag/damon/patches/v34 Development Trees ----------------- There are a couple of trees for entire DAMON patchset series and features for future release. - For latest release: https://github.com/sjp38/linux/tree/damon/master - For next release: https://github.com/sjp38/linux/tree/damon/next Long-term Support Trees ----------------------- For people who want to test DAMON but using LTS kernels, there are another couple of trees based on two latest LTS kernels respectively and containing the 'damon/master' backports. - For v5.4.y: https://github.com/sjp38/linux/tree/damon/for-v5.4.y - For v5.10.y: https://github.com/sjp38/linux/tree/damon/for-v5.10.y Amazon Linux Kernel Trees ------------------------- DAMON is also merged in two public Amazon Linux kernel trees that based on v5.4.y[1] and v5.10.y[2]. [1] https://github.com/amazonlinux/linux/tree/amazon-5.4.y/master/mm/damon [2] https://github.com/amazonlinux/linux/tree/amazon-5.10.y/master/mm/damon Git Tree for Diff of Patches ============================ For easy review of diff between different versions of each patch, I prepared a git tree containing all versions of the DAMON patchset series: https://github.com/sjp38/damon-patches You can clone it and use 'diff' for easy review of changes between different versions of the patchset. For example: $ git clone https://github.com/sjp38/damon-patches && cd damon-patches $ diff -u damon/v33 damon/v34 Sequence Of Patches =================== First three patches implement the core logics of DAMON. The 1st patch introduces basic sampling based hotness monitoring for arbitrary types of targets. Following two patches implement the core mechanisms for control of overhead and accuracy, namely regions based sampling (patch 2) and adaptive regions adjustment (patch 3). Now the essential parts of DAMON is complete, but it cannot work unless someone provides monitoring primitives for a specific use case. The following two patches make it just work for virtual address spaces monitoring. The 4th patch makes 'PG_idle' can be used by DAMON and the 5th patch implements the virtual memory address space specific monitoring primitives using page table Accessed bits and the 'PG_idle' page flag. Now DAMON just works for virtual address space monitoring via the kernel space api. To let the user space users can use DAMON, following four patches add interfaces for them. The 6th patch adds a tracepoint for monitoring results. The 7th patch implements a DAMON application kernel module, namely damon-dbgfs, that simply wraps DAMON and exposes DAMON interface to the user space via the debugfs interface. The 8th patch further exports pid of monitoring thread (kdamond) to user space for easier cpu usage accounting, and the 9th patch makes the debugfs interface to support multiple contexts. Three patches for maintainability follows. The 10th patch adds documentations for both the user space and the kernel space. The 11th patch provides unit tests (based on the kunit) while the 12th patch adds user space tests (based on the kselftest). Finally, the last patch (13th) updates the MAINTAINERS file. This patch (of 13): DAMON is a data access monitoring framework for the Linux kernel. The core mechanisms of DAMON make it - accurate (the monitoring output is useful enough for DRAM level performance-centric memory management; It might be inappropriate for CPU cache levels, though), - light-weight (the monitoring overhead is normally low enough to be applied online), and - scalable (the upper-bound of the overhead is in constant range regardless of the size of target workloads). Using this framework, hence, we can easily write efficient kernel space data access monitoring applications. For example, the kernel's memory management mechanisms can make advanced decisions using this. Experimental data access aware optimization works that incurring high access monitoring overhead could again be implemented on top of this. Due to its simple and flexible interface, providing user space interface would be also easy. Then, user space users who have some special workloads can write personalized applications for better understanding and optimizations of their workloads and systems. === Nevertheless, this commit is defining and implementing only basic access check part without the overhead-accuracy handling core logic. The basic access check is as below. The output of DAMON says what memory regions are how frequently accessed for a given duration. The resolution of the access frequency is controlled by setting ``sampling interval`` and ``aggregation interval``. In detail, DAMON checks access to each page per ``sampling interval`` and aggregates the results. In other words, counts the number of the accesses to each region. After each ``aggregation interval`` passes, DAMON calls callback functions that previously registered by users so that users can read the aggregated results and then clears the results. This can be described in below simple pseudo-code:: init() while monitoring_on: for page in monitoring_target: if accessed(page): nr_accesses[page] += 1 if time() % aggregation_interval == 0: for callback in user_registered_callbacks: callback(monitoring_target, nr_accesses) for page in monitoring_target: nr_accesses[page] = 0 if time() % update_interval == 0: update() sleep(sampling interval) The target regions constructed at the beginning of the monitoring and updated after each ``regions_update_interval``, because the target regions could be dynamically changed (e.g., mmap() or memory hotplug). The monitoring overhead of this mechanism will arbitrarily increase as the size of the target workload grows. The basic monitoring primitives for actual access check and dynamic target regions construction aren't in the core part of DAMON. Instead, it allows users to implement their own primitives that are optimized for their use case and configure DAMON to use those. In other words, users cannot use current version of DAMON without some additional works. Following commits will implement the core mechanisms for the overhead-accuracy control and default primitives implementations. Link: https://lkml.kernel.org/r/20210716081449.22187-1-sj38.park@gmail.com Link: https://lkml.kernel.org/r/20210716081449.22187-2-sj38.park@gmail.com Signed-off-by: SeongJae Park <sjpark@amazon.de> Reviewed-by: Leonard Foerster <foersleo@amazon.de> Reviewed-by: Fernand Sieber <sieberf@amazon.com> Acked-by: Shakeel Butt <shakeelb@google.com> Cc: Jonathan Cameron <Jonathan.Cameron@huawei.com> Cc: Alexander Shishkin <alexander.shishkin@linux.intel.com> Cc: Amit Shah <amit@kernel.org> Cc: Benjamin Herrenschmidt <benh@kernel.crashing.org> Cc: Jonathan Corbet <corbet@lwn.net> Cc: David Hildenbrand <david@redhat.com> Cc: David Woodhouse <dwmw@amazon.com> Cc: Marco Elver <elver@google.com> Cc: Fan Du <fan.du@intel.com> Cc: Greg Kroah-Hartman <greg@kroah.com> Cc: Greg Thelen <gthelen@google.com> Cc: Joe Perches <joe@perches.com> Cc: Mel Gorman <mgorman@suse.de> Cc: Maximilian Heyne <mheyne@amazon.de> Cc: Minchan Kim <minchan@kernel.org> Cc: Ingo Molnar <mingo@redhat.com> Cc: Namhyung Kim <namhyung@kernel.org> Cc: Peter Zijlstra <peterz@infradead.org> Cc: Rik van Riel <riel@surriel.com> Cc: David Rientjes <rientjes@google.com> Cc: Steven Rostedt (VMware) <rostedt@goodmis.org> Cc: Shuah Khan <shuah@kernel.org> Cc: Vlastimil Babka <vbabka@suse.cz> Cc: Vladimir Davydov <vdavydov.dev@gmail.com> Cc: Brendan Higgins <brendanhiggins@google.com> Cc: Markus Boehme <markubo@amazon.de> Signed-off-by: Andrew Morton <akpm@linux-foundation.org> Signed-off-by: Linus Torvalds <torvalds@linux-foundation.org>
2021-09-08 10:56:28 +08:00
* @after_sampling: Called after each sampling.
* @after_aggregation: Called after each aggregation.
* @before_terminate: Called before terminating the monitoring.
* @private: User private data.
*
* The monitoring thread (&damon_ctx.kdamond) calls @before_start and
* @before_terminate just before starting and finishing the monitoring,
* respectively. Therefore, those are good places for installing and cleaning
* @private.
*
mm/damon/core: add a new callback for watermarks checks Patch series "mm/damon: Support online tuning". Effects of DAMON and DAMON-based Operation Schemes highly depends on the configurations. Wrong configurations could even result in unexpected efficiency degradations. For finding a best configuration, repeating incremental configuration changes and results measurements, in other words, online tuning, could be helpful. Nevertheless, DAMON kernel API supports only restrictive online tuning. Worse yet, the sysfs-based DAMON user interface doesn't support online tuning at all. DAMON_RECLAIM also doesn't support online tuning. This patchset makes the DAMON kernel API, DAMON sysfs interface, and DAMON_RECLAIM supports online tuning. Sequence of patches ------------------- First two patches enhance DAMON online tuning for kernel API users. Specifically, patch 1 let kernel API users to be able to do DAMON online tuning without a restriction, and patch 2 makes error handling easier. Following seven patches (patches 3-9) refactor code for better readability and easier reuse of code fragments that will be useful for online tuning support. Patch 10 introduces DAMON callback based user request handling structure for DAMON sysfs interface, and patch 11 enables DAMON online tuning via DAMON sysfs interface. Documentation patch (patch 12) for usage of it follows. Patch 13 enables online tuning of DAMON_RECLAIM and finally patch 14 documents the DAMON_RECLAIM online tuning usage. This patch (of 14): For updating input parameters for running DAMON contexts, DAMON kernel API users can use the contexts' callbacks, as it is the safe place for context internal data accesses. When the context has DAMON-based operation schemes and all schemes are deactivated due to their watermarks, however, DAMON does nothing but only watermarks checks. As a result, no callbacks will be called back, and therefore the kernel API users cannot update the input parameters including monitoring attributes, DAMON-based operation schemes, and watermarks. To let users easily update such DAMON input parameters in such a case, this commit adds a new callback, 'after_wmarks_check()'. It will be called after each watermarks check. Users can do the online input parameters update in the callback even under the schemes deactivated case. Link: https://lkml.kernel.org/r/20220429160606.127307-2-sj@kernel.org Signed-off-by: SeongJae Park <sj@kernel.org> Signed-off-by: Andrew Morton <akpm@linux-foundation.org>
2022-05-10 09:20:54 +08:00
* The monitoring thread calls @after_wmarks_check after each DAMON-based
* operation schemes' watermarks check. If users need to make changes to the
* attributes of the monitoring context while it's deactivated due to the
* watermarks, this is the good place to do.
*
mm: introduce Data Access MONitor (DAMON) Patch series "Introduce Data Access MONitor (DAMON)", v34. Introduction ============ DAMON is a data access monitoring framework for the Linux kernel. The core mechanisms of DAMON called 'region based sampling' and 'adaptive regions adjustment' (refer to 'mechanisms.rst' in the 11th patch of this patchset for the detail) make it - accurate (The monitored information is useful for DRAM level memory management. It might not appropriate for Cache-level accuracy, though.), - light-weight (The monitoring overhead is low enough to be applied online while making no impact on the performance of the target workloads.), and - scalable (the upper-bound of the instrumentation overhead is controllable regardless of the size of target workloads.). Using this framework, therefore, several memory management mechanisms such as reclamation and THP can be optimized to aware real data access patterns. Experimental access pattern aware memory management optimization works that incurring high instrumentation overhead will be able to have another try. Though DAMON is for kernel subsystems, it can be easily exposed to the user space by writing a DAMON-wrapper kernel subsystem. Then, user space users who have some special workloads will be able to write personalized tools or applications for deeper understanding and specialized optimizations of their systems. DAMON is also merged in two public Amazon Linux kernel trees that based on v5.4.y[1] and v5.10.y[2]. [1] https://github.com/amazonlinux/linux/tree/amazon-5.4.y/master/mm/damon [2] https://github.com/amazonlinux/linux/tree/amazon-5.10.y/master/mm/damon The userspace tool[1] is available, released under GPLv2, and actively being maintained. I am also planning to implement another basic user interface in perf[2]. Also, the basic test suite for DAMON is available under GPLv2[3]. [1] https://github.com/awslabs/damo [2] https://lore.kernel.org/linux-mm/20210107120729.22328-1-sjpark@amazon.com/ [3] https://github.com/awslabs/damon-tests Long-term Plan -------------- DAMON is a part of a project called Data Access-aware Operating System (DAOS). As the name implies, I want to improve the performance and efficiency of systems using fine-grained data access patterns. The optimizations are for both kernel and user spaces. I will therefore modify or create kernel subsystems, export some of those to user space and implement user space library / tools. Below shows the layers and components for the project. --------------------------------------------------------------------------- Primitives: PTE Accessed bit, PG_idle, rmap, (Intel CMT), ... Framework: DAMON Features: DAMOS, virtual addr, physical addr, ... Applications: DAMON-debugfs, (DARC), ... ^^^^^^^^^^^^^^^^^^^^^^^ KERNEL SPACE ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ Raw Interface: debugfs, (sysfs), (damonfs), tracepoints, (sys_damon), ... vvvvvvvvvvvvvvvvvvvvvvv USER SPACE vvvvvvvvvvvvvvvvvvvvvvvvvvvvvvvv Library: (libdamon), ... Tools: DAMO, (perf), ... --------------------------------------------------------------------------- The components in parentheses or marked as '...' are not implemented yet but in the future plan. IOW, those are the TODO tasks of DAOS project. For more detail, please refer to the plans: https://lore.kernel.org/linux-mm/20201202082731.24828-1-sjpark@amazon.com/ Evaluations =========== We evaluated DAMON's overhead, monitoring quality and usefulness using 24 realistic workloads on my QEMU/KVM based virtual machine running a kernel that v24 DAMON patchset is applied. DAMON is lightweight. It increases system memory usage by 0.39% and slows target workloads down by 1.16%. DAMON is accurate and useful for memory management optimizations. An experimental DAMON-based operation scheme for THP, namely 'ethp', removes 76.15% of THP memory overheads while preserving 51.25% of THP speedup. Another experimental DAMON-based 'proactive reclamation' implementation, 'prcl', reduces 93.38% of residential sets and 23.63% of system memory footprint while incurring only 1.22% runtime overhead in the best case (parsec3/freqmine). NOTE that the experimental THP optimization and proactive reclamation are not for production but only for proof of concepts. Please refer to the official document[1] or "Documentation/admin-guide/mm: Add a document for DAMON" patch in this patchset for detailed evaluation setup and results. [1] https://damonitor.github.io/doc/html/latest-damon/admin-guide/mm/damon/eval.html Real-world User Story ===================== In summary, DAMON has used on production systems and proved its usefulness. DAMON as a profiler ------------------- We analyzed characteristics of a large scale production systems of our customers using DAMON. The systems utilize 70GB DRAM and 36 CPUs. From this, we were able to find interesting things below. There were obviously different access pattern under idle workload and active workload. Under the idle workload, it accessed large memory regions with low frequency, while the active workload accessed small memory regions with high freuqnecy. DAMON found a 7GB memory region that showing obviously high access frequency under the active workload. We believe this is the performance-effective working set and need to be protected. There was a 4KB memory region that showing highest access frequency under not only active but also idle workloads. We think this must be a hottest code section like thing that should never be paged out. For this analysis, DAMON used only 0.3-1% of single CPU time. Because we used recording-based analysis, it consumed about 3-12 MB of disk space per 20 minutes. This is only small amount of disk space, but we can further reduce the disk usage by using non-recording-based DAMON features. I'd like to argue that only DAMON can do such detailed analysis (finding 4KB highest region in 70GB memory) with the light overhead. DAMON as a system optimization tool ----------------------------------- We also found below potential performance problems on the systems and made DAMON-based solutions. The system doesn't want to make the workload suffer from the page reclamation and thus it utilizes enough DRAM but no swap device. However, we found the system is actively reclaiming file-backed pages, because the system has intensive file IO. The file IO turned out to be not performance critical for the workload, but the customer wanted to ensure performance critical file-backed pages like code section to not mistakenly be evicted. Using direct IO should or `mlock()` would be a straightforward solution, but modifying the user space code is not easy for the customer. Alternatively, we could use DAMON-based operation scheme[1]. By using it, we can ask DAMON to track access frequency of each region and make 'process_madvise(MADV_WILLNEED)[2]' call for regions having specific size and access frequency for a time interval. We also found the system is having high number of TLB misses. We tried 'always' THP enabled policy and it greatly reduced TLB misses, but the page reclamation also been more frequent due to the THP internal fragmentation caused memory bloat. We could try another DAMON-based operation scheme that applies 'MADV_HUGEPAGE' to memory regions having >=2MB size and high access frequency, while applying 'MADV_NOHUGEPAGE' to regions having <2MB size and low access frequency. We do not own the systems so we only reported the analysis results and possible optimization solutions to the customers. The customers satisfied about the analysis results and promised to try the optimization guides. [1] https://lore.kernel.org/linux-mm/20201006123931.5847-1-sjpark@amazon.com/ [2] https://lore.kernel.org/linux-api/20200622192900.22757-4-minchan@kernel.org/ Comparison with Idle Page Tracking ================================== Idle Page Tracking allows users to set and read idleness of pages using a bitmap file which represents each page with each bit of the file. One recommended usage of it is working set size detection. Users can do that by 1. find PFN of each page for workloads in interest, 2. set all the pages as idle by doing writes to the bitmap file, 3. wait until the workload accesses its working set, and 4. read the idleness of the pages again and count pages became not idle. NOTE: While Idle Page Tracking is for user space users, DAMON is primarily designed for kernel subsystems though it can easily exposed to the user space. Hence, this section only assumes such user space use of DAMON. For what use cases Idle Page Tracking would be better? ------------------------------------------------------ 1. Flexible usecases other than hotness monitoring. Because Idle Page Tracking allows users to control the primitive (Page idleness) by themselves, Idle Page Tracking users can do anything they want. Meanwhile, DAMON is primarily designed to monitor the hotness of each memory region. For this, DAMON asks users to provide sampling interval and aggregation interval. For the reason, there could be some use case that using Idle Page Tracking is simpler. 2. Physical memory monitoring. Idle Page Tracking receives PFN range as input, so natively supports physical memory monitoring. DAMON is designed to be extensible for multiple address spaces and use cases by implementing and using primitives for the given use case. Therefore, by theory, DAMON has no limitation in the type of target address space as long as primitives for the given address space exists. However, the default primitives introduced by this patchset supports only virtual address spaces. Therefore, for physical memory monitoring, you should implement your own primitives and use it, or simply use Idle Page Tracking. Nonetheless, RFC patchsets[1] for the physical memory address space primitives is already available. It also supports user memory same to Idle Page Tracking. [1] https://lore.kernel.org/linux-mm/20200831104730.28970-1-sjpark@amazon.com/ For what use cases DAMON is better? ----------------------------------- 1. Hotness Monitoring. Idle Page Tracking let users know only if a page frame is accessed or not. For hotness check, the user should write more code and use more memory. DAMON do that by itself. 2. Low Monitoring Overhead DAMON receives user's monitoring request with one step and then provide the results. So, roughly speaking, DAMON require only O(1) user/kernel context switches. In case of Idle Page Tracking, however, because the interface receives contiguous page frames, the number of user/kernel context switches increases as the monitoring target becomes complex and huge. As a result, the context switch overhead could be not negligible. Moreover, DAMON is born to handle with the monitoring overhead. Because the core mechanism is pure logical, Idle Page Tracking users might be able to implement the mechanism on their own, but it would be time consuming and the user/kernel context switching will still more frequent than that of DAMON. Also, the kernel subsystems cannot use the logic in this case. 3. Page granularity working set size detection. Until v22 of this patchset, this was categorized as the thing Idle Page Tracking could do better, because DAMON basically maintains additional metadata for each of the monitoring target regions. So, in the page granularity working set size detection use case, DAMON would incur (number of monitoring target pages * size of metadata) memory overhead. Size of the single metadata item is about 54 bytes, so assuming 4KB pages, about 1.3% of monitoring target pages will be additionally used. All essential metadata for Idle Page Tracking are embedded in 'struct page' and page table entries. Therefore, in this use case, only one counter variable for working set size accounting is required if Idle Page Tracking is used. There are more details to consider, but roughly speaking, this is true in most cases. However, the situation changed from v23. Now DAMON supports arbitrary types of monitoring targets, which don't use the metadata. Using that, DAMON can do the working set size detection with no additional space overhead but less user-kernel context switch. A first draft for the implementation of monitoring primitives for this usage is available in a DAMON development tree[1]. An RFC patchset for it based on this patchset will also be available soon. Since v24, the arbitrary type support is dropped from this patchset because this patchset doesn't introduce real use of the type. You can still get it from the DAMON development tree[2], though. [1] https://github.com/sjp38/linux/tree/damon/pgidle_hack [2] https://github.com/sjp38/linux/tree/damon/master 4. More future usecases While Idle Page Tracking has tight coupling with base primitives (PG_Idle and page table Accessed bits), DAMON is designed to be extensible for many use cases and address spaces. If you need some special address type or want to use special h/w access check primitives, you can write your own primitives for that and configure DAMON to use those. Therefore, if your use case could be changed a lot in future, using DAMON could be better. Can I use both Idle Page Tracking and DAMON? -------------------------------------------- Yes, though using them concurrently for overlapping memory regions could result in interference to each other. Nevertheless, such use case would be rare or makes no sense at all. Even in the case, the noise would bot be really significant. So, you can choose whatever you want depending on the characteristics of your use cases. More Information ================ We prepared a showcase web site[1] that you can get more information. There are - the official documentations[2], - the heatmap format dynamic access pattern of various realistic workloads for heap area[3], mmap()-ed area[4], and stack[5] area, - the dynamic working set size distribution[6] and chronological working set size changes[7], and - the latest performance test results[8]. [1] https://damonitor.github.io/_index [2] https://damonitor.github.io/doc/html/latest-damon [3] https://damonitor.github.io/test/result/visual/latest/rec.heatmap.0.png.html [4] https://damonitor.github.io/test/result/visual/latest/rec.heatmap.1.png.html [5] https://damonitor.github.io/test/result/visual/latest/rec.heatmap.2.png.html [6] https://damonitor.github.io/test/result/visual/latest/rec.wss_sz.png.html [7] https://damonitor.github.io/test/result/visual/latest/rec.wss_time.png.html [8] https://damonitor.github.io/test/result/perf/latest/html/index.html Baseline and Complete Git Trees =============================== The patches are based on the latest -mm tree, specifically v5.14-rc1-mmots-2021-07-15-18-47 of https://github.com/hnaz/linux-mm. You can also clone the complete git tree: $ git clone git://github.com/sjp38/linux -b damon/patches/v34 The web is also available: https://github.com/sjp38/linux/releases/tag/damon/patches/v34 Development Trees ----------------- There are a couple of trees for entire DAMON patchset series and features for future release. - For latest release: https://github.com/sjp38/linux/tree/damon/master - For next release: https://github.com/sjp38/linux/tree/damon/next Long-term Support Trees ----------------------- For people who want to test DAMON but using LTS kernels, there are another couple of trees based on two latest LTS kernels respectively and containing the 'damon/master' backports. - For v5.4.y: https://github.com/sjp38/linux/tree/damon/for-v5.4.y - For v5.10.y: https://github.com/sjp38/linux/tree/damon/for-v5.10.y Amazon Linux Kernel Trees ------------------------- DAMON is also merged in two public Amazon Linux kernel trees that based on v5.4.y[1] and v5.10.y[2]. [1] https://github.com/amazonlinux/linux/tree/amazon-5.4.y/master/mm/damon [2] https://github.com/amazonlinux/linux/tree/amazon-5.10.y/master/mm/damon Git Tree for Diff of Patches ============================ For easy review of diff between different versions of each patch, I prepared a git tree containing all versions of the DAMON patchset series: https://github.com/sjp38/damon-patches You can clone it and use 'diff' for easy review of changes between different versions of the patchset. For example: $ git clone https://github.com/sjp38/damon-patches && cd damon-patches $ diff -u damon/v33 damon/v34 Sequence Of Patches =================== First three patches implement the core logics of DAMON. The 1st patch introduces basic sampling based hotness monitoring for arbitrary types of targets. Following two patches implement the core mechanisms for control of overhead and accuracy, namely regions based sampling (patch 2) and adaptive regions adjustment (patch 3). Now the essential parts of DAMON is complete, but it cannot work unless someone provides monitoring primitives for a specific use case. The following two patches make it just work for virtual address spaces monitoring. The 4th patch makes 'PG_idle' can be used by DAMON and the 5th patch implements the virtual memory address space specific monitoring primitives using page table Accessed bits and the 'PG_idle' page flag. Now DAMON just works for virtual address space monitoring via the kernel space api. To let the user space users can use DAMON, following four patches add interfaces for them. The 6th patch adds a tracepoint for monitoring results. The 7th patch implements a DAMON application kernel module, namely damon-dbgfs, that simply wraps DAMON and exposes DAMON interface to the user space via the debugfs interface. The 8th patch further exports pid of monitoring thread (kdamond) to user space for easier cpu usage accounting, and the 9th patch makes the debugfs interface to support multiple contexts. Three patches for maintainability follows. The 10th patch adds documentations for both the user space and the kernel space. The 11th patch provides unit tests (based on the kunit) while the 12th patch adds user space tests (based on the kselftest). Finally, the last patch (13th) updates the MAINTAINERS file. This patch (of 13): DAMON is a data access monitoring framework for the Linux kernel. The core mechanisms of DAMON make it - accurate (the monitoring output is useful enough for DRAM level performance-centric memory management; It might be inappropriate for CPU cache levels, though), - light-weight (the monitoring overhead is normally low enough to be applied online), and - scalable (the upper-bound of the overhead is in constant range regardless of the size of target workloads). Using this framework, hence, we can easily write efficient kernel space data access monitoring applications. For example, the kernel's memory management mechanisms can make advanced decisions using this. Experimental data access aware optimization works that incurring high access monitoring overhead could again be implemented on top of this. Due to its simple and flexible interface, providing user space interface would be also easy. Then, user space users who have some special workloads can write personalized applications for better understanding and optimizations of their workloads and systems. === Nevertheless, this commit is defining and implementing only basic access check part without the overhead-accuracy handling core logic. The basic access check is as below. The output of DAMON says what memory regions are how frequently accessed for a given duration. The resolution of the access frequency is controlled by setting ``sampling interval`` and ``aggregation interval``. In detail, DAMON checks access to each page per ``sampling interval`` and aggregates the results. In other words, counts the number of the accesses to each region. After each ``aggregation interval`` passes, DAMON calls callback functions that previously registered by users so that users can read the aggregated results and then clears the results. This can be described in below simple pseudo-code:: init() while monitoring_on: for page in monitoring_target: if accessed(page): nr_accesses[page] += 1 if time() % aggregation_interval == 0: for callback in user_registered_callbacks: callback(monitoring_target, nr_accesses) for page in monitoring_target: nr_accesses[page] = 0 if time() % update_interval == 0: update() sleep(sampling interval) The target regions constructed at the beginning of the monitoring and updated after each ``regions_update_interval``, because the target regions could be dynamically changed (e.g., mmap() or memory hotplug). The monitoring overhead of this mechanism will arbitrarily increase as the size of the target workload grows. The basic monitoring primitives for actual access check and dynamic target regions construction aren't in the core part of DAMON. Instead, it allows users to implement their own primitives that are optimized for their use case and configure DAMON to use those. In other words, users cannot use current version of DAMON without some additional works. Following commits will implement the core mechanisms for the overhead-accuracy control and default primitives implementations. Link: https://lkml.kernel.org/r/20210716081449.22187-1-sj38.park@gmail.com Link: https://lkml.kernel.org/r/20210716081449.22187-2-sj38.park@gmail.com Signed-off-by: SeongJae Park <sjpark@amazon.de> Reviewed-by: Leonard Foerster <foersleo@amazon.de> Reviewed-by: Fernand Sieber <sieberf@amazon.com> Acked-by: Shakeel Butt <shakeelb@google.com> Cc: Jonathan Cameron <Jonathan.Cameron@huawei.com> Cc: Alexander Shishkin <alexander.shishkin@linux.intel.com> Cc: Amit Shah <amit@kernel.org> Cc: Benjamin Herrenschmidt <benh@kernel.crashing.org> Cc: Jonathan Corbet <corbet@lwn.net> Cc: David Hildenbrand <david@redhat.com> Cc: David Woodhouse <dwmw@amazon.com> Cc: Marco Elver <elver@google.com> Cc: Fan Du <fan.du@intel.com> Cc: Greg Kroah-Hartman <greg@kroah.com> Cc: Greg Thelen <gthelen@google.com> Cc: Joe Perches <joe@perches.com> Cc: Mel Gorman <mgorman@suse.de> Cc: Maximilian Heyne <mheyne@amazon.de> Cc: Minchan Kim <minchan@kernel.org> Cc: Ingo Molnar <mingo@redhat.com> Cc: Namhyung Kim <namhyung@kernel.org> Cc: Peter Zijlstra <peterz@infradead.org> Cc: Rik van Riel <riel@surriel.com> Cc: David Rientjes <rientjes@google.com> Cc: Steven Rostedt (VMware) <rostedt@goodmis.org> Cc: Shuah Khan <shuah@kernel.org> Cc: Vlastimil Babka <vbabka@suse.cz> Cc: Vladimir Davydov <vdavydov.dev@gmail.com> Cc: Brendan Higgins <brendanhiggins@google.com> Cc: Markus Boehme <markubo@amazon.de> Signed-off-by: Andrew Morton <akpm@linux-foundation.org> Signed-off-by: Linus Torvalds <torvalds@linux-foundation.org>
2021-09-08 10:56:28 +08:00
* The monitoring thread calls @after_sampling and @after_aggregation for each
* of the sampling intervals and aggregation intervals, respectively.
* Therefore, users can safely access the monitoring results without additional
* protection. For the reason, users are recommended to use these callback for
* the accesses to the results.
*
* If any callback returns non-zero, monitoring stops.
*/
struct damon_callback {
void *private;
int (*before_start)(struct damon_ctx *context);
mm/damon/core: add a new callback for watermarks checks Patch series "mm/damon: Support online tuning". Effects of DAMON and DAMON-based Operation Schemes highly depends on the configurations. Wrong configurations could even result in unexpected efficiency degradations. For finding a best configuration, repeating incremental configuration changes and results measurements, in other words, online tuning, could be helpful. Nevertheless, DAMON kernel API supports only restrictive online tuning. Worse yet, the sysfs-based DAMON user interface doesn't support online tuning at all. DAMON_RECLAIM also doesn't support online tuning. This patchset makes the DAMON kernel API, DAMON sysfs interface, and DAMON_RECLAIM supports online tuning. Sequence of patches ------------------- First two patches enhance DAMON online tuning for kernel API users. Specifically, patch 1 let kernel API users to be able to do DAMON online tuning without a restriction, and patch 2 makes error handling easier. Following seven patches (patches 3-9) refactor code for better readability and easier reuse of code fragments that will be useful for online tuning support. Patch 10 introduces DAMON callback based user request handling structure for DAMON sysfs interface, and patch 11 enables DAMON online tuning via DAMON sysfs interface. Documentation patch (patch 12) for usage of it follows. Patch 13 enables online tuning of DAMON_RECLAIM and finally patch 14 documents the DAMON_RECLAIM online tuning usage. This patch (of 14): For updating input parameters for running DAMON contexts, DAMON kernel API users can use the contexts' callbacks, as it is the safe place for context internal data accesses. When the context has DAMON-based operation schemes and all schemes are deactivated due to their watermarks, however, DAMON does nothing but only watermarks checks. As a result, no callbacks will be called back, and therefore the kernel API users cannot update the input parameters including monitoring attributes, DAMON-based operation schemes, and watermarks. To let users easily update such DAMON input parameters in such a case, this commit adds a new callback, 'after_wmarks_check()'. It will be called after each watermarks check. Users can do the online input parameters update in the callback even under the schemes deactivated case. Link: https://lkml.kernel.org/r/20220429160606.127307-2-sj@kernel.org Signed-off-by: SeongJae Park <sj@kernel.org> Signed-off-by: Andrew Morton <akpm@linux-foundation.org>
2022-05-10 09:20:54 +08:00
int (*after_wmarks_check)(struct damon_ctx *context);
mm: introduce Data Access MONitor (DAMON) Patch series "Introduce Data Access MONitor (DAMON)", v34. Introduction ============ DAMON is a data access monitoring framework for the Linux kernel. The core mechanisms of DAMON called 'region based sampling' and 'adaptive regions adjustment' (refer to 'mechanisms.rst' in the 11th patch of this patchset for the detail) make it - accurate (The monitored information is useful for DRAM level memory management. It might not appropriate for Cache-level accuracy, though.), - light-weight (The monitoring overhead is low enough to be applied online while making no impact on the performance of the target workloads.), and - scalable (the upper-bound of the instrumentation overhead is controllable regardless of the size of target workloads.). Using this framework, therefore, several memory management mechanisms such as reclamation and THP can be optimized to aware real data access patterns. Experimental access pattern aware memory management optimization works that incurring high instrumentation overhead will be able to have another try. Though DAMON is for kernel subsystems, it can be easily exposed to the user space by writing a DAMON-wrapper kernel subsystem. Then, user space users who have some special workloads will be able to write personalized tools or applications for deeper understanding and specialized optimizations of their systems. DAMON is also merged in two public Amazon Linux kernel trees that based on v5.4.y[1] and v5.10.y[2]. [1] https://github.com/amazonlinux/linux/tree/amazon-5.4.y/master/mm/damon [2] https://github.com/amazonlinux/linux/tree/amazon-5.10.y/master/mm/damon The userspace tool[1] is available, released under GPLv2, and actively being maintained. I am also planning to implement another basic user interface in perf[2]. Also, the basic test suite for DAMON is available under GPLv2[3]. [1] https://github.com/awslabs/damo [2] https://lore.kernel.org/linux-mm/20210107120729.22328-1-sjpark@amazon.com/ [3] https://github.com/awslabs/damon-tests Long-term Plan -------------- DAMON is a part of a project called Data Access-aware Operating System (DAOS). As the name implies, I want to improve the performance and efficiency of systems using fine-grained data access patterns. The optimizations are for both kernel and user spaces. I will therefore modify or create kernel subsystems, export some of those to user space and implement user space library / tools. Below shows the layers and components for the project. --------------------------------------------------------------------------- Primitives: PTE Accessed bit, PG_idle, rmap, (Intel CMT), ... Framework: DAMON Features: DAMOS, virtual addr, physical addr, ... Applications: DAMON-debugfs, (DARC), ... ^^^^^^^^^^^^^^^^^^^^^^^ KERNEL SPACE ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ Raw Interface: debugfs, (sysfs), (damonfs), tracepoints, (sys_damon), ... vvvvvvvvvvvvvvvvvvvvvvv USER SPACE vvvvvvvvvvvvvvvvvvvvvvvvvvvvvvvv Library: (libdamon), ... Tools: DAMO, (perf), ... --------------------------------------------------------------------------- The components in parentheses or marked as '...' are not implemented yet but in the future plan. IOW, those are the TODO tasks of DAOS project. For more detail, please refer to the plans: https://lore.kernel.org/linux-mm/20201202082731.24828-1-sjpark@amazon.com/ Evaluations =========== We evaluated DAMON's overhead, monitoring quality and usefulness using 24 realistic workloads on my QEMU/KVM based virtual machine running a kernel that v24 DAMON patchset is applied. DAMON is lightweight. It increases system memory usage by 0.39% and slows target workloads down by 1.16%. DAMON is accurate and useful for memory management optimizations. An experimental DAMON-based operation scheme for THP, namely 'ethp', removes 76.15% of THP memory overheads while preserving 51.25% of THP speedup. Another experimental DAMON-based 'proactive reclamation' implementation, 'prcl', reduces 93.38% of residential sets and 23.63% of system memory footprint while incurring only 1.22% runtime overhead in the best case (parsec3/freqmine). NOTE that the experimental THP optimization and proactive reclamation are not for production but only for proof of concepts. Please refer to the official document[1] or "Documentation/admin-guide/mm: Add a document for DAMON" patch in this patchset for detailed evaluation setup and results. [1] https://damonitor.github.io/doc/html/latest-damon/admin-guide/mm/damon/eval.html Real-world User Story ===================== In summary, DAMON has used on production systems and proved its usefulness. DAMON as a profiler ------------------- We analyzed characteristics of a large scale production systems of our customers using DAMON. The systems utilize 70GB DRAM and 36 CPUs. From this, we were able to find interesting things below. There were obviously different access pattern under idle workload and active workload. Under the idle workload, it accessed large memory regions with low frequency, while the active workload accessed small memory regions with high freuqnecy. DAMON found a 7GB memory region that showing obviously high access frequency under the active workload. We believe this is the performance-effective working set and need to be protected. There was a 4KB memory region that showing highest access frequency under not only active but also idle workloads. We think this must be a hottest code section like thing that should never be paged out. For this analysis, DAMON used only 0.3-1% of single CPU time. Because we used recording-based analysis, it consumed about 3-12 MB of disk space per 20 minutes. This is only small amount of disk space, but we can further reduce the disk usage by using non-recording-based DAMON features. I'd like to argue that only DAMON can do such detailed analysis (finding 4KB highest region in 70GB memory) with the light overhead. DAMON as a system optimization tool ----------------------------------- We also found below potential performance problems on the systems and made DAMON-based solutions. The system doesn't want to make the workload suffer from the page reclamation and thus it utilizes enough DRAM but no swap device. However, we found the system is actively reclaiming file-backed pages, because the system has intensive file IO. The file IO turned out to be not performance critical for the workload, but the customer wanted to ensure performance critical file-backed pages like code section to not mistakenly be evicted. Using direct IO should or `mlock()` would be a straightforward solution, but modifying the user space code is not easy for the customer. Alternatively, we could use DAMON-based operation scheme[1]. By using it, we can ask DAMON to track access frequency of each region and make 'process_madvise(MADV_WILLNEED)[2]' call for regions having specific size and access frequency for a time interval. We also found the system is having high number of TLB misses. We tried 'always' THP enabled policy and it greatly reduced TLB misses, but the page reclamation also been more frequent due to the THP internal fragmentation caused memory bloat. We could try another DAMON-based operation scheme that applies 'MADV_HUGEPAGE' to memory regions having >=2MB size and high access frequency, while applying 'MADV_NOHUGEPAGE' to regions having <2MB size and low access frequency. We do not own the systems so we only reported the analysis results and possible optimization solutions to the customers. The customers satisfied about the analysis results and promised to try the optimization guides. [1] https://lore.kernel.org/linux-mm/20201006123931.5847-1-sjpark@amazon.com/ [2] https://lore.kernel.org/linux-api/20200622192900.22757-4-minchan@kernel.org/ Comparison with Idle Page Tracking ================================== Idle Page Tracking allows users to set and read idleness of pages using a bitmap file which represents each page with each bit of the file. One recommended usage of it is working set size detection. Users can do that by 1. find PFN of each page for workloads in interest, 2. set all the pages as idle by doing writes to the bitmap file, 3. wait until the workload accesses its working set, and 4. read the idleness of the pages again and count pages became not idle. NOTE: While Idle Page Tracking is for user space users, DAMON is primarily designed for kernel subsystems though it can easily exposed to the user space. Hence, this section only assumes such user space use of DAMON. For what use cases Idle Page Tracking would be better? ------------------------------------------------------ 1. Flexible usecases other than hotness monitoring. Because Idle Page Tracking allows users to control the primitive (Page idleness) by themselves, Idle Page Tracking users can do anything they want. Meanwhile, DAMON is primarily designed to monitor the hotness of each memory region. For this, DAMON asks users to provide sampling interval and aggregation interval. For the reason, there could be some use case that using Idle Page Tracking is simpler. 2. Physical memory monitoring. Idle Page Tracking receives PFN range as input, so natively supports physical memory monitoring. DAMON is designed to be extensible for multiple address spaces and use cases by implementing and using primitives for the given use case. Therefore, by theory, DAMON has no limitation in the type of target address space as long as primitives for the given address space exists. However, the default primitives introduced by this patchset supports only virtual address spaces. Therefore, for physical memory monitoring, you should implement your own primitives and use it, or simply use Idle Page Tracking. Nonetheless, RFC patchsets[1] for the physical memory address space primitives is already available. It also supports user memory same to Idle Page Tracking. [1] https://lore.kernel.org/linux-mm/20200831104730.28970-1-sjpark@amazon.com/ For what use cases DAMON is better? ----------------------------------- 1. Hotness Monitoring. Idle Page Tracking let users know only if a page frame is accessed or not. For hotness check, the user should write more code and use more memory. DAMON do that by itself. 2. Low Monitoring Overhead DAMON receives user's monitoring request with one step and then provide the results. So, roughly speaking, DAMON require only O(1) user/kernel context switches. In case of Idle Page Tracking, however, because the interface receives contiguous page frames, the number of user/kernel context switches increases as the monitoring target becomes complex and huge. As a result, the context switch overhead could be not negligible. Moreover, DAMON is born to handle with the monitoring overhead. Because the core mechanism is pure logical, Idle Page Tracking users might be able to implement the mechanism on their own, but it would be time consuming and the user/kernel context switching will still more frequent than that of DAMON. Also, the kernel subsystems cannot use the logic in this case. 3. Page granularity working set size detection. Until v22 of this patchset, this was categorized as the thing Idle Page Tracking could do better, because DAMON basically maintains additional metadata for each of the monitoring target regions. So, in the page granularity working set size detection use case, DAMON would incur (number of monitoring target pages * size of metadata) memory overhead. Size of the single metadata item is about 54 bytes, so assuming 4KB pages, about 1.3% of monitoring target pages will be additionally used. All essential metadata for Idle Page Tracking are embedded in 'struct page' and page table entries. Therefore, in this use case, only one counter variable for working set size accounting is required if Idle Page Tracking is used. There are more details to consider, but roughly speaking, this is true in most cases. However, the situation changed from v23. Now DAMON supports arbitrary types of monitoring targets, which don't use the metadata. Using that, DAMON can do the working set size detection with no additional space overhead but less user-kernel context switch. A first draft for the implementation of monitoring primitives for this usage is available in a DAMON development tree[1]. An RFC patchset for it based on this patchset will also be available soon. Since v24, the arbitrary type support is dropped from this patchset because this patchset doesn't introduce real use of the type. You can still get it from the DAMON development tree[2], though. [1] https://github.com/sjp38/linux/tree/damon/pgidle_hack [2] https://github.com/sjp38/linux/tree/damon/master 4. More future usecases While Idle Page Tracking has tight coupling with base primitives (PG_Idle and page table Accessed bits), DAMON is designed to be extensible for many use cases and address spaces. If you need some special address type or want to use special h/w access check primitives, you can write your own primitives for that and configure DAMON to use those. Therefore, if your use case could be changed a lot in future, using DAMON could be better. Can I use both Idle Page Tracking and DAMON? -------------------------------------------- Yes, though using them concurrently for overlapping memory regions could result in interference to each other. Nevertheless, such use case would be rare or makes no sense at all. Even in the case, the noise would bot be really significant. So, you can choose whatever you want depending on the characteristics of your use cases. More Information ================ We prepared a showcase web site[1] that you can get more information. There are - the official documentations[2], - the heatmap format dynamic access pattern of various realistic workloads for heap area[3], mmap()-ed area[4], and stack[5] area, - the dynamic working set size distribution[6] and chronological working set size changes[7], and - the latest performance test results[8]. [1] https://damonitor.github.io/_index [2] https://damonitor.github.io/doc/html/latest-damon [3] https://damonitor.github.io/test/result/visual/latest/rec.heatmap.0.png.html [4] https://damonitor.github.io/test/result/visual/latest/rec.heatmap.1.png.html [5] https://damonitor.github.io/test/result/visual/latest/rec.heatmap.2.png.html [6] https://damonitor.github.io/test/result/visual/latest/rec.wss_sz.png.html [7] https://damonitor.github.io/test/result/visual/latest/rec.wss_time.png.html [8] https://damonitor.github.io/test/result/perf/latest/html/index.html Baseline and Complete Git Trees =============================== The patches are based on the latest -mm tree, specifically v5.14-rc1-mmots-2021-07-15-18-47 of https://github.com/hnaz/linux-mm. You can also clone the complete git tree: $ git clone git://github.com/sjp38/linux -b damon/patches/v34 The web is also available: https://github.com/sjp38/linux/releases/tag/damon/patches/v34 Development Trees ----------------- There are a couple of trees for entire DAMON patchset series and features for future release. - For latest release: https://github.com/sjp38/linux/tree/damon/master - For next release: https://github.com/sjp38/linux/tree/damon/next Long-term Support Trees ----------------------- For people who want to test DAMON but using LTS kernels, there are another couple of trees based on two latest LTS kernels respectively and containing the 'damon/master' backports. - For v5.4.y: https://github.com/sjp38/linux/tree/damon/for-v5.4.y - For v5.10.y: https://github.com/sjp38/linux/tree/damon/for-v5.10.y Amazon Linux Kernel Trees ------------------------- DAMON is also merged in two public Amazon Linux kernel trees that based on v5.4.y[1] and v5.10.y[2]. [1] https://github.com/amazonlinux/linux/tree/amazon-5.4.y/master/mm/damon [2] https://github.com/amazonlinux/linux/tree/amazon-5.10.y/master/mm/damon Git Tree for Diff of Patches ============================ For easy review of diff between different versions of each patch, I prepared a git tree containing all versions of the DAMON patchset series: https://github.com/sjp38/damon-patches You can clone it and use 'diff' for easy review of changes between different versions of the patchset. For example: $ git clone https://github.com/sjp38/damon-patches && cd damon-patches $ diff -u damon/v33 damon/v34 Sequence Of Patches =================== First three patches implement the core logics of DAMON. The 1st patch introduces basic sampling based hotness monitoring for arbitrary types of targets. Following two patches implement the core mechanisms for control of overhead and accuracy, namely regions based sampling (patch 2) and adaptive regions adjustment (patch 3). Now the essential parts of DAMON is complete, but it cannot work unless someone provides monitoring primitives for a specific use case. The following two patches make it just work for virtual address spaces monitoring. The 4th patch makes 'PG_idle' can be used by DAMON and the 5th patch implements the virtual memory address space specific monitoring primitives using page table Accessed bits and the 'PG_idle' page flag. Now DAMON just works for virtual address space monitoring via the kernel space api. To let the user space users can use DAMON, following four patches add interfaces for them. The 6th patch adds a tracepoint for monitoring results. The 7th patch implements a DAMON application kernel module, namely damon-dbgfs, that simply wraps DAMON and exposes DAMON interface to the user space via the debugfs interface. The 8th patch further exports pid of monitoring thread (kdamond) to user space for easier cpu usage accounting, and the 9th patch makes the debugfs interface to support multiple contexts. Three patches for maintainability follows. The 10th patch adds documentations for both the user space and the kernel space. The 11th patch provides unit tests (based on the kunit) while the 12th patch adds user space tests (based on the kselftest). Finally, the last patch (13th) updates the MAINTAINERS file. This patch (of 13): DAMON is a data access monitoring framework for the Linux kernel. The core mechanisms of DAMON make it - accurate (the monitoring output is useful enough for DRAM level performance-centric memory management; It might be inappropriate for CPU cache levels, though), - light-weight (the monitoring overhead is normally low enough to be applied online), and - scalable (the upper-bound of the overhead is in constant range regardless of the size of target workloads). Using this framework, hence, we can easily write efficient kernel space data access monitoring applications. For example, the kernel's memory management mechanisms can make advanced decisions using this. Experimental data access aware optimization works that incurring high access monitoring overhead could again be implemented on top of this. Due to its simple and flexible interface, providing user space interface would be also easy. Then, user space users who have some special workloads can write personalized applications for better understanding and optimizations of their workloads and systems. === Nevertheless, this commit is defining and implementing only basic access check part without the overhead-accuracy handling core logic. The basic access check is as below. The output of DAMON says what memory regions are how frequently accessed for a given duration. The resolution of the access frequency is controlled by setting ``sampling interval`` and ``aggregation interval``. In detail, DAMON checks access to each page per ``sampling interval`` and aggregates the results. In other words, counts the number of the accesses to each region. After each ``aggregation interval`` passes, DAMON calls callback functions that previously registered by users so that users can read the aggregated results and then clears the results. This can be described in below simple pseudo-code:: init() while monitoring_on: for page in monitoring_target: if accessed(page): nr_accesses[page] += 1 if time() % aggregation_interval == 0: for callback in user_registered_callbacks: callback(monitoring_target, nr_accesses) for page in monitoring_target: nr_accesses[page] = 0 if time() % update_interval == 0: update() sleep(sampling interval) The target regions constructed at the beginning of the monitoring and updated after each ``regions_update_interval``, because the target regions could be dynamically changed (e.g., mmap() or memory hotplug). The monitoring overhead of this mechanism will arbitrarily increase as the size of the target workload grows. The basic monitoring primitives for actual access check and dynamic target regions construction aren't in the core part of DAMON. Instead, it allows users to implement their own primitives that are optimized for their use case and configure DAMON to use those. In other words, users cannot use current version of DAMON without some additional works. Following commits will implement the core mechanisms for the overhead-accuracy control and default primitives implementations. Link: https://lkml.kernel.org/r/20210716081449.22187-1-sj38.park@gmail.com Link: https://lkml.kernel.org/r/20210716081449.22187-2-sj38.park@gmail.com Signed-off-by: SeongJae Park <sjpark@amazon.de> Reviewed-by: Leonard Foerster <foersleo@amazon.de> Reviewed-by: Fernand Sieber <sieberf@amazon.com> Acked-by: Shakeel Butt <shakeelb@google.com> Cc: Jonathan Cameron <Jonathan.Cameron@huawei.com> Cc: Alexander Shishkin <alexander.shishkin@linux.intel.com> Cc: Amit Shah <amit@kernel.org> Cc: Benjamin Herrenschmidt <benh@kernel.crashing.org> Cc: Jonathan Corbet <corbet@lwn.net> Cc: David Hildenbrand <david@redhat.com> Cc: David Woodhouse <dwmw@amazon.com> Cc: Marco Elver <elver@google.com> Cc: Fan Du <fan.du@intel.com> Cc: Greg Kroah-Hartman <greg@kroah.com> Cc: Greg Thelen <gthelen@google.com> Cc: Joe Perches <joe@perches.com> Cc: Mel Gorman <mgorman@suse.de> Cc: Maximilian Heyne <mheyne@amazon.de> Cc: Minchan Kim <minchan@kernel.org> Cc: Ingo Molnar <mingo@redhat.com> Cc: Namhyung Kim <namhyung@kernel.org> Cc: Peter Zijlstra <peterz@infradead.org> Cc: Rik van Riel <riel@surriel.com> Cc: David Rientjes <rientjes@google.com> Cc: Steven Rostedt (VMware) <rostedt@goodmis.org> Cc: Shuah Khan <shuah@kernel.org> Cc: Vlastimil Babka <vbabka@suse.cz> Cc: Vladimir Davydov <vdavydov.dev@gmail.com> Cc: Brendan Higgins <brendanhiggins@google.com> Cc: Markus Boehme <markubo@amazon.de> Signed-off-by: Andrew Morton <akpm@linux-foundation.org> Signed-off-by: Linus Torvalds <torvalds@linux-foundation.org>
2021-09-08 10:56:28 +08:00
int (*after_sampling)(struct damon_ctx *context);
int (*after_aggregation)(struct damon_ctx *context);
void (*before_terminate)(struct damon_ctx *context);
mm: introduce Data Access MONitor (DAMON) Patch series "Introduce Data Access MONitor (DAMON)", v34. Introduction ============ DAMON is a data access monitoring framework for the Linux kernel. The core mechanisms of DAMON called 'region based sampling' and 'adaptive regions adjustment' (refer to 'mechanisms.rst' in the 11th patch of this patchset for the detail) make it - accurate (The monitored information is useful for DRAM level memory management. It might not appropriate for Cache-level accuracy, though.), - light-weight (The monitoring overhead is low enough to be applied online while making no impact on the performance of the target workloads.), and - scalable (the upper-bound of the instrumentation overhead is controllable regardless of the size of target workloads.). Using this framework, therefore, several memory management mechanisms such as reclamation and THP can be optimized to aware real data access patterns. Experimental access pattern aware memory management optimization works that incurring high instrumentation overhead will be able to have another try. Though DAMON is for kernel subsystems, it can be easily exposed to the user space by writing a DAMON-wrapper kernel subsystem. Then, user space users who have some special workloads will be able to write personalized tools or applications for deeper understanding and specialized optimizations of their systems. DAMON is also merged in two public Amazon Linux kernel trees that based on v5.4.y[1] and v5.10.y[2]. [1] https://github.com/amazonlinux/linux/tree/amazon-5.4.y/master/mm/damon [2] https://github.com/amazonlinux/linux/tree/amazon-5.10.y/master/mm/damon The userspace tool[1] is available, released under GPLv2, and actively being maintained. I am also planning to implement another basic user interface in perf[2]. Also, the basic test suite for DAMON is available under GPLv2[3]. [1] https://github.com/awslabs/damo [2] https://lore.kernel.org/linux-mm/20210107120729.22328-1-sjpark@amazon.com/ [3] https://github.com/awslabs/damon-tests Long-term Plan -------------- DAMON is a part of a project called Data Access-aware Operating System (DAOS). As the name implies, I want to improve the performance and efficiency of systems using fine-grained data access patterns. The optimizations are for both kernel and user spaces. I will therefore modify or create kernel subsystems, export some of those to user space and implement user space library / tools. Below shows the layers and components for the project. --------------------------------------------------------------------------- Primitives: PTE Accessed bit, PG_idle, rmap, (Intel CMT), ... Framework: DAMON Features: DAMOS, virtual addr, physical addr, ... Applications: DAMON-debugfs, (DARC), ... ^^^^^^^^^^^^^^^^^^^^^^^ KERNEL SPACE ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ Raw Interface: debugfs, (sysfs), (damonfs), tracepoints, (sys_damon), ... vvvvvvvvvvvvvvvvvvvvvvv USER SPACE vvvvvvvvvvvvvvvvvvvvvvvvvvvvvvvv Library: (libdamon), ... Tools: DAMO, (perf), ... --------------------------------------------------------------------------- The components in parentheses or marked as '...' are not implemented yet but in the future plan. IOW, those are the TODO tasks of DAOS project. For more detail, please refer to the plans: https://lore.kernel.org/linux-mm/20201202082731.24828-1-sjpark@amazon.com/ Evaluations =========== We evaluated DAMON's overhead, monitoring quality and usefulness using 24 realistic workloads on my QEMU/KVM based virtual machine running a kernel that v24 DAMON patchset is applied. DAMON is lightweight. It increases system memory usage by 0.39% and slows target workloads down by 1.16%. DAMON is accurate and useful for memory management optimizations. An experimental DAMON-based operation scheme for THP, namely 'ethp', removes 76.15% of THP memory overheads while preserving 51.25% of THP speedup. Another experimental DAMON-based 'proactive reclamation' implementation, 'prcl', reduces 93.38% of residential sets and 23.63% of system memory footprint while incurring only 1.22% runtime overhead in the best case (parsec3/freqmine). NOTE that the experimental THP optimization and proactive reclamation are not for production but only for proof of concepts. Please refer to the official document[1] or "Documentation/admin-guide/mm: Add a document for DAMON" patch in this patchset for detailed evaluation setup and results. [1] https://damonitor.github.io/doc/html/latest-damon/admin-guide/mm/damon/eval.html Real-world User Story ===================== In summary, DAMON has used on production systems and proved its usefulness. DAMON as a profiler ------------------- We analyzed characteristics of a large scale production systems of our customers using DAMON. The systems utilize 70GB DRAM and 36 CPUs. From this, we were able to find interesting things below. There were obviously different access pattern under idle workload and active workload. Under the idle workload, it accessed large memory regions with low frequency, while the active workload accessed small memory regions with high freuqnecy. DAMON found a 7GB memory region that showing obviously high access frequency under the active workload. We believe this is the performance-effective working set and need to be protected. There was a 4KB memory region that showing highest access frequency under not only active but also idle workloads. We think this must be a hottest code section like thing that should never be paged out. For this analysis, DAMON used only 0.3-1% of single CPU time. Because we used recording-based analysis, it consumed about 3-12 MB of disk space per 20 minutes. This is only small amount of disk space, but we can further reduce the disk usage by using non-recording-based DAMON features. I'd like to argue that only DAMON can do such detailed analysis (finding 4KB highest region in 70GB memory) with the light overhead. DAMON as a system optimization tool ----------------------------------- We also found below potential performance problems on the systems and made DAMON-based solutions. The system doesn't want to make the workload suffer from the page reclamation and thus it utilizes enough DRAM but no swap device. However, we found the system is actively reclaiming file-backed pages, because the system has intensive file IO. The file IO turned out to be not performance critical for the workload, but the customer wanted to ensure performance critical file-backed pages like code section to not mistakenly be evicted. Using direct IO should or `mlock()` would be a straightforward solution, but modifying the user space code is not easy for the customer. Alternatively, we could use DAMON-based operation scheme[1]. By using it, we can ask DAMON to track access frequency of each region and make 'process_madvise(MADV_WILLNEED)[2]' call for regions having specific size and access frequency for a time interval. We also found the system is having high number of TLB misses. We tried 'always' THP enabled policy and it greatly reduced TLB misses, but the page reclamation also been more frequent due to the THP internal fragmentation caused memory bloat. We could try another DAMON-based operation scheme that applies 'MADV_HUGEPAGE' to memory regions having >=2MB size and high access frequency, while applying 'MADV_NOHUGEPAGE' to regions having <2MB size and low access frequency. We do not own the systems so we only reported the analysis results and possible optimization solutions to the customers. The customers satisfied about the analysis results and promised to try the optimization guides. [1] https://lore.kernel.org/linux-mm/20201006123931.5847-1-sjpark@amazon.com/ [2] https://lore.kernel.org/linux-api/20200622192900.22757-4-minchan@kernel.org/ Comparison with Idle Page Tracking ================================== Idle Page Tracking allows users to set and read idleness of pages using a bitmap file which represents each page with each bit of the file. One recommended usage of it is working set size detection. Users can do that by 1. find PFN of each page for workloads in interest, 2. set all the pages as idle by doing writes to the bitmap file, 3. wait until the workload accesses its working set, and 4. read the idleness of the pages again and count pages became not idle. NOTE: While Idle Page Tracking is for user space users, DAMON is primarily designed for kernel subsystems though it can easily exposed to the user space. Hence, this section only assumes such user space use of DAMON. For what use cases Idle Page Tracking would be better? ------------------------------------------------------ 1. Flexible usecases other than hotness monitoring. Because Idle Page Tracking allows users to control the primitive (Page idleness) by themselves, Idle Page Tracking users can do anything they want. Meanwhile, DAMON is primarily designed to monitor the hotness of each memory region. For this, DAMON asks users to provide sampling interval and aggregation interval. For the reason, there could be some use case that using Idle Page Tracking is simpler. 2. Physical memory monitoring. Idle Page Tracking receives PFN range as input, so natively supports physical memory monitoring. DAMON is designed to be extensible for multiple address spaces and use cases by implementing and using primitives for the given use case. Therefore, by theory, DAMON has no limitation in the type of target address space as long as primitives for the given address space exists. However, the default primitives introduced by this patchset supports only virtual address spaces. Therefore, for physical memory monitoring, you should implement your own primitives and use it, or simply use Idle Page Tracking. Nonetheless, RFC patchsets[1] for the physical memory address space primitives is already available. It also supports user memory same to Idle Page Tracking. [1] https://lore.kernel.org/linux-mm/20200831104730.28970-1-sjpark@amazon.com/ For what use cases DAMON is better? ----------------------------------- 1. Hotness Monitoring. Idle Page Tracking let users know only if a page frame is accessed or not. For hotness check, the user should write more code and use more memory. DAMON do that by itself. 2. Low Monitoring Overhead DAMON receives user's monitoring request with one step and then provide the results. So, roughly speaking, DAMON require only O(1) user/kernel context switches. In case of Idle Page Tracking, however, because the interface receives contiguous page frames, the number of user/kernel context switches increases as the monitoring target becomes complex and huge. As a result, the context switch overhead could be not negligible. Moreover, DAMON is born to handle with the monitoring overhead. Because the core mechanism is pure logical, Idle Page Tracking users might be able to implement the mechanism on their own, but it would be time consuming and the user/kernel context switching will still more frequent than that of DAMON. Also, the kernel subsystems cannot use the logic in this case. 3. Page granularity working set size detection. Until v22 of this patchset, this was categorized as the thing Idle Page Tracking could do better, because DAMON basically maintains additional metadata for each of the monitoring target regions. So, in the page granularity working set size detection use case, DAMON would incur (number of monitoring target pages * size of metadata) memory overhead. Size of the single metadata item is about 54 bytes, so assuming 4KB pages, about 1.3% of monitoring target pages will be additionally used. All essential metadata for Idle Page Tracking are embedded in 'struct page' and page table entries. Therefore, in this use case, only one counter variable for working set size accounting is required if Idle Page Tracking is used. There are more details to consider, but roughly speaking, this is true in most cases. However, the situation changed from v23. Now DAMON supports arbitrary types of monitoring targets, which don't use the metadata. Using that, DAMON can do the working set size detection with no additional space overhead but less user-kernel context switch. A first draft for the implementation of monitoring primitives for this usage is available in a DAMON development tree[1]. An RFC patchset for it based on this patchset will also be available soon. Since v24, the arbitrary type support is dropped from this patchset because this patchset doesn't introduce real use of the type. You can still get it from the DAMON development tree[2], though. [1] https://github.com/sjp38/linux/tree/damon/pgidle_hack [2] https://github.com/sjp38/linux/tree/damon/master 4. More future usecases While Idle Page Tracking has tight coupling with base primitives (PG_Idle and page table Accessed bits), DAMON is designed to be extensible for many use cases and address spaces. If you need some special address type or want to use special h/w access check primitives, you can write your own primitives for that and configure DAMON to use those. Therefore, if your use case could be changed a lot in future, using DAMON could be better. Can I use both Idle Page Tracking and DAMON? -------------------------------------------- Yes, though using them concurrently for overlapping memory regions could result in interference to each other. Nevertheless, such use case would be rare or makes no sense at all. Even in the case, the noise would bot be really significant. So, you can choose whatever you want depending on the characteristics of your use cases. More Information ================ We prepared a showcase web site[1] that you can get more information. There are - the official documentations[2], - the heatmap format dynamic access pattern of various realistic workloads for heap area[3], mmap()-ed area[4], and stack[5] area, - the dynamic working set size distribution[6] and chronological working set size changes[7], and - the latest performance test results[8]. [1] https://damonitor.github.io/_index [2] https://damonitor.github.io/doc/html/latest-damon [3] https://damonitor.github.io/test/result/visual/latest/rec.heatmap.0.png.html [4] https://damonitor.github.io/test/result/visual/latest/rec.heatmap.1.png.html [5] https://damonitor.github.io/test/result/visual/latest/rec.heatmap.2.png.html [6] https://damonitor.github.io/test/result/visual/latest/rec.wss_sz.png.html [7] https://damonitor.github.io/test/result/visual/latest/rec.wss_time.png.html [8] https://damonitor.github.io/test/result/perf/latest/html/index.html Baseline and Complete Git Trees =============================== The patches are based on the latest -mm tree, specifically v5.14-rc1-mmots-2021-07-15-18-47 of https://github.com/hnaz/linux-mm. You can also clone the complete git tree: $ git clone git://github.com/sjp38/linux -b damon/patches/v34 The web is also available: https://github.com/sjp38/linux/releases/tag/damon/patches/v34 Development Trees ----------------- There are a couple of trees for entire DAMON patchset series and features for future release. - For latest release: https://github.com/sjp38/linux/tree/damon/master - For next release: https://github.com/sjp38/linux/tree/damon/next Long-term Support Trees ----------------------- For people who want to test DAMON but using LTS kernels, there are another couple of trees based on two latest LTS kernels respectively and containing the 'damon/master' backports. - For v5.4.y: https://github.com/sjp38/linux/tree/damon/for-v5.4.y - For v5.10.y: https://github.com/sjp38/linux/tree/damon/for-v5.10.y Amazon Linux Kernel Trees ------------------------- DAMON is also merged in two public Amazon Linux kernel trees that based on v5.4.y[1] and v5.10.y[2]. [1] https://github.com/amazonlinux/linux/tree/amazon-5.4.y/master/mm/damon [2] https://github.com/amazonlinux/linux/tree/amazon-5.10.y/master/mm/damon Git Tree for Diff of Patches ============================ For easy review of diff between different versions of each patch, I prepared a git tree containing all versions of the DAMON patchset series: https://github.com/sjp38/damon-patches You can clone it and use 'diff' for easy review of changes between different versions of the patchset. For example: $ git clone https://github.com/sjp38/damon-patches && cd damon-patches $ diff -u damon/v33 damon/v34 Sequence Of Patches =================== First three patches implement the core logics of DAMON. The 1st patch introduces basic sampling based hotness monitoring for arbitrary types of targets. Following two patches implement the core mechanisms for control of overhead and accuracy, namely regions based sampling (patch 2) and adaptive regions adjustment (patch 3). Now the essential parts of DAMON is complete, but it cannot work unless someone provides monitoring primitives for a specific use case. The following two patches make it just work for virtual address spaces monitoring. The 4th patch makes 'PG_idle' can be used by DAMON and the 5th patch implements the virtual memory address space specific monitoring primitives using page table Accessed bits and the 'PG_idle' page flag. Now DAMON just works for virtual address space monitoring via the kernel space api. To let the user space users can use DAMON, following four patches add interfaces for them. The 6th patch adds a tracepoint for monitoring results. The 7th patch implements a DAMON application kernel module, namely damon-dbgfs, that simply wraps DAMON and exposes DAMON interface to the user space via the debugfs interface. The 8th patch further exports pid of monitoring thread (kdamond) to user space for easier cpu usage accounting, and the 9th patch makes the debugfs interface to support multiple contexts. Three patches for maintainability follows. The 10th patch adds documentations for both the user space and the kernel space. The 11th patch provides unit tests (based on the kunit) while the 12th patch adds user space tests (based on the kselftest). Finally, the last patch (13th) updates the MAINTAINERS file. This patch (of 13): DAMON is a data access monitoring framework for the Linux kernel. The core mechanisms of DAMON make it - accurate (the monitoring output is useful enough for DRAM level performance-centric memory management; It might be inappropriate for CPU cache levels, though), - light-weight (the monitoring overhead is normally low enough to be applied online), and - scalable (the upper-bound of the overhead is in constant range regardless of the size of target workloads). Using this framework, hence, we can easily write efficient kernel space data access monitoring applications. For example, the kernel's memory management mechanisms can make advanced decisions using this. Experimental data access aware optimization works that incurring high access monitoring overhead could again be implemented on top of this. Due to its simple and flexible interface, providing user space interface would be also easy. Then, user space users who have some special workloads can write personalized applications for better understanding and optimizations of their workloads and systems. === Nevertheless, this commit is defining and implementing only basic access check part without the overhead-accuracy handling core logic. The basic access check is as below. The output of DAMON says what memory regions are how frequently accessed for a given duration. The resolution of the access frequency is controlled by setting ``sampling interval`` and ``aggregation interval``. In detail, DAMON checks access to each page per ``sampling interval`` and aggregates the results. In other words, counts the number of the accesses to each region. After each ``aggregation interval`` passes, DAMON calls callback functions that previously registered by users so that users can read the aggregated results and then clears the results. This can be described in below simple pseudo-code:: init() while monitoring_on: for page in monitoring_target: if accessed(page): nr_accesses[page] += 1 if time() % aggregation_interval == 0: for callback in user_registered_callbacks: callback(monitoring_target, nr_accesses) for page in monitoring_target: nr_accesses[page] = 0 if time() % update_interval == 0: update() sleep(sampling interval) The target regions constructed at the beginning of the monitoring and updated after each ``regions_update_interval``, because the target regions could be dynamically changed (e.g., mmap() or memory hotplug). The monitoring overhead of this mechanism will arbitrarily increase as the size of the target workload grows. The basic monitoring primitives for actual access check and dynamic target regions construction aren't in the core part of DAMON. Instead, it allows users to implement their own primitives that are optimized for their use case and configure DAMON to use those. In other words, users cannot use current version of DAMON without some additional works. Following commits will implement the core mechanisms for the overhead-accuracy control and default primitives implementations. Link: https://lkml.kernel.org/r/20210716081449.22187-1-sj38.park@gmail.com Link: https://lkml.kernel.org/r/20210716081449.22187-2-sj38.park@gmail.com Signed-off-by: SeongJae Park <sjpark@amazon.de> Reviewed-by: Leonard Foerster <foersleo@amazon.de> Reviewed-by: Fernand Sieber <sieberf@amazon.com> Acked-by: Shakeel Butt <shakeelb@google.com> Cc: Jonathan Cameron <Jonathan.Cameron@huawei.com> Cc: Alexander Shishkin <alexander.shishkin@linux.intel.com> Cc: Amit Shah <amit@kernel.org> Cc: Benjamin Herrenschmidt <benh@kernel.crashing.org> Cc: Jonathan Corbet <corbet@lwn.net> Cc: David Hildenbrand <david@redhat.com> Cc: David Woodhouse <dwmw@amazon.com> Cc: Marco Elver <elver@google.com> Cc: Fan Du <fan.du@intel.com> Cc: Greg Kroah-Hartman <greg@kroah.com> Cc: Greg Thelen <gthelen@google.com> Cc: Joe Perches <joe@perches.com> Cc: Mel Gorman <mgorman@suse.de> Cc: Maximilian Heyne <mheyne@amazon.de> Cc: Minchan Kim <minchan@kernel.org> Cc: Ingo Molnar <mingo@redhat.com> Cc: Namhyung Kim <namhyung@kernel.org> Cc: Peter Zijlstra <peterz@infradead.org> Cc: Rik van Riel <riel@surriel.com> Cc: David Rientjes <rientjes@google.com> Cc: Steven Rostedt (VMware) <rostedt@goodmis.org> Cc: Shuah Khan <shuah@kernel.org> Cc: Vlastimil Babka <vbabka@suse.cz> Cc: Vladimir Davydov <vdavydov.dev@gmail.com> Cc: Brendan Higgins <brendanhiggins@google.com> Cc: Markus Boehme <markubo@amazon.de> Signed-off-by: Andrew Morton <akpm@linux-foundation.org> Signed-off-by: Linus Torvalds <torvalds@linux-foundation.org>
2021-09-08 10:56:28 +08:00
};
/**
* struct damon_ctx - Represents a context for each monitoring. This is the
* main interface that allows users to set the attributes and get the results
* of the monitoring.
*
* @sample_interval: The time between access samplings.
* @aggr_interval: The time between monitor results aggregations.
mm/damon: rename damon_primitives to damon_operations Patch series "Allow DAMON user code independent of monitoring primitives". In-kernel DAMON user code is required to configure the monitoring context (struct damon_ctx) with proper monitoring primitives (struct damon_primitive). This makes the user code dependent to all supporting monitoring primitives. For example, DAMON debugfs interface depends on both DAMON_VADDR and DAMON_PADDR, though some users have interest in only one use case. As more monitoring primitives are introduced, the problem will be bigger. To minimize such unnecessary dependency, this patchset makes monitoring primitives can be registered by the implemnting code and later dynamically searched and selected by the user code. In addition to that, this patchset renames monitoring primitives to monitoring operations, which is more easy to intuitively understand what it means and how it would be structed. This patch (of 8): DAMON has a set of callback functions called monitoring primitives and let it can be configured with various implementations for easy extension for different address spaces and usages. However, the word 'primitive' is not so explicit. Meanwhile, many other structs resembles similar purpose calls themselves 'operations'. To make the code easier to be understood, this commit renames 'damon_primitives' to 'damon_operations' before it is too late to rename. Link: https://lkml.kernel.org/r/20220215184603.1479-1-sj@kernel.org Link: https://lkml.kernel.org/r/20220215184603.1479-2-sj@kernel.org Signed-off-by: SeongJae Park <sj@kernel.org> Cc: Xin Hao <xhao@linux.alibaba.com> Cc: David Rientjes <rientjes@google.com> Signed-off-by: Andrew Morton <akpm@linux-foundation.org> Signed-off-by: Linus Torvalds <torvalds@linux-foundation.org>
2022-03-23 05:48:46 +08:00
* @ops_update_interval: The time between monitoring operations updates.
mm: introduce Data Access MONitor (DAMON) Patch series "Introduce Data Access MONitor (DAMON)", v34. Introduction ============ DAMON is a data access monitoring framework for the Linux kernel. The core mechanisms of DAMON called 'region based sampling' and 'adaptive regions adjustment' (refer to 'mechanisms.rst' in the 11th patch of this patchset for the detail) make it - accurate (The monitored information is useful for DRAM level memory management. It might not appropriate for Cache-level accuracy, though.), - light-weight (The monitoring overhead is low enough to be applied online while making no impact on the performance of the target workloads.), and - scalable (the upper-bound of the instrumentation overhead is controllable regardless of the size of target workloads.). Using this framework, therefore, several memory management mechanisms such as reclamation and THP can be optimized to aware real data access patterns. Experimental access pattern aware memory management optimization works that incurring high instrumentation overhead will be able to have another try. Though DAMON is for kernel subsystems, it can be easily exposed to the user space by writing a DAMON-wrapper kernel subsystem. Then, user space users who have some special workloads will be able to write personalized tools or applications for deeper understanding and specialized optimizations of their systems. DAMON is also merged in two public Amazon Linux kernel trees that based on v5.4.y[1] and v5.10.y[2]. [1] https://github.com/amazonlinux/linux/tree/amazon-5.4.y/master/mm/damon [2] https://github.com/amazonlinux/linux/tree/amazon-5.10.y/master/mm/damon The userspace tool[1] is available, released under GPLv2, and actively being maintained. I am also planning to implement another basic user interface in perf[2]. Also, the basic test suite for DAMON is available under GPLv2[3]. [1] https://github.com/awslabs/damo [2] https://lore.kernel.org/linux-mm/20210107120729.22328-1-sjpark@amazon.com/ [3] https://github.com/awslabs/damon-tests Long-term Plan -------------- DAMON is a part of a project called Data Access-aware Operating System (DAOS). As the name implies, I want to improve the performance and efficiency of systems using fine-grained data access patterns. The optimizations are for both kernel and user spaces. I will therefore modify or create kernel subsystems, export some of those to user space and implement user space library / tools. Below shows the layers and components for the project. --------------------------------------------------------------------------- Primitives: PTE Accessed bit, PG_idle, rmap, (Intel CMT), ... Framework: DAMON Features: DAMOS, virtual addr, physical addr, ... Applications: DAMON-debugfs, (DARC), ... ^^^^^^^^^^^^^^^^^^^^^^^ KERNEL SPACE ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ Raw Interface: debugfs, (sysfs), (damonfs), tracepoints, (sys_damon), ... vvvvvvvvvvvvvvvvvvvvvvv USER SPACE vvvvvvvvvvvvvvvvvvvvvvvvvvvvvvvv Library: (libdamon), ... Tools: DAMO, (perf), ... --------------------------------------------------------------------------- The components in parentheses or marked as '...' are not implemented yet but in the future plan. IOW, those are the TODO tasks of DAOS project. For more detail, please refer to the plans: https://lore.kernel.org/linux-mm/20201202082731.24828-1-sjpark@amazon.com/ Evaluations =========== We evaluated DAMON's overhead, monitoring quality and usefulness using 24 realistic workloads on my QEMU/KVM based virtual machine running a kernel that v24 DAMON patchset is applied. DAMON is lightweight. It increases system memory usage by 0.39% and slows target workloads down by 1.16%. DAMON is accurate and useful for memory management optimizations. An experimental DAMON-based operation scheme for THP, namely 'ethp', removes 76.15% of THP memory overheads while preserving 51.25% of THP speedup. Another experimental DAMON-based 'proactive reclamation' implementation, 'prcl', reduces 93.38% of residential sets and 23.63% of system memory footprint while incurring only 1.22% runtime overhead in the best case (parsec3/freqmine). NOTE that the experimental THP optimization and proactive reclamation are not for production but only for proof of concepts. Please refer to the official document[1] or "Documentation/admin-guide/mm: Add a document for DAMON" patch in this patchset for detailed evaluation setup and results. [1] https://damonitor.github.io/doc/html/latest-damon/admin-guide/mm/damon/eval.html Real-world User Story ===================== In summary, DAMON has used on production systems and proved its usefulness. DAMON as a profiler ------------------- We analyzed characteristics of a large scale production systems of our customers using DAMON. The systems utilize 70GB DRAM and 36 CPUs. From this, we were able to find interesting things below. There were obviously different access pattern under idle workload and active workload. Under the idle workload, it accessed large memory regions with low frequency, while the active workload accessed small memory regions with high freuqnecy. DAMON found a 7GB memory region that showing obviously high access frequency under the active workload. We believe this is the performance-effective working set and need to be protected. There was a 4KB memory region that showing highest access frequency under not only active but also idle workloads. We think this must be a hottest code section like thing that should never be paged out. For this analysis, DAMON used only 0.3-1% of single CPU time. Because we used recording-based analysis, it consumed about 3-12 MB of disk space per 20 minutes. This is only small amount of disk space, but we can further reduce the disk usage by using non-recording-based DAMON features. I'd like to argue that only DAMON can do such detailed analysis (finding 4KB highest region in 70GB memory) with the light overhead. DAMON as a system optimization tool ----------------------------------- We also found below potential performance problems on the systems and made DAMON-based solutions. The system doesn't want to make the workload suffer from the page reclamation and thus it utilizes enough DRAM but no swap device. However, we found the system is actively reclaiming file-backed pages, because the system has intensive file IO. The file IO turned out to be not performance critical for the workload, but the customer wanted to ensure performance critical file-backed pages like code section to not mistakenly be evicted. Using direct IO should or `mlock()` would be a straightforward solution, but modifying the user space code is not easy for the customer. Alternatively, we could use DAMON-based operation scheme[1]. By using it, we can ask DAMON to track access frequency of each region and make 'process_madvise(MADV_WILLNEED)[2]' call for regions having specific size and access frequency for a time interval. We also found the system is having high number of TLB misses. We tried 'always' THP enabled policy and it greatly reduced TLB misses, but the page reclamation also been more frequent due to the THP internal fragmentation caused memory bloat. We could try another DAMON-based operation scheme that applies 'MADV_HUGEPAGE' to memory regions having >=2MB size and high access frequency, while applying 'MADV_NOHUGEPAGE' to regions having <2MB size and low access frequency. We do not own the systems so we only reported the analysis results and possible optimization solutions to the customers. The customers satisfied about the analysis results and promised to try the optimization guides. [1] https://lore.kernel.org/linux-mm/20201006123931.5847-1-sjpark@amazon.com/ [2] https://lore.kernel.org/linux-api/20200622192900.22757-4-minchan@kernel.org/ Comparison with Idle Page Tracking ================================== Idle Page Tracking allows users to set and read idleness of pages using a bitmap file which represents each page with each bit of the file. One recommended usage of it is working set size detection. Users can do that by 1. find PFN of each page for workloads in interest, 2. set all the pages as idle by doing writes to the bitmap file, 3. wait until the workload accesses its working set, and 4. read the idleness of the pages again and count pages became not idle. NOTE: While Idle Page Tracking is for user space users, DAMON is primarily designed for kernel subsystems though it can easily exposed to the user space. Hence, this section only assumes such user space use of DAMON. For what use cases Idle Page Tracking would be better? ------------------------------------------------------ 1. Flexible usecases other than hotness monitoring. Because Idle Page Tracking allows users to control the primitive (Page idleness) by themselves, Idle Page Tracking users can do anything they want. Meanwhile, DAMON is primarily designed to monitor the hotness of each memory region. For this, DAMON asks users to provide sampling interval and aggregation interval. For the reason, there could be some use case that using Idle Page Tracking is simpler. 2. Physical memory monitoring. Idle Page Tracking receives PFN range as input, so natively supports physical memory monitoring. DAMON is designed to be extensible for multiple address spaces and use cases by implementing and using primitives for the given use case. Therefore, by theory, DAMON has no limitation in the type of target address space as long as primitives for the given address space exists. However, the default primitives introduced by this patchset supports only virtual address spaces. Therefore, for physical memory monitoring, you should implement your own primitives and use it, or simply use Idle Page Tracking. Nonetheless, RFC patchsets[1] for the physical memory address space primitives is already available. It also supports user memory same to Idle Page Tracking. [1] https://lore.kernel.org/linux-mm/20200831104730.28970-1-sjpark@amazon.com/ For what use cases DAMON is better? ----------------------------------- 1. Hotness Monitoring. Idle Page Tracking let users know only if a page frame is accessed or not. For hotness check, the user should write more code and use more memory. DAMON do that by itself. 2. Low Monitoring Overhead DAMON receives user's monitoring request with one step and then provide the results. So, roughly speaking, DAMON require only O(1) user/kernel context switches. In case of Idle Page Tracking, however, because the interface receives contiguous page frames, the number of user/kernel context switches increases as the monitoring target becomes complex and huge. As a result, the context switch overhead could be not negligible. Moreover, DAMON is born to handle with the monitoring overhead. Because the core mechanism is pure logical, Idle Page Tracking users might be able to implement the mechanism on their own, but it would be time consuming and the user/kernel context switching will still more frequent than that of DAMON. Also, the kernel subsystems cannot use the logic in this case. 3. Page granularity working set size detection. Until v22 of this patchset, this was categorized as the thing Idle Page Tracking could do better, because DAMON basically maintains additional metadata for each of the monitoring target regions. So, in the page granularity working set size detection use case, DAMON would incur (number of monitoring target pages * size of metadata) memory overhead. Size of the single metadata item is about 54 bytes, so assuming 4KB pages, about 1.3% of monitoring target pages will be additionally used. All essential metadata for Idle Page Tracking are embedded in 'struct page' and page table entries. Therefore, in this use case, only one counter variable for working set size accounting is required if Idle Page Tracking is used. There are more details to consider, but roughly speaking, this is true in most cases. However, the situation changed from v23. Now DAMON supports arbitrary types of monitoring targets, which don't use the metadata. Using that, DAMON can do the working set size detection with no additional space overhead but less user-kernel context switch. A first draft for the implementation of monitoring primitives for this usage is available in a DAMON development tree[1]. An RFC patchset for it based on this patchset will also be available soon. Since v24, the arbitrary type support is dropped from this patchset because this patchset doesn't introduce real use of the type. You can still get it from the DAMON development tree[2], though. [1] https://github.com/sjp38/linux/tree/damon/pgidle_hack [2] https://github.com/sjp38/linux/tree/damon/master 4. More future usecases While Idle Page Tracking has tight coupling with base primitives (PG_Idle and page table Accessed bits), DAMON is designed to be extensible for many use cases and address spaces. If you need some special address type or want to use special h/w access check primitives, you can write your own primitives for that and configure DAMON to use those. Therefore, if your use case could be changed a lot in future, using DAMON could be better. Can I use both Idle Page Tracking and DAMON? -------------------------------------------- Yes, though using them concurrently for overlapping memory regions could result in interference to each other. Nevertheless, such use case would be rare or makes no sense at all. Even in the case, the noise would bot be really significant. So, you can choose whatever you want depending on the characteristics of your use cases. More Information ================ We prepared a showcase web site[1] that you can get more information. There are - the official documentations[2], - the heatmap format dynamic access pattern of various realistic workloads for heap area[3], mmap()-ed area[4], and stack[5] area, - the dynamic working set size distribution[6] and chronological working set size changes[7], and - the latest performance test results[8]. [1] https://damonitor.github.io/_index [2] https://damonitor.github.io/doc/html/latest-damon [3] https://damonitor.github.io/test/result/visual/latest/rec.heatmap.0.png.html [4] https://damonitor.github.io/test/result/visual/latest/rec.heatmap.1.png.html [5] https://damonitor.github.io/test/result/visual/latest/rec.heatmap.2.png.html [6] https://damonitor.github.io/test/result/visual/latest/rec.wss_sz.png.html [7] https://damonitor.github.io/test/result/visual/latest/rec.wss_time.png.html [8] https://damonitor.github.io/test/result/perf/latest/html/index.html Baseline and Complete Git Trees =============================== The patches are based on the latest -mm tree, specifically v5.14-rc1-mmots-2021-07-15-18-47 of https://github.com/hnaz/linux-mm. You can also clone the complete git tree: $ git clone git://github.com/sjp38/linux -b damon/patches/v34 The web is also available: https://github.com/sjp38/linux/releases/tag/damon/patches/v34 Development Trees ----------------- There are a couple of trees for entire DAMON patchset series and features for future release. - For latest release: https://github.com/sjp38/linux/tree/damon/master - For next release: https://github.com/sjp38/linux/tree/damon/next Long-term Support Trees ----------------------- For people who want to test DAMON but using LTS kernels, there are another couple of trees based on two latest LTS kernels respectively and containing the 'damon/master' backports. - For v5.4.y: https://github.com/sjp38/linux/tree/damon/for-v5.4.y - For v5.10.y: https://github.com/sjp38/linux/tree/damon/for-v5.10.y Amazon Linux Kernel Trees ------------------------- DAMON is also merged in two public Amazon Linux kernel trees that based on v5.4.y[1] and v5.10.y[2]. [1] https://github.com/amazonlinux/linux/tree/amazon-5.4.y/master/mm/damon [2] https://github.com/amazonlinux/linux/tree/amazon-5.10.y/master/mm/damon Git Tree for Diff of Patches ============================ For easy review of diff between different versions of each patch, I prepared a git tree containing all versions of the DAMON patchset series: https://github.com/sjp38/damon-patches You can clone it and use 'diff' for easy review of changes between different versions of the patchset. For example: $ git clone https://github.com/sjp38/damon-patches && cd damon-patches $ diff -u damon/v33 damon/v34 Sequence Of Patches =================== First three patches implement the core logics of DAMON. The 1st patch introduces basic sampling based hotness monitoring for arbitrary types of targets. Following two patches implement the core mechanisms for control of overhead and accuracy, namely regions based sampling (patch 2) and adaptive regions adjustment (patch 3). Now the essential parts of DAMON is complete, but it cannot work unless someone provides monitoring primitives for a specific use case. The following two patches make it just work for virtual address spaces monitoring. The 4th patch makes 'PG_idle' can be used by DAMON and the 5th patch implements the virtual memory address space specific monitoring primitives using page table Accessed bits and the 'PG_idle' page flag. Now DAMON just works for virtual address space monitoring via the kernel space api. To let the user space users can use DAMON, following four patches add interfaces for them. The 6th patch adds a tracepoint for monitoring results. The 7th patch implements a DAMON application kernel module, namely damon-dbgfs, that simply wraps DAMON and exposes DAMON interface to the user space via the debugfs interface. The 8th patch further exports pid of monitoring thread (kdamond) to user space for easier cpu usage accounting, and the 9th patch makes the debugfs interface to support multiple contexts. Three patches for maintainability follows. The 10th patch adds documentations for both the user space and the kernel space. The 11th patch provides unit tests (based on the kunit) while the 12th patch adds user space tests (based on the kselftest). Finally, the last patch (13th) updates the MAINTAINERS file. This patch (of 13): DAMON is a data access monitoring framework for the Linux kernel. The core mechanisms of DAMON make it - accurate (the monitoring output is useful enough for DRAM level performance-centric memory management; It might be inappropriate for CPU cache levels, though), - light-weight (the monitoring overhead is normally low enough to be applied online), and - scalable (the upper-bound of the overhead is in constant range regardless of the size of target workloads). Using this framework, hence, we can easily write efficient kernel space data access monitoring applications. For example, the kernel's memory management mechanisms can make advanced decisions using this. Experimental data access aware optimization works that incurring high access monitoring overhead could again be implemented on top of this. Due to its simple and flexible interface, providing user space interface would be also easy. Then, user space users who have some special workloads can write personalized applications for better understanding and optimizations of their workloads and systems. === Nevertheless, this commit is defining and implementing only basic access check part without the overhead-accuracy handling core logic. The basic access check is as below. The output of DAMON says what memory regions are how frequently accessed for a given duration. The resolution of the access frequency is controlled by setting ``sampling interval`` and ``aggregation interval``. In detail, DAMON checks access to each page per ``sampling interval`` and aggregates the results. In other words, counts the number of the accesses to each region. After each ``aggregation interval`` passes, DAMON calls callback functions that previously registered by users so that users can read the aggregated results and then clears the results. This can be described in below simple pseudo-code:: init() while monitoring_on: for page in monitoring_target: if accessed(page): nr_accesses[page] += 1 if time() % aggregation_interval == 0: for callback in user_registered_callbacks: callback(monitoring_target, nr_accesses) for page in monitoring_target: nr_accesses[page] = 0 if time() % update_interval == 0: update() sleep(sampling interval) The target regions constructed at the beginning of the monitoring and updated after each ``regions_update_interval``, because the target regions could be dynamically changed (e.g., mmap() or memory hotplug). The monitoring overhead of this mechanism will arbitrarily increase as the size of the target workload grows. The basic monitoring primitives for actual access check and dynamic target regions construction aren't in the core part of DAMON. Instead, it allows users to implement their own primitives that are optimized for their use case and configure DAMON to use those. In other words, users cannot use current version of DAMON without some additional works. Following commits will implement the core mechanisms for the overhead-accuracy control and default primitives implementations. Link: https://lkml.kernel.org/r/20210716081449.22187-1-sj38.park@gmail.com Link: https://lkml.kernel.org/r/20210716081449.22187-2-sj38.park@gmail.com Signed-off-by: SeongJae Park <sjpark@amazon.de> Reviewed-by: Leonard Foerster <foersleo@amazon.de> Reviewed-by: Fernand Sieber <sieberf@amazon.com> Acked-by: Shakeel Butt <shakeelb@google.com> Cc: Jonathan Cameron <Jonathan.Cameron@huawei.com> Cc: Alexander Shishkin <alexander.shishkin@linux.intel.com> Cc: Amit Shah <amit@kernel.org> Cc: Benjamin Herrenschmidt <benh@kernel.crashing.org> Cc: Jonathan Corbet <corbet@lwn.net> Cc: David Hildenbrand <david@redhat.com> Cc: David Woodhouse <dwmw@amazon.com> Cc: Marco Elver <elver@google.com> Cc: Fan Du <fan.du@intel.com> Cc: Greg Kroah-Hartman <greg@kroah.com> Cc: Greg Thelen <gthelen@google.com> Cc: Joe Perches <joe@perches.com> Cc: Mel Gorman <mgorman@suse.de> Cc: Maximilian Heyne <mheyne@amazon.de> Cc: Minchan Kim <minchan@kernel.org> Cc: Ingo Molnar <mingo@redhat.com> Cc: Namhyung Kim <namhyung@kernel.org> Cc: Peter Zijlstra <peterz@infradead.org> Cc: Rik van Riel <riel@surriel.com> Cc: David Rientjes <rientjes@google.com> Cc: Steven Rostedt (VMware) <rostedt@goodmis.org> Cc: Shuah Khan <shuah@kernel.org> Cc: Vlastimil Babka <vbabka@suse.cz> Cc: Vladimir Davydov <vdavydov.dev@gmail.com> Cc: Brendan Higgins <brendanhiggins@google.com> Cc: Markus Boehme <markubo@amazon.de> Signed-off-by: Andrew Morton <akpm@linux-foundation.org> Signed-off-by: Linus Torvalds <torvalds@linux-foundation.org>
2021-09-08 10:56:28 +08:00
*
* For each @sample_interval, DAMON checks whether each region is accessed or
* not. It aggregates and keeps the access information (number of accesses to
* each region) for @aggr_interval time. DAMON also checks whether the target
* memory regions need update (e.g., by ``mmap()`` calls from the application,
* in case of virtual memory monitoring) and applies the changes for each
mm/damon: rename damon_primitives to damon_operations Patch series "Allow DAMON user code independent of monitoring primitives". In-kernel DAMON user code is required to configure the monitoring context (struct damon_ctx) with proper monitoring primitives (struct damon_primitive). This makes the user code dependent to all supporting monitoring primitives. For example, DAMON debugfs interface depends on both DAMON_VADDR and DAMON_PADDR, though some users have interest in only one use case. As more monitoring primitives are introduced, the problem will be bigger. To minimize such unnecessary dependency, this patchset makes monitoring primitives can be registered by the implemnting code and later dynamically searched and selected by the user code. In addition to that, this patchset renames monitoring primitives to monitoring operations, which is more easy to intuitively understand what it means and how it would be structed. This patch (of 8): DAMON has a set of callback functions called monitoring primitives and let it can be configured with various implementations for easy extension for different address spaces and usages. However, the word 'primitive' is not so explicit. Meanwhile, many other structs resembles similar purpose calls themselves 'operations'. To make the code easier to be understood, this commit renames 'damon_primitives' to 'damon_operations' before it is too late to rename. Link: https://lkml.kernel.org/r/20220215184603.1479-1-sj@kernel.org Link: https://lkml.kernel.org/r/20220215184603.1479-2-sj@kernel.org Signed-off-by: SeongJae Park <sj@kernel.org> Cc: Xin Hao <xhao@linux.alibaba.com> Cc: David Rientjes <rientjes@google.com> Signed-off-by: Andrew Morton <akpm@linux-foundation.org> Signed-off-by: Linus Torvalds <torvalds@linux-foundation.org>
2022-03-23 05:48:46 +08:00
* @ops_update_interval. All time intervals are in micro-seconds.
* Please refer to &struct damon_operations and &struct damon_callback for more
mm: introduce Data Access MONitor (DAMON) Patch series "Introduce Data Access MONitor (DAMON)", v34. Introduction ============ DAMON is a data access monitoring framework for the Linux kernel. The core mechanisms of DAMON called 'region based sampling' and 'adaptive regions adjustment' (refer to 'mechanisms.rst' in the 11th patch of this patchset for the detail) make it - accurate (The monitored information is useful for DRAM level memory management. It might not appropriate for Cache-level accuracy, though.), - light-weight (The monitoring overhead is low enough to be applied online while making no impact on the performance of the target workloads.), and - scalable (the upper-bound of the instrumentation overhead is controllable regardless of the size of target workloads.). Using this framework, therefore, several memory management mechanisms such as reclamation and THP can be optimized to aware real data access patterns. Experimental access pattern aware memory management optimization works that incurring high instrumentation overhead will be able to have another try. Though DAMON is for kernel subsystems, it can be easily exposed to the user space by writing a DAMON-wrapper kernel subsystem. Then, user space users who have some special workloads will be able to write personalized tools or applications for deeper understanding and specialized optimizations of their systems. DAMON is also merged in two public Amazon Linux kernel trees that based on v5.4.y[1] and v5.10.y[2]. [1] https://github.com/amazonlinux/linux/tree/amazon-5.4.y/master/mm/damon [2] https://github.com/amazonlinux/linux/tree/amazon-5.10.y/master/mm/damon The userspace tool[1] is available, released under GPLv2, and actively being maintained. I am also planning to implement another basic user interface in perf[2]. Also, the basic test suite for DAMON is available under GPLv2[3]. [1] https://github.com/awslabs/damo [2] https://lore.kernel.org/linux-mm/20210107120729.22328-1-sjpark@amazon.com/ [3] https://github.com/awslabs/damon-tests Long-term Plan -------------- DAMON is a part of a project called Data Access-aware Operating System (DAOS). As the name implies, I want to improve the performance and efficiency of systems using fine-grained data access patterns. The optimizations are for both kernel and user spaces. I will therefore modify or create kernel subsystems, export some of those to user space and implement user space library / tools. Below shows the layers and components for the project. --------------------------------------------------------------------------- Primitives: PTE Accessed bit, PG_idle, rmap, (Intel CMT), ... Framework: DAMON Features: DAMOS, virtual addr, physical addr, ... Applications: DAMON-debugfs, (DARC), ... ^^^^^^^^^^^^^^^^^^^^^^^ KERNEL SPACE ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ Raw Interface: debugfs, (sysfs), (damonfs), tracepoints, (sys_damon), ... vvvvvvvvvvvvvvvvvvvvvvv USER SPACE vvvvvvvvvvvvvvvvvvvvvvvvvvvvvvvv Library: (libdamon), ... Tools: DAMO, (perf), ... --------------------------------------------------------------------------- The components in parentheses or marked as '...' are not implemented yet but in the future plan. IOW, those are the TODO tasks of DAOS project. For more detail, please refer to the plans: https://lore.kernel.org/linux-mm/20201202082731.24828-1-sjpark@amazon.com/ Evaluations =========== We evaluated DAMON's overhead, monitoring quality and usefulness using 24 realistic workloads on my QEMU/KVM based virtual machine running a kernel that v24 DAMON patchset is applied. DAMON is lightweight. It increases system memory usage by 0.39% and slows target workloads down by 1.16%. DAMON is accurate and useful for memory management optimizations. An experimental DAMON-based operation scheme for THP, namely 'ethp', removes 76.15% of THP memory overheads while preserving 51.25% of THP speedup. Another experimental DAMON-based 'proactive reclamation' implementation, 'prcl', reduces 93.38% of residential sets and 23.63% of system memory footprint while incurring only 1.22% runtime overhead in the best case (parsec3/freqmine). NOTE that the experimental THP optimization and proactive reclamation are not for production but only for proof of concepts. Please refer to the official document[1] or "Documentation/admin-guide/mm: Add a document for DAMON" patch in this patchset for detailed evaluation setup and results. [1] https://damonitor.github.io/doc/html/latest-damon/admin-guide/mm/damon/eval.html Real-world User Story ===================== In summary, DAMON has used on production systems and proved its usefulness. DAMON as a profiler ------------------- We analyzed characteristics of a large scale production systems of our customers using DAMON. The systems utilize 70GB DRAM and 36 CPUs. From this, we were able to find interesting things below. There were obviously different access pattern under idle workload and active workload. Under the idle workload, it accessed large memory regions with low frequency, while the active workload accessed small memory regions with high freuqnecy. DAMON found a 7GB memory region that showing obviously high access frequency under the active workload. We believe this is the performance-effective working set and need to be protected. There was a 4KB memory region that showing highest access frequency under not only active but also idle workloads. We think this must be a hottest code section like thing that should never be paged out. For this analysis, DAMON used only 0.3-1% of single CPU time. Because we used recording-based analysis, it consumed about 3-12 MB of disk space per 20 minutes. This is only small amount of disk space, but we can further reduce the disk usage by using non-recording-based DAMON features. I'd like to argue that only DAMON can do such detailed analysis (finding 4KB highest region in 70GB memory) with the light overhead. DAMON as a system optimization tool ----------------------------------- We also found below potential performance problems on the systems and made DAMON-based solutions. The system doesn't want to make the workload suffer from the page reclamation and thus it utilizes enough DRAM but no swap device. However, we found the system is actively reclaiming file-backed pages, because the system has intensive file IO. The file IO turned out to be not performance critical for the workload, but the customer wanted to ensure performance critical file-backed pages like code section to not mistakenly be evicted. Using direct IO should or `mlock()` would be a straightforward solution, but modifying the user space code is not easy for the customer. Alternatively, we could use DAMON-based operation scheme[1]. By using it, we can ask DAMON to track access frequency of each region and make 'process_madvise(MADV_WILLNEED)[2]' call for regions having specific size and access frequency for a time interval. We also found the system is having high number of TLB misses. We tried 'always' THP enabled policy and it greatly reduced TLB misses, but the page reclamation also been more frequent due to the THP internal fragmentation caused memory bloat. We could try another DAMON-based operation scheme that applies 'MADV_HUGEPAGE' to memory regions having >=2MB size and high access frequency, while applying 'MADV_NOHUGEPAGE' to regions having <2MB size and low access frequency. We do not own the systems so we only reported the analysis results and possible optimization solutions to the customers. The customers satisfied about the analysis results and promised to try the optimization guides. [1] https://lore.kernel.org/linux-mm/20201006123931.5847-1-sjpark@amazon.com/ [2] https://lore.kernel.org/linux-api/20200622192900.22757-4-minchan@kernel.org/ Comparison with Idle Page Tracking ================================== Idle Page Tracking allows users to set and read idleness of pages using a bitmap file which represents each page with each bit of the file. One recommended usage of it is working set size detection. Users can do that by 1. find PFN of each page for workloads in interest, 2. set all the pages as idle by doing writes to the bitmap file, 3. wait until the workload accesses its working set, and 4. read the idleness of the pages again and count pages became not idle. NOTE: While Idle Page Tracking is for user space users, DAMON is primarily designed for kernel subsystems though it can easily exposed to the user space. Hence, this section only assumes such user space use of DAMON. For what use cases Idle Page Tracking would be better? ------------------------------------------------------ 1. Flexible usecases other than hotness monitoring. Because Idle Page Tracking allows users to control the primitive (Page idleness) by themselves, Idle Page Tracking users can do anything they want. Meanwhile, DAMON is primarily designed to monitor the hotness of each memory region. For this, DAMON asks users to provide sampling interval and aggregation interval. For the reason, there could be some use case that using Idle Page Tracking is simpler. 2. Physical memory monitoring. Idle Page Tracking receives PFN range as input, so natively supports physical memory monitoring. DAMON is designed to be extensible for multiple address spaces and use cases by implementing and using primitives for the given use case. Therefore, by theory, DAMON has no limitation in the type of target address space as long as primitives for the given address space exists. However, the default primitives introduced by this patchset supports only virtual address spaces. Therefore, for physical memory monitoring, you should implement your own primitives and use it, or simply use Idle Page Tracking. Nonetheless, RFC patchsets[1] for the physical memory address space primitives is already available. It also supports user memory same to Idle Page Tracking. [1] https://lore.kernel.org/linux-mm/20200831104730.28970-1-sjpark@amazon.com/ For what use cases DAMON is better? ----------------------------------- 1. Hotness Monitoring. Idle Page Tracking let users know only if a page frame is accessed or not. For hotness check, the user should write more code and use more memory. DAMON do that by itself. 2. Low Monitoring Overhead DAMON receives user's monitoring request with one step and then provide the results. So, roughly speaking, DAMON require only O(1) user/kernel context switches. In case of Idle Page Tracking, however, because the interface receives contiguous page frames, the number of user/kernel context switches increases as the monitoring target becomes complex and huge. As a result, the context switch overhead could be not negligible. Moreover, DAMON is born to handle with the monitoring overhead. Because the core mechanism is pure logical, Idle Page Tracking users might be able to implement the mechanism on their own, but it would be time consuming and the user/kernel context switching will still more frequent than that of DAMON. Also, the kernel subsystems cannot use the logic in this case. 3. Page granularity working set size detection. Until v22 of this patchset, this was categorized as the thing Idle Page Tracking could do better, because DAMON basically maintains additional metadata for each of the monitoring target regions. So, in the page granularity working set size detection use case, DAMON would incur (number of monitoring target pages * size of metadata) memory overhead. Size of the single metadata item is about 54 bytes, so assuming 4KB pages, about 1.3% of monitoring target pages will be additionally used. All essential metadata for Idle Page Tracking are embedded in 'struct page' and page table entries. Therefore, in this use case, only one counter variable for working set size accounting is required if Idle Page Tracking is used. There are more details to consider, but roughly speaking, this is true in most cases. However, the situation changed from v23. Now DAMON supports arbitrary types of monitoring targets, which don't use the metadata. Using that, DAMON can do the working set size detection with no additional space overhead but less user-kernel context switch. A first draft for the implementation of monitoring primitives for this usage is available in a DAMON development tree[1]. An RFC patchset for it based on this patchset will also be available soon. Since v24, the arbitrary type support is dropped from this patchset because this patchset doesn't introduce real use of the type. You can still get it from the DAMON development tree[2], though. [1] https://github.com/sjp38/linux/tree/damon/pgidle_hack [2] https://github.com/sjp38/linux/tree/damon/master 4. More future usecases While Idle Page Tracking has tight coupling with base primitives (PG_Idle and page table Accessed bits), DAMON is designed to be extensible for many use cases and address spaces. If you need some special address type or want to use special h/w access check primitives, you can write your own primitives for that and configure DAMON to use those. Therefore, if your use case could be changed a lot in future, using DAMON could be better. Can I use both Idle Page Tracking and DAMON? -------------------------------------------- Yes, though using them concurrently for overlapping memory regions could result in interference to each other. Nevertheless, such use case would be rare or makes no sense at all. Even in the case, the noise would bot be really significant. So, you can choose whatever you want depending on the characteristics of your use cases. More Information ================ We prepared a showcase web site[1] that you can get more information. There are - the official documentations[2], - the heatmap format dynamic access pattern of various realistic workloads for heap area[3], mmap()-ed area[4], and stack[5] area, - the dynamic working set size distribution[6] and chronological working set size changes[7], and - the latest performance test results[8]. [1] https://damonitor.github.io/_index [2] https://damonitor.github.io/doc/html/latest-damon [3] https://damonitor.github.io/test/result/visual/latest/rec.heatmap.0.png.html [4] https://damonitor.github.io/test/result/visual/latest/rec.heatmap.1.png.html [5] https://damonitor.github.io/test/result/visual/latest/rec.heatmap.2.png.html [6] https://damonitor.github.io/test/result/visual/latest/rec.wss_sz.png.html [7] https://damonitor.github.io/test/result/visual/latest/rec.wss_time.png.html [8] https://damonitor.github.io/test/result/perf/latest/html/index.html Baseline and Complete Git Trees =============================== The patches are based on the latest -mm tree, specifically v5.14-rc1-mmots-2021-07-15-18-47 of https://github.com/hnaz/linux-mm. You can also clone the complete git tree: $ git clone git://github.com/sjp38/linux -b damon/patches/v34 The web is also available: https://github.com/sjp38/linux/releases/tag/damon/patches/v34 Development Trees ----------------- There are a couple of trees for entire DAMON patchset series and features for future release. - For latest release: https://github.com/sjp38/linux/tree/damon/master - For next release: https://github.com/sjp38/linux/tree/damon/next Long-term Support Trees ----------------------- For people who want to test DAMON but using LTS kernels, there are another couple of trees based on two latest LTS kernels respectively and containing the 'damon/master' backports. - For v5.4.y: https://github.com/sjp38/linux/tree/damon/for-v5.4.y - For v5.10.y: https://github.com/sjp38/linux/tree/damon/for-v5.10.y Amazon Linux Kernel Trees ------------------------- DAMON is also merged in two public Amazon Linux kernel trees that based on v5.4.y[1] and v5.10.y[2]. [1] https://github.com/amazonlinux/linux/tree/amazon-5.4.y/master/mm/damon [2] https://github.com/amazonlinux/linux/tree/amazon-5.10.y/master/mm/damon Git Tree for Diff of Patches ============================ For easy review of diff between different versions of each patch, I prepared a git tree containing all versions of the DAMON patchset series: https://github.com/sjp38/damon-patches You can clone it and use 'diff' for easy review of changes between different versions of the patchset. For example: $ git clone https://github.com/sjp38/damon-patches && cd damon-patches $ diff -u damon/v33 damon/v34 Sequence Of Patches =================== First three patches implement the core logics of DAMON. The 1st patch introduces basic sampling based hotness monitoring for arbitrary types of targets. Following two patches implement the core mechanisms for control of overhead and accuracy, namely regions based sampling (patch 2) and adaptive regions adjustment (patch 3). Now the essential parts of DAMON is complete, but it cannot work unless someone provides monitoring primitives for a specific use case. The following two patches make it just work for virtual address spaces monitoring. The 4th patch makes 'PG_idle' can be used by DAMON and the 5th patch implements the virtual memory address space specific monitoring primitives using page table Accessed bits and the 'PG_idle' page flag. Now DAMON just works for virtual address space monitoring via the kernel space api. To let the user space users can use DAMON, following four patches add interfaces for them. The 6th patch adds a tracepoint for monitoring results. The 7th patch implements a DAMON application kernel module, namely damon-dbgfs, that simply wraps DAMON and exposes DAMON interface to the user space via the debugfs interface. The 8th patch further exports pid of monitoring thread (kdamond) to user space for easier cpu usage accounting, and the 9th patch makes the debugfs interface to support multiple contexts. Three patches for maintainability follows. The 10th patch adds documentations for both the user space and the kernel space. The 11th patch provides unit tests (based on the kunit) while the 12th patch adds user space tests (based on the kselftest). Finally, the last patch (13th) updates the MAINTAINERS file. This patch (of 13): DAMON is a data access monitoring framework for the Linux kernel. The core mechanisms of DAMON make it - accurate (the monitoring output is useful enough for DRAM level performance-centric memory management; It might be inappropriate for CPU cache levels, though), - light-weight (the monitoring overhead is normally low enough to be applied online), and - scalable (the upper-bound of the overhead is in constant range regardless of the size of target workloads). Using this framework, hence, we can easily write efficient kernel space data access monitoring applications. For example, the kernel's memory management mechanisms can make advanced decisions using this. Experimental data access aware optimization works that incurring high access monitoring overhead could again be implemented on top of this. Due to its simple and flexible interface, providing user space interface would be also easy. Then, user space users who have some special workloads can write personalized applications for better understanding and optimizations of their workloads and systems. === Nevertheless, this commit is defining and implementing only basic access check part without the overhead-accuracy handling core logic. The basic access check is as below. The output of DAMON says what memory regions are how frequently accessed for a given duration. The resolution of the access frequency is controlled by setting ``sampling interval`` and ``aggregation interval``. In detail, DAMON checks access to each page per ``sampling interval`` and aggregates the results. In other words, counts the number of the accesses to each region. After each ``aggregation interval`` passes, DAMON calls callback functions that previously registered by users so that users can read the aggregated results and then clears the results. This can be described in below simple pseudo-code:: init() while monitoring_on: for page in monitoring_target: if accessed(page): nr_accesses[page] += 1 if time() % aggregation_interval == 0: for callback in user_registered_callbacks: callback(monitoring_target, nr_accesses) for page in monitoring_target: nr_accesses[page] = 0 if time() % update_interval == 0: update() sleep(sampling interval) The target regions constructed at the beginning of the monitoring and updated after each ``regions_update_interval``, because the target regions could be dynamically changed (e.g., mmap() or memory hotplug). The monitoring overhead of this mechanism will arbitrarily increase as the size of the target workload grows. The basic monitoring primitives for actual access check and dynamic target regions construction aren't in the core part of DAMON. Instead, it allows users to implement their own primitives that are optimized for their use case and configure DAMON to use those. In other words, users cannot use current version of DAMON without some additional works. Following commits will implement the core mechanisms for the overhead-accuracy control and default primitives implementations. Link: https://lkml.kernel.org/r/20210716081449.22187-1-sj38.park@gmail.com Link: https://lkml.kernel.org/r/20210716081449.22187-2-sj38.park@gmail.com Signed-off-by: SeongJae Park <sjpark@amazon.de> Reviewed-by: Leonard Foerster <foersleo@amazon.de> Reviewed-by: Fernand Sieber <sieberf@amazon.com> Acked-by: Shakeel Butt <shakeelb@google.com> Cc: Jonathan Cameron <Jonathan.Cameron@huawei.com> Cc: Alexander Shishkin <alexander.shishkin@linux.intel.com> Cc: Amit Shah <amit@kernel.org> Cc: Benjamin Herrenschmidt <benh@kernel.crashing.org> Cc: Jonathan Corbet <corbet@lwn.net> Cc: David Hildenbrand <david@redhat.com> Cc: David Woodhouse <dwmw@amazon.com> Cc: Marco Elver <elver@google.com> Cc: Fan Du <fan.du@intel.com> Cc: Greg Kroah-Hartman <greg@kroah.com> Cc: Greg Thelen <gthelen@google.com> Cc: Joe Perches <joe@perches.com> Cc: Mel Gorman <mgorman@suse.de> Cc: Maximilian Heyne <mheyne@amazon.de> Cc: Minchan Kim <minchan@kernel.org> Cc: Ingo Molnar <mingo@redhat.com> Cc: Namhyung Kim <namhyung@kernel.org> Cc: Peter Zijlstra <peterz@infradead.org> Cc: Rik van Riel <riel@surriel.com> Cc: David Rientjes <rientjes@google.com> Cc: Steven Rostedt (VMware) <rostedt@goodmis.org> Cc: Shuah Khan <shuah@kernel.org> Cc: Vlastimil Babka <vbabka@suse.cz> Cc: Vladimir Davydov <vdavydov.dev@gmail.com> Cc: Brendan Higgins <brendanhiggins@google.com> Cc: Markus Boehme <markubo@amazon.de> Signed-off-by: Andrew Morton <akpm@linux-foundation.org> Signed-off-by: Linus Torvalds <torvalds@linux-foundation.org>
2021-09-08 10:56:28 +08:00
* detail.
*
* @kdamond: Kernel thread who does the monitoring.
* @kdamond_stop: Notifies whether kdamond should stop.
* @kdamond_lock: Mutex for the synchronizations with @kdamond.
*
* For each monitoring context, one kernel thread for the monitoring is
* created. The pointer to the thread is stored in @kdamond.
*
* Once started, the monitoring thread runs until explicitly required to be
* terminated or every monitoring target is invalid. The validity of the
mm/damon: rename damon_primitives to damon_operations Patch series "Allow DAMON user code independent of monitoring primitives". In-kernel DAMON user code is required to configure the monitoring context (struct damon_ctx) with proper monitoring primitives (struct damon_primitive). This makes the user code dependent to all supporting monitoring primitives. For example, DAMON debugfs interface depends on both DAMON_VADDR and DAMON_PADDR, though some users have interest in only one use case. As more monitoring primitives are introduced, the problem will be bigger. To minimize such unnecessary dependency, this patchset makes monitoring primitives can be registered by the implemnting code and later dynamically searched and selected by the user code. In addition to that, this patchset renames monitoring primitives to monitoring operations, which is more easy to intuitively understand what it means and how it would be structed. This patch (of 8): DAMON has a set of callback functions called monitoring primitives and let it can be configured with various implementations for easy extension for different address spaces and usages. However, the word 'primitive' is not so explicit. Meanwhile, many other structs resembles similar purpose calls themselves 'operations'. To make the code easier to be understood, this commit renames 'damon_primitives' to 'damon_operations' before it is too late to rename. Link: https://lkml.kernel.org/r/20220215184603.1479-1-sj@kernel.org Link: https://lkml.kernel.org/r/20220215184603.1479-2-sj@kernel.org Signed-off-by: SeongJae Park <sj@kernel.org> Cc: Xin Hao <xhao@linux.alibaba.com> Cc: David Rientjes <rientjes@google.com> Signed-off-by: Andrew Morton <akpm@linux-foundation.org> Signed-off-by: Linus Torvalds <torvalds@linux-foundation.org>
2022-03-23 05:48:46 +08:00
* targets is checked via the &damon_operations.target_valid of @ops. The
mm: introduce Data Access MONitor (DAMON) Patch series "Introduce Data Access MONitor (DAMON)", v34. Introduction ============ DAMON is a data access monitoring framework for the Linux kernel. The core mechanisms of DAMON called 'region based sampling' and 'adaptive regions adjustment' (refer to 'mechanisms.rst' in the 11th patch of this patchset for the detail) make it - accurate (The monitored information is useful for DRAM level memory management. It might not appropriate for Cache-level accuracy, though.), - light-weight (The monitoring overhead is low enough to be applied online while making no impact on the performance of the target workloads.), and - scalable (the upper-bound of the instrumentation overhead is controllable regardless of the size of target workloads.). Using this framework, therefore, several memory management mechanisms such as reclamation and THP can be optimized to aware real data access patterns. Experimental access pattern aware memory management optimization works that incurring high instrumentation overhead will be able to have another try. Though DAMON is for kernel subsystems, it can be easily exposed to the user space by writing a DAMON-wrapper kernel subsystem. Then, user space users who have some special workloads will be able to write personalized tools or applications for deeper understanding and specialized optimizations of their systems. DAMON is also merged in two public Amazon Linux kernel trees that based on v5.4.y[1] and v5.10.y[2]. [1] https://github.com/amazonlinux/linux/tree/amazon-5.4.y/master/mm/damon [2] https://github.com/amazonlinux/linux/tree/amazon-5.10.y/master/mm/damon The userspace tool[1] is available, released under GPLv2, and actively being maintained. I am also planning to implement another basic user interface in perf[2]. Also, the basic test suite for DAMON is available under GPLv2[3]. [1] https://github.com/awslabs/damo [2] https://lore.kernel.org/linux-mm/20210107120729.22328-1-sjpark@amazon.com/ [3] https://github.com/awslabs/damon-tests Long-term Plan -------------- DAMON is a part of a project called Data Access-aware Operating System (DAOS). As the name implies, I want to improve the performance and efficiency of systems using fine-grained data access patterns. The optimizations are for both kernel and user spaces. I will therefore modify or create kernel subsystems, export some of those to user space and implement user space library / tools. Below shows the layers and components for the project. --------------------------------------------------------------------------- Primitives: PTE Accessed bit, PG_idle, rmap, (Intel CMT), ... Framework: DAMON Features: DAMOS, virtual addr, physical addr, ... Applications: DAMON-debugfs, (DARC), ... ^^^^^^^^^^^^^^^^^^^^^^^ KERNEL SPACE ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ Raw Interface: debugfs, (sysfs), (damonfs), tracepoints, (sys_damon), ... vvvvvvvvvvvvvvvvvvvvvvv USER SPACE vvvvvvvvvvvvvvvvvvvvvvvvvvvvvvvv Library: (libdamon), ... Tools: DAMO, (perf), ... --------------------------------------------------------------------------- The components in parentheses or marked as '...' are not implemented yet but in the future plan. IOW, those are the TODO tasks of DAOS project. For more detail, please refer to the plans: https://lore.kernel.org/linux-mm/20201202082731.24828-1-sjpark@amazon.com/ Evaluations =========== We evaluated DAMON's overhead, monitoring quality and usefulness using 24 realistic workloads on my QEMU/KVM based virtual machine running a kernel that v24 DAMON patchset is applied. DAMON is lightweight. It increases system memory usage by 0.39% and slows target workloads down by 1.16%. DAMON is accurate and useful for memory management optimizations. An experimental DAMON-based operation scheme for THP, namely 'ethp', removes 76.15% of THP memory overheads while preserving 51.25% of THP speedup. Another experimental DAMON-based 'proactive reclamation' implementation, 'prcl', reduces 93.38% of residential sets and 23.63% of system memory footprint while incurring only 1.22% runtime overhead in the best case (parsec3/freqmine). NOTE that the experimental THP optimization and proactive reclamation are not for production but only for proof of concepts. Please refer to the official document[1] or "Documentation/admin-guide/mm: Add a document for DAMON" patch in this patchset for detailed evaluation setup and results. [1] https://damonitor.github.io/doc/html/latest-damon/admin-guide/mm/damon/eval.html Real-world User Story ===================== In summary, DAMON has used on production systems and proved its usefulness. DAMON as a profiler ------------------- We analyzed characteristics of a large scale production systems of our customers using DAMON. The systems utilize 70GB DRAM and 36 CPUs. From this, we were able to find interesting things below. There were obviously different access pattern under idle workload and active workload. Under the idle workload, it accessed large memory regions with low frequency, while the active workload accessed small memory regions with high freuqnecy. DAMON found a 7GB memory region that showing obviously high access frequency under the active workload. We believe this is the performance-effective working set and need to be protected. There was a 4KB memory region that showing highest access frequency under not only active but also idle workloads. We think this must be a hottest code section like thing that should never be paged out. For this analysis, DAMON used only 0.3-1% of single CPU time. Because we used recording-based analysis, it consumed about 3-12 MB of disk space per 20 minutes. This is only small amount of disk space, but we can further reduce the disk usage by using non-recording-based DAMON features. I'd like to argue that only DAMON can do such detailed analysis (finding 4KB highest region in 70GB memory) with the light overhead. DAMON as a system optimization tool ----------------------------------- We also found below potential performance problems on the systems and made DAMON-based solutions. The system doesn't want to make the workload suffer from the page reclamation and thus it utilizes enough DRAM but no swap device. However, we found the system is actively reclaiming file-backed pages, because the system has intensive file IO. The file IO turned out to be not performance critical for the workload, but the customer wanted to ensure performance critical file-backed pages like code section to not mistakenly be evicted. Using direct IO should or `mlock()` would be a straightforward solution, but modifying the user space code is not easy for the customer. Alternatively, we could use DAMON-based operation scheme[1]. By using it, we can ask DAMON to track access frequency of each region and make 'process_madvise(MADV_WILLNEED)[2]' call for regions having specific size and access frequency for a time interval. We also found the system is having high number of TLB misses. We tried 'always' THP enabled policy and it greatly reduced TLB misses, but the page reclamation also been more frequent due to the THP internal fragmentation caused memory bloat. We could try another DAMON-based operation scheme that applies 'MADV_HUGEPAGE' to memory regions having >=2MB size and high access frequency, while applying 'MADV_NOHUGEPAGE' to regions having <2MB size and low access frequency. We do not own the systems so we only reported the analysis results and possible optimization solutions to the customers. The customers satisfied about the analysis results and promised to try the optimization guides. [1] https://lore.kernel.org/linux-mm/20201006123931.5847-1-sjpark@amazon.com/ [2] https://lore.kernel.org/linux-api/20200622192900.22757-4-minchan@kernel.org/ Comparison with Idle Page Tracking ================================== Idle Page Tracking allows users to set and read idleness of pages using a bitmap file which represents each page with each bit of the file. One recommended usage of it is working set size detection. Users can do that by 1. find PFN of each page for workloads in interest, 2. set all the pages as idle by doing writes to the bitmap file, 3. wait until the workload accesses its working set, and 4. read the idleness of the pages again and count pages became not idle. NOTE: While Idle Page Tracking is for user space users, DAMON is primarily designed for kernel subsystems though it can easily exposed to the user space. Hence, this section only assumes such user space use of DAMON. For what use cases Idle Page Tracking would be better? ------------------------------------------------------ 1. Flexible usecases other than hotness monitoring. Because Idle Page Tracking allows users to control the primitive (Page idleness) by themselves, Idle Page Tracking users can do anything they want. Meanwhile, DAMON is primarily designed to monitor the hotness of each memory region. For this, DAMON asks users to provide sampling interval and aggregation interval. For the reason, there could be some use case that using Idle Page Tracking is simpler. 2. Physical memory monitoring. Idle Page Tracking receives PFN range as input, so natively supports physical memory monitoring. DAMON is designed to be extensible for multiple address spaces and use cases by implementing and using primitives for the given use case. Therefore, by theory, DAMON has no limitation in the type of target address space as long as primitives for the given address space exists. However, the default primitives introduced by this patchset supports only virtual address spaces. Therefore, for physical memory monitoring, you should implement your own primitives and use it, or simply use Idle Page Tracking. Nonetheless, RFC patchsets[1] for the physical memory address space primitives is already available. It also supports user memory same to Idle Page Tracking. [1] https://lore.kernel.org/linux-mm/20200831104730.28970-1-sjpark@amazon.com/ For what use cases DAMON is better? ----------------------------------- 1. Hotness Monitoring. Idle Page Tracking let users know only if a page frame is accessed or not. For hotness check, the user should write more code and use more memory. DAMON do that by itself. 2. Low Monitoring Overhead DAMON receives user's monitoring request with one step and then provide the results. So, roughly speaking, DAMON require only O(1) user/kernel context switches. In case of Idle Page Tracking, however, because the interface receives contiguous page frames, the number of user/kernel context switches increases as the monitoring target becomes complex and huge. As a result, the context switch overhead could be not negligible. Moreover, DAMON is born to handle with the monitoring overhead. Because the core mechanism is pure logical, Idle Page Tracking users might be able to implement the mechanism on their own, but it would be time consuming and the user/kernel context switching will still more frequent than that of DAMON. Also, the kernel subsystems cannot use the logic in this case. 3. Page granularity working set size detection. Until v22 of this patchset, this was categorized as the thing Idle Page Tracking could do better, because DAMON basically maintains additional metadata for each of the monitoring target regions. So, in the page granularity working set size detection use case, DAMON would incur (number of monitoring target pages * size of metadata) memory overhead. Size of the single metadata item is about 54 bytes, so assuming 4KB pages, about 1.3% of monitoring target pages will be additionally used. All essential metadata for Idle Page Tracking are embedded in 'struct page' and page table entries. Therefore, in this use case, only one counter variable for working set size accounting is required if Idle Page Tracking is used. There are more details to consider, but roughly speaking, this is true in most cases. However, the situation changed from v23. Now DAMON supports arbitrary types of monitoring targets, which don't use the metadata. Using that, DAMON can do the working set size detection with no additional space overhead but less user-kernel context switch. A first draft for the implementation of monitoring primitives for this usage is available in a DAMON development tree[1]. An RFC patchset for it based on this patchset will also be available soon. Since v24, the arbitrary type support is dropped from this patchset because this patchset doesn't introduce real use of the type. You can still get it from the DAMON development tree[2], though. [1] https://github.com/sjp38/linux/tree/damon/pgidle_hack [2] https://github.com/sjp38/linux/tree/damon/master 4. More future usecases While Idle Page Tracking has tight coupling with base primitives (PG_Idle and page table Accessed bits), DAMON is designed to be extensible for many use cases and address spaces. If you need some special address type or want to use special h/w access check primitives, you can write your own primitives for that and configure DAMON to use those. Therefore, if your use case could be changed a lot in future, using DAMON could be better. Can I use both Idle Page Tracking and DAMON? -------------------------------------------- Yes, though using them concurrently for overlapping memory regions could result in interference to each other. Nevertheless, such use case would be rare or makes no sense at all. Even in the case, the noise would bot be really significant. So, you can choose whatever you want depending on the characteristics of your use cases. More Information ================ We prepared a showcase web site[1] that you can get more information. There are - the official documentations[2], - the heatmap format dynamic access pattern of various realistic workloads for heap area[3], mmap()-ed area[4], and stack[5] area, - the dynamic working set size distribution[6] and chronological working set size changes[7], and - the latest performance test results[8]. [1] https://damonitor.github.io/_index [2] https://damonitor.github.io/doc/html/latest-damon [3] https://damonitor.github.io/test/result/visual/latest/rec.heatmap.0.png.html [4] https://damonitor.github.io/test/result/visual/latest/rec.heatmap.1.png.html [5] https://damonitor.github.io/test/result/visual/latest/rec.heatmap.2.png.html [6] https://damonitor.github.io/test/result/visual/latest/rec.wss_sz.png.html [7] https://damonitor.github.io/test/result/visual/latest/rec.wss_time.png.html [8] https://damonitor.github.io/test/result/perf/latest/html/index.html Baseline and Complete Git Trees =============================== The patches are based on the latest -mm tree, specifically v5.14-rc1-mmots-2021-07-15-18-47 of https://github.com/hnaz/linux-mm. You can also clone the complete git tree: $ git clone git://github.com/sjp38/linux -b damon/patches/v34 The web is also available: https://github.com/sjp38/linux/releases/tag/damon/patches/v34 Development Trees ----------------- There are a couple of trees for entire DAMON patchset series and features for future release. - For latest release: https://github.com/sjp38/linux/tree/damon/master - For next release: https://github.com/sjp38/linux/tree/damon/next Long-term Support Trees ----------------------- For people who want to test DAMON but using LTS kernels, there are another couple of trees based on two latest LTS kernels respectively and containing the 'damon/master' backports. - For v5.4.y: https://github.com/sjp38/linux/tree/damon/for-v5.4.y - For v5.10.y: https://github.com/sjp38/linux/tree/damon/for-v5.10.y Amazon Linux Kernel Trees ------------------------- DAMON is also merged in two public Amazon Linux kernel trees that based on v5.4.y[1] and v5.10.y[2]. [1] https://github.com/amazonlinux/linux/tree/amazon-5.4.y/master/mm/damon [2] https://github.com/amazonlinux/linux/tree/amazon-5.10.y/master/mm/damon Git Tree for Diff of Patches ============================ For easy review of diff between different versions of each patch, I prepared a git tree containing all versions of the DAMON patchset series: https://github.com/sjp38/damon-patches You can clone it and use 'diff' for easy review of changes between different versions of the patchset. For example: $ git clone https://github.com/sjp38/damon-patches && cd damon-patches $ diff -u damon/v33 damon/v34 Sequence Of Patches =================== First three patches implement the core logics of DAMON. The 1st patch introduces basic sampling based hotness monitoring for arbitrary types of targets. Following two patches implement the core mechanisms for control of overhead and accuracy, namely regions based sampling (patch 2) and adaptive regions adjustment (patch 3). Now the essential parts of DAMON is complete, but it cannot work unless someone provides monitoring primitives for a specific use case. The following two patches make it just work for virtual address spaces monitoring. The 4th patch makes 'PG_idle' can be used by DAMON and the 5th patch implements the virtual memory address space specific monitoring primitives using page table Accessed bits and the 'PG_idle' page flag. Now DAMON just works for virtual address space monitoring via the kernel space api. To let the user space users can use DAMON, following four patches add interfaces for them. The 6th patch adds a tracepoint for monitoring results. The 7th patch implements a DAMON application kernel module, namely damon-dbgfs, that simply wraps DAMON and exposes DAMON interface to the user space via the debugfs interface. The 8th patch further exports pid of monitoring thread (kdamond) to user space for easier cpu usage accounting, and the 9th patch makes the debugfs interface to support multiple contexts. Three patches for maintainability follows. The 10th patch adds documentations for both the user space and the kernel space. The 11th patch provides unit tests (based on the kunit) while the 12th patch adds user space tests (based on the kselftest). Finally, the last patch (13th) updates the MAINTAINERS file. This patch (of 13): DAMON is a data access monitoring framework for the Linux kernel. The core mechanisms of DAMON make it - accurate (the monitoring output is useful enough for DRAM level performance-centric memory management; It might be inappropriate for CPU cache levels, though), - light-weight (the monitoring overhead is normally low enough to be applied online), and - scalable (the upper-bound of the overhead is in constant range regardless of the size of target workloads). Using this framework, hence, we can easily write efficient kernel space data access monitoring applications. For example, the kernel's memory management mechanisms can make advanced decisions using this. Experimental data access aware optimization works that incurring high access monitoring overhead could again be implemented on top of this. Due to its simple and flexible interface, providing user space interface would be also easy. Then, user space users who have some special workloads can write personalized applications for better understanding and optimizations of their workloads and systems. === Nevertheless, this commit is defining and implementing only basic access check part without the overhead-accuracy handling core logic. The basic access check is as below. The output of DAMON says what memory regions are how frequently accessed for a given duration. The resolution of the access frequency is controlled by setting ``sampling interval`` and ``aggregation interval``. In detail, DAMON checks access to each page per ``sampling interval`` and aggregates the results. In other words, counts the number of the accesses to each region. After each ``aggregation interval`` passes, DAMON calls callback functions that previously registered by users so that users can read the aggregated results and then clears the results. This can be described in below simple pseudo-code:: init() while monitoring_on: for page in monitoring_target: if accessed(page): nr_accesses[page] += 1 if time() % aggregation_interval == 0: for callback in user_registered_callbacks: callback(monitoring_target, nr_accesses) for page in monitoring_target: nr_accesses[page] = 0 if time() % update_interval == 0: update() sleep(sampling interval) The target regions constructed at the beginning of the monitoring and updated after each ``regions_update_interval``, because the target regions could be dynamically changed (e.g., mmap() or memory hotplug). The monitoring overhead of this mechanism will arbitrarily increase as the size of the target workload grows. The basic monitoring primitives for actual access check and dynamic target regions construction aren't in the core part of DAMON. Instead, it allows users to implement their own primitives that are optimized for their use case and configure DAMON to use those. In other words, users cannot use current version of DAMON without some additional works. Following commits will implement the core mechanisms for the overhead-accuracy control and default primitives implementations. Link: https://lkml.kernel.org/r/20210716081449.22187-1-sj38.park@gmail.com Link: https://lkml.kernel.org/r/20210716081449.22187-2-sj38.park@gmail.com Signed-off-by: SeongJae Park <sjpark@amazon.de> Reviewed-by: Leonard Foerster <foersleo@amazon.de> Reviewed-by: Fernand Sieber <sieberf@amazon.com> Acked-by: Shakeel Butt <shakeelb@google.com> Cc: Jonathan Cameron <Jonathan.Cameron@huawei.com> Cc: Alexander Shishkin <alexander.shishkin@linux.intel.com> Cc: Amit Shah <amit@kernel.org> Cc: Benjamin Herrenschmidt <benh@kernel.crashing.org> Cc: Jonathan Corbet <corbet@lwn.net> Cc: David Hildenbrand <david@redhat.com> Cc: David Woodhouse <dwmw@amazon.com> Cc: Marco Elver <elver@google.com> Cc: Fan Du <fan.du@intel.com> Cc: Greg Kroah-Hartman <greg@kroah.com> Cc: Greg Thelen <gthelen@google.com> Cc: Joe Perches <joe@perches.com> Cc: Mel Gorman <mgorman@suse.de> Cc: Maximilian Heyne <mheyne@amazon.de> Cc: Minchan Kim <minchan@kernel.org> Cc: Ingo Molnar <mingo@redhat.com> Cc: Namhyung Kim <namhyung@kernel.org> Cc: Peter Zijlstra <peterz@infradead.org> Cc: Rik van Riel <riel@surriel.com> Cc: David Rientjes <rientjes@google.com> Cc: Steven Rostedt (VMware) <rostedt@goodmis.org> Cc: Shuah Khan <shuah@kernel.org> Cc: Vlastimil Babka <vbabka@suse.cz> Cc: Vladimir Davydov <vdavydov.dev@gmail.com> Cc: Brendan Higgins <brendanhiggins@google.com> Cc: Markus Boehme <markubo@amazon.de> Signed-off-by: Andrew Morton <akpm@linux-foundation.org> Signed-off-by: Linus Torvalds <torvalds@linux-foundation.org>
2021-09-08 10:56:28 +08:00
* termination can also be explicitly requested by writing non-zero to
* @kdamond_stop. The thread sets @kdamond to NULL when it terminates.
* Therefore, users can know whether the monitoring is ongoing or terminated by
* reading @kdamond. Reads and writes to @kdamond and @kdamond_stop from
* outside of the monitoring thread must be protected by @kdamond_lock.
*
* Note that the monitoring thread protects only @kdamond and @kdamond_stop via
* @kdamond_lock. Accesses to other fields must be protected by themselves.
*
mm/damon: rename damon_primitives to damon_operations Patch series "Allow DAMON user code independent of monitoring primitives". In-kernel DAMON user code is required to configure the monitoring context (struct damon_ctx) with proper monitoring primitives (struct damon_primitive). This makes the user code dependent to all supporting monitoring primitives. For example, DAMON debugfs interface depends on both DAMON_VADDR and DAMON_PADDR, though some users have interest in only one use case. As more monitoring primitives are introduced, the problem will be bigger. To minimize such unnecessary dependency, this patchset makes monitoring primitives can be registered by the implemnting code and later dynamically searched and selected by the user code. In addition to that, this patchset renames monitoring primitives to monitoring operations, which is more easy to intuitively understand what it means and how it would be structed. This patch (of 8): DAMON has a set of callback functions called monitoring primitives and let it can be configured with various implementations for easy extension for different address spaces and usages. However, the word 'primitive' is not so explicit. Meanwhile, many other structs resembles similar purpose calls themselves 'operations'. To make the code easier to be understood, this commit renames 'damon_primitives' to 'damon_operations' before it is too late to rename. Link: https://lkml.kernel.org/r/20220215184603.1479-1-sj@kernel.org Link: https://lkml.kernel.org/r/20220215184603.1479-2-sj@kernel.org Signed-off-by: SeongJae Park <sj@kernel.org> Cc: Xin Hao <xhao@linux.alibaba.com> Cc: David Rientjes <rientjes@google.com> Signed-off-by: Andrew Morton <akpm@linux-foundation.org> Signed-off-by: Linus Torvalds <torvalds@linux-foundation.org>
2022-03-23 05:48:46 +08:00
* @ops: Set of monitoring operations for given use cases.
mm: introduce Data Access MONitor (DAMON) Patch series "Introduce Data Access MONitor (DAMON)", v34. Introduction ============ DAMON is a data access monitoring framework for the Linux kernel. The core mechanisms of DAMON called 'region based sampling' and 'adaptive regions adjustment' (refer to 'mechanisms.rst' in the 11th patch of this patchset for the detail) make it - accurate (The monitored information is useful for DRAM level memory management. It might not appropriate for Cache-level accuracy, though.), - light-weight (The monitoring overhead is low enough to be applied online while making no impact on the performance of the target workloads.), and - scalable (the upper-bound of the instrumentation overhead is controllable regardless of the size of target workloads.). Using this framework, therefore, several memory management mechanisms such as reclamation and THP can be optimized to aware real data access patterns. Experimental access pattern aware memory management optimization works that incurring high instrumentation overhead will be able to have another try. Though DAMON is for kernel subsystems, it can be easily exposed to the user space by writing a DAMON-wrapper kernel subsystem. Then, user space users who have some special workloads will be able to write personalized tools or applications for deeper understanding and specialized optimizations of their systems. DAMON is also merged in two public Amazon Linux kernel trees that based on v5.4.y[1] and v5.10.y[2]. [1] https://github.com/amazonlinux/linux/tree/amazon-5.4.y/master/mm/damon [2] https://github.com/amazonlinux/linux/tree/amazon-5.10.y/master/mm/damon The userspace tool[1] is available, released under GPLv2, and actively being maintained. I am also planning to implement another basic user interface in perf[2]. Also, the basic test suite for DAMON is available under GPLv2[3]. [1] https://github.com/awslabs/damo [2] https://lore.kernel.org/linux-mm/20210107120729.22328-1-sjpark@amazon.com/ [3] https://github.com/awslabs/damon-tests Long-term Plan -------------- DAMON is a part of a project called Data Access-aware Operating System (DAOS). As the name implies, I want to improve the performance and efficiency of systems using fine-grained data access patterns. The optimizations are for both kernel and user spaces. I will therefore modify or create kernel subsystems, export some of those to user space and implement user space library / tools. Below shows the layers and components for the project. --------------------------------------------------------------------------- Primitives: PTE Accessed bit, PG_idle, rmap, (Intel CMT), ... Framework: DAMON Features: DAMOS, virtual addr, physical addr, ... Applications: DAMON-debugfs, (DARC), ... ^^^^^^^^^^^^^^^^^^^^^^^ KERNEL SPACE ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ Raw Interface: debugfs, (sysfs), (damonfs), tracepoints, (sys_damon), ... vvvvvvvvvvvvvvvvvvvvvvv USER SPACE vvvvvvvvvvvvvvvvvvvvvvvvvvvvvvvv Library: (libdamon), ... Tools: DAMO, (perf), ... --------------------------------------------------------------------------- The components in parentheses or marked as '...' are not implemented yet but in the future plan. IOW, those are the TODO tasks of DAOS project. For more detail, please refer to the plans: https://lore.kernel.org/linux-mm/20201202082731.24828-1-sjpark@amazon.com/ Evaluations =========== We evaluated DAMON's overhead, monitoring quality and usefulness using 24 realistic workloads on my QEMU/KVM based virtual machine running a kernel that v24 DAMON patchset is applied. DAMON is lightweight. It increases system memory usage by 0.39% and slows target workloads down by 1.16%. DAMON is accurate and useful for memory management optimizations. An experimental DAMON-based operation scheme for THP, namely 'ethp', removes 76.15% of THP memory overheads while preserving 51.25% of THP speedup. Another experimental DAMON-based 'proactive reclamation' implementation, 'prcl', reduces 93.38% of residential sets and 23.63% of system memory footprint while incurring only 1.22% runtime overhead in the best case (parsec3/freqmine). NOTE that the experimental THP optimization and proactive reclamation are not for production but only for proof of concepts. Please refer to the official document[1] or "Documentation/admin-guide/mm: Add a document for DAMON" patch in this patchset for detailed evaluation setup and results. [1] https://damonitor.github.io/doc/html/latest-damon/admin-guide/mm/damon/eval.html Real-world User Story ===================== In summary, DAMON has used on production systems and proved its usefulness. DAMON as a profiler ------------------- We analyzed characteristics of a large scale production systems of our customers using DAMON. The systems utilize 70GB DRAM and 36 CPUs. From this, we were able to find interesting things below. There were obviously different access pattern under idle workload and active workload. Under the idle workload, it accessed large memory regions with low frequency, while the active workload accessed small memory regions with high freuqnecy. DAMON found a 7GB memory region that showing obviously high access frequency under the active workload. We believe this is the performance-effective working set and need to be protected. There was a 4KB memory region that showing highest access frequency under not only active but also idle workloads. We think this must be a hottest code section like thing that should never be paged out. For this analysis, DAMON used only 0.3-1% of single CPU time. Because we used recording-based analysis, it consumed about 3-12 MB of disk space per 20 minutes. This is only small amount of disk space, but we can further reduce the disk usage by using non-recording-based DAMON features. I'd like to argue that only DAMON can do such detailed analysis (finding 4KB highest region in 70GB memory) with the light overhead. DAMON as a system optimization tool ----------------------------------- We also found below potential performance problems on the systems and made DAMON-based solutions. The system doesn't want to make the workload suffer from the page reclamation and thus it utilizes enough DRAM but no swap device. However, we found the system is actively reclaiming file-backed pages, because the system has intensive file IO. The file IO turned out to be not performance critical for the workload, but the customer wanted to ensure performance critical file-backed pages like code section to not mistakenly be evicted. Using direct IO should or `mlock()` would be a straightforward solution, but modifying the user space code is not easy for the customer. Alternatively, we could use DAMON-based operation scheme[1]. By using it, we can ask DAMON to track access frequency of each region and make 'process_madvise(MADV_WILLNEED)[2]' call for regions having specific size and access frequency for a time interval. We also found the system is having high number of TLB misses. We tried 'always' THP enabled policy and it greatly reduced TLB misses, but the page reclamation also been more frequent due to the THP internal fragmentation caused memory bloat. We could try another DAMON-based operation scheme that applies 'MADV_HUGEPAGE' to memory regions having >=2MB size and high access frequency, while applying 'MADV_NOHUGEPAGE' to regions having <2MB size and low access frequency. We do not own the systems so we only reported the analysis results and possible optimization solutions to the customers. The customers satisfied about the analysis results and promised to try the optimization guides. [1] https://lore.kernel.org/linux-mm/20201006123931.5847-1-sjpark@amazon.com/ [2] https://lore.kernel.org/linux-api/20200622192900.22757-4-minchan@kernel.org/ Comparison with Idle Page Tracking ================================== Idle Page Tracking allows users to set and read idleness of pages using a bitmap file which represents each page with each bit of the file. One recommended usage of it is working set size detection. Users can do that by 1. find PFN of each page for workloads in interest, 2. set all the pages as idle by doing writes to the bitmap file, 3. wait until the workload accesses its working set, and 4. read the idleness of the pages again and count pages became not idle. NOTE: While Idle Page Tracking is for user space users, DAMON is primarily designed for kernel subsystems though it can easily exposed to the user space. Hence, this section only assumes such user space use of DAMON. For what use cases Idle Page Tracking would be better? ------------------------------------------------------ 1. Flexible usecases other than hotness monitoring. Because Idle Page Tracking allows users to control the primitive (Page idleness) by themselves, Idle Page Tracking users can do anything they want. Meanwhile, DAMON is primarily designed to monitor the hotness of each memory region. For this, DAMON asks users to provide sampling interval and aggregation interval. For the reason, there could be some use case that using Idle Page Tracking is simpler. 2. Physical memory monitoring. Idle Page Tracking receives PFN range as input, so natively supports physical memory monitoring. DAMON is designed to be extensible for multiple address spaces and use cases by implementing and using primitives for the given use case. Therefore, by theory, DAMON has no limitation in the type of target address space as long as primitives for the given address space exists. However, the default primitives introduced by this patchset supports only virtual address spaces. Therefore, for physical memory monitoring, you should implement your own primitives and use it, or simply use Idle Page Tracking. Nonetheless, RFC patchsets[1] for the physical memory address space primitives is already available. It also supports user memory same to Idle Page Tracking. [1] https://lore.kernel.org/linux-mm/20200831104730.28970-1-sjpark@amazon.com/ For what use cases DAMON is better? ----------------------------------- 1. Hotness Monitoring. Idle Page Tracking let users know only if a page frame is accessed or not. For hotness check, the user should write more code and use more memory. DAMON do that by itself. 2. Low Monitoring Overhead DAMON receives user's monitoring request with one step and then provide the results. So, roughly speaking, DAMON require only O(1) user/kernel context switches. In case of Idle Page Tracking, however, because the interface receives contiguous page frames, the number of user/kernel context switches increases as the monitoring target becomes complex and huge. As a result, the context switch overhead could be not negligible. Moreover, DAMON is born to handle with the monitoring overhead. Because the core mechanism is pure logical, Idle Page Tracking users might be able to implement the mechanism on their own, but it would be time consuming and the user/kernel context switching will still more frequent than that of DAMON. Also, the kernel subsystems cannot use the logic in this case. 3. Page granularity working set size detection. Until v22 of this patchset, this was categorized as the thing Idle Page Tracking could do better, because DAMON basically maintains additional metadata for each of the monitoring target regions. So, in the page granularity working set size detection use case, DAMON would incur (number of monitoring target pages * size of metadata) memory overhead. Size of the single metadata item is about 54 bytes, so assuming 4KB pages, about 1.3% of monitoring target pages will be additionally used. All essential metadata for Idle Page Tracking are embedded in 'struct page' and page table entries. Therefore, in this use case, only one counter variable for working set size accounting is required if Idle Page Tracking is used. There are more details to consider, but roughly speaking, this is true in most cases. However, the situation changed from v23. Now DAMON supports arbitrary types of monitoring targets, which don't use the metadata. Using that, DAMON can do the working set size detection with no additional space overhead but less user-kernel context switch. A first draft for the implementation of monitoring primitives for this usage is available in a DAMON development tree[1]. An RFC patchset for it based on this patchset will also be available soon. Since v24, the arbitrary type support is dropped from this patchset because this patchset doesn't introduce real use of the type. You can still get it from the DAMON development tree[2], though. [1] https://github.com/sjp38/linux/tree/damon/pgidle_hack [2] https://github.com/sjp38/linux/tree/damon/master 4. More future usecases While Idle Page Tracking has tight coupling with base primitives (PG_Idle and page table Accessed bits), DAMON is designed to be extensible for many use cases and address spaces. If you need some special address type or want to use special h/w access check primitives, you can write your own primitives for that and configure DAMON to use those. Therefore, if your use case could be changed a lot in future, using DAMON could be better. Can I use both Idle Page Tracking and DAMON? -------------------------------------------- Yes, though using them concurrently for overlapping memory regions could result in interference to each other. Nevertheless, such use case would be rare or makes no sense at all. Even in the case, the noise would bot be really significant. So, you can choose whatever you want depending on the characteristics of your use cases. More Information ================ We prepared a showcase web site[1] that you can get more information. There are - the official documentations[2], - the heatmap format dynamic access pattern of various realistic workloads for heap area[3], mmap()-ed area[4], and stack[5] area, - the dynamic working set size distribution[6] and chronological working set size changes[7], and - the latest performance test results[8]. [1] https://damonitor.github.io/_index [2] https://damonitor.github.io/doc/html/latest-damon [3] https://damonitor.github.io/test/result/visual/latest/rec.heatmap.0.png.html [4] https://damonitor.github.io/test/result/visual/latest/rec.heatmap.1.png.html [5] https://damonitor.github.io/test/result/visual/latest/rec.heatmap.2.png.html [6] https://damonitor.github.io/test/result/visual/latest/rec.wss_sz.png.html [7] https://damonitor.github.io/test/result/visual/latest/rec.wss_time.png.html [8] https://damonitor.github.io/test/result/perf/latest/html/index.html Baseline and Complete Git Trees =============================== The patches are based on the latest -mm tree, specifically v5.14-rc1-mmots-2021-07-15-18-47 of https://github.com/hnaz/linux-mm. You can also clone the complete git tree: $ git clone git://github.com/sjp38/linux -b damon/patches/v34 The web is also available: https://github.com/sjp38/linux/releases/tag/damon/patches/v34 Development Trees ----------------- There are a couple of trees for entire DAMON patchset series and features for future release. - For latest release: https://github.com/sjp38/linux/tree/damon/master - For next release: https://github.com/sjp38/linux/tree/damon/next Long-term Support Trees ----------------------- For people who want to test DAMON but using LTS kernels, there are another couple of trees based on two latest LTS kernels respectively and containing the 'damon/master' backports. - For v5.4.y: https://github.com/sjp38/linux/tree/damon/for-v5.4.y - For v5.10.y: https://github.com/sjp38/linux/tree/damon/for-v5.10.y Amazon Linux Kernel Trees ------------------------- DAMON is also merged in two public Amazon Linux kernel trees that based on v5.4.y[1] and v5.10.y[2]. [1] https://github.com/amazonlinux/linux/tree/amazon-5.4.y/master/mm/damon [2] https://github.com/amazonlinux/linux/tree/amazon-5.10.y/master/mm/damon Git Tree for Diff of Patches ============================ For easy review of diff between different versions of each patch, I prepared a git tree containing all versions of the DAMON patchset series: https://github.com/sjp38/damon-patches You can clone it and use 'diff' for easy review of changes between different versions of the patchset. For example: $ git clone https://github.com/sjp38/damon-patches && cd damon-patches $ diff -u damon/v33 damon/v34 Sequence Of Patches =================== First three patches implement the core logics of DAMON. The 1st patch introduces basic sampling based hotness monitoring for arbitrary types of targets. Following two patches implement the core mechanisms for control of overhead and accuracy, namely regions based sampling (patch 2) and adaptive regions adjustment (patch 3). Now the essential parts of DAMON is complete, but it cannot work unless someone provides monitoring primitives for a specific use case. The following two patches make it just work for virtual address spaces monitoring. The 4th patch makes 'PG_idle' can be used by DAMON and the 5th patch implements the virtual memory address space specific monitoring primitives using page table Accessed bits and the 'PG_idle' page flag. Now DAMON just works for virtual address space monitoring via the kernel space api. To let the user space users can use DAMON, following four patches add interfaces for them. The 6th patch adds a tracepoint for monitoring results. The 7th patch implements a DAMON application kernel module, namely damon-dbgfs, that simply wraps DAMON and exposes DAMON interface to the user space via the debugfs interface. The 8th patch further exports pid of monitoring thread (kdamond) to user space for easier cpu usage accounting, and the 9th patch makes the debugfs interface to support multiple contexts. Three patches for maintainability follows. The 10th patch adds documentations for both the user space and the kernel space. The 11th patch provides unit tests (based on the kunit) while the 12th patch adds user space tests (based on the kselftest). Finally, the last patch (13th) updates the MAINTAINERS file. This patch (of 13): DAMON is a data access monitoring framework for the Linux kernel. The core mechanisms of DAMON make it - accurate (the monitoring output is useful enough for DRAM level performance-centric memory management; It might be inappropriate for CPU cache levels, though), - light-weight (the monitoring overhead is normally low enough to be applied online), and - scalable (the upper-bound of the overhead is in constant range regardless of the size of target workloads). Using this framework, hence, we can easily write efficient kernel space data access monitoring applications. For example, the kernel's memory management mechanisms can make advanced decisions using this. Experimental data access aware optimization works that incurring high access monitoring overhead could again be implemented on top of this. Due to its simple and flexible interface, providing user space interface would be also easy. Then, user space users who have some special workloads can write personalized applications for better understanding and optimizations of their workloads and systems. === Nevertheless, this commit is defining and implementing only basic access check part without the overhead-accuracy handling core logic. The basic access check is as below. The output of DAMON says what memory regions are how frequently accessed for a given duration. The resolution of the access frequency is controlled by setting ``sampling interval`` and ``aggregation interval``. In detail, DAMON checks access to each page per ``sampling interval`` and aggregates the results. In other words, counts the number of the accesses to each region. After each ``aggregation interval`` passes, DAMON calls callback functions that previously registered by users so that users can read the aggregated results and then clears the results. This can be described in below simple pseudo-code:: init() while monitoring_on: for page in monitoring_target: if accessed(page): nr_accesses[page] += 1 if time() % aggregation_interval == 0: for callback in user_registered_callbacks: callback(monitoring_target, nr_accesses) for page in monitoring_target: nr_accesses[page] = 0 if time() % update_interval == 0: update() sleep(sampling interval) The target regions constructed at the beginning of the monitoring and updated after each ``regions_update_interval``, because the target regions could be dynamically changed (e.g., mmap() or memory hotplug). The monitoring overhead of this mechanism will arbitrarily increase as the size of the target workload grows. The basic monitoring primitives for actual access check and dynamic target regions construction aren't in the core part of DAMON. Instead, it allows users to implement their own primitives that are optimized for their use case and configure DAMON to use those. In other words, users cannot use current version of DAMON without some additional works. Following commits will implement the core mechanisms for the overhead-accuracy control and default primitives implementations. Link: https://lkml.kernel.org/r/20210716081449.22187-1-sj38.park@gmail.com Link: https://lkml.kernel.org/r/20210716081449.22187-2-sj38.park@gmail.com Signed-off-by: SeongJae Park <sjpark@amazon.de> Reviewed-by: Leonard Foerster <foersleo@amazon.de> Reviewed-by: Fernand Sieber <sieberf@amazon.com> Acked-by: Shakeel Butt <shakeelb@google.com> Cc: Jonathan Cameron <Jonathan.Cameron@huawei.com> Cc: Alexander Shishkin <alexander.shishkin@linux.intel.com> Cc: Amit Shah <amit@kernel.org> Cc: Benjamin Herrenschmidt <benh@kernel.crashing.org> Cc: Jonathan Corbet <corbet@lwn.net> Cc: David Hildenbrand <david@redhat.com> Cc: David Woodhouse <dwmw@amazon.com> Cc: Marco Elver <elver@google.com> Cc: Fan Du <fan.du@intel.com> Cc: Greg Kroah-Hartman <greg@kroah.com> Cc: Greg Thelen <gthelen@google.com> Cc: Joe Perches <joe@perches.com> Cc: Mel Gorman <mgorman@suse.de> Cc: Maximilian Heyne <mheyne@amazon.de> Cc: Minchan Kim <minchan@kernel.org> Cc: Ingo Molnar <mingo@redhat.com> Cc: Namhyung Kim <namhyung@kernel.org> Cc: Peter Zijlstra <peterz@infradead.org> Cc: Rik van Riel <riel@surriel.com> Cc: David Rientjes <rientjes@google.com> Cc: Steven Rostedt (VMware) <rostedt@goodmis.org> Cc: Shuah Khan <shuah@kernel.org> Cc: Vlastimil Babka <vbabka@suse.cz> Cc: Vladimir Davydov <vdavydov.dev@gmail.com> Cc: Brendan Higgins <brendanhiggins@google.com> Cc: Markus Boehme <markubo@amazon.de> Signed-off-by: Andrew Morton <akpm@linux-foundation.org> Signed-off-by: Linus Torvalds <torvalds@linux-foundation.org>
2021-09-08 10:56:28 +08:00
* @callback: Set of callbacks for monitoring events notifications.
*
mm/damon: adaptively adjust regions Even somehow the initial monitoring target regions are well constructed to fulfill the assumption (pages in same region have similar access frequencies), the data access pattern can be dynamically changed. This will result in low monitoring quality. To keep the assumption as much as possible, DAMON adaptively merges and splits each region based on their access frequency. For each ``aggregation interval``, it compares the access frequencies of adjacent regions and merges those if the frequency difference is small. Then, after it reports and clears the aggregated access frequency of each region, it splits each region into two or three regions if the total number of regions will not exceed the user-specified maximum number of regions after the split. In this way, DAMON provides its best-effort quality and minimal overhead while keeping the upper-bound overhead that users set. Link: https://lkml.kernel.org/r/20210716081449.22187-4-sj38.park@gmail.com Signed-off-by: SeongJae Park <sjpark@amazon.de> Reviewed-by: Leonard Foerster <foersleo@amazon.de> Reviewed-by: Fernand Sieber <sieberf@amazon.com> Acked-by: Shakeel Butt <shakeelb@google.com> Cc: Alexander Shishkin <alexander.shishkin@linux.intel.com> Cc: Amit Shah <amit@kernel.org> Cc: Benjamin Herrenschmidt <benh@kernel.crashing.org> Cc: Brendan Higgins <brendanhiggins@google.com> Cc: David Hildenbrand <david@redhat.com> Cc: David Rientjes <rientjes@google.com> Cc: David Woodhouse <dwmw@amazon.com> Cc: Fan Du <fan.du@intel.com> Cc: Greg Kroah-Hartman <greg@kroah.com> Cc: Greg Thelen <gthelen@google.com> Cc: Ingo Molnar <mingo@redhat.com> Cc: Joe Perches <joe@perches.com> Cc: Jonathan Cameron <Jonathan.Cameron@huawei.com> Cc: Jonathan Corbet <corbet@lwn.net> Cc: Marco Elver <elver@google.com> Cc: Markus Boehme <markubo@amazon.de> Cc: Maximilian Heyne <mheyne@amazon.de> Cc: Mel Gorman <mgorman@suse.de> Cc: Minchan Kim <minchan@kernel.org> Cc: Namhyung Kim <namhyung@kernel.org> Cc: Peter Zijlstra <peterz@infradead.org> Cc: Rik van Riel <riel@surriel.com> Cc: Shuah Khan <shuah@kernel.org> Cc: Steven Rostedt (VMware) <rostedt@goodmis.org> Cc: Vladimir Davydov <vdavydov.dev@gmail.com> Cc: Vlastimil Babka <vbabka@suse.cz> Signed-off-by: Andrew Morton <akpm@linux-foundation.org> Signed-off-by: Linus Torvalds <torvalds@linux-foundation.org>
2021-09-08 10:56:36 +08:00
* @min_nr_regions: The minimum number of adaptive monitoring regions.
* @max_nr_regions: The maximum number of adaptive monitoring regions.
* @adaptive_targets: Head of monitoring targets (&damon_target) list.
mm/damon/core: implement DAMON-based Operation Schemes (DAMOS) In many cases, users might use DAMON for simple data access aware memory management optimizations such as applying an operation scheme to a memory region of a specific size having a specific access frequency for a specific time. For example, "page out a memory region larger than 100 MiB but having a low access frequency more than 10 minutes", or "Use THP for a memory region larger than 2 MiB having a high access frequency for more than 2 seconds". Most simple form of the solution would be doing offline data access pattern profiling using DAMON and modifying the application source code or system configuration based on the profiling results. Or, developing a daemon constructed with two modules (one for access monitoring and the other for applying memory management actions via mlock(), madvise(), sysctl, etc) is imaginable. To avoid users spending their time for implementation of such simple data access monitoring-based operation schemes, this makes DAMON to handle such schemes directly. With this change, users can simply specify their desired schemes to DAMON. Then, DAMON will automatically apply the schemes to the user-specified target processes. Each of the schemes is composed with conditions for filtering of the target memory regions and desired memory management action for the target. Specifically, the format is:: <min/max size> <min/max access frequency> <min/max age> <action> The filtering conditions are size of memory region, number of accesses to the region monitored by DAMON, and the age of the region. The age of region is incremented periodically but reset when its addresses or access frequency has significantly changed or the action of a scheme was applied. For the action, current implementation supports a few of madvise()-like hints, ``WILLNEED``, ``COLD``, ``PAGEOUT``, ``HUGEPAGE``, and ``NOHUGEPAGE``. Because DAMON supports various address spaces and application of the actions to a monitoring target region is dependent to the type of the target address space, the application code should be implemented by each primitives and registered to the framework. Note that this only implements the framework part. Following commit will implement the action applications for virtual address spaces primitives. Link: https://lkml.kernel.org/r/20211001125604.29660-3-sj@kernel.org Signed-off-by: SeongJae Park <sj@kernel.org> Cc: Amit Shah <amit@kernel.org> Cc: Benjamin Herrenschmidt <benh@kernel.crashing.org> Cc: David Hildenbrand <david@redhat.com> Cc: David Rienjes <rientjes@google.com> Cc: David Woodhouse <dwmw@amazon.com> Cc: Greg Thelen <gthelen@google.com> Cc: Jonathan Cameron <Jonathan.Cameron@huawei.com> Cc: Jonathan Corbet <corbet@lwn.net> Cc: Leonard Foerster <foersleo@amazon.de> Cc: Marco Elver <elver@google.com> Cc: Markus Boehme <markubo@amazon.de> Cc: Shakeel Butt <shakeelb@google.com> Cc: Shuah Khan <shuah@kernel.org> Signed-off-by: Andrew Morton <akpm@linux-foundation.org> Signed-off-by: Linus Torvalds <torvalds@linux-foundation.org>
2021-11-06 04:46:22 +08:00
* @schemes: Head of schemes (&damos) list.
mm: introduce Data Access MONitor (DAMON) Patch series "Introduce Data Access MONitor (DAMON)", v34. Introduction ============ DAMON is a data access monitoring framework for the Linux kernel. The core mechanisms of DAMON called 'region based sampling' and 'adaptive regions adjustment' (refer to 'mechanisms.rst' in the 11th patch of this patchset for the detail) make it - accurate (The monitored information is useful for DRAM level memory management. It might not appropriate for Cache-level accuracy, though.), - light-weight (The monitoring overhead is low enough to be applied online while making no impact on the performance of the target workloads.), and - scalable (the upper-bound of the instrumentation overhead is controllable regardless of the size of target workloads.). Using this framework, therefore, several memory management mechanisms such as reclamation and THP can be optimized to aware real data access patterns. Experimental access pattern aware memory management optimization works that incurring high instrumentation overhead will be able to have another try. Though DAMON is for kernel subsystems, it can be easily exposed to the user space by writing a DAMON-wrapper kernel subsystem. Then, user space users who have some special workloads will be able to write personalized tools or applications for deeper understanding and specialized optimizations of their systems. DAMON is also merged in two public Amazon Linux kernel trees that based on v5.4.y[1] and v5.10.y[2]. [1] https://github.com/amazonlinux/linux/tree/amazon-5.4.y/master/mm/damon [2] https://github.com/amazonlinux/linux/tree/amazon-5.10.y/master/mm/damon The userspace tool[1] is available, released under GPLv2, and actively being maintained. I am also planning to implement another basic user interface in perf[2]. Also, the basic test suite for DAMON is available under GPLv2[3]. [1] https://github.com/awslabs/damo [2] https://lore.kernel.org/linux-mm/20210107120729.22328-1-sjpark@amazon.com/ [3] https://github.com/awslabs/damon-tests Long-term Plan -------------- DAMON is a part of a project called Data Access-aware Operating System (DAOS). As the name implies, I want to improve the performance and efficiency of systems using fine-grained data access patterns. The optimizations are for both kernel and user spaces. I will therefore modify or create kernel subsystems, export some of those to user space and implement user space library / tools. Below shows the layers and components for the project. --------------------------------------------------------------------------- Primitives: PTE Accessed bit, PG_idle, rmap, (Intel CMT), ... Framework: DAMON Features: DAMOS, virtual addr, physical addr, ... Applications: DAMON-debugfs, (DARC), ... ^^^^^^^^^^^^^^^^^^^^^^^ KERNEL SPACE ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ Raw Interface: debugfs, (sysfs), (damonfs), tracepoints, (sys_damon), ... vvvvvvvvvvvvvvvvvvvvvvv USER SPACE vvvvvvvvvvvvvvvvvvvvvvvvvvvvvvvv Library: (libdamon), ... Tools: DAMO, (perf), ... --------------------------------------------------------------------------- The components in parentheses or marked as '...' are not implemented yet but in the future plan. IOW, those are the TODO tasks of DAOS project. For more detail, please refer to the plans: https://lore.kernel.org/linux-mm/20201202082731.24828-1-sjpark@amazon.com/ Evaluations =========== We evaluated DAMON's overhead, monitoring quality and usefulness using 24 realistic workloads on my QEMU/KVM based virtual machine running a kernel that v24 DAMON patchset is applied. DAMON is lightweight. It increases system memory usage by 0.39% and slows target workloads down by 1.16%. DAMON is accurate and useful for memory management optimizations. An experimental DAMON-based operation scheme for THP, namely 'ethp', removes 76.15% of THP memory overheads while preserving 51.25% of THP speedup. Another experimental DAMON-based 'proactive reclamation' implementation, 'prcl', reduces 93.38% of residential sets and 23.63% of system memory footprint while incurring only 1.22% runtime overhead in the best case (parsec3/freqmine). NOTE that the experimental THP optimization and proactive reclamation are not for production but only for proof of concepts. Please refer to the official document[1] or "Documentation/admin-guide/mm: Add a document for DAMON" patch in this patchset for detailed evaluation setup and results. [1] https://damonitor.github.io/doc/html/latest-damon/admin-guide/mm/damon/eval.html Real-world User Story ===================== In summary, DAMON has used on production systems and proved its usefulness. DAMON as a profiler ------------------- We analyzed characteristics of a large scale production systems of our customers using DAMON. The systems utilize 70GB DRAM and 36 CPUs. From this, we were able to find interesting things below. There were obviously different access pattern under idle workload and active workload. Under the idle workload, it accessed large memory regions with low frequency, while the active workload accessed small memory regions with high freuqnecy. DAMON found a 7GB memory region that showing obviously high access frequency under the active workload. We believe this is the performance-effective working set and need to be protected. There was a 4KB memory region that showing highest access frequency under not only active but also idle workloads. We think this must be a hottest code section like thing that should never be paged out. For this analysis, DAMON used only 0.3-1% of single CPU time. Because we used recording-based analysis, it consumed about 3-12 MB of disk space per 20 minutes. This is only small amount of disk space, but we can further reduce the disk usage by using non-recording-based DAMON features. I'd like to argue that only DAMON can do such detailed analysis (finding 4KB highest region in 70GB memory) with the light overhead. DAMON as a system optimization tool ----------------------------------- We also found below potential performance problems on the systems and made DAMON-based solutions. The system doesn't want to make the workload suffer from the page reclamation and thus it utilizes enough DRAM but no swap device. However, we found the system is actively reclaiming file-backed pages, because the system has intensive file IO. The file IO turned out to be not performance critical for the workload, but the customer wanted to ensure performance critical file-backed pages like code section to not mistakenly be evicted. Using direct IO should or `mlock()` would be a straightforward solution, but modifying the user space code is not easy for the customer. Alternatively, we could use DAMON-based operation scheme[1]. By using it, we can ask DAMON to track access frequency of each region and make 'process_madvise(MADV_WILLNEED)[2]' call for regions having specific size and access frequency for a time interval. We also found the system is having high number of TLB misses. We tried 'always' THP enabled policy and it greatly reduced TLB misses, but the page reclamation also been more frequent due to the THP internal fragmentation caused memory bloat. We could try another DAMON-based operation scheme that applies 'MADV_HUGEPAGE' to memory regions having >=2MB size and high access frequency, while applying 'MADV_NOHUGEPAGE' to regions having <2MB size and low access frequency. We do not own the systems so we only reported the analysis results and possible optimization solutions to the customers. The customers satisfied about the analysis results and promised to try the optimization guides. [1] https://lore.kernel.org/linux-mm/20201006123931.5847-1-sjpark@amazon.com/ [2] https://lore.kernel.org/linux-api/20200622192900.22757-4-minchan@kernel.org/ Comparison with Idle Page Tracking ================================== Idle Page Tracking allows users to set and read idleness of pages using a bitmap file which represents each page with each bit of the file. One recommended usage of it is working set size detection. Users can do that by 1. find PFN of each page for workloads in interest, 2. set all the pages as idle by doing writes to the bitmap file, 3. wait until the workload accesses its working set, and 4. read the idleness of the pages again and count pages became not idle. NOTE: While Idle Page Tracking is for user space users, DAMON is primarily designed for kernel subsystems though it can easily exposed to the user space. Hence, this section only assumes such user space use of DAMON. For what use cases Idle Page Tracking would be better? ------------------------------------------------------ 1. Flexible usecases other than hotness monitoring. Because Idle Page Tracking allows users to control the primitive (Page idleness) by themselves, Idle Page Tracking users can do anything they want. Meanwhile, DAMON is primarily designed to monitor the hotness of each memory region. For this, DAMON asks users to provide sampling interval and aggregation interval. For the reason, there could be some use case that using Idle Page Tracking is simpler. 2. Physical memory monitoring. Idle Page Tracking receives PFN range as input, so natively supports physical memory monitoring. DAMON is designed to be extensible for multiple address spaces and use cases by implementing and using primitives for the given use case. Therefore, by theory, DAMON has no limitation in the type of target address space as long as primitives for the given address space exists. However, the default primitives introduced by this patchset supports only virtual address spaces. Therefore, for physical memory monitoring, you should implement your own primitives and use it, or simply use Idle Page Tracking. Nonetheless, RFC patchsets[1] for the physical memory address space primitives is already available. It also supports user memory same to Idle Page Tracking. [1] https://lore.kernel.org/linux-mm/20200831104730.28970-1-sjpark@amazon.com/ For what use cases DAMON is better? ----------------------------------- 1. Hotness Monitoring. Idle Page Tracking let users know only if a page frame is accessed or not. For hotness check, the user should write more code and use more memory. DAMON do that by itself. 2. Low Monitoring Overhead DAMON receives user's monitoring request with one step and then provide the results. So, roughly speaking, DAMON require only O(1) user/kernel context switches. In case of Idle Page Tracking, however, because the interface receives contiguous page frames, the number of user/kernel context switches increases as the monitoring target becomes complex and huge. As a result, the context switch overhead could be not negligible. Moreover, DAMON is born to handle with the monitoring overhead. Because the core mechanism is pure logical, Idle Page Tracking users might be able to implement the mechanism on their own, but it would be time consuming and the user/kernel context switching will still more frequent than that of DAMON. Also, the kernel subsystems cannot use the logic in this case. 3. Page granularity working set size detection. Until v22 of this patchset, this was categorized as the thing Idle Page Tracking could do better, because DAMON basically maintains additional metadata for each of the monitoring target regions. So, in the page granularity working set size detection use case, DAMON would incur (number of monitoring target pages * size of metadata) memory overhead. Size of the single metadata item is about 54 bytes, so assuming 4KB pages, about 1.3% of monitoring target pages will be additionally used. All essential metadata for Idle Page Tracking are embedded in 'struct page' and page table entries. Therefore, in this use case, only one counter variable for working set size accounting is required if Idle Page Tracking is used. There are more details to consider, but roughly speaking, this is true in most cases. However, the situation changed from v23. Now DAMON supports arbitrary types of monitoring targets, which don't use the metadata. Using that, DAMON can do the working set size detection with no additional space overhead but less user-kernel context switch. A first draft for the implementation of monitoring primitives for this usage is available in a DAMON development tree[1]. An RFC patchset for it based on this patchset will also be available soon. Since v24, the arbitrary type support is dropped from this patchset because this patchset doesn't introduce real use of the type. You can still get it from the DAMON development tree[2], though. [1] https://github.com/sjp38/linux/tree/damon/pgidle_hack [2] https://github.com/sjp38/linux/tree/damon/master 4. More future usecases While Idle Page Tracking has tight coupling with base primitives (PG_Idle and page table Accessed bits), DAMON is designed to be extensible for many use cases and address spaces. If you need some special address type or want to use special h/w access check primitives, you can write your own primitives for that and configure DAMON to use those. Therefore, if your use case could be changed a lot in future, using DAMON could be better. Can I use both Idle Page Tracking and DAMON? -------------------------------------------- Yes, though using them concurrently for overlapping memory regions could result in interference to each other. Nevertheless, such use case would be rare or makes no sense at all. Even in the case, the noise would bot be really significant. So, you can choose whatever you want depending on the characteristics of your use cases. More Information ================ We prepared a showcase web site[1] that you can get more information. There are - the official documentations[2], - the heatmap format dynamic access pattern of various realistic workloads for heap area[3], mmap()-ed area[4], and stack[5] area, - the dynamic working set size distribution[6] and chronological working set size changes[7], and - the latest performance test results[8]. [1] https://damonitor.github.io/_index [2] https://damonitor.github.io/doc/html/latest-damon [3] https://damonitor.github.io/test/result/visual/latest/rec.heatmap.0.png.html [4] https://damonitor.github.io/test/result/visual/latest/rec.heatmap.1.png.html [5] https://damonitor.github.io/test/result/visual/latest/rec.heatmap.2.png.html [6] https://damonitor.github.io/test/result/visual/latest/rec.wss_sz.png.html [7] https://damonitor.github.io/test/result/visual/latest/rec.wss_time.png.html [8] https://damonitor.github.io/test/result/perf/latest/html/index.html Baseline and Complete Git Trees =============================== The patches are based on the latest -mm tree, specifically v5.14-rc1-mmots-2021-07-15-18-47 of https://github.com/hnaz/linux-mm. You can also clone the complete git tree: $ git clone git://github.com/sjp38/linux -b damon/patches/v34 The web is also available: https://github.com/sjp38/linux/releases/tag/damon/patches/v34 Development Trees ----------------- There are a couple of trees for entire DAMON patchset series and features for future release. - For latest release: https://github.com/sjp38/linux/tree/damon/master - For next release: https://github.com/sjp38/linux/tree/damon/next Long-term Support Trees ----------------------- For people who want to test DAMON but using LTS kernels, there are another couple of trees based on two latest LTS kernels respectively and containing the 'damon/master' backports. - For v5.4.y: https://github.com/sjp38/linux/tree/damon/for-v5.4.y - For v5.10.y: https://github.com/sjp38/linux/tree/damon/for-v5.10.y Amazon Linux Kernel Trees ------------------------- DAMON is also merged in two public Amazon Linux kernel trees that based on v5.4.y[1] and v5.10.y[2]. [1] https://github.com/amazonlinux/linux/tree/amazon-5.4.y/master/mm/damon [2] https://github.com/amazonlinux/linux/tree/amazon-5.10.y/master/mm/damon Git Tree for Diff of Patches ============================ For easy review of diff between different versions of each patch, I prepared a git tree containing all versions of the DAMON patchset series: https://github.com/sjp38/damon-patches You can clone it and use 'diff' for easy review of changes between different versions of the patchset. For example: $ git clone https://github.com/sjp38/damon-patches && cd damon-patches $ diff -u damon/v33 damon/v34 Sequence Of Patches =================== First three patches implement the core logics of DAMON. The 1st patch introduces basic sampling based hotness monitoring for arbitrary types of targets. Following two patches implement the core mechanisms for control of overhead and accuracy, namely regions based sampling (patch 2) and adaptive regions adjustment (patch 3). Now the essential parts of DAMON is complete, but it cannot work unless someone provides monitoring primitives for a specific use case. The following two patches make it just work for virtual address spaces monitoring. The 4th patch makes 'PG_idle' can be used by DAMON and the 5th patch implements the virtual memory address space specific monitoring primitives using page table Accessed bits and the 'PG_idle' page flag. Now DAMON just works for virtual address space monitoring via the kernel space api. To let the user space users can use DAMON, following four patches add interfaces for them. The 6th patch adds a tracepoint for monitoring results. The 7th patch implements a DAMON application kernel module, namely damon-dbgfs, that simply wraps DAMON and exposes DAMON interface to the user space via the debugfs interface. The 8th patch further exports pid of monitoring thread (kdamond) to user space for easier cpu usage accounting, and the 9th patch makes the debugfs interface to support multiple contexts. Three patches for maintainability follows. The 10th patch adds documentations for both the user space and the kernel space. The 11th patch provides unit tests (based on the kunit) while the 12th patch adds user space tests (based on the kselftest). Finally, the last patch (13th) updates the MAINTAINERS file. This patch (of 13): DAMON is a data access monitoring framework for the Linux kernel. The core mechanisms of DAMON make it - accurate (the monitoring output is useful enough for DRAM level performance-centric memory management; It might be inappropriate for CPU cache levels, though), - light-weight (the monitoring overhead is normally low enough to be applied online), and - scalable (the upper-bound of the overhead is in constant range regardless of the size of target workloads). Using this framework, hence, we can easily write efficient kernel space data access monitoring applications. For example, the kernel's memory management mechanisms can make advanced decisions using this. Experimental data access aware optimization works that incurring high access monitoring overhead could again be implemented on top of this. Due to its simple and flexible interface, providing user space interface would be also easy. Then, user space users who have some special workloads can write personalized applications for better understanding and optimizations of their workloads and systems. === Nevertheless, this commit is defining and implementing only basic access check part without the overhead-accuracy handling core logic. The basic access check is as below. The output of DAMON says what memory regions are how frequently accessed for a given duration. The resolution of the access frequency is controlled by setting ``sampling interval`` and ``aggregation interval``. In detail, DAMON checks access to each page per ``sampling interval`` and aggregates the results. In other words, counts the number of the accesses to each region. After each ``aggregation interval`` passes, DAMON calls callback functions that previously registered by users so that users can read the aggregated results and then clears the results. This can be described in below simple pseudo-code:: init() while monitoring_on: for page in monitoring_target: if accessed(page): nr_accesses[page] += 1 if time() % aggregation_interval == 0: for callback in user_registered_callbacks: callback(monitoring_target, nr_accesses) for page in monitoring_target: nr_accesses[page] = 0 if time() % update_interval == 0: update() sleep(sampling interval) The target regions constructed at the beginning of the monitoring and updated after each ``regions_update_interval``, because the target regions could be dynamically changed (e.g., mmap() or memory hotplug). The monitoring overhead of this mechanism will arbitrarily increase as the size of the target workload grows. The basic monitoring primitives for actual access check and dynamic target regions construction aren't in the core part of DAMON. Instead, it allows users to implement their own primitives that are optimized for their use case and configure DAMON to use those. In other words, users cannot use current version of DAMON without some additional works. Following commits will implement the core mechanisms for the overhead-accuracy control and default primitives implementations. Link: https://lkml.kernel.org/r/20210716081449.22187-1-sj38.park@gmail.com Link: https://lkml.kernel.org/r/20210716081449.22187-2-sj38.park@gmail.com Signed-off-by: SeongJae Park <sjpark@amazon.de> Reviewed-by: Leonard Foerster <foersleo@amazon.de> Reviewed-by: Fernand Sieber <sieberf@amazon.com> Acked-by: Shakeel Butt <shakeelb@google.com> Cc: Jonathan Cameron <Jonathan.Cameron@huawei.com> Cc: Alexander Shishkin <alexander.shishkin@linux.intel.com> Cc: Amit Shah <amit@kernel.org> Cc: Benjamin Herrenschmidt <benh@kernel.crashing.org> Cc: Jonathan Corbet <corbet@lwn.net> Cc: David Hildenbrand <david@redhat.com> Cc: David Woodhouse <dwmw@amazon.com> Cc: Marco Elver <elver@google.com> Cc: Fan Du <fan.du@intel.com> Cc: Greg Kroah-Hartman <greg@kroah.com> Cc: Greg Thelen <gthelen@google.com> Cc: Joe Perches <joe@perches.com> Cc: Mel Gorman <mgorman@suse.de> Cc: Maximilian Heyne <mheyne@amazon.de> Cc: Minchan Kim <minchan@kernel.org> Cc: Ingo Molnar <mingo@redhat.com> Cc: Namhyung Kim <namhyung@kernel.org> Cc: Peter Zijlstra <peterz@infradead.org> Cc: Rik van Riel <riel@surriel.com> Cc: David Rientjes <rientjes@google.com> Cc: Steven Rostedt (VMware) <rostedt@goodmis.org> Cc: Shuah Khan <shuah@kernel.org> Cc: Vlastimil Babka <vbabka@suse.cz> Cc: Vladimir Davydov <vdavydov.dev@gmail.com> Cc: Brendan Higgins <brendanhiggins@google.com> Cc: Markus Boehme <markubo@amazon.de> Signed-off-by: Andrew Morton <akpm@linux-foundation.org> Signed-off-by: Linus Torvalds <torvalds@linux-foundation.org>
2021-09-08 10:56:28 +08:00
*/
struct damon_ctx {
unsigned long sample_interval;
unsigned long aggr_interval;
mm/damon: rename damon_primitives to damon_operations Patch series "Allow DAMON user code independent of monitoring primitives". In-kernel DAMON user code is required to configure the monitoring context (struct damon_ctx) with proper monitoring primitives (struct damon_primitive). This makes the user code dependent to all supporting monitoring primitives. For example, DAMON debugfs interface depends on both DAMON_VADDR and DAMON_PADDR, though some users have interest in only one use case. As more monitoring primitives are introduced, the problem will be bigger. To minimize such unnecessary dependency, this patchset makes monitoring primitives can be registered by the implemnting code and later dynamically searched and selected by the user code. In addition to that, this patchset renames monitoring primitives to monitoring operations, which is more easy to intuitively understand what it means and how it would be structed. This patch (of 8): DAMON has a set of callback functions called monitoring primitives and let it can be configured with various implementations for easy extension for different address spaces and usages. However, the word 'primitive' is not so explicit. Meanwhile, many other structs resembles similar purpose calls themselves 'operations'. To make the code easier to be understood, this commit renames 'damon_primitives' to 'damon_operations' before it is too late to rename. Link: https://lkml.kernel.org/r/20220215184603.1479-1-sj@kernel.org Link: https://lkml.kernel.org/r/20220215184603.1479-2-sj@kernel.org Signed-off-by: SeongJae Park <sj@kernel.org> Cc: Xin Hao <xhao@linux.alibaba.com> Cc: David Rientjes <rientjes@google.com> Signed-off-by: Andrew Morton <akpm@linux-foundation.org> Signed-off-by: Linus Torvalds <torvalds@linux-foundation.org>
2022-03-23 05:48:46 +08:00
unsigned long ops_update_interval;
mm: introduce Data Access MONitor (DAMON) Patch series "Introduce Data Access MONitor (DAMON)", v34. Introduction ============ DAMON is a data access monitoring framework for the Linux kernel. The core mechanisms of DAMON called 'region based sampling' and 'adaptive regions adjustment' (refer to 'mechanisms.rst' in the 11th patch of this patchset for the detail) make it - accurate (The monitored information is useful for DRAM level memory management. It might not appropriate for Cache-level accuracy, though.), - light-weight (The monitoring overhead is low enough to be applied online while making no impact on the performance of the target workloads.), and - scalable (the upper-bound of the instrumentation overhead is controllable regardless of the size of target workloads.). Using this framework, therefore, several memory management mechanisms such as reclamation and THP can be optimized to aware real data access patterns. Experimental access pattern aware memory management optimization works that incurring high instrumentation overhead will be able to have another try. Though DAMON is for kernel subsystems, it can be easily exposed to the user space by writing a DAMON-wrapper kernel subsystem. Then, user space users who have some special workloads will be able to write personalized tools or applications for deeper understanding and specialized optimizations of their systems. DAMON is also merged in two public Amazon Linux kernel trees that based on v5.4.y[1] and v5.10.y[2]. [1] https://github.com/amazonlinux/linux/tree/amazon-5.4.y/master/mm/damon [2] https://github.com/amazonlinux/linux/tree/amazon-5.10.y/master/mm/damon The userspace tool[1] is available, released under GPLv2, and actively being maintained. I am also planning to implement another basic user interface in perf[2]. Also, the basic test suite for DAMON is available under GPLv2[3]. [1] https://github.com/awslabs/damo [2] https://lore.kernel.org/linux-mm/20210107120729.22328-1-sjpark@amazon.com/ [3] https://github.com/awslabs/damon-tests Long-term Plan -------------- DAMON is a part of a project called Data Access-aware Operating System (DAOS). As the name implies, I want to improve the performance and efficiency of systems using fine-grained data access patterns. The optimizations are for both kernel and user spaces. I will therefore modify or create kernel subsystems, export some of those to user space and implement user space library / tools. Below shows the layers and components for the project. --------------------------------------------------------------------------- Primitives: PTE Accessed bit, PG_idle, rmap, (Intel CMT), ... Framework: DAMON Features: DAMOS, virtual addr, physical addr, ... Applications: DAMON-debugfs, (DARC), ... ^^^^^^^^^^^^^^^^^^^^^^^ KERNEL SPACE ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ Raw Interface: debugfs, (sysfs), (damonfs), tracepoints, (sys_damon), ... vvvvvvvvvvvvvvvvvvvvvvv USER SPACE vvvvvvvvvvvvvvvvvvvvvvvvvvvvvvvv Library: (libdamon), ... Tools: DAMO, (perf), ... --------------------------------------------------------------------------- The components in parentheses or marked as '...' are not implemented yet but in the future plan. IOW, those are the TODO tasks of DAOS project. For more detail, please refer to the plans: https://lore.kernel.org/linux-mm/20201202082731.24828-1-sjpark@amazon.com/ Evaluations =========== We evaluated DAMON's overhead, monitoring quality and usefulness using 24 realistic workloads on my QEMU/KVM based virtual machine running a kernel that v24 DAMON patchset is applied. DAMON is lightweight. It increases system memory usage by 0.39% and slows target workloads down by 1.16%. DAMON is accurate and useful for memory management optimizations. An experimental DAMON-based operation scheme for THP, namely 'ethp', removes 76.15% of THP memory overheads while preserving 51.25% of THP speedup. Another experimental DAMON-based 'proactive reclamation' implementation, 'prcl', reduces 93.38% of residential sets and 23.63% of system memory footprint while incurring only 1.22% runtime overhead in the best case (parsec3/freqmine). NOTE that the experimental THP optimization and proactive reclamation are not for production but only for proof of concepts. Please refer to the official document[1] or "Documentation/admin-guide/mm: Add a document for DAMON" patch in this patchset for detailed evaluation setup and results. [1] https://damonitor.github.io/doc/html/latest-damon/admin-guide/mm/damon/eval.html Real-world User Story ===================== In summary, DAMON has used on production systems and proved its usefulness. DAMON as a profiler ------------------- We analyzed characteristics of a large scale production systems of our customers using DAMON. The systems utilize 70GB DRAM and 36 CPUs. From this, we were able to find interesting things below. There were obviously different access pattern under idle workload and active workload. Under the idle workload, it accessed large memory regions with low frequency, while the active workload accessed small memory regions with high freuqnecy. DAMON found a 7GB memory region that showing obviously high access frequency under the active workload. We believe this is the performance-effective working set and need to be protected. There was a 4KB memory region that showing highest access frequency under not only active but also idle workloads. We think this must be a hottest code section like thing that should never be paged out. For this analysis, DAMON used only 0.3-1% of single CPU time. Because we used recording-based analysis, it consumed about 3-12 MB of disk space per 20 minutes. This is only small amount of disk space, but we can further reduce the disk usage by using non-recording-based DAMON features. I'd like to argue that only DAMON can do such detailed analysis (finding 4KB highest region in 70GB memory) with the light overhead. DAMON as a system optimization tool ----------------------------------- We also found below potential performance problems on the systems and made DAMON-based solutions. The system doesn't want to make the workload suffer from the page reclamation and thus it utilizes enough DRAM but no swap device. However, we found the system is actively reclaiming file-backed pages, because the system has intensive file IO. The file IO turned out to be not performance critical for the workload, but the customer wanted to ensure performance critical file-backed pages like code section to not mistakenly be evicted. Using direct IO should or `mlock()` would be a straightforward solution, but modifying the user space code is not easy for the customer. Alternatively, we could use DAMON-based operation scheme[1]. By using it, we can ask DAMON to track access frequency of each region and make 'process_madvise(MADV_WILLNEED)[2]' call for regions having specific size and access frequency for a time interval. We also found the system is having high number of TLB misses. We tried 'always' THP enabled policy and it greatly reduced TLB misses, but the page reclamation also been more frequent due to the THP internal fragmentation caused memory bloat. We could try another DAMON-based operation scheme that applies 'MADV_HUGEPAGE' to memory regions having >=2MB size and high access frequency, while applying 'MADV_NOHUGEPAGE' to regions having <2MB size and low access frequency. We do not own the systems so we only reported the analysis results and possible optimization solutions to the customers. The customers satisfied about the analysis results and promised to try the optimization guides. [1] https://lore.kernel.org/linux-mm/20201006123931.5847-1-sjpark@amazon.com/ [2] https://lore.kernel.org/linux-api/20200622192900.22757-4-minchan@kernel.org/ Comparison with Idle Page Tracking ================================== Idle Page Tracking allows users to set and read idleness of pages using a bitmap file which represents each page with each bit of the file. One recommended usage of it is working set size detection. Users can do that by 1. find PFN of each page for workloads in interest, 2. set all the pages as idle by doing writes to the bitmap file, 3. wait until the workload accesses its working set, and 4. read the idleness of the pages again and count pages became not idle. NOTE: While Idle Page Tracking is for user space users, DAMON is primarily designed for kernel subsystems though it can easily exposed to the user space. Hence, this section only assumes such user space use of DAMON. For what use cases Idle Page Tracking would be better? ------------------------------------------------------ 1. Flexible usecases other than hotness monitoring. Because Idle Page Tracking allows users to control the primitive (Page idleness) by themselves, Idle Page Tracking users can do anything they want. Meanwhile, DAMON is primarily designed to monitor the hotness of each memory region. For this, DAMON asks users to provide sampling interval and aggregation interval. For the reason, there could be some use case that using Idle Page Tracking is simpler. 2. Physical memory monitoring. Idle Page Tracking receives PFN range as input, so natively supports physical memory monitoring. DAMON is designed to be extensible for multiple address spaces and use cases by implementing and using primitives for the given use case. Therefore, by theory, DAMON has no limitation in the type of target address space as long as primitives for the given address space exists. However, the default primitives introduced by this patchset supports only virtual address spaces. Therefore, for physical memory monitoring, you should implement your own primitives and use it, or simply use Idle Page Tracking. Nonetheless, RFC patchsets[1] for the physical memory address space primitives is already available. It also supports user memory same to Idle Page Tracking. [1] https://lore.kernel.org/linux-mm/20200831104730.28970-1-sjpark@amazon.com/ For what use cases DAMON is better? ----------------------------------- 1. Hotness Monitoring. Idle Page Tracking let users know only if a page frame is accessed or not. For hotness check, the user should write more code and use more memory. DAMON do that by itself. 2. Low Monitoring Overhead DAMON receives user's monitoring request with one step and then provide the results. So, roughly speaking, DAMON require only O(1) user/kernel context switches. In case of Idle Page Tracking, however, because the interface receives contiguous page frames, the number of user/kernel context switches increases as the monitoring target becomes complex and huge. As a result, the context switch overhead could be not negligible. Moreover, DAMON is born to handle with the monitoring overhead. Because the core mechanism is pure logical, Idle Page Tracking users might be able to implement the mechanism on their own, but it would be time consuming and the user/kernel context switching will still more frequent than that of DAMON. Also, the kernel subsystems cannot use the logic in this case. 3. Page granularity working set size detection. Until v22 of this patchset, this was categorized as the thing Idle Page Tracking could do better, because DAMON basically maintains additional metadata for each of the monitoring target regions. So, in the page granularity working set size detection use case, DAMON would incur (number of monitoring target pages * size of metadata) memory overhead. Size of the single metadata item is about 54 bytes, so assuming 4KB pages, about 1.3% of monitoring target pages will be additionally used. All essential metadata for Idle Page Tracking are embedded in 'struct page' and page table entries. Therefore, in this use case, only one counter variable for working set size accounting is required if Idle Page Tracking is used. There are more details to consider, but roughly speaking, this is true in most cases. However, the situation changed from v23. Now DAMON supports arbitrary types of monitoring targets, which don't use the metadata. Using that, DAMON can do the working set size detection with no additional space overhead but less user-kernel context switch. A first draft for the implementation of monitoring primitives for this usage is available in a DAMON development tree[1]. An RFC patchset for it based on this patchset will also be available soon. Since v24, the arbitrary type support is dropped from this patchset because this patchset doesn't introduce real use of the type. You can still get it from the DAMON development tree[2], though. [1] https://github.com/sjp38/linux/tree/damon/pgidle_hack [2] https://github.com/sjp38/linux/tree/damon/master 4. More future usecases While Idle Page Tracking has tight coupling with base primitives (PG_Idle and page table Accessed bits), DAMON is designed to be extensible for many use cases and address spaces. If you need some special address type or want to use special h/w access check primitives, you can write your own primitives for that and configure DAMON to use those. Therefore, if your use case could be changed a lot in future, using DAMON could be better. Can I use both Idle Page Tracking and DAMON? -------------------------------------------- Yes, though using them concurrently for overlapping memory regions could result in interference to each other. Nevertheless, such use case would be rare or makes no sense at all. Even in the case, the noise would bot be really significant. So, you can choose whatever you want depending on the characteristics of your use cases. More Information ================ We prepared a showcase web site[1] that you can get more information. There are - the official documentations[2], - the heatmap format dynamic access pattern of various realistic workloads for heap area[3], mmap()-ed area[4], and stack[5] area, - the dynamic working set size distribution[6] and chronological working set size changes[7], and - the latest performance test results[8]. [1] https://damonitor.github.io/_index [2] https://damonitor.github.io/doc/html/latest-damon [3] https://damonitor.github.io/test/result/visual/latest/rec.heatmap.0.png.html [4] https://damonitor.github.io/test/result/visual/latest/rec.heatmap.1.png.html [5] https://damonitor.github.io/test/result/visual/latest/rec.heatmap.2.png.html [6] https://damonitor.github.io/test/result/visual/latest/rec.wss_sz.png.html [7] https://damonitor.github.io/test/result/visual/latest/rec.wss_time.png.html [8] https://damonitor.github.io/test/result/perf/latest/html/index.html Baseline and Complete Git Trees =============================== The patches are based on the latest -mm tree, specifically v5.14-rc1-mmots-2021-07-15-18-47 of https://github.com/hnaz/linux-mm. You can also clone the complete git tree: $ git clone git://github.com/sjp38/linux -b damon/patches/v34 The web is also available: https://github.com/sjp38/linux/releases/tag/damon/patches/v34 Development Trees ----------------- There are a couple of trees for entire DAMON patchset series and features for future release. - For latest release: https://github.com/sjp38/linux/tree/damon/master - For next release: https://github.com/sjp38/linux/tree/damon/next Long-term Support Trees ----------------------- For people who want to test DAMON but using LTS kernels, there are another couple of trees based on two latest LTS kernels respectively and containing the 'damon/master' backports. - For v5.4.y: https://github.com/sjp38/linux/tree/damon/for-v5.4.y - For v5.10.y: https://github.com/sjp38/linux/tree/damon/for-v5.10.y Amazon Linux Kernel Trees ------------------------- DAMON is also merged in two public Amazon Linux kernel trees that based on v5.4.y[1] and v5.10.y[2]. [1] https://github.com/amazonlinux/linux/tree/amazon-5.4.y/master/mm/damon [2] https://github.com/amazonlinux/linux/tree/amazon-5.10.y/master/mm/damon Git Tree for Diff of Patches ============================ For easy review of diff between different versions of each patch, I prepared a git tree containing all versions of the DAMON patchset series: https://github.com/sjp38/damon-patches You can clone it and use 'diff' for easy review of changes between different versions of the patchset. For example: $ git clone https://github.com/sjp38/damon-patches && cd damon-patches $ diff -u damon/v33 damon/v34 Sequence Of Patches =================== First three patches implement the core logics of DAMON. The 1st patch introduces basic sampling based hotness monitoring for arbitrary types of targets. Following two patches implement the core mechanisms for control of overhead and accuracy, namely regions based sampling (patch 2) and adaptive regions adjustment (patch 3). Now the essential parts of DAMON is complete, but it cannot work unless someone provides monitoring primitives for a specific use case. The following two patches make it just work for virtual address spaces monitoring. The 4th patch makes 'PG_idle' can be used by DAMON and the 5th patch implements the virtual memory address space specific monitoring primitives using page table Accessed bits and the 'PG_idle' page flag. Now DAMON just works for virtual address space monitoring via the kernel space api. To let the user space users can use DAMON, following four patches add interfaces for them. The 6th patch adds a tracepoint for monitoring results. The 7th patch implements a DAMON application kernel module, namely damon-dbgfs, that simply wraps DAMON and exposes DAMON interface to the user space via the debugfs interface. The 8th patch further exports pid of monitoring thread (kdamond) to user space for easier cpu usage accounting, and the 9th patch makes the debugfs interface to support multiple contexts. Three patches for maintainability follows. The 10th patch adds documentations for both the user space and the kernel space. The 11th patch provides unit tests (based on the kunit) while the 12th patch adds user space tests (based on the kselftest). Finally, the last patch (13th) updates the MAINTAINERS file. This patch (of 13): DAMON is a data access monitoring framework for the Linux kernel. The core mechanisms of DAMON make it - accurate (the monitoring output is useful enough for DRAM level performance-centric memory management; It might be inappropriate for CPU cache levels, though), - light-weight (the monitoring overhead is normally low enough to be applied online), and - scalable (the upper-bound of the overhead is in constant range regardless of the size of target workloads). Using this framework, hence, we can easily write efficient kernel space data access monitoring applications. For example, the kernel's memory management mechanisms can make advanced decisions using this. Experimental data access aware optimization works that incurring high access monitoring overhead could again be implemented on top of this. Due to its simple and flexible interface, providing user space interface would be also easy. Then, user space users who have some special workloads can write personalized applications for better understanding and optimizations of their workloads and systems. === Nevertheless, this commit is defining and implementing only basic access check part without the overhead-accuracy handling core logic. The basic access check is as below. The output of DAMON says what memory regions are how frequently accessed for a given duration. The resolution of the access frequency is controlled by setting ``sampling interval`` and ``aggregation interval``. In detail, DAMON checks access to each page per ``sampling interval`` and aggregates the results. In other words, counts the number of the accesses to each region. After each ``aggregation interval`` passes, DAMON calls callback functions that previously registered by users so that users can read the aggregated results and then clears the results. This can be described in below simple pseudo-code:: init() while monitoring_on: for page in monitoring_target: if accessed(page): nr_accesses[page] += 1 if time() % aggregation_interval == 0: for callback in user_registered_callbacks: callback(monitoring_target, nr_accesses) for page in monitoring_target: nr_accesses[page] = 0 if time() % update_interval == 0: update() sleep(sampling interval) The target regions constructed at the beginning of the monitoring and updated after each ``regions_update_interval``, because the target regions could be dynamically changed (e.g., mmap() or memory hotplug). The monitoring overhead of this mechanism will arbitrarily increase as the size of the target workload grows. The basic monitoring primitives for actual access check and dynamic target regions construction aren't in the core part of DAMON. Instead, it allows users to implement their own primitives that are optimized for their use case and configure DAMON to use those. In other words, users cannot use current version of DAMON without some additional works. Following commits will implement the core mechanisms for the overhead-accuracy control and default primitives implementations. Link: https://lkml.kernel.org/r/20210716081449.22187-1-sj38.park@gmail.com Link: https://lkml.kernel.org/r/20210716081449.22187-2-sj38.park@gmail.com Signed-off-by: SeongJae Park <sjpark@amazon.de> Reviewed-by: Leonard Foerster <foersleo@amazon.de> Reviewed-by: Fernand Sieber <sieberf@amazon.com> Acked-by: Shakeel Butt <shakeelb@google.com> Cc: Jonathan Cameron <Jonathan.Cameron@huawei.com> Cc: Alexander Shishkin <alexander.shishkin@linux.intel.com> Cc: Amit Shah <amit@kernel.org> Cc: Benjamin Herrenschmidt <benh@kernel.crashing.org> Cc: Jonathan Corbet <corbet@lwn.net> Cc: David Hildenbrand <david@redhat.com> Cc: David Woodhouse <dwmw@amazon.com> Cc: Marco Elver <elver@google.com> Cc: Fan Du <fan.du@intel.com> Cc: Greg Kroah-Hartman <greg@kroah.com> Cc: Greg Thelen <gthelen@google.com> Cc: Joe Perches <joe@perches.com> Cc: Mel Gorman <mgorman@suse.de> Cc: Maximilian Heyne <mheyne@amazon.de> Cc: Minchan Kim <minchan@kernel.org> Cc: Ingo Molnar <mingo@redhat.com> Cc: Namhyung Kim <namhyung@kernel.org> Cc: Peter Zijlstra <peterz@infradead.org> Cc: Rik van Riel <riel@surriel.com> Cc: David Rientjes <rientjes@google.com> Cc: Steven Rostedt (VMware) <rostedt@goodmis.org> Cc: Shuah Khan <shuah@kernel.org> Cc: Vlastimil Babka <vbabka@suse.cz> Cc: Vladimir Davydov <vdavydov.dev@gmail.com> Cc: Brendan Higgins <brendanhiggins@google.com> Cc: Markus Boehme <markubo@amazon.de> Signed-off-by: Andrew Morton <akpm@linux-foundation.org> Signed-off-by: Linus Torvalds <torvalds@linux-foundation.org>
2021-09-08 10:56:28 +08:00
/* private: internal use only */
struct timespec64 last_aggregation;
mm/damon: rename damon_primitives to damon_operations Patch series "Allow DAMON user code independent of monitoring primitives". In-kernel DAMON user code is required to configure the monitoring context (struct damon_ctx) with proper monitoring primitives (struct damon_primitive). This makes the user code dependent to all supporting monitoring primitives. For example, DAMON debugfs interface depends on both DAMON_VADDR and DAMON_PADDR, though some users have interest in only one use case. As more monitoring primitives are introduced, the problem will be bigger. To minimize such unnecessary dependency, this patchset makes monitoring primitives can be registered by the implemnting code and later dynamically searched and selected by the user code. In addition to that, this patchset renames monitoring primitives to monitoring operations, which is more easy to intuitively understand what it means and how it would be structed. This patch (of 8): DAMON has a set of callback functions called monitoring primitives and let it can be configured with various implementations for easy extension for different address spaces and usages. However, the word 'primitive' is not so explicit. Meanwhile, many other structs resembles similar purpose calls themselves 'operations'. To make the code easier to be understood, this commit renames 'damon_primitives' to 'damon_operations' before it is too late to rename. Link: https://lkml.kernel.org/r/20220215184603.1479-1-sj@kernel.org Link: https://lkml.kernel.org/r/20220215184603.1479-2-sj@kernel.org Signed-off-by: SeongJae Park <sj@kernel.org> Cc: Xin Hao <xhao@linux.alibaba.com> Cc: David Rientjes <rientjes@google.com> Signed-off-by: Andrew Morton <akpm@linux-foundation.org> Signed-off-by: Linus Torvalds <torvalds@linux-foundation.org>
2022-03-23 05:48:46 +08:00
struct timespec64 last_ops_update;
mm: introduce Data Access MONitor (DAMON) Patch series "Introduce Data Access MONitor (DAMON)", v34. Introduction ============ DAMON is a data access monitoring framework for the Linux kernel. The core mechanisms of DAMON called 'region based sampling' and 'adaptive regions adjustment' (refer to 'mechanisms.rst' in the 11th patch of this patchset for the detail) make it - accurate (The monitored information is useful for DRAM level memory management. It might not appropriate for Cache-level accuracy, though.), - light-weight (The monitoring overhead is low enough to be applied online while making no impact on the performance of the target workloads.), and - scalable (the upper-bound of the instrumentation overhead is controllable regardless of the size of target workloads.). Using this framework, therefore, several memory management mechanisms such as reclamation and THP can be optimized to aware real data access patterns. Experimental access pattern aware memory management optimization works that incurring high instrumentation overhead will be able to have another try. Though DAMON is for kernel subsystems, it can be easily exposed to the user space by writing a DAMON-wrapper kernel subsystem. Then, user space users who have some special workloads will be able to write personalized tools or applications for deeper understanding and specialized optimizations of their systems. DAMON is also merged in two public Amazon Linux kernel trees that based on v5.4.y[1] and v5.10.y[2]. [1] https://github.com/amazonlinux/linux/tree/amazon-5.4.y/master/mm/damon [2] https://github.com/amazonlinux/linux/tree/amazon-5.10.y/master/mm/damon The userspace tool[1] is available, released under GPLv2, and actively being maintained. I am also planning to implement another basic user interface in perf[2]. Also, the basic test suite for DAMON is available under GPLv2[3]. [1] https://github.com/awslabs/damo [2] https://lore.kernel.org/linux-mm/20210107120729.22328-1-sjpark@amazon.com/ [3] https://github.com/awslabs/damon-tests Long-term Plan -------------- DAMON is a part of a project called Data Access-aware Operating System (DAOS). As the name implies, I want to improve the performance and efficiency of systems using fine-grained data access patterns. The optimizations are for both kernel and user spaces. I will therefore modify or create kernel subsystems, export some of those to user space and implement user space library / tools. Below shows the layers and components for the project. --------------------------------------------------------------------------- Primitives: PTE Accessed bit, PG_idle, rmap, (Intel CMT), ... Framework: DAMON Features: DAMOS, virtual addr, physical addr, ... Applications: DAMON-debugfs, (DARC), ... ^^^^^^^^^^^^^^^^^^^^^^^ KERNEL SPACE ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ Raw Interface: debugfs, (sysfs), (damonfs), tracepoints, (sys_damon), ... vvvvvvvvvvvvvvvvvvvvvvv USER SPACE vvvvvvvvvvvvvvvvvvvvvvvvvvvvvvvv Library: (libdamon), ... Tools: DAMO, (perf), ... --------------------------------------------------------------------------- The components in parentheses or marked as '...' are not implemented yet but in the future plan. IOW, those are the TODO tasks of DAOS project. For more detail, please refer to the plans: https://lore.kernel.org/linux-mm/20201202082731.24828-1-sjpark@amazon.com/ Evaluations =========== We evaluated DAMON's overhead, monitoring quality and usefulness using 24 realistic workloads on my QEMU/KVM based virtual machine running a kernel that v24 DAMON patchset is applied. DAMON is lightweight. It increases system memory usage by 0.39% and slows target workloads down by 1.16%. DAMON is accurate and useful for memory management optimizations. An experimental DAMON-based operation scheme for THP, namely 'ethp', removes 76.15% of THP memory overheads while preserving 51.25% of THP speedup. Another experimental DAMON-based 'proactive reclamation' implementation, 'prcl', reduces 93.38% of residential sets and 23.63% of system memory footprint while incurring only 1.22% runtime overhead in the best case (parsec3/freqmine). NOTE that the experimental THP optimization and proactive reclamation are not for production but only for proof of concepts. Please refer to the official document[1] or "Documentation/admin-guide/mm: Add a document for DAMON" patch in this patchset for detailed evaluation setup and results. [1] https://damonitor.github.io/doc/html/latest-damon/admin-guide/mm/damon/eval.html Real-world User Story ===================== In summary, DAMON has used on production systems and proved its usefulness. DAMON as a profiler ------------------- We analyzed characteristics of a large scale production systems of our customers using DAMON. The systems utilize 70GB DRAM and 36 CPUs. From this, we were able to find interesting things below. There were obviously different access pattern under idle workload and active workload. Under the idle workload, it accessed large memory regions with low frequency, while the active workload accessed small memory regions with high freuqnecy. DAMON found a 7GB memory region that showing obviously high access frequency under the active workload. We believe this is the performance-effective working set and need to be protected. There was a 4KB memory region that showing highest access frequency under not only active but also idle workloads. We think this must be a hottest code section like thing that should never be paged out. For this analysis, DAMON used only 0.3-1% of single CPU time. Because we used recording-based analysis, it consumed about 3-12 MB of disk space per 20 minutes. This is only small amount of disk space, but we can further reduce the disk usage by using non-recording-based DAMON features. I'd like to argue that only DAMON can do such detailed analysis (finding 4KB highest region in 70GB memory) with the light overhead. DAMON as a system optimization tool ----------------------------------- We also found below potential performance problems on the systems and made DAMON-based solutions. The system doesn't want to make the workload suffer from the page reclamation and thus it utilizes enough DRAM but no swap device. However, we found the system is actively reclaiming file-backed pages, because the system has intensive file IO. The file IO turned out to be not performance critical for the workload, but the customer wanted to ensure performance critical file-backed pages like code section to not mistakenly be evicted. Using direct IO should or `mlock()` would be a straightforward solution, but modifying the user space code is not easy for the customer. Alternatively, we could use DAMON-based operation scheme[1]. By using it, we can ask DAMON to track access frequency of each region and make 'process_madvise(MADV_WILLNEED)[2]' call for regions having specific size and access frequency for a time interval. We also found the system is having high number of TLB misses. We tried 'always' THP enabled policy and it greatly reduced TLB misses, but the page reclamation also been more frequent due to the THP internal fragmentation caused memory bloat. We could try another DAMON-based operation scheme that applies 'MADV_HUGEPAGE' to memory regions having >=2MB size and high access frequency, while applying 'MADV_NOHUGEPAGE' to regions having <2MB size and low access frequency. We do not own the systems so we only reported the analysis results and possible optimization solutions to the customers. The customers satisfied about the analysis results and promised to try the optimization guides. [1] https://lore.kernel.org/linux-mm/20201006123931.5847-1-sjpark@amazon.com/ [2] https://lore.kernel.org/linux-api/20200622192900.22757-4-minchan@kernel.org/ Comparison with Idle Page Tracking ================================== Idle Page Tracking allows users to set and read idleness of pages using a bitmap file which represents each page with each bit of the file. One recommended usage of it is working set size detection. Users can do that by 1. find PFN of each page for workloads in interest, 2. set all the pages as idle by doing writes to the bitmap file, 3. wait until the workload accesses its working set, and 4. read the idleness of the pages again and count pages became not idle. NOTE: While Idle Page Tracking is for user space users, DAMON is primarily designed for kernel subsystems though it can easily exposed to the user space. Hence, this section only assumes such user space use of DAMON. For what use cases Idle Page Tracking would be better? ------------------------------------------------------ 1. Flexible usecases other than hotness monitoring. Because Idle Page Tracking allows users to control the primitive (Page idleness) by themselves, Idle Page Tracking users can do anything they want. Meanwhile, DAMON is primarily designed to monitor the hotness of each memory region. For this, DAMON asks users to provide sampling interval and aggregation interval. For the reason, there could be some use case that using Idle Page Tracking is simpler. 2. Physical memory monitoring. Idle Page Tracking receives PFN range as input, so natively supports physical memory monitoring. DAMON is designed to be extensible for multiple address spaces and use cases by implementing and using primitives for the given use case. Therefore, by theory, DAMON has no limitation in the type of target address space as long as primitives for the given address space exists. However, the default primitives introduced by this patchset supports only virtual address spaces. Therefore, for physical memory monitoring, you should implement your own primitives and use it, or simply use Idle Page Tracking. Nonetheless, RFC patchsets[1] for the physical memory address space primitives is already available. It also supports user memory same to Idle Page Tracking. [1] https://lore.kernel.org/linux-mm/20200831104730.28970-1-sjpark@amazon.com/ For what use cases DAMON is better? ----------------------------------- 1. Hotness Monitoring. Idle Page Tracking let users know only if a page frame is accessed or not. For hotness check, the user should write more code and use more memory. DAMON do that by itself. 2. Low Monitoring Overhead DAMON receives user's monitoring request with one step and then provide the results. So, roughly speaking, DAMON require only O(1) user/kernel context switches. In case of Idle Page Tracking, however, because the interface receives contiguous page frames, the number of user/kernel context switches increases as the monitoring target becomes complex and huge. As a result, the context switch overhead could be not negligible. Moreover, DAMON is born to handle with the monitoring overhead. Because the core mechanism is pure logical, Idle Page Tracking users might be able to implement the mechanism on their own, but it would be time consuming and the user/kernel context switching will still more frequent than that of DAMON. Also, the kernel subsystems cannot use the logic in this case. 3. Page granularity working set size detection. Until v22 of this patchset, this was categorized as the thing Idle Page Tracking could do better, because DAMON basically maintains additional metadata for each of the monitoring target regions. So, in the page granularity working set size detection use case, DAMON would incur (number of monitoring target pages * size of metadata) memory overhead. Size of the single metadata item is about 54 bytes, so assuming 4KB pages, about 1.3% of monitoring target pages will be additionally used. All essential metadata for Idle Page Tracking are embedded in 'struct page' and page table entries. Therefore, in this use case, only one counter variable for working set size accounting is required if Idle Page Tracking is used. There are more details to consider, but roughly speaking, this is true in most cases. However, the situation changed from v23. Now DAMON supports arbitrary types of monitoring targets, which don't use the metadata. Using that, DAMON can do the working set size detection with no additional space overhead but less user-kernel context switch. A first draft for the implementation of monitoring primitives for this usage is available in a DAMON development tree[1]. An RFC patchset for it based on this patchset will also be available soon. Since v24, the arbitrary type support is dropped from this patchset because this patchset doesn't introduce real use of the type. You can still get it from the DAMON development tree[2], though. [1] https://github.com/sjp38/linux/tree/damon/pgidle_hack [2] https://github.com/sjp38/linux/tree/damon/master 4. More future usecases While Idle Page Tracking has tight coupling with base primitives (PG_Idle and page table Accessed bits), DAMON is designed to be extensible for many use cases and address spaces. If you need some special address type or want to use special h/w access check primitives, you can write your own primitives for that and configure DAMON to use those. Therefore, if your use case could be changed a lot in future, using DAMON could be better. Can I use both Idle Page Tracking and DAMON? -------------------------------------------- Yes, though using them concurrently for overlapping memory regions could result in interference to each other. Nevertheless, such use case would be rare or makes no sense at all. Even in the case, the noise would bot be really significant. So, you can choose whatever you want depending on the characteristics of your use cases. More Information ================ We prepared a showcase web site[1] that you can get more information. There are - the official documentations[2], - the heatmap format dynamic access pattern of various realistic workloads for heap area[3], mmap()-ed area[4], and stack[5] area, - the dynamic working set size distribution[6] and chronological working set size changes[7], and - the latest performance test results[8]. [1] https://damonitor.github.io/_index [2] https://damonitor.github.io/doc/html/latest-damon [3] https://damonitor.github.io/test/result/visual/latest/rec.heatmap.0.png.html [4] https://damonitor.github.io/test/result/visual/latest/rec.heatmap.1.png.html [5] https://damonitor.github.io/test/result/visual/latest/rec.heatmap.2.png.html [6] https://damonitor.github.io/test/result/visual/latest/rec.wss_sz.png.html [7] https://damonitor.github.io/test/result/visual/latest/rec.wss_time.png.html [8] https://damonitor.github.io/test/result/perf/latest/html/index.html Baseline and Complete Git Trees =============================== The patches are based on the latest -mm tree, specifically v5.14-rc1-mmots-2021-07-15-18-47 of https://github.com/hnaz/linux-mm. You can also clone the complete git tree: $ git clone git://github.com/sjp38/linux -b damon/patches/v34 The web is also available: https://github.com/sjp38/linux/releases/tag/damon/patches/v34 Development Trees ----------------- There are a couple of trees for entire DAMON patchset series and features for future release. - For latest release: https://github.com/sjp38/linux/tree/damon/master - For next release: https://github.com/sjp38/linux/tree/damon/next Long-term Support Trees ----------------------- For people who want to test DAMON but using LTS kernels, there are another couple of trees based on two latest LTS kernels respectively and containing the 'damon/master' backports. - For v5.4.y: https://github.com/sjp38/linux/tree/damon/for-v5.4.y - For v5.10.y: https://github.com/sjp38/linux/tree/damon/for-v5.10.y Amazon Linux Kernel Trees ------------------------- DAMON is also merged in two public Amazon Linux kernel trees that based on v5.4.y[1] and v5.10.y[2]. [1] https://github.com/amazonlinux/linux/tree/amazon-5.4.y/master/mm/damon [2] https://github.com/amazonlinux/linux/tree/amazon-5.10.y/master/mm/damon Git Tree for Diff of Patches ============================ For easy review of diff between different versions of each patch, I prepared a git tree containing all versions of the DAMON patchset series: https://github.com/sjp38/damon-patches You can clone it and use 'diff' for easy review of changes between different versions of the patchset. For example: $ git clone https://github.com/sjp38/damon-patches && cd damon-patches $ diff -u damon/v33 damon/v34 Sequence Of Patches =================== First three patches implement the core logics of DAMON. The 1st patch introduces basic sampling based hotness monitoring for arbitrary types of targets. Following two patches implement the core mechanisms for control of overhead and accuracy, namely regions based sampling (patch 2) and adaptive regions adjustment (patch 3). Now the essential parts of DAMON is complete, but it cannot work unless someone provides monitoring primitives for a specific use case. The following two patches make it just work for virtual address spaces monitoring. The 4th patch makes 'PG_idle' can be used by DAMON and the 5th patch implements the virtual memory address space specific monitoring primitives using page table Accessed bits and the 'PG_idle' page flag. Now DAMON just works for virtual address space monitoring via the kernel space api. To let the user space users can use DAMON, following four patches add interfaces for them. The 6th patch adds a tracepoint for monitoring results. The 7th patch implements a DAMON application kernel module, namely damon-dbgfs, that simply wraps DAMON and exposes DAMON interface to the user space via the debugfs interface. The 8th patch further exports pid of monitoring thread (kdamond) to user space for easier cpu usage accounting, and the 9th patch makes the debugfs interface to support multiple contexts. Three patches for maintainability follows. The 10th patch adds documentations for both the user space and the kernel space. The 11th patch provides unit tests (based on the kunit) while the 12th patch adds user space tests (based on the kselftest). Finally, the last patch (13th) updates the MAINTAINERS file. This patch (of 13): DAMON is a data access monitoring framework for the Linux kernel. The core mechanisms of DAMON make it - accurate (the monitoring output is useful enough for DRAM level performance-centric memory management; It might be inappropriate for CPU cache levels, though), - light-weight (the monitoring overhead is normally low enough to be applied online), and - scalable (the upper-bound of the overhead is in constant range regardless of the size of target workloads). Using this framework, hence, we can easily write efficient kernel space data access monitoring applications. For example, the kernel's memory management mechanisms can make advanced decisions using this. Experimental data access aware optimization works that incurring high access monitoring overhead could again be implemented on top of this. Due to its simple and flexible interface, providing user space interface would be also easy. Then, user space users who have some special workloads can write personalized applications for better understanding and optimizations of their workloads and systems. === Nevertheless, this commit is defining and implementing only basic access check part without the overhead-accuracy handling core logic. The basic access check is as below. The output of DAMON says what memory regions are how frequently accessed for a given duration. The resolution of the access frequency is controlled by setting ``sampling interval`` and ``aggregation interval``. In detail, DAMON checks access to each page per ``sampling interval`` and aggregates the results. In other words, counts the number of the accesses to each region. After each ``aggregation interval`` passes, DAMON calls callback functions that previously registered by users so that users can read the aggregated results and then clears the results. This can be described in below simple pseudo-code:: init() while monitoring_on: for page in monitoring_target: if accessed(page): nr_accesses[page] += 1 if time() % aggregation_interval == 0: for callback in user_registered_callbacks: callback(monitoring_target, nr_accesses) for page in monitoring_target: nr_accesses[page] = 0 if time() % update_interval == 0: update() sleep(sampling interval) The target regions constructed at the beginning of the monitoring and updated after each ``regions_update_interval``, because the target regions could be dynamically changed (e.g., mmap() or memory hotplug). The monitoring overhead of this mechanism will arbitrarily increase as the size of the target workload grows. The basic monitoring primitives for actual access check and dynamic target regions construction aren't in the core part of DAMON. Instead, it allows users to implement their own primitives that are optimized for their use case and configure DAMON to use those. In other words, users cannot use current version of DAMON without some additional works. Following commits will implement the core mechanisms for the overhead-accuracy control and default primitives implementations. Link: https://lkml.kernel.org/r/20210716081449.22187-1-sj38.park@gmail.com Link: https://lkml.kernel.org/r/20210716081449.22187-2-sj38.park@gmail.com Signed-off-by: SeongJae Park <sjpark@amazon.de> Reviewed-by: Leonard Foerster <foersleo@amazon.de> Reviewed-by: Fernand Sieber <sieberf@amazon.com> Acked-by: Shakeel Butt <shakeelb@google.com> Cc: Jonathan Cameron <Jonathan.Cameron@huawei.com> Cc: Alexander Shishkin <alexander.shishkin@linux.intel.com> Cc: Amit Shah <amit@kernel.org> Cc: Benjamin Herrenschmidt <benh@kernel.crashing.org> Cc: Jonathan Corbet <corbet@lwn.net> Cc: David Hildenbrand <david@redhat.com> Cc: David Woodhouse <dwmw@amazon.com> Cc: Marco Elver <elver@google.com> Cc: Fan Du <fan.du@intel.com> Cc: Greg Kroah-Hartman <greg@kroah.com> Cc: Greg Thelen <gthelen@google.com> Cc: Joe Perches <joe@perches.com> Cc: Mel Gorman <mgorman@suse.de> Cc: Maximilian Heyne <mheyne@amazon.de> Cc: Minchan Kim <minchan@kernel.org> Cc: Ingo Molnar <mingo@redhat.com> Cc: Namhyung Kim <namhyung@kernel.org> Cc: Peter Zijlstra <peterz@infradead.org> Cc: Rik van Riel <riel@surriel.com> Cc: David Rientjes <rientjes@google.com> Cc: Steven Rostedt (VMware) <rostedt@goodmis.org> Cc: Shuah Khan <shuah@kernel.org> Cc: Vlastimil Babka <vbabka@suse.cz> Cc: Vladimir Davydov <vdavydov.dev@gmail.com> Cc: Brendan Higgins <brendanhiggins@google.com> Cc: Markus Boehme <markubo@amazon.de> Signed-off-by: Andrew Morton <akpm@linux-foundation.org> Signed-off-by: Linus Torvalds <torvalds@linux-foundation.org>
2021-09-08 10:56:28 +08:00
/* public: */
struct task_struct *kdamond;
struct mutex kdamond_lock;
mm/damon: rename damon_primitives to damon_operations Patch series "Allow DAMON user code independent of monitoring primitives". In-kernel DAMON user code is required to configure the monitoring context (struct damon_ctx) with proper monitoring primitives (struct damon_primitive). This makes the user code dependent to all supporting monitoring primitives. For example, DAMON debugfs interface depends on both DAMON_VADDR and DAMON_PADDR, though some users have interest in only one use case. As more monitoring primitives are introduced, the problem will be bigger. To minimize such unnecessary dependency, this patchset makes monitoring primitives can be registered by the implemnting code and later dynamically searched and selected by the user code. In addition to that, this patchset renames monitoring primitives to monitoring operations, which is more easy to intuitively understand what it means and how it would be structed. This patch (of 8): DAMON has a set of callback functions called monitoring primitives and let it can be configured with various implementations for easy extension for different address spaces and usages. However, the word 'primitive' is not so explicit. Meanwhile, many other structs resembles similar purpose calls themselves 'operations'. To make the code easier to be understood, this commit renames 'damon_primitives' to 'damon_operations' before it is too late to rename. Link: https://lkml.kernel.org/r/20220215184603.1479-1-sj@kernel.org Link: https://lkml.kernel.org/r/20220215184603.1479-2-sj@kernel.org Signed-off-by: SeongJae Park <sj@kernel.org> Cc: Xin Hao <xhao@linux.alibaba.com> Cc: David Rientjes <rientjes@google.com> Signed-off-by: Andrew Morton <akpm@linux-foundation.org> Signed-off-by: Linus Torvalds <torvalds@linux-foundation.org>
2022-03-23 05:48:46 +08:00
struct damon_operations ops;
mm: introduce Data Access MONitor (DAMON) Patch series "Introduce Data Access MONitor (DAMON)", v34. Introduction ============ DAMON is a data access monitoring framework for the Linux kernel. The core mechanisms of DAMON called 'region based sampling' and 'adaptive regions adjustment' (refer to 'mechanisms.rst' in the 11th patch of this patchset for the detail) make it - accurate (The monitored information is useful for DRAM level memory management. It might not appropriate for Cache-level accuracy, though.), - light-weight (The monitoring overhead is low enough to be applied online while making no impact on the performance of the target workloads.), and - scalable (the upper-bound of the instrumentation overhead is controllable regardless of the size of target workloads.). Using this framework, therefore, several memory management mechanisms such as reclamation and THP can be optimized to aware real data access patterns. Experimental access pattern aware memory management optimization works that incurring high instrumentation overhead will be able to have another try. Though DAMON is for kernel subsystems, it can be easily exposed to the user space by writing a DAMON-wrapper kernel subsystem. Then, user space users who have some special workloads will be able to write personalized tools or applications for deeper understanding and specialized optimizations of their systems. DAMON is also merged in two public Amazon Linux kernel trees that based on v5.4.y[1] and v5.10.y[2]. [1] https://github.com/amazonlinux/linux/tree/amazon-5.4.y/master/mm/damon [2] https://github.com/amazonlinux/linux/tree/amazon-5.10.y/master/mm/damon The userspace tool[1] is available, released under GPLv2, and actively being maintained. I am also planning to implement another basic user interface in perf[2]. Also, the basic test suite for DAMON is available under GPLv2[3]. [1] https://github.com/awslabs/damo [2] https://lore.kernel.org/linux-mm/20210107120729.22328-1-sjpark@amazon.com/ [3] https://github.com/awslabs/damon-tests Long-term Plan -------------- DAMON is a part of a project called Data Access-aware Operating System (DAOS). As the name implies, I want to improve the performance and efficiency of systems using fine-grained data access patterns. The optimizations are for both kernel and user spaces. I will therefore modify or create kernel subsystems, export some of those to user space and implement user space library / tools. Below shows the layers and components for the project. --------------------------------------------------------------------------- Primitives: PTE Accessed bit, PG_idle, rmap, (Intel CMT), ... Framework: DAMON Features: DAMOS, virtual addr, physical addr, ... Applications: DAMON-debugfs, (DARC), ... ^^^^^^^^^^^^^^^^^^^^^^^ KERNEL SPACE ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ Raw Interface: debugfs, (sysfs), (damonfs), tracepoints, (sys_damon), ... vvvvvvvvvvvvvvvvvvvvvvv USER SPACE vvvvvvvvvvvvvvvvvvvvvvvvvvvvvvvv Library: (libdamon), ... Tools: DAMO, (perf), ... --------------------------------------------------------------------------- The components in parentheses or marked as '...' are not implemented yet but in the future plan. IOW, those are the TODO tasks of DAOS project. For more detail, please refer to the plans: https://lore.kernel.org/linux-mm/20201202082731.24828-1-sjpark@amazon.com/ Evaluations =========== We evaluated DAMON's overhead, monitoring quality and usefulness using 24 realistic workloads on my QEMU/KVM based virtual machine running a kernel that v24 DAMON patchset is applied. DAMON is lightweight. It increases system memory usage by 0.39% and slows target workloads down by 1.16%. DAMON is accurate and useful for memory management optimizations. An experimental DAMON-based operation scheme for THP, namely 'ethp', removes 76.15% of THP memory overheads while preserving 51.25% of THP speedup. Another experimental DAMON-based 'proactive reclamation' implementation, 'prcl', reduces 93.38% of residential sets and 23.63% of system memory footprint while incurring only 1.22% runtime overhead in the best case (parsec3/freqmine). NOTE that the experimental THP optimization and proactive reclamation are not for production but only for proof of concepts. Please refer to the official document[1] or "Documentation/admin-guide/mm: Add a document for DAMON" patch in this patchset for detailed evaluation setup and results. [1] https://damonitor.github.io/doc/html/latest-damon/admin-guide/mm/damon/eval.html Real-world User Story ===================== In summary, DAMON has used on production systems and proved its usefulness. DAMON as a profiler ------------------- We analyzed characteristics of a large scale production systems of our customers using DAMON. The systems utilize 70GB DRAM and 36 CPUs. From this, we were able to find interesting things below. There were obviously different access pattern under idle workload and active workload. Under the idle workload, it accessed large memory regions with low frequency, while the active workload accessed small memory regions with high freuqnecy. DAMON found a 7GB memory region that showing obviously high access frequency under the active workload. We believe this is the performance-effective working set and need to be protected. There was a 4KB memory region that showing highest access frequency under not only active but also idle workloads. We think this must be a hottest code section like thing that should never be paged out. For this analysis, DAMON used only 0.3-1% of single CPU time. Because we used recording-based analysis, it consumed about 3-12 MB of disk space per 20 minutes. This is only small amount of disk space, but we can further reduce the disk usage by using non-recording-based DAMON features. I'd like to argue that only DAMON can do such detailed analysis (finding 4KB highest region in 70GB memory) with the light overhead. DAMON as a system optimization tool ----------------------------------- We also found below potential performance problems on the systems and made DAMON-based solutions. The system doesn't want to make the workload suffer from the page reclamation and thus it utilizes enough DRAM but no swap device. However, we found the system is actively reclaiming file-backed pages, because the system has intensive file IO. The file IO turned out to be not performance critical for the workload, but the customer wanted to ensure performance critical file-backed pages like code section to not mistakenly be evicted. Using direct IO should or `mlock()` would be a straightforward solution, but modifying the user space code is not easy for the customer. Alternatively, we could use DAMON-based operation scheme[1]. By using it, we can ask DAMON to track access frequency of each region and make 'process_madvise(MADV_WILLNEED)[2]' call for regions having specific size and access frequency for a time interval. We also found the system is having high number of TLB misses. We tried 'always' THP enabled policy and it greatly reduced TLB misses, but the page reclamation also been more frequent due to the THP internal fragmentation caused memory bloat. We could try another DAMON-based operation scheme that applies 'MADV_HUGEPAGE' to memory regions having >=2MB size and high access frequency, while applying 'MADV_NOHUGEPAGE' to regions having <2MB size and low access frequency. We do not own the systems so we only reported the analysis results and possible optimization solutions to the customers. The customers satisfied about the analysis results and promised to try the optimization guides. [1] https://lore.kernel.org/linux-mm/20201006123931.5847-1-sjpark@amazon.com/ [2] https://lore.kernel.org/linux-api/20200622192900.22757-4-minchan@kernel.org/ Comparison with Idle Page Tracking ================================== Idle Page Tracking allows users to set and read idleness of pages using a bitmap file which represents each page with each bit of the file. One recommended usage of it is working set size detection. Users can do that by 1. find PFN of each page for workloads in interest, 2. set all the pages as idle by doing writes to the bitmap file, 3. wait until the workload accesses its working set, and 4. read the idleness of the pages again and count pages became not idle. NOTE: While Idle Page Tracking is for user space users, DAMON is primarily designed for kernel subsystems though it can easily exposed to the user space. Hence, this section only assumes such user space use of DAMON. For what use cases Idle Page Tracking would be better? ------------------------------------------------------ 1. Flexible usecases other than hotness monitoring. Because Idle Page Tracking allows users to control the primitive (Page idleness) by themselves, Idle Page Tracking users can do anything they want. Meanwhile, DAMON is primarily designed to monitor the hotness of each memory region. For this, DAMON asks users to provide sampling interval and aggregation interval. For the reason, there could be some use case that using Idle Page Tracking is simpler. 2. Physical memory monitoring. Idle Page Tracking receives PFN range as input, so natively supports physical memory monitoring. DAMON is designed to be extensible for multiple address spaces and use cases by implementing and using primitives for the given use case. Therefore, by theory, DAMON has no limitation in the type of target address space as long as primitives for the given address space exists. However, the default primitives introduced by this patchset supports only virtual address spaces. Therefore, for physical memory monitoring, you should implement your own primitives and use it, or simply use Idle Page Tracking. Nonetheless, RFC patchsets[1] for the physical memory address space primitives is already available. It also supports user memory same to Idle Page Tracking. [1] https://lore.kernel.org/linux-mm/20200831104730.28970-1-sjpark@amazon.com/ For what use cases DAMON is better? ----------------------------------- 1. Hotness Monitoring. Idle Page Tracking let users know only if a page frame is accessed or not. For hotness check, the user should write more code and use more memory. DAMON do that by itself. 2. Low Monitoring Overhead DAMON receives user's monitoring request with one step and then provide the results. So, roughly speaking, DAMON require only O(1) user/kernel context switches. In case of Idle Page Tracking, however, because the interface receives contiguous page frames, the number of user/kernel context switches increases as the monitoring target becomes complex and huge. As a result, the context switch overhead could be not negligible. Moreover, DAMON is born to handle with the monitoring overhead. Because the core mechanism is pure logical, Idle Page Tracking users might be able to implement the mechanism on their own, but it would be time consuming and the user/kernel context switching will still more frequent than that of DAMON. Also, the kernel subsystems cannot use the logic in this case. 3. Page granularity working set size detection. Until v22 of this patchset, this was categorized as the thing Idle Page Tracking could do better, because DAMON basically maintains additional metadata for each of the monitoring target regions. So, in the page granularity working set size detection use case, DAMON would incur (number of monitoring target pages * size of metadata) memory overhead. Size of the single metadata item is about 54 bytes, so assuming 4KB pages, about 1.3% of monitoring target pages will be additionally used. All essential metadata for Idle Page Tracking are embedded in 'struct page' and page table entries. Therefore, in this use case, only one counter variable for working set size accounting is required if Idle Page Tracking is used. There are more details to consider, but roughly speaking, this is true in most cases. However, the situation changed from v23. Now DAMON supports arbitrary types of monitoring targets, which don't use the metadata. Using that, DAMON can do the working set size detection with no additional space overhead but less user-kernel context switch. A first draft for the implementation of monitoring primitives for this usage is available in a DAMON development tree[1]. An RFC patchset for it based on this patchset will also be available soon. Since v24, the arbitrary type support is dropped from this patchset because this patchset doesn't introduce real use of the type. You can still get it from the DAMON development tree[2], though. [1] https://github.com/sjp38/linux/tree/damon/pgidle_hack [2] https://github.com/sjp38/linux/tree/damon/master 4. More future usecases While Idle Page Tracking has tight coupling with base primitives (PG_Idle and page table Accessed bits), DAMON is designed to be extensible for many use cases and address spaces. If you need some special address type or want to use special h/w access check primitives, you can write your own primitives for that and configure DAMON to use those. Therefore, if your use case could be changed a lot in future, using DAMON could be better. Can I use both Idle Page Tracking and DAMON? -------------------------------------------- Yes, though using them concurrently for overlapping memory regions could result in interference to each other. Nevertheless, such use case would be rare or makes no sense at all. Even in the case, the noise would bot be really significant. So, you can choose whatever you want depending on the characteristics of your use cases. More Information ================ We prepared a showcase web site[1] that you can get more information. There are - the official documentations[2], - the heatmap format dynamic access pattern of various realistic workloads for heap area[3], mmap()-ed area[4], and stack[5] area, - the dynamic working set size distribution[6] and chronological working set size changes[7], and - the latest performance test results[8]. [1] https://damonitor.github.io/_index [2] https://damonitor.github.io/doc/html/latest-damon [3] https://damonitor.github.io/test/result/visual/latest/rec.heatmap.0.png.html [4] https://damonitor.github.io/test/result/visual/latest/rec.heatmap.1.png.html [5] https://damonitor.github.io/test/result/visual/latest/rec.heatmap.2.png.html [6] https://damonitor.github.io/test/result/visual/latest/rec.wss_sz.png.html [7] https://damonitor.github.io/test/result/visual/latest/rec.wss_time.png.html [8] https://damonitor.github.io/test/result/perf/latest/html/index.html Baseline and Complete Git Trees =============================== The patches are based on the latest -mm tree, specifically v5.14-rc1-mmots-2021-07-15-18-47 of https://github.com/hnaz/linux-mm. You can also clone the complete git tree: $ git clone git://github.com/sjp38/linux -b damon/patches/v34 The web is also available: https://github.com/sjp38/linux/releases/tag/damon/patches/v34 Development Trees ----------------- There are a couple of trees for entire DAMON patchset series and features for future release. - For latest release: https://github.com/sjp38/linux/tree/damon/master - For next release: https://github.com/sjp38/linux/tree/damon/next Long-term Support Trees ----------------------- For people who want to test DAMON but using LTS kernels, there are another couple of trees based on two latest LTS kernels respectively and containing the 'damon/master' backports. - For v5.4.y: https://github.com/sjp38/linux/tree/damon/for-v5.4.y - For v5.10.y: https://github.com/sjp38/linux/tree/damon/for-v5.10.y Amazon Linux Kernel Trees ------------------------- DAMON is also merged in two public Amazon Linux kernel trees that based on v5.4.y[1] and v5.10.y[2]. [1] https://github.com/amazonlinux/linux/tree/amazon-5.4.y/master/mm/damon [2] https://github.com/amazonlinux/linux/tree/amazon-5.10.y/master/mm/damon Git Tree for Diff of Patches ============================ For easy review of diff between different versions of each patch, I prepared a git tree containing all versions of the DAMON patchset series: https://github.com/sjp38/damon-patches You can clone it and use 'diff' for easy review of changes between different versions of the patchset. For example: $ git clone https://github.com/sjp38/damon-patches && cd damon-patches $ diff -u damon/v33 damon/v34 Sequence Of Patches =================== First three patches implement the core logics of DAMON. The 1st patch introduces basic sampling based hotness monitoring for arbitrary types of targets. Following two patches implement the core mechanisms for control of overhead and accuracy, namely regions based sampling (patch 2) and adaptive regions adjustment (patch 3). Now the essential parts of DAMON is complete, but it cannot work unless someone provides monitoring primitives for a specific use case. The following two patches make it just work for virtual address spaces monitoring. The 4th patch makes 'PG_idle' can be used by DAMON and the 5th patch implements the virtual memory address space specific monitoring primitives using page table Accessed bits and the 'PG_idle' page flag. Now DAMON just works for virtual address space monitoring via the kernel space api. To let the user space users can use DAMON, following four patches add interfaces for them. The 6th patch adds a tracepoint for monitoring results. The 7th patch implements a DAMON application kernel module, namely damon-dbgfs, that simply wraps DAMON and exposes DAMON interface to the user space via the debugfs interface. The 8th patch further exports pid of monitoring thread (kdamond) to user space for easier cpu usage accounting, and the 9th patch makes the debugfs interface to support multiple contexts. Three patches for maintainability follows. The 10th patch adds documentations for both the user space and the kernel space. The 11th patch provides unit tests (based on the kunit) while the 12th patch adds user space tests (based on the kselftest). Finally, the last patch (13th) updates the MAINTAINERS file. This patch (of 13): DAMON is a data access monitoring framework for the Linux kernel. The core mechanisms of DAMON make it - accurate (the monitoring output is useful enough for DRAM level performance-centric memory management; It might be inappropriate for CPU cache levels, though), - light-weight (the monitoring overhead is normally low enough to be applied online), and - scalable (the upper-bound of the overhead is in constant range regardless of the size of target workloads). Using this framework, hence, we can easily write efficient kernel space data access monitoring applications. For example, the kernel's memory management mechanisms can make advanced decisions using this. Experimental data access aware optimization works that incurring high access monitoring overhead could again be implemented on top of this. Due to its simple and flexible interface, providing user space interface would be also easy. Then, user space users who have some special workloads can write personalized applications for better understanding and optimizations of their workloads and systems. === Nevertheless, this commit is defining and implementing only basic access check part without the overhead-accuracy handling core logic. The basic access check is as below. The output of DAMON says what memory regions are how frequently accessed for a given duration. The resolution of the access frequency is controlled by setting ``sampling interval`` and ``aggregation interval``. In detail, DAMON checks access to each page per ``sampling interval`` and aggregates the results. In other words, counts the number of the accesses to each region. After each ``aggregation interval`` passes, DAMON calls callback functions that previously registered by users so that users can read the aggregated results and then clears the results. This can be described in below simple pseudo-code:: init() while monitoring_on: for page in monitoring_target: if accessed(page): nr_accesses[page] += 1 if time() % aggregation_interval == 0: for callback in user_registered_callbacks: callback(monitoring_target, nr_accesses) for page in monitoring_target: nr_accesses[page] = 0 if time() % update_interval == 0: update() sleep(sampling interval) The target regions constructed at the beginning of the monitoring and updated after each ``regions_update_interval``, because the target regions could be dynamically changed (e.g., mmap() or memory hotplug). The monitoring overhead of this mechanism will arbitrarily increase as the size of the target workload grows. The basic monitoring primitives for actual access check and dynamic target regions construction aren't in the core part of DAMON. Instead, it allows users to implement their own primitives that are optimized for their use case and configure DAMON to use those. In other words, users cannot use current version of DAMON without some additional works. Following commits will implement the core mechanisms for the overhead-accuracy control and default primitives implementations. Link: https://lkml.kernel.org/r/20210716081449.22187-1-sj38.park@gmail.com Link: https://lkml.kernel.org/r/20210716081449.22187-2-sj38.park@gmail.com Signed-off-by: SeongJae Park <sjpark@amazon.de> Reviewed-by: Leonard Foerster <foersleo@amazon.de> Reviewed-by: Fernand Sieber <sieberf@amazon.com> Acked-by: Shakeel Butt <shakeelb@google.com> Cc: Jonathan Cameron <Jonathan.Cameron@huawei.com> Cc: Alexander Shishkin <alexander.shishkin@linux.intel.com> Cc: Amit Shah <amit@kernel.org> Cc: Benjamin Herrenschmidt <benh@kernel.crashing.org> Cc: Jonathan Corbet <corbet@lwn.net> Cc: David Hildenbrand <david@redhat.com> Cc: David Woodhouse <dwmw@amazon.com> Cc: Marco Elver <elver@google.com> Cc: Fan Du <fan.du@intel.com> Cc: Greg Kroah-Hartman <greg@kroah.com> Cc: Greg Thelen <gthelen@google.com> Cc: Joe Perches <joe@perches.com> Cc: Mel Gorman <mgorman@suse.de> Cc: Maximilian Heyne <mheyne@amazon.de> Cc: Minchan Kim <minchan@kernel.org> Cc: Ingo Molnar <mingo@redhat.com> Cc: Namhyung Kim <namhyung@kernel.org> Cc: Peter Zijlstra <peterz@infradead.org> Cc: Rik van Riel <riel@surriel.com> Cc: David Rientjes <rientjes@google.com> Cc: Steven Rostedt (VMware) <rostedt@goodmis.org> Cc: Shuah Khan <shuah@kernel.org> Cc: Vlastimil Babka <vbabka@suse.cz> Cc: Vladimir Davydov <vdavydov.dev@gmail.com> Cc: Brendan Higgins <brendanhiggins@google.com> Cc: Markus Boehme <markubo@amazon.de> Signed-off-by: Andrew Morton <akpm@linux-foundation.org> Signed-off-by: Linus Torvalds <torvalds@linux-foundation.org>
2021-09-08 10:56:28 +08:00
struct damon_callback callback;
mm/damon: adaptively adjust regions Even somehow the initial monitoring target regions are well constructed to fulfill the assumption (pages in same region have similar access frequencies), the data access pattern can be dynamically changed. This will result in low monitoring quality. To keep the assumption as much as possible, DAMON adaptively merges and splits each region based on their access frequency. For each ``aggregation interval``, it compares the access frequencies of adjacent regions and merges those if the frequency difference is small. Then, after it reports and clears the aggregated access frequency of each region, it splits each region into two or three regions if the total number of regions will not exceed the user-specified maximum number of regions after the split. In this way, DAMON provides its best-effort quality and minimal overhead while keeping the upper-bound overhead that users set. Link: https://lkml.kernel.org/r/20210716081449.22187-4-sj38.park@gmail.com Signed-off-by: SeongJae Park <sjpark@amazon.de> Reviewed-by: Leonard Foerster <foersleo@amazon.de> Reviewed-by: Fernand Sieber <sieberf@amazon.com> Acked-by: Shakeel Butt <shakeelb@google.com> Cc: Alexander Shishkin <alexander.shishkin@linux.intel.com> Cc: Amit Shah <amit@kernel.org> Cc: Benjamin Herrenschmidt <benh@kernel.crashing.org> Cc: Brendan Higgins <brendanhiggins@google.com> Cc: David Hildenbrand <david@redhat.com> Cc: David Rientjes <rientjes@google.com> Cc: David Woodhouse <dwmw@amazon.com> Cc: Fan Du <fan.du@intel.com> Cc: Greg Kroah-Hartman <greg@kroah.com> Cc: Greg Thelen <gthelen@google.com> Cc: Ingo Molnar <mingo@redhat.com> Cc: Joe Perches <joe@perches.com> Cc: Jonathan Cameron <Jonathan.Cameron@huawei.com> Cc: Jonathan Corbet <corbet@lwn.net> Cc: Marco Elver <elver@google.com> Cc: Markus Boehme <markubo@amazon.de> Cc: Maximilian Heyne <mheyne@amazon.de> Cc: Mel Gorman <mgorman@suse.de> Cc: Minchan Kim <minchan@kernel.org> Cc: Namhyung Kim <namhyung@kernel.org> Cc: Peter Zijlstra <peterz@infradead.org> Cc: Rik van Riel <riel@surriel.com> Cc: Shuah Khan <shuah@kernel.org> Cc: Steven Rostedt (VMware) <rostedt@goodmis.org> Cc: Vladimir Davydov <vdavydov.dev@gmail.com> Cc: Vlastimil Babka <vbabka@suse.cz> Signed-off-by: Andrew Morton <akpm@linux-foundation.org> Signed-off-by: Linus Torvalds <torvalds@linux-foundation.org>
2021-09-08 10:56:36 +08:00
unsigned long min_nr_regions;
unsigned long max_nr_regions;
struct list_head adaptive_targets;
mm/damon/core: implement DAMON-based Operation Schemes (DAMOS) In many cases, users might use DAMON for simple data access aware memory management optimizations such as applying an operation scheme to a memory region of a specific size having a specific access frequency for a specific time. For example, "page out a memory region larger than 100 MiB but having a low access frequency more than 10 minutes", or "Use THP for a memory region larger than 2 MiB having a high access frequency for more than 2 seconds". Most simple form of the solution would be doing offline data access pattern profiling using DAMON and modifying the application source code or system configuration based on the profiling results. Or, developing a daemon constructed with two modules (one for access monitoring and the other for applying memory management actions via mlock(), madvise(), sysctl, etc) is imaginable. To avoid users spending their time for implementation of such simple data access monitoring-based operation schemes, this makes DAMON to handle such schemes directly. With this change, users can simply specify their desired schemes to DAMON. Then, DAMON will automatically apply the schemes to the user-specified target processes. Each of the schemes is composed with conditions for filtering of the target memory regions and desired memory management action for the target. Specifically, the format is:: <min/max size> <min/max access frequency> <min/max age> <action> The filtering conditions are size of memory region, number of accesses to the region monitored by DAMON, and the age of the region. The age of region is incremented periodically but reset when its addresses or access frequency has significantly changed or the action of a scheme was applied. For the action, current implementation supports a few of madvise()-like hints, ``WILLNEED``, ``COLD``, ``PAGEOUT``, ``HUGEPAGE``, and ``NOHUGEPAGE``. Because DAMON supports various address spaces and application of the actions to a monitoring target region is dependent to the type of the target address space, the application code should be implemented by each primitives and registered to the framework. Note that this only implements the framework part. Following commit will implement the action applications for virtual address spaces primitives. Link: https://lkml.kernel.org/r/20211001125604.29660-3-sj@kernel.org Signed-off-by: SeongJae Park <sj@kernel.org> Cc: Amit Shah <amit@kernel.org> Cc: Benjamin Herrenschmidt <benh@kernel.crashing.org> Cc: David Hildenbrand <david@redhat.com> Cc: David Rienjes <rientjes@google.com> Cc: David Woodhouse <dwmw@amazon.com> Cc: Greg Thelen <gthelen@google.com> Cc: Jonathan Cameron <Jonathan.Cameron@huawei.com> Cc: Jonathan Corbet <corbet@lwn.net> Cc: Leonard Foerster <foersleo@amazon.de> Cc: Marco Elver <elver@google.com> Cc: Markus Boehme <markubo@amazon.de> Cc: Shakeel Butt <shakeelb@google.com> Cc: Shuah Khan <shuah@kernel.org> Signed-off-by: Andrew Morton <akpm@linux-foundation.org> Signed-off-by: Linus Torvalds <torvalds@linux-foundation.org>
2021-11-06 04:46:22 +08:00
struct list_head schemes;
mm: introduce Data Access MONitor (DAMON) Patch series "Introduce Data Access MONitor (DAMON)", v34. Introduction ============ DAMON is a data access monitoring framework for the Linux kernel. The core mechanisms of DAMON called 'region based sampling' and 'adaptive regions adjustment' (refer to 'mechanisms.rst' in the 11th patch of this patchset for the detail) make it - accurate (The monitored information is useful for DRAM level memory management. It might not appropriate for Cache-level accuracy, though.), - light-weight (The monitoring overhead is low enough to be applied online while making no impact on the performance of the target workloads.), and - scalable (the upper-bound of the instrumentation overhead is controllable regardless of the size of target workloads.). Using this framework, therefore, several memory management mechanisms such as reclamation and THP can be optimized to aware real data access patterns. Experimental access pattern aware memory management optimization works that incurring high instrumentation overhead will be able to have another try. Though DAMON is for kernel subsystems, it can be easily exposed to the user space by writing a DAMON-wrapper kernel subsystem. Then, user space users who have some special workloads will be able to write personalized tools or applications for deeper understanding and specialized optimizations of their systems. DAMON is also merged in two public Amazon Linux kernel trees that based on v5.4.y[1] and v5.10.y[2]. [1] https://github.com/amazonlinux/linux/tree/amazon-5.4.y/master/mm/damon [2] https://github.com/amazonlinux/linux/tree/amazon-5.10.y/master/mm/damon The userspace tool[1] is available, released under GPLv2, and actively being maintained. I am also planning to implement another basic user interface in perf[2]. Also, the basic test suite for DAMON is available under GPLv2[3]. [1] https://github.com/awslabs/damo [2] https://lore.kernel.org/linux-mm/20210107120729.22328-1-sjpark@amazon.com/ [3] https://github.com/awslabs/damon-tests Long-term Plan -------------- DAMON is a part of a project called Data Access-aware Operating System (DAOS). As the name implies, I want to improve the performance and efficiency of systems using fine-grained data access patterns. The optimizations are for both kernel and user spaces. I will therefore modify or create kernel subsystems, export some of those to user space and implement user space library / tools. Below shows the layers and components for the project. --------------------------------------------------------------------------- Primitives: PTE Accessed bit, PG_idle, rmap, (Intel CMT), ... Framework: DAMON Features: DAMOS, virtual addr, physical addr, ... Applications: DAMON-debugfs, (DARC), ... ^^^^^^^^^^^^^^^^^^^^^^^ KERNEL SPACE ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ Raw Interface: debugfs, (sysfs), (damonfs), tracepoints, (sys_damon), ... vvvvvvvvvvvvvvvvvvvvvvv USER SPACE vvvvvvvvvvvvvvvvvvvvvvvvvvvvvvvv Library: (libdamon), ... Tools: DAMO, (perf), ... --------------------------------------------------------------------------- The components in parentheses or marked as '...' are not implemented yet but in the future plan. IOW, those are the TODO tasks of DAOS project. For more detail, please refer to the plans: https://lore.kernel.org/linux-mm/20201202082731.24828-1-sjpark@amazon.com/ Evaluations =========== We evaluated DAMON's overhead, monitoring quality and usefulness using 24 realistic workloads on my QEMU/KVM based virtual machine running a kernel that v24 DAMON patchset is applied. DAMON is lightweight. It increases system memory usage by 0.39% and slows target workloads down by 1.16%. DAMON is accurate and useful for memory management optimizations. An experimental DAMON-based operation scheme for THP, namely 'ethp', removes 76.15% of THP memory overheads while preserving 51.25% of THP speedup. Another experimental DAMON-based 'proactive reclamation' implementation, 'prcl', reduces 93.38% of residential sets and 23.63% of system memory footprint while incurring only 1.22% runtime overhead in the best case (parsec3/freqmine). NOTE that the experimental THP optimization and proactive reclamation are not for production but only for proof of concepts. Please refer to the official document[1] or "Documentation/admin-guide/mm: Add a document for DAMON" patch in this patchset for detailed evaluation setup and results. [1] https://damonitor.github.io/doc/html/latest-damon/admin-guide/mm/damon/eval.html Real-world User Story ===================== In summary, DAMON has used on production systems and proved its usefulness. DAMON as a profiler ------------------- We analyzed characteristics of a large scale production systems of our customers using DAMON. The systems utilize 70GB DRAM and 36 CPUs. From this, we were able to find interesting things below. There were obviously different access pattern under idle workload and active workload. Under the idle workload, it accessed large memory regions with low frequency, while the active workload accessed small memory regions with high freuqnecy. DAMON found a 7GB memory region that showing obviously high access frequency under the active workload. We believe this is the performance-effective working set and need to be protected. There was a 4KB memory region that showing highest access frequency under not only active but also idle workloads. We think this must be a hottest code section like thing that should never be paged out. For this analysis, DAMON used only 0.3-1% of single CPU time. Because we used recording-based analysis, it consumed about 3-12 MB of disk space per 20 minutes. This is only small amount of disk space, but we can further reduce the disk usage by using non-recording-based DAMON features. I'd like to argue that only DAMON can do such detailed analysis (finding 4KB highest region in 70GB memory) with the light overhead. DAMON as a system optimization tool ----------------------------------- We also found below potential performance problems on the systems and made DAMON-based solutions. The system doesn't want to make the workload suffer from the page reclamation and thus it utilizes enough DRAM but no swap device. However, we found the system is actively reclaiming file-backed pages, because the system has intensive file IO. The file IO turned out to be not performance critical for the workload, but the customer wanted to ensure performance critical file-backed pages like code section to not mistakenly be evicted. Using direct IO should or `mlock()` would be a straightforward solution, but modifying the user space code is not easy for the customer. Alternatively, we could use DAMON-based operation scheme[1]. By using it, we can ask DAMON to track access frequency of each region and make 'process_madvise(MADV_WILLNEED)[2]' call for regions having specific size and access frequency for a time interval. We also found the system is having high number of TLB misses. We tried 'always' THP enabled policy and it greatly reduced TLB misses, but the page reclamation also been more frequent due to the THP internal fragmentation caused memory bloat. We could try another DAMON-based operation scheme that applies 'MADV_HUGEPAGE' to memory regions having >=2MB size and high access frequency, while applying 'MADV_NOHUGEPAGE' to regions having <2MB size and low access frequency. We do not own the systems so we only reported the analysis results and possible optimization solutions to the customers. The customers satisfied about the analysis results and promised to try the optimization guides. [1] https://lore.kernel.org/linux-mm/20201006123931.5847-1-sjpark@amazon.com/ [2] https://lore.kernel.org/linux-api/20200622192900.22757-4-minchan@kernel.org/ Comparison with Idle Page Tracking ================================== Idle Page Tracking allows users to set and read idleness of pages using a bitmap file which represents each page with each bit of the file. One recommended usage of it is working set size detection. Users can do that by 1. find PFN of each page for workloads in interest, 2. set all the pages as idle by doing writes to the bitmap file, 3. wait until the workload accesses its working set, and 4. read the idleness of the pages again and count pages became not idle. NOTE: While Idle Page Tracking is for user space users, DAMON is primarily designed for kernel subsystems though it can easily exposed to the user space. Hence, this section only assumes such user space use of DAMON. For what use cases Idle Page Tracking would be better? ------------------------------------------------------ 1. Flexible usecases other than hotness monitoring. Because Idle Page Tracking allows users to control the primitive (Page idleness) by themselves, Idle Page Tracking users can do anything they want. Meanwhile, DAMON is primarily designed to monitor the hotness of each memory region. For this, DAMON asks users to provide sampling interval and aggregation interval. For the reason, there could be some use case that using Idle Page Tracking is simpler. 2. Physical memory monitoring. Idle Page Tracking receives PFN range as input, so natively supports physical memory monitoring. DAMON is designed to be extensible for multiple address spaces and use cases by implementing and using primitives for the given use case. Therefore, by theory, DAMON has no limitation in the type of target address space as long as primitives for the given address space exists. However, the default primitives introduced by this patchset supports only virtual address spaces. Therefore, for physical memory monitoring, you should implement your own primitives and use it, or simply use Idle Page Tracking. Nonetheless, RFC patchsets[1] for the physical memory address space primitives is already available. It also supports user memory same to Idle Page Tracking. [1] https://lore.kernel.org/linux-mm/20200831104730.28970-1-sjpark@amazon.com/ For what use cases DAMON is better? ----------------------------------- 1. Hotness Monitoring. Idle Page Tracking let users know only if a page frame is accessed or not. For hotness check, the user should write more code and use more memory. DAMON do that by itself. 2. Low Monitoring Overhead DAMON receives user's monitoring request with one step and then provide the results. So, roughly speaking, DAMON require only O(1) user/kernel context switches. In case of Idle Page Tracking, however, because the interface receives contiguous page frames, the number of user/kernel context switches increases as the monitoring target becomes complex and huge. As a result, the context switch overhead could be not negligible. Moreover, DAMON is born to handle with the monitoring overhead. Because the core mechanism is pure logical, Idle Page Tracking users might be able to implement the mechanism on their own, but it would be time consuming and the user/kernel context switching will still more frequent than that of DAMON. Also, the kernel subsystems cannot use the logic in this case. 3. Page granularity working set size detection. Until v22 of this patchset, this was categorized as the thing Idle Page Tracking could do better, because DAMON basically maintains additional metadata for each of the monitoring target regions. So, in the page granularity working set size detection use case, DAMON would incur (number of monitoring target pages * size of metadata) memory overhead. Size of the single metadata item is about 54 bytes, so assuming 4KB pages, about 1.3% of monitoring target pages will be additionally used. All essential metadata for Idle Page Tracking are embedded in 'struct page' and page table entries. Therefore, in this use case, only one counter variable for working set size accounting is required if Idle Page Tracking is used. There are more details to consider, but roughly speaking, this is true in most cases. However, the situation changed from v23. Now DAMON supports arbitrary types of monitoring targets, which don't use the metadata. Using that, DAMON can do the working set size detection with no additional space overhead but less user-kernel context switch. A first draft for the implementation of monitoring primitives for this usage is available in a DAMON development tree[1]. An RFC patchset for it based on this patchset will also be available soon. Since v24, the arbitrary type support is dropped from this patchset because this patchset doesn't introduce real use of the type. You can still get it from the DAMON development tree[2], though. [1] https://github.com/sjp38/linux/tree/damon/pgidle_hack [2] https://github.com/sjp38/linux/tree/damon/master 4. More future usecases While Idle Page Tracking has tight coupling with base primitives (PG_Idle and page table Accessed bits), DAMON is designed to be extensible for many use cases and address spaces. If you need some special address type or want to use special h/w access check primitives, you can write your own primitives for that and configure DAMON to use those. Therefore, if your use case could be changed a lot in future, using DAMON could be better. Can I use both Idle Page Tracking and DAMON? -------------------------------------------- Yes, though using them concurrently for overlapping memory regions could result in interference to each other. Nevertheless, such use case would be rare or makes no sense at all. Even in the case, the noise would bot be really significant. So, you can choose whatever you want depending on the characteristics of your use cases. More Information ================ We prepared a showcase web site[1] that you can get more information. There are - the official documentations[2], - the heatmap format dynamic access pattern of various realistic workloads for heap area[3], mmap()-ed area[4], and stack[5] area, - the dynamic working set size distribution[6] and chronological working set size changes[7], and - the latest performance test results[8]. [1] https://damonitor.github.io/_index [2] https://damonitor.github.io/doc/html/latest-damon [3] https://damonitor.github.io/test/result/visual/latest/rec.heatmap.0.png.html [4] https://damonitor.github.io/test/result/visual/latest/rec.heatmap.1.png.html [5] https://damonitor.github.io/test/result/visual/latest/rec.heatmap.2.png.html [6] https://damonitor.github.io/test/result/visual/latest/rec.wss_sz.png.html [7] https://damonitor.github.io/test/result/visual/latest/rec.wss_time.png.html [8] https://damonitor.github.io/test/result/perf/latest/html/index.html Baseline and Complete Git Trees =============================== The patches are based on the latest -mm tree, specifically v5.14-rc1-mmots-2021-07-15-18-47 of https://github.com/hnaz/linux-mm. You can also clone the complete git tree: $ git clone git://github.com/sjp38/linux -b damon/patches/v34 The web is also available: https://github.com/sjp38/linux/releases/tag/damon/patches/v34 Development Trees ----------------- There are a couple of trees for entire DAMON patchset series and features for future release. - For latest release: https://github.com/sjp38/linux/tree/damon/master - For next release: https://github.com/sjp38/linux/tree/damon/next Long-term Support Trees ----------------------- For people who want to test DAMON but using LTS kernels, there are another couple of trees based on two latest LTS kernels respectively and containing the 'damon/master' backports. - For v5.4.y: https://github.com/sjp38/linux/tree/damon/for-v5.4.y - For v5.10.y: https://github.com/sjp38/linux/tree/damon/for-v5.10.y Amazon Linux Kernel Trees ------------------------- DAMON is also merged in two public Amazon Linux kernel trees that based on v5.4.y[1] and v5.10.y[2]. [1] https://github.com/amazonlinux/linux/tree/amazon-5.4.y/master/mm/damon [2] https://github.com/amazonlinux/linux/tree/amazon-5.10.y/master/mm/damon Git Tree for Diff of Patches ============================ For easy review of diff between different versions of each patch, I prepared a git tree containing all versions of the DAMON patchset series: https://github.com/sjp38/damon-patches You can clone it and use 'diff' for easy review of changes between different versions of the patchset. For example: $ git clone https://github.com/sjp38/damon-patches && cd damon-patches $ diff -u damon/v33 damon/v34 Sequence Of Patches =================== First three patches implement the core logics of DAMON. The 1st patch introduces basic sampling based hotness monitoring for arbitrary types of targets. Following two patches implement the core mechanisms for control of overhead and accuracy, namely regions based sampling (patch 2) and adaptive regions adjustment (patch 3). Now the essential parts of DAMON is complete, but it cannot work unless someone provides monitoring primitives for a specific use case. The following two patches make it just work for virtual address spaces monitoring. The 4th patch makes 'PG_idle' can be used by DAMON and the 5th patch implements the virtual memory address space specific monitoring primitives using page table Accessed bits and the 'PG_idle' page flag. Now DAMON just works for virtual address space monitoring via the kernel space api. To let the user space users can use DAMON, following four patches add interfaces for them. The 6th patch adds a tracepoint for monitoring results. The 7th patch implements a DAMON application kernel module, namely damon-dbgfs, that simply wraps DAMON and exposes DAMON interface to the user space via the debugfs interface. The 8th patch further exports pid of monitoring thread (kdamond) to user space for easier cpu usage accounting, and the 9th patch makes the debugfs interface to support multiple contexts. Three patches for maintainability follows. The 10th patch adds documentations for both the user space and the kernel space. The 11th patch provides unit tests (based on the kunit) while the 12th patch adds user space tests (based on the kselftest). Finally, the last patch (13th) updates the MAINTAINERS file. This patch (of 13): DAMON is a data access monitoring framework for the Linux kernel. The core mechanisms of DAMON make it - accurate (the monitoring output is useful enough for DRAM level performance-centric memory management; It might be inappropriate for CPU cache levels, though), - light-weight (the monitoring overhead is normally low enough to be applied online), and - scalable (the upper-bound of the overhead is in constant range regardless of the size of target workloads). Using this framework, hence, we can easily write efficient kernel space data access monitoring applications. For example, the kernel's memory management mechanisms can make advanced decisions using this. Experimental data access aware optimization works that incurring high access monitoring overhead could again be implemented on top of this. Due to its simple and flexible interface, providing user space interface would be also easy. Then, user space users who have some special workloads can write personalized applications for better understanding and optimizations of their workloads and systems. === Nevertheless, this commit is defining and implementing only basic access check part without the overhead-accuracy handling core logic. The basic access check is as below. The output of DAMON says what memory regions are how frequently accessed for a given duration. The resolution of the access frequency is controlled by setting ``sampling interval`` and ``aggregation interval``. In detail, DAMON checks access to each page per ``sampling interval`` and aggregates the results. In other words, counts the number of the accesses to each region. After each ``aggregation interval`` passes, DAMON calls callback functions that previously registered by users so that users can read the aggregated results and then clears the results. This can be described in below simple pseudo-code:: init() while monitoring_on: for page in monitoring_target: if accessed(page): nr_accesses[page] += 1 if time() % aggregation_interval == 0: for callback in user_registered_callbacks: callback(monitoring_target, nr_accesses) for page in monitoring_target: nr_accesses[page] = 0 if time() % update_interval == 0: update() sleep(sampling interval) The target regions constructed at the beginning of the monitoring and updated after each ``regions_update_interval``, because the target regions could be dynamically changed (e.g., mmap() or memory hotplug). The monitoring overhead of this mechanism will arbitrarily increase as the size of the target workload grows. The basic monitoring primitives for actual access check and dynamic target regions construction aren't in the core part of DAMON. Instead, it allows users to implement their own primitives that are optimized for their use case and configure DAMON to use those. In other words, users cannot use current version of DAMON without some additional works. Following commits will implement the core mechanisms for the overhead-accuracy control and default primitives implementations. Link: https://lkml.kernel.org/r/20210716081449.22187-1-sj38.park@gmail.com Link: https://lkml.kernel.org/r/20210716081449.22187-2-sj38.park@gmail.com Signed-off-by: SeongJae Park <sjpark@amazon.de> Reviewed-by: Leonard Foerster <foersleo@amazon.de> Reviewed-by: Fernand Sieber <sieberf@amazon.com> Acked-by: Shakeel Butt <shakeelb@google.com> Cc: Jonathan Cameron <Jonathan.Cameron@huawei.com> Cc: Alexander Shishkin <alexander.shishkin@linux.intel.com> Cc: Amit Shah <amit@kernel.org> Cc: Benjamin Herrenschmidt <benh@kernel.crashing.org> Cc: Jonathan Corbet <corbet@lwn.net> Cc: David Hildenbrand <david@redhat.com> Cc: David Woodhouse <dwmw@amazon.com> Cc: Marco Elver <elver@google.com> Cc: Fan Du <fan.du@intel.com> Cc: Greg Kroah-Hartman <greg@kroah.com> Cc: Greg Thelen <gthelen@google.com> Cc: Joe Perches <joe@perches.com> Cc: Mel Gorman <mgorman@suse.de> Cc: Maximilian Heyne <mheyne@amazon.de> Cc: Minchan Kim <minchan@kernel.org> Cc: Ingo Molnar <mingo@redhat.com> Cc: Namhyung Kim <namhyung@kernel.org> Cc: Peter Zijlstra <peterz@infradead.org> Cc: Rik van Riel <riel@surriel.com> Cc: David Rientjes <rientjes@google.com> Cc: Steven Rostedt (VMware) <rostedt@goodmis.org> Cc: Shuah Khan <shuah@kernel.org> Cc: Vlastimil Babka <vbabka@suse.cz> Cc: Vladimir Davydov <vdavydov.dev@gmail.com> Cc: Brendan Higgins <brendanhiggins@google.com> Cc: Markus Boehme <markubo@amazon.de> Signed-off-by: Andrew Morton <akpm@linux-foundation.org> Signed-off-by: Linus Torvalds <torvalds@linux-foundation.org>
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};
static inline struct damon_region *damon_next_region(struct damon_region *r)
{
return container_of(r->list.next, struct damon_region, list);
}
mm/damon/core: implement region-based sampling To avoid the unbounded increase of the overhead, DAMON groups adjacent pages that are assumed to have the same access frequencies into a region. As long as the assumption (pages in a region have the same access frequencies) is kept, only one page in the region is required to be checked. Thus, for each ``sampling interval``, 1. the 'prepare_access_checks' primitive picks one page in each region, 2. waits for one ``sampling interval``, 3. checks whether the page is accessed meanwhile, and 4. increases the access count of the region if so. Therefore, the monitoring overhead is controllable by adjusting the number of regions. DAMON allows both the underlying primitives and user callbacks to adjust regions for the trade-off. In other words, this commit makes DAMON to use not only time-based sampling but also space-based sampling. This scheme, however, cannot preserve the quality of the output if the assumption is not guaranteed. Next commit will address this problem. Link: https://lkml.kernel.org/r/20210716081449.22187-3-sj38.park@gmail.com Signed-off-by: SeongJae Park <sjpark@amazon.de> Reviewed-by: Leonard Foerster <foersleo@amazon.de> Reviewed-by: Fernand Sieber <sieberf@amazon.com> Acked-by: Shakeel Butt <shakeelb@google.com> Cc: Alexander Shishkin <alexander.shishkin@linux.intel.com> Cc: Amit Shah <amit@kernel.org> Cc: Benjamin Herrenschmidt <benh@kernel.crashing.org> Cc: Brendan Higgins <brendanhiggins@google.com> Cc: David Hildenbrand <david@redhat.com> Cc: David Rientjes <rientjes@google.com> Cc: David Woodhouse <dwmw@amazon.com> Cc: Fan Du <fan.du@intel.com> Cc: Greg Kroah-Hartman <greg@kroah.com> Cc: Greg Thelen <gthelen@google.com> Cc: Ingo Molnar <mingo@redhat.com> Cc: Joe Perches <joe@perches.com> Cc: Jonathan Cameron <Jonathan.Cameron@huawei.com> Cc: Jonathan Corbet <corbet@lwn.net> Cc: Marco Elver <elver@google.com> Cc: Markus Boehme <markubo@amazon.de> Cc: Maximilian Heyne <mheyne@amazon.de> Cc: Mel Gorman <mgorman@suse.de> Cc: Minchan Kim <minchan@kernel.org> Cc: Namhyung Kim <namhyung@kernel.org> Cc: Peter Zijlstra <peterz@infradead.org> Cc: Rik van Riel <riel@surriel.com> Cc: Shuah Khan <shuah@kernel.org> Cc: Steven Rostedt (VMware) <rostedt@goodmis.org> Cc: Vladimir Davydov <vdavydov.dev@gmail.com> Cc: Vlastimil Babka <vbabka@suse.cz> Signed-off-by: Andrew Morton <akpm@linux-foundation.org> Signed-off-by: Linus Torvalds <torvalds@linux-foundation.org>
2021-09-08 10:56:32 +08:00
static inline struct damon_region *damon_prev_region(struct damon_region *r)
{
return container_of(r->list.prev, struct damon_region, list);
}
mm/damon/core: implement region-based sampling To avoid the unbounded increase of the overhead, DAMON groups adjacent pages that are assumed to have the same access frequencies into a region. As long as the assumption (pages in a region have the same access frequencies) is kept, only one page in the region is required to be checked. Thus, for each ``sampling interval``, 1. the 'prepare_access_checks' primitive picks one page in each region, 2. waits for one ``sampling interval``, 3. checks whether the page is accessed meanwhile, and 4. increases the access count of the region if so. Therefore, the monitoring overhead is controllable by adjusting the number of regions. DAMON allows both the underlying primitives and user callbacks to adjust regions for the trade-off. In other words, this commit makes DAMON to use not only time-based sampling but also space-based sampling. This scheme, however, cannot preserve the quality of the output if the assumption is not guaranteed. Next commit will address this problem. Link: https://lkml.kernel.org/r/20210716081449.22187-3-sj38.park@gmail.com Signed-off-by: SeongJae Park <sjpark@amazon.de> Reviewed-by: Leonard Foerster <foersleo@amazon.de> Reviewed-by: Fernand Sieber <sieberf@amazon.com> Acked-by: Shakeel Butt <shakeelb@google.com> Cc: Alexander Shishkin <alexander.shishkin@linux.intel.com> Cc: Amit Shah <amit@kernel.org> Cc: Benjamin Herrenschmidt <benh@kernel.crashing.org> Cc: Brendan Higgins <brendanhiggins@google.com> Cc: David Hildenbrand <david@redhat.com> Cc: David Rientjes <rientjes@google.com> Cc: David Woodhouse <dwmw@amazon.com> Cc: Fan Du <fan.du@intel.com> Cc: Greg Kroah-Hartman <greg@kroah.com> Cc: Greg Thelen <gthelen@google.com> Cc: Ingo Molnar <mingo@redhat.com> Cc: Joe Perches <joe@perches.com> Cc: Jonathan Cameron <Jonathan.Cameron@huawei.com> Cc: Jonathan Corbet <corbet@lwn.net> Cc: Marco Elver <elver@google.com> Cc: Markus Boehme <markubo@amazon.de> Cc: Maximilian Heyne <mheyne@amazon.de> Cc: Mel Gorman <mgorman@suse.de> Cc: Minchan Kim <minchan@kernel.org> Cc: Namhyung Kim <namhyung@kernel.org> Cc: Peter Zijlstra <peterz@infradead.org> Cc: Rik van Riel <riel@surriel.com> Cc: Shuah Khan <shuah@kernel.org> Cc: Steven Rostedt (VMware) <rostedt@goodmis.org> Cc: Vladimir Davydov <vdavydov.dev@gmail.com> Cc: Vlastimil Babka <vbabka@suse.cz> Signed-off-by: Andrew Morton <akpm@linux-foundation.org> Signed-off-by: Linus Torvalds <torvalds@linux-foundation.org>
2021-09-08 10:56:32 +08:00
static inline struct damon_region *damon_last_region(struct damon_target *t)
{
return list_last_entry(&t->regions_list, struct damon_region, list);
}
mm/damon/core: implement region-based sampling To avoid the unbounded increase of the overhead, DAMON groups adjacent pages that are assumed to have the same access frequencies into a region. As long as the assumption (pages in a region have the same access frequencies) is kept, only one page in the region is required to be checked. Thus, for each ``sampling interval``, 1. the 'prepare_access_checks' primitive picks one page in each region, 2. waits for one ``sampling interval``, 3. checks whether the page is accessed meanwhile, and 4. increases the access count of the region if so. Therefore, the monitoring overhead is controllable by adjusting the number of regions. DAMON allows both the underlying primitives and user callbacks to adjust regions for the trade-off. In other words, this commit makes DAMON to use not only time-based sampling but also space-based sampling. This scheme, however, cannot preserve the quality of the output if the assumption is not guaranteed. Next commit will address this problem. Link: https://lkml.kernel.org/r/20210716081449.22187-3-sj38.park@gmail.com Signed-off-by: SeongJae Park <sjpark@amazon.de> Reviewed-by: Leonard Foerster <foersleo@amazon.de> Reviewed-by: Fernand Sieber <sieberf@amazon.com> Acked-by: Shakeel Butt <shakeelb@google.com> Cc: Alexander Shishkin <alexander.shishkin@linux.intel.com> Cc: Amit Shah <amit@kernel.org> Cc: Benjamin Herrenschmidt <benh@kernel.crashing.org> Cc: Brendan Higgins <brendanhiggins@google.com> Cc: David Hildenbrand <david@redhat.com> Cc: David Rientjes <rientjes@google.com> Cc: David Woodhouse <dwmw@amazon.com> Cc: Fan Du <fan.du@intel.com> Cc: Greg Kroah-Hartman <greg@kroah.com> Cc: Greg Thelen <gthelen@google.com> Cc: Ingo Molnar <mingo@redhat.com> Cc: Joe Perches <joe@perches.com> Cc: Jonathan Cameron <Jonathan.Cameron@huawei.com> Cc: Jonathan Corbet <corbet@lwn.net> Cc: Marco Elver <elver@google.com> Cc: Markus Boehme <markubo@amazon.de> Cc: Maximilian Heyne <mheyne@amazon.de> Cc: Mel Gorman <mgorman@suse.de> Cc: Minchan Kim <minchan@kernel.org> Cc: Namhyung Kim <namhyung@kernel.org> Cc: Peter Zijlstra <peterz@infradead.org> Cc: Rik van Riel <riel@surriel.com> Cc: Shuah Khan <shuah@kernel.org> Cc: Steven Rostedt (VMware) <rostedt@goodmis.org> Cc: Vladimir Davydov <vdavydov.dev@gmail.com> Cc: Vlastimil Babka <vbabka@suse.cz> Signed-off-by: Andrew Morton <akpm@linux-foundation.org> Signed-off-by: Linus Torvalds <torvalds@linux-foundation.org>
2021-09-08 10:56:32 +08:00
#define damon_for_each_region(r, t) \
list_for_each_entry(r, &t->regions_list, list)
#define damon_for_each_region_safe(r, next, t) \
list_for_each_entry_safe(r, next, &t->regions_list, list)
#define damon_for_each_target(t, ctx) \
mm/damon: adaptively adjust regions Even somehow the initial monitoring target regions are well constructed to fulfill the assumption (pages in same region have similar access frequencies), the data access pattern can be dynamically changed. This will result in low monitoring quality. To keep the assumption as much as possible, DAMON adaptively merges and splits each region based on their access frequency. For each ``aggregation interval``, it compares the access frequencies of adjacent regions and merges those if the frequency difference is small. Then, after it reports and clears the aggregated access frequency of each region, it splits each region into two or three regions if the total number of regions will not exceed the user-specified maximum number of regions after the split. In this way, DAMON provides its best-effort quality and minimal overhead while keeping the upper-bound overhead that users set. Link: https://lkml.kernel.org/r/20210716081449.22187-4-sj38.park@gmail.com Signed-off-by: SeongJae Park <sjpark@amazon.de> Reviewed-by: Leonard Foerster <foersleo@amazon.de> Reviewed-by: Fernand Sieber <sieberf@amazon.com> Acked-by: Shakeel Butt <shakeelb@google.com> Cc: Alexander Shishkin <alexander.shishkin@linux.intel.com> Cc: Amit Shah <amit@kernel.org> Cc: Benjamin Herrenschmidt <benh@kernel.crashing.org> Cc: Brendan Higgins <brendanhiggins@google.com> Cc: David Hildenbrand <david@redhat.com> Cc: David Rientjes <rientjes@google.com> Cc: David Woodhouse <dwmw@amazon.com> Cc: Fan Du <fan.du@intel.com> Cc: Greg Kroah-Hartman <greg@kroah.com> Cc: Greg Thelen <gthelen@google.com> Cc: Ingo Molnar <mingo@redhat.com> Cc: Joe Perches <joe@perches.com> Cc: Jonathan Cameron <Jonathan.Cameron@huawei.com> Cc: Jonathan Corbet <corbet@lwn.net> Cc: Marco Elver <elver@google.com> Cc: Markus Boehme <markubo@amazon.de> Cc: Maximilian Heyne <mheyne@amazon.de> Cc: Mel Gorman <mgorman@suse.de> Cc: Minchan Kim <minchan@kernel.org> Cc: Namhyung Kim <namhyung@kernel.org> Cc: Peter Zijlstra <peterz@infradead.org> Cc: Rik van Riel <riel@surriel.com> Cc: Shuah Khan <shuah@kernel.org> Cc: Steven Rostedt (VMware) <rostedt@goodmis.org> Cc: Vladimir Davydov <vdavydov.dev@gmail.com> Cc: Vlastimil Babka <vbabka@suse.cz> Signed-off-by: Andrew Morton <akpm@linux-foundation.org> Signed-off-by: Linus Torvalds <torvalds@linux-foundation.org>
2021-09-08 10:56:36 +08:00
list_for_each_entry(t, &(ctx)->adaptive_targets, list)
mm/damon/core: implement region-based sampling To avoid the unbounded increase of the overhead, DAMON groups adjacent pages that are assumed to have the same access frequencies into a region. As long as the assumption (pages in a region have the same access frequencies) is kept, only one page in the region is required to be checked. Thus, for each ``sampling interval``, 1. the 'prepare_access_checks' primitive picks one page in each region, 2. waits for one ``sampling interval``, 3. checks whether the page is accessed meanwhile, and 4. increases the access count of the region if so. Therefore, the monitoring overhead is controllable by adjusting the number of regions. DAMON allows both the underlying primitives and user callbacks to adjust regions for the trade-off. In other words, this commit makes DAMON to use not only time-based sampling but also space-based sampling. This scheme, however, cannot preserve the quality of the output if the assumption is not guaranteed. Next commit will address this problem. Link: https://lkml.kernel.org/r/20210716081449.22187-3-sj38.park@gmail.com Signed-off-by: SeongJae Park <sjpark@amazon.de> Reviewed-by: Leonard Foerster <foersleo@amazon.de> Reviewed-by: Fernand Sieber <sieberf@amazon.com> Acked-by: Shakeel Butt <shakeelb@google.com> Cc: Alexander Shishkin <alexander.shishkin@linux.intel.com> Cc: Amit Shah <amit@kernel.org> Cc: Benjamin Herrenschmidt <benh@kernel.crashing.org> Cc: Brendan Higgins <brendanhiggins@google.com> Cc: David Hildenbrand <david@redhat.com> Cc: David Rientjes <rientjes@google.com> Cc: David Woodhouse <dwmw@amazon.com> Cc: Fan Du <fan.du@intel.com> Cc: Greg Kroah-Hartman <greg@kroah.com> Cc: Greg Thelen <gthelen@google.com> Cc: Ingo Molnar <mingo@redhat.com> Cc: Joe Perches <joe@perches.com> Cc: Jonathan Cameron <Jonathan.Cameron@huawei.com> Cc: Jonathan Corbet <corbet@lwn.net> Cc: Marco Elver <elver@google.com> Cc: Markus Boehme <markubo@amazon.de> Cc: Maximilian Heyne <mheyne@amazon.de> Cc: Mel Gorman <mgorman@suse.de> Cc: Minchan Kim <minchan@kernel.org> Cc: Namhyung Kim <namhyung@kernel.org> Cc: Peter Zijlstra <peterz@infradead.org> Cc: Rik van Riel <riel@surriel.com> Cc: Shuah Khan <shuah@kernel.org> Cc: Steven Rostedt (VMware) <rostedt@goodmis.org> Cc: Vladimir Davydov <vdavydov.dev@gmail.com> Cc: Vlastimil Babka <vbabka@suse.cz> Signed-off-by: Andrew Morton <akpm@linux-foundation.org> Signed-off-by: Linus Torvalds <torvalds@linux-foundation.org>
2021-09-08 10:56:32 +08:00
#define damon_for_each_target_safe(t, next, ctx) \
mm/damon: adaptively adjust regions Even somehow the initial monitoring target regions are well constructed to fulfill the assumption (pages in same region have similar access frequencies), the data access pattern can be dynamically changed. This will result in low monitoring quality. To keep the assumption as much as possible, DAMON adaptively merges and splits each region based on their access frequency. For each ``aggregation interval``, it compares the access frequencies of adjacent regions and merges those if the frequency difference is small. Then, after it reports and clears the aggregated access frequency of each region, it splits each region into two or three regions if the total number of regions will not exceed the user-specified maximum number of regions after the split. In this way, DAMON provides its best-effort quality and minimal overhead while keeping the upper-bound overhead that users set. Link: https://lkml.kernel.org/r/20210716081449.22187-4-sj38.park@gmail.com Signed-off-by: SeongJae Park <sjpark@amazon.de> Reviewed-by: Leonard Foerster <foersleo@amazon.de> Reviewed-by: Fernand Sieber <sieberf@amazon.com> Acked-by: Shakeel Butt <shakeelb@google.com> Cc: Alexander Shishkin <alexander.shishkin@linux.intel.com> Cc: Amit Shah <amit@kernel.org> Cc: Benjamin Herrenschmidt <benh@kernel.crashing.org> Cc: Brendan Higgins <brendanhiggins@google.com> Cc: David Hildenbrand <david@redhat.com> Cc: David Rientjes <rientjes@google.com> Cc: David Woodhouse <dwmw@amazon.com> Cc: Fan Du <fan.du@intel.com> Cc: Greg Kroah-Hartman <greg@kroah.com> Cc: Greg Thelen <gthelen@google.com> Cc: Ingo Molnar <mingo@redhat.com> Cc: Joe Perches <joe@perches.com> Cc: Jonathan Cameron <Jonathan.Cameron@huawei.com> Cc: Jonathan Corbet <corbet@lwn.net> Cc: Marco Elver <elver@google.com> Cc: Markus Boehme <markubo@amazon.de> Cc: Maximilian Heyne <mheyne@amazon.de> Cc: Mel Gorman <mgorman@suse.de> Cc: Minchan Kim <minchan@kernel.org> Cc: Namhyung Kim <namhyung@kernel.org> Cc: Peter Zijlstra <peterz@infradead.org> Cc: Rik van Riel <riel@surriel.com> Cc: Shuah Khan <shuah@kernel.org> Cc: Steven Rostedt (VMware) <rostedt@goodmis.org> Cc: Vladimir Davydov <vdavydov.dev@gmail.com> Cc: Vlastimil Babka <vbabka@suse.cz> Signed-off-by: Andrew Morton <akpm@linux-foundation.org> Signed-off-by: Linus Torvalds <torvalds@linux-foundation.org>
2021-09-08 10:56:36 +08:00
list_for_each_entry_safe(t, next, &(ctx)->adaptive_targets, list)
mm/damon/core: implement region-based sampling To avoid the unbounded increase of the overhead, DAMON groups adjacent pages that are assumed to have the same access frequencies into a region. As long as the assumption (pages in a region have the same access frequencies) is kept, only one page in the region is required to be checked. Thus, for each ``sampling interval``, 1. the 'prepare_access_checks' primitive picks one page in each region, 2. waits for one ``sampling interval``, 3. checks whether the page is accessed meanwhile, and 4. increases the access count of the region if so. Therefore, the monitoring overhead is controllable by adjusting the number of regions. DAMON allows both the underlying primitives and user callbacks to adjust regions for the trade-off. In other words, this commit makes DAMON to use not only time-based sampling but also space-based sampling. This scheme, however, cannot preserve the quality of the output if the assumption is not guaranteed. Next commit will address this problem. Link: https://lkml.kernel.org/r/20210716081449.22187-3-sj38.park@gmail.com Signed-off-by: SeongJae Park <sjpark@amazon.de> Reviewed-by: Leonard Foerster <foersleo@amazon.de> Reviewed-by: Fernand Sieber <sieberf@amazon.com> Acked-by: Shakeel Butt <shakeelb@google.com> Cc: Alexander Shishkin <alexander.shishkin@linux.intel.com> Cc: Amit Shah <amit@kernel.org> Cc: Benjamin Herrenschmidt <benh@kernel.crashing.org> Cc: Brendan Higgins <brendanhiggins@google.com> Cc: David Hildenbrand <david@redhat.com> Cc: David Rientjes <rientjes@google.com> Cc: David Woodhouse <dwmw@amazon.com> Cc: Fan Du <fan.du@intel.com> Cc: Greg Kroah-Hartman <greg@kroah.com> Cc: Greg Thelen <gthelen@google.com> Cc: Ingo Molnar <mingo@redhat.com> Cc: Joe Perches <joe@perches.com> Cc: Jonathan Cameron <Jonathan.Cameron@huawei.com> Cc: Jonathan Corbet <corbet@lwn.net> Cc: Marco Elver <elver@google.com> Cc: Markus Boehme <markubo@amazon.de> Cc: Maximilian Heyne <mheyne@amazon.de> Cc: Mel Gorman <mgorman@suse.de> Cc: Minchan Kim <minchan@kernel.org> Cc: Namhyung Kim <namhyung@kernel.org> Cc: Peter Zijlstra <peterz@infradead.org> Cc: Rik van Riel <riel@surriel.com> Cc: Shuah Khan <shuah@kernel.org> Cc: Steven Rostedt (VMware) <rostedt@goodmis.org> Cc: Vladimir Davydov <vdavydov.dev@gmail.com> Cc: Vlastimil Babka <vbabka@suse.cz> Signed-off-by: Andrew Morton <akpm@linux-foundation.org> Signed-off-by: Linus Torvalds <torvalds@linux-foundation.org>
2021-09-08 10:56:32 +08:00
mm/damon/core: implement DAMON-based Operation Schemes (DAMOS) In many cases, users might use DAMON for simple data access aware memory management optimizations such as applying an operation scheme to a memory region of a specific size having a specific access frequency for a specific time. For example, "page out a memory region larger than 100 MiB but having a low access frequency more than 10 minutes", or "Use THP for a memory region larger than 2 MiB having a high access frequency for more than 2 seconds". Most simple form of the solution would be doing offline data access pattern profiling using DAMON and modifying the application source code or system configuration based on the profiling results. Or, developing a daemon constructed with two modules (one for access monitoring and the other for applying memory management actions via mlock(), madvise(), sysctl, etc) is imaginable. To avoid users spending their time for implementation of such simple data access monitoring-based operation schemes, this makes DAMON to handle such schemes directly. With this change, users can simply specify their desired schemes to DAMON. Then, DAMON will automatically apply the schemes to the user-specified target processes. Each of the schemes is composed with conditions for filtering of the target memory regions and desired memory management action for the target. Specifically, the format is:: <min/max size> <min/max access frequency> <min/max age> <action> The filtering conditions are size of memory region, number of accesses to the region monitored by DAMON, and the age of the region. The age of region is incremented periodically but reset when its addresses or access frequency has significantly changed or the action of a scheme was applied. For the action, current implementation supports a few of madvise()-like hints, ``WILLNEED``, ``COLD``, ``PAGEOUT``, ``HUGEPAGE``, and ``NOHUGEPAGE``. Because DAMON supports various address spaces and application of the actions to a monitoring target region is dependent to the type of the target address space, the application code should be implemented by each primitives and registered to the framework. Note that this only implements the framework part. Following commit will implement the action applications for virtual address spaces primitives. Link: https://lkml.kernel.org/r/20211001125604.29660-3-sj@kernel.org Signed-off-by: SeongJae Park <sj@kernel.org> Cc: Amit Shah <amit@kernel.org> Cc: Benjamin Herrenschmidt <benh@kernel.crashing.org> Cc: David Hildenbrand <david@redhat.com> Cc: David Rienjes <rientjes@google.com> Cc: David Woodhouse <dwmw@amazon.com> Cc: Greg Thelen <gthelen@google.com> Cc: Jonathan Cameron <Jonathan.Cameron@huawei.com> Cc: Jonathan Corbet <corbet@lwn.net> Cc: Leonard Foerster <foersleo@amazon.de> Cc: Marco Elver <elver@google.com> Cc: Markus Boehme <markubo@amazon.de> Cc: Shakeel Butt <shakeelb@google.com> Cc: Shuah Khan <shuah@kernel.org> Signed-off-by: Andrew Morton <akpm@linux-foundation.org> Signed-off-by: Linus Torvalds <torvalds@linux-foundation.org>
2021-11-06 04:46:22 +08:00
#define damon_for_each_scheme(s, ctx) \
list_for_each_entry(s, &(ctx)->schemes, list)
#define damon_for_each_scheme_safe(s, next, ctx) \
list_for_each_entry_safe(s, next, &(ctx)->schemes, list)
mm: introduce Data Access MONitor (DAMON) Patch series "Introduce Data Access MONitor (DAMON)", v34. Introduction ============ DAMON is a data access monitoring framework for the Linux kernel. The core mechanisms of DAMON called 'region based sampling' and 'adaptive regions adjustment' (refer to 'mechanisms.rst' in the 11th patch of this patchset for the detail) make it - accurate (The monitored information is useful for DRAM level memory management. It might not appropriate for Cache-level accuracy, though.), - light-weight (The monitoring overhead is low enough to be applied online while making no impact on the performance of the target workloads.), and - scalable (the upper-bound of the instrumentation overhead is controllable regardless of the size of target workloads.). Using this framework, therefore, several memory management mechanisms such as reclamation and THP can be optimized to aware real data access patterns. Experimental access pattern aware memory management optimization works that incurring high instrumentation overhead will be able to have another try. Though DAMON is for kernel subsystems, it can be easily exposed to the user space by writing a DAMON-wrapper kernel subsystem. Then, user space users who have some special workloads will be able to write personalized tools or applications for deeper understanding and specialized optimizations of their systems. DAMON is also merged in two public Amazon Linux kernel trees that based on v5.4.y[1] and v5.10.y[2]. [1] https://github.com/amazonlinux/linux/tree/amazon-5.4.y/master/mm/damon [2] https://github.com/amazonlinux/linux/tree/amazon-5.10.y/master/mm/damon The userspace tool[1] is available, released under GPLv2, and actively being maintained. I am also planning to implement another basic user interface in perf[2]. Also, the basic test suite for DAMON is available under GPLv2[3]. [1] https://github.com/awslabs/damo [2] https://lore.kernel.org/linux-mm/20210107120729.22328-1-sjpark@amazon.com/ [3] https://github.com/awslabs/damon-tests Long-term Plan -------------- DAMON is a part of a project called Data Access-aware Operating System (DAOS). As the name implies, I want to improve the performance and efficiency of systems using fine-grained data access patterns. The optimizations are for both kernel and user spaces. I will therefore modify or create kernel subsystems, export some of those to user space and implement user space library / tools. Below shows the layers and components for the project. --------------------------------------------------------------------------- Primitives: PTE Accessed bit, PG_idle, rmap, (Intel CMT), ... Framework: DAMON Features: DAMOS, virtual addr, physical addr, ... Applications: DAMON-debugfs, (DARC), ... ^^^^^^^^^^^^^^^^^^^^^^^ KERNEL SPACE ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ Raw Interface: debugfs, (sysfs), (damonfs), tracepoints, (sys_damon), ... vvvvvvvvvvvvvvvvvvvvvvv USER SPACE vvvvvvvvvvvvvvvvvvvvvvvvvvvvvvvv Library: (libdamon), ... Tools: DAMO, (perf), ... --------------------------------------------------------------------------- The components in parentheses or marked as '...' are not implemented yet but in the future plan. IOW, those are the TODO tasks of DAOS project. For more detail, please refer to the plans: https://lore.kernel.org/linux-mm/20201202082731.24828-1-sjpark@amazon.com/ Evaluations =========== We evaluated DAMON's overhead, monitoring quality and usefulness using 24 realistic workloads on my QEMU/KVM based virtual machine running a kernel that v24 DAMON patchset is applied. DAMON is lightweight. It increases system memory usage by 0.39% and slows target workloads down by 1.16%. DAMON is accurate and useful for memory management optimizations. An experimental DAMON-based operation scheme for THP, namely 'ethp', removes 76.15% of THP memory overheads while preserving 51.25% of THP speedup. Another experimental DAMON-based 'proactive reclamation' implementation, 'prcl', reduces 93.38% of residential sets and 23.63% of system memory footprint while incurring only 1.22% runtime overhead in the best case (parsec3/freqmine). NOTE that the experimental THP optimization and proactive reclamation are not for production but only for proof of concepts. Please refer to the official document[1] or "Documentation/admin-guide/mm: Add a document for DAMON" patch in this patchset for detailed evaluation setup and results. [1] https://damonitor.github.io/doc/html/latest-damon/admin-guide/mm/damon/eval.html Real-world User Story ===================== In summary, DAMON has used on production systems and proved its usefulness. DAMON as a profiler ------------------- We analyzed characteristics of a large scale production systems of our customers using DAMON. The systems utilize 70GB DRAM and 36 CPUs. From this, we were able to find interesting things below. There were obviously different access pattern under idle workload and active workload. Under the idle workload, it accessed large memory regions with low frequency, while the active workload accessed small memory regions with high freuqnecy. DAMON found a 7GB memory region that showing obviously high access frequency under the active workload. We believe this is the performance-effective working set and need to be protected. There was a 4KB memory region that showing highest access frequency under not only active but also idle workloads. We think this must be a hottest code section like thing that should never be paged out. For this analysis, DAMON used only 0.3-1% of single CPU time. Because we used recording-based analysis, it consumed about 3-12 MB of disk space per 20 minutes. This is only small amount of disk space, but we can further reduce the disk usage by using non-recording-based DAMON features. I'd like to argue that only DAMON can do such detailed analysis (finding 4KB highest region in 70GB memory) with the light overhead. DAMON as a system optimization tool ----------------------------------- We also found below potential performance problems on the systems and made DAMON-based solutions. The system doesn't want to make the workload suffer from the page reclamation and thus it utilizes enough DRAM but no swap device. However, we found the system is actively reclaiming file-backed pages, because the system has intensive file IO. The file IO turned out to be not performance critical for the workload, but the customer wanted to ensure performance critical file-backed pages like code section to not mistakenly be evicted. Using direct IO should or `mlock()` would be a straightforward solution, but modifying the user space code is not easy for the customer. Alternatively, we could use DAMON-based operation scheme[1]. By using it, we can ask DAMON to track access frequency of each region and make 'process_madvise(MADV_WILLNEED)[2]' call for regions having specific size and access frequency for a time interval. We also found the system is having high number of TLB misses. We tried 'always' THP enabled policy and it greatly reduced TLB misses, but the page reclamation also been more frequent due to the THP internal fragmentation caused memory bloat. We could try another DAMON-based operation scheme that applies 'MADV_HUGEPAGE' to memory regions having >=2MB size and high access frequency, while applying 'MADV_NOHUGEPAGE' to regions having <2MB size and low access frequency. We do not own the systems so we only reported the analysis results and possible optimization solutions to the customers. The customers satisfied about the analysis results and promised to try the optimization guides. [1] https://lore.kernel.org/linux-mm/20201006123931.5847-1-sjpark@amazon.com/ [2] https://lore.kernel.org/linux-api/20200622192900.22757-4-minchan@kernel.org/ Comparison with Idle Page Tracking ================================== Idle Page Tracking allows users to set and read idleness of pages using a bitmap file which represents each page with each bit of the file. One recommended usage of it is working set size detection. Users can do that by 1. find PFN of each page for workloads in interest, 2. set all the pages as idle by doing writes to the bitmap file, 3. wait until the workload accesses its working set, and 4. read the idleness of the pages again and count pages became not idle. NOTE: While Idle Page Tracking is for user space users, DAMON is primarily designed for kernel subsystems though it can easily exposed to the user space. Hence, this section only assumes such user space use of DAMON. For what use cases Idle Page Tracking would be better? ------------------------------------------------------ 1. Flexible usecases other than hotness monitoring. Because Idle Page Tracking allows users to control the primitive (Page idleness) by themselves, Idle Page Tracking users can do anything they want. Meanwhile, DAMON is primarily designed to monitor the hotness of each memory region. For this, DAMON asks users to provide sampling interval and aggregation interval. For the reason, there could be some use case that using Idle Page Tracking is simpler. 2. Physical memory monitoring. Idle Page Tracking receives PFN range as input, so natively supports physical memory monitoring. DAMON is designed to be extensible for multiple address spaces and use cases by implementing and using primitives for the given use case. Therefore, by theory, DAMON has no limitation in the type of target address space as long as primitives for the given address space exists. However, the default primitives introduced by this patchset supports only virtual address spaces. Therefore, for physical memory monitoring, you should implement your own primitives and use it, or simply use Idle Page Tracking. Nonetheless, RFC patchsets[1] for the physical memory address space primitives is already available. It also supports user memory same to Idle Page Tracking. [1] https://lore.kernel.org/linux-mm/20200831104730.28970-1-sjpark@amazon.com/ For what use cases DAMON is better? ----------------------------------- 1. Hotness Monitoring. Idle Page Tracking let users know only if a page frame is accessed or not. For hotness check, the user should write more code and use more memory. DAMON do that by itself. 2. Low Monitoring Overhead DAMON receives user's monitoring request with one step and then provide the results. So, roughly speaking, DAMON require only O(1) user/kernel context switches. In case of Idle Page Tracking, however, because the interface receives contiguous page frames, the number of user/kernel context switches increases as the monitoring target becomes complex and huge. As a result, the context switch overhead could be not negligible. Moreover, DAMON is born to handle with the monitoring overhead. Because the core mechanism is pure logical, Idle Page Tracking users might be able to implement the mechanism on their own, but it would be time consuming and the user/kernel context switching will still more frequent than that of DAMON. Also, the kernel subsystems cannot use the logic in this case. 3. Page granularity working set size detection. Until v22 of this patchset, this was categorized as the thing Idle Page Tracking could do better, because DAMON basically maintains additional metadata for each of the monitoring target regions. So, in the page granularity working set size detection use case, DAMON would incur (number of monitoring target pages * size of metadata) memory overhead. Size of the single metadata item is about 54 bytes, so assuming 4KB pages, about 1.3% of monitoring target pages will be additionally used. All essential metadata for Idle Page Tracking are embedded in 'struct page' and page table entries. Therefore, in this use case, only one counter variable for working set size accounting is required if Idle Page Tracking is used. There are more details to consider, but roughly speaking, this is true in most cases. However, the situation changed from v23. Now DAMON supports arbitrary types of monitoring targets, which don't use the metadata. Using that, DAMON can do the working set size detection with no additional space overhead but less user-kernel context switch. A first draft for the implementation of monitoring primitives for this usage is available in a DAMON development tree[1]. An RFC patchset for it based on this patchset will also be available soon. Since v24, the arbitrary type support is dropped from this patchset because this patchset doesn't introduce real use of the type. You can still get it from the DAMON development tree[2], though. [1] https://github.com/sjp38/linux/tree/damon/pgidle_hack [2] https://github.com/sjp38/linux/tree/damon/master 4. More future usecases While Idle Page Tracking has tight coupling with base primitives (PG_Idle and page table Accessed bits), DAMON is designed to be extensible for many use cases and address spaces. If you need some special address type or want to use special h/w access check primitives, you can write your own primitives for that and configure DAMON to use those. Therefore, if your use case could be changed a lot in future, using DAMON could be better. Can I use both Idle Page Tracking and DAMON? -------------------------------------------- Yes, though using them concurrently for overlapping memory regions could result in interference to each other. Nevertheless, such use case would be rare or makes no sense at all. Even in the case, the noise would bot be really significant. So, you can choose whatever you want depending on the characteristics of your use cases. More Information ================ We prepared a showcase web site[1] that you can get more information. There are - the official documentations[2], - the heatmap format dynamic access pattern of various realistic workloads for heap area[3], mmap()-ed area[4], and stack[5] area, - the dynamic working set size distribution[6] and chronological working set size changes[7], and - the latest performance test results[8]. [1] https://damonitor.github.io/_index [2] https://damonitor.github.io/doc/html/latest-damon [3] https://damonitor.github.io/test/result/visual/latest/rec.heatmap.0.png.html [4] https://damonitor.github.io/test/result/visual/latest/rec.heatmap.1.png.html [5] https://damonitor.github.io/test/result/visual/latest/rec.heatmap.2.png.html [6] https://damonitor.github.io/test/result/visual/latest/rec.wss_sz.png.html [7] https://damonitor.github.io/test/result/visual/latest/rec.wss_time.png.html [8] https://damonitor.github.io/test/result/perf/latest/html/index.html Baseline and Complete Git Trees =============================== The patches are based on the latest -mm tree, specifically v5.14-rc1-mmots-2021-07-15-18-47 of https://github.com/hnaz/linux-mm. You can also clone the complete git tree: $ git clone git://github.com/sjp38/linux -b damon/patches/v34 The web is also available: https://github.com/sjp38/linux/releases/tag/damon/patches/v34 Development Trees ----------------- There are a couple of trees for entire DAMON patchset series and features for future release. - For latest release: https://github.com/sjp38/linux/tree/damon/master - For next release: https://github.com/sjp38/linux/tree/damon/next Long-term Support Trees ----------------------- For people who want to test DAMON but using LTS kernels, there are another couple of trees based on two latest LTS kernels respectively and containing the 'damon/master' backports. - For v5.4.y: https://github.com/sjp38/linux/tree/damon/for-v5.4.y - For v5.10.y: https://github.com/sjp38/linux/tree/damon/for-v5.10.y Amazon Linux Kernel Trees ------------------------- DAMON is also merged in two public Amazon Linux kernel trees that based on v5.4.y[1] and v5.10.y[2]. [1] https://github.com/amazonlinux/linux/tree/amazon-5.4.y/master/mm/damon [2] https://github.com/amazonlinux/linux/tree/amazon-5.10.y/master/mm/damon Git Tree for Diff of Patches ============================ For easy review of diff between different versions of each patch, I prepared a git tree containing all versions of the DAMON patchset series: https://github.com/sjp38/damon-patches You can clone it and use 'diff' for easy review of changes between different versions of the patchset. For example: $ git clone https://github.com/sjp38/damon-patches && cd damon-patches $ diff -u damon/v33 damon/v34 Sequence Of Patches =================== First three patches implement the core logics of DAMON. The 1st patch introduces basic sampling based hotness monitoring for arbitrary types of targets. Following two patches implement the core mechanisms for control of overhead and accuracy, namely regions based sampling (patch 2) and adaptive regions adjustment (patch 3). Now the essential parts of DAMON is complete, but it cannot work unless someone provides monitoring primitives for a specific use case. The following two patches make it just work for virtual address spaces monitoring. The 4th patch makes 'PG_idle' can be used by DAMON and the 5th patch implements the virtual memory address space specific monitoring primitives using page table Accessed bits and the 'PG_idle' page flag. Now DAMON just works for virtual address space monitoring via the kernel space api. To let the user space users can use DAMON, following four patches add interfaces for them. The 6th patch adds a tracepoint for monitoring results. The 7th patch implements a DAMON application kernel module, namely damon-dbgfs, that simply wraps DAMON and exposes DAMON interface to the user space via the debugfs interface. The 8th patch further exports pid of monitoring thread (kdamond) to user space for easier cpu usage accounting, and the 9th patch makes the debugfs interface to support multiple contexts. Three patches for maintainability follows. The 10th patch adds documentations for both the user space and the kernel space. The 11th patch provides unit tests (based on the kunit) while the 12th patch adds user space tests (based on the kselftest). Finally, the last patch (13th) updates the MAINTAINERS file. This patch (of 13): DAMON is a data access monitoring framework for the Linux kernel. The core mechanisms of DAMON make it - accurate (the monitoring output is useful enough for DRAM level performance-centric memory management; It might be inappropriate for CPU cache levels, though), - light-weight (the monitoring overhead is normally low enough to be applied online), and - scalable (the upper-bound of the overhead is in constant range regardless of the size of target workloads). Using this framework, hence, we can easily write efficient kernel space data access monitoring applications. For example, the kernel's memory management mechanisms can make advanced decisions using this. Experimental data access aware optimization works that incurring high access monitoring overhead could again be implemented on top of this. Due to its simple and flexible interface, providing user space interface would be also easy. Then, user space users who have some special workloads can write personalized applications for better understanding and optimizations of their workloads and systems. === Nevertheless, this commit is defining and implementing only basic access check part without the overhead-accuracy handling core logic. The basic access check is as below. The output of DAMON says what memory regions are how frequently accessed for a given duration. The resolution of the access frequency is controlled by setting ``sampling interval`` and ``aggregation interval``. In detail, DAMON checks access to each page per ``sampling interval`` and aggregates the results. In other words, counts the number of the accesses to each region. After each ``aggregation interval`` passes, DAMON calls callback functions that previously registered by users so that users can read the aggregated results and then clears the results. This can be described in below simple pseudo-code:: init() while monitoring_on: for page in monitoring_target: if accessed(page): nr_accesses[page] += 1 if time() % aggregation_interval == 0: for callback in user_registered_callbacks: callback(monitoring_target, nr_accesses) for page in monitoring_target: nr_accesses[page] = 0 if time() % update_interval == 0: update() sleep(sampling interval) The target regions constructed at the beginning of the monitoring and updated after each ``regions_update_interval``, because the target regions could be dynamically changed (e.g., mmap() or memory hotplug). The monitoring overhead of this mechanism will arbitrarily increase as the size of the target workload grows. The basic monitoring primitives for actual access check and dynamic target regions construction aren't in the core part of DAMON. Instead, it allows users to implement their own primitives that are optimized for their use case and configure DAMON to use those. In other words, users cannot use current version of DAMON without some additional works. Following commits will implement the core mechanisms for the overhead-accuracy control and default primitives implementations. Link: https://lkml.kernel.org/r/20210716081449.22187-1-sj38.park@gmail.com Link: https://lkml.kernel.org/r/20210716081449.22187-2-sj38.park@gmail.com Signed-off-by: SeongJae Park <sjpark@amazon.de> Reviewed-by: Leonard Foerster <foersleo@amazon.de> Reviewed-by: Fernand Sieber <sieberf@amazon.com> Acked-by: Shakeel Butt <shakeelb@google.com> Cc: Jonathan Cameron <Jonathan.Cameron@huawei.com> Cc: Alexander Shishkin <alexander.shishkin@linux.intel.com> Cc: Amit Shah <amit@kernel.org> Cc: Benjamin Herrenschmidt <benh@kernel.crashing.org> Cc: Jonathan Corbet <corbet@lwn.net> Cc: David Hildenbrand <david@redhat.com> Cc: David Woodhouse <dwmw@amazon.com> Cc: Marco Elver <elver@google.com> Cc: Fan Du <fan.du@intel.com> Cc: Greg Kroah-Hartman <greg@kroah.com> Cc: Greg Thelen <gthelen@google.com> Cc: Joe Perches <joe@perches.com> Cc: Mel Gorman <mgorman@suse.de> Cc: Maximilian Heyne <mheyne@amazon.de> Cc: Minchan Kim <minchan@kernel.org> Cc: Ingo Molnar <mingo@redhat.com> Cc: Namhyung Kim <namhyung@kernel.org> Cc: Peter Zijlstra <peterz@infradead.org> Cc: Rik van Riel <riel@surriel.com> Cc: David Rientjes <rientjes@google.com> Cc: Steven Rostedt (VMware) <rostedt@goodmis.org> Cc: Shuah Khan <shuah@kernel.org> Cc: Vlastimil Babka <vbabka@suse.cz> Cc: Vladimir Davydov <vdavydov.dev@gmail.com> Cc: Brendan Higgins <brendanhiggins@google.com> Cc: Markus Boehme <markubo@amazon.de> Signed-off-by: Andrew Morton <akpm@linux-foundation.org> Signed-off-by: Linus Torvalds <torvalds@linux-foundation.org>
2021-09-08 10:56:28 +08:00
#ifdef CONFIG_DAMON
mm/damon/core: implement region-based sampling To avoid the unbounded increase of the overhead, DAMON groups adjacent pages that are assumed to have the same access frequencies into a region. As long as the assumption (pages in a region have the same access frequencies) is kept, only one page in the region is required to be checked. Thus, for each ``sampling interval``, 1. the 'prepare_access_checks' primitive picks one page in each region, 2. waits for one ``sampling interval``, 3. checks whether the page is accessed meanwhile, and 4. increases the access count of the region if so. Therefore, the monitoring overhead is controllable by adjusting the number of regions. DAMON allows both the underlying primitives and user callbacks to adjust regions for the trade-off. In other words, this commit makes DAMON to use not only time-based sampling but also space-based sampling. This scheme, however, cannot preserve the quality of the output if the assumption is not guaranteed. Next commit will address this problem. Link: https://lkml.kernel.org/r/20210716081449.22187-3-sj38.park@gmail.com Signed-off-by: SeongJae Park <sjpark@amazon.de> Reviewed-by: Leonard Foerster <foersleo@amazon.de> Reviewed-by: Fernand Sieber <sieberf@amazon.com> Acked-by: Shakeel Butt <shakeelb@google.com> Cc: Alexander Shishkin <alexander.shishkin@linux.intel.com> Cc: Amit Shah <amit@kernel.org> Cc: Benjamin Herrenschmidt <benh@kernel.crashing.org> Cc: Brendan Higgins <brendanhiggins@google.com> Cc: David Hildenbrand <david@redhat.com> Cc: David Rientjes <rientjes@google.com> Cc: David Woodhouse <dwmw@amazon.com> Cc: Fan Du <fan.du@intel.com> Cc: Greg Kroah-Hartman <greg@kroah.com> Cc: Greg Thelen <gthelen@google.com> Cc: Ingo Molnar <mingo@redhat.com> Cc: Joe Perches <joe@perches.com> Cc: Jonathan Cameron <Jonathan.Cameron@huawei.com> Cc: Jonathan Corbet <corbet@lwn.net> Cc: Marco Elver <elver@google.com> Cc: Markus Boehme <markubo@amazon.de> Cc: Maximilian Heyne <mheyne@amazon.de> Cc: Mel Gorman <mgorman@suse.de> Cc: Minchan Kim <minchan@kernel.org> Cc: Namhyung Kim <namhyung@kernel.org> Cc: Peter Zijlstra <peterz@infradead.org> Cc: Rik van Riel <riel@surriel.com> Cc: Shuah Khan <shuah@kernel.org> Cc: Steven Rostedt (VMware) <rostedt@goodmis.org> Cc: Vladimir Davydov <vdavydov.dev@gmail.com> Cc: Vlastimil Babka <vbabka@suse.cz> Signed-off-by: Andrew Morton <akpm@linux-foundation.org> Signed-off-by: Linus Torvalds <torvalds@linux-foundation.org>
2021-09-08 10:56:32 +08:00
struct damon_region *damon_new_region(unsigned long start, unsigned long end);
/*
* Add a region between two other regions
*/
static inline void damon_insert_region(struct damon_region *r,
mm/damon: adaptively adjust regions Even somehow the initial monitoring target regions are well constructed to fulfill the assumption (pages in same region have similar access frequencies), the data access pattern can be dynamically changed. This will result in low monitoring quality. To keep the assumption as much as possible, DAMON adaptively merges and splits each region based on their access frequency. For each ``aggregation interval``, it compares the access frequencies of adjacent regions and merges those if the frequency difference is small. Then, after it reports and clears the aggregated access frequency of each region, it splits each region into two or three regions if the total number of regions will not exceed the user-specified maximum number of regions after the split. In this way, DAMON provides its best-effort quality and minimal overhead while keeping the upper-bound overhead that users set. Link: https://lkml.kernel.org/r/20210716081449.22187-4-sj38.park@gmail.com Signed-off-by: SeongJae Park <sjpark@amazon.de> Reviewed-by: Leonard Foerster <foersleo@amazon.de> Reviewed-by: Fernand Sieber <sieberf@amazon.com> Acked-by: Shakeel Butt <shakeelb@google.com> Cc: Alexander Shishkin <alexander.shishkin@linux.intel.com> Cc: Amit Shah <amit@kernel.org> Cc: Benjamin Herrenschmidt <benh@kernel.crashing.org> Cc: Brendan Higgins <brendanhiggins@google.com> Cc: David Hildenbrand <david@redhat.com> Cc: David Rientjes <rientjes@google.com> Cc: David Woodhouse <dwmw@amazon.com> Cc: Fan Du <fan.du@intel.com> Cc: Greg Kroah-Hartman <greg@kroah.com> Cc: Greg Thelen <gthelen@google.com> Cc: Ingo Molnar <mingo@redhat.com> Cc: Joe Perches <joe@perches.com> Cc: Jonathan Cameron <Jonathan.Cameron@huawei.com> Cc: Jonathan Corbet <corbet@lwn.net> Cc: Marco Elver <elver@google.com> Cc: Markus Boehme <markubo@amazon.de> Cc: Maximilian Heyne <mheyne@amazon.de> Cc: Mel Gorman <mgorman@suse.de> Cc: Minchan Kim <minchan@kernel.org> Cc: Namhyung Kim <namhyung@kernel.org> Cc: Peter Zijlstra <peterz@infradead.org> Cc: Rik van Riel <riel@surriel.com> Cc: Shuah Khan <shuah@kernel.org> Cc: Steven Rostedt (VMware) <rostedt@goodmis.org> Cc: Vladimir Davydov <vdavydov.dev@gmail.com> Cc: Vlastimil Babka <vbabka@suse.cz> Signed-off-by: Andrew Morton <akpm@linux-foundation.org> Signed-off-by: Linus Torvalds <torvalds@linux-foundation.org>
2021-09-08 10:56:36 +08:00
struct damon_region *prev, struct damon_region *next,
struct damon_target *t)
{
__list_add(&r->list, &prev->list, &next->list);
t->nr_regions++;
}
mm/damon/core: implement region-based sampling To avoid the unbounded increase of the overhead, DAMON groups adjacent pages that are assumed to have the same access frequencies into a region. As long as the assumption (pages in a region have the same access frequencies) is kept, only one page in the region is required to be checked. Thus, for each ``sampling interval``, 1. the 'prepare_access_checks' primitive picks one page in each region, 2. waits for one ``sampling interval``, 3. checks whether the page is accessed meanwhile, and 4. increases the access count of the region if so. Therefore, the monitoring overhead is controllable by adjusting the number of regions. DAMON allows both the underlying primitives and user callbacks to adjust regions for the trade-off. In other words, this commit makes DAMON to use not only time-based sampling but also space-based sampling. This scheme, however, cannot preserve the quality of the output if the assumption is not guaranteed. Next commit will address this problem. Link: https://lkml.kernel.org/r/20210716081449.22187-3-sj38.park@gmail.com Signed-off-by: SeongJae Park <sjpark@amazon.de> Reviewed-by: Leonard Foerster <foersleo@amazon.de> Reviewed-by: Fernand Sieber <sieberf@amazon.com> Acked-by: Shakeel Butt <shakeelb@google.com> Cc: Alexander Shishkin <alexander.shishkin@linux.intel.com> Cc: Amit Shah <amit@kernel.org> Cc: Benjamin Herrenschmidt <benh@kernel.crashing.org> Cc: Brendan Higgins <brendanhiggins@google.com> Cc: David Hildenbrand <david@redhat.com> Cc: David Rientjes <rientjes@google.com> Cc: David Woodhouse <dwmw@amazon.com> Cc: Fan Du <fan.du@intel.com> Cc: Greg Kroah-Hartman <greg@kroah.com> Cc: Greg Thelen <gthelen@google.com> Cc: Ingo Molnar <mingo@redhat.com> Cc: Joe Perches <joe@perches.com> Cc: Jonathan Cameron <Jonathan.Cameron@huawei.com> Cc: Jonathan Corbet <corbet@lwn.net> Cc: Marco Elver <elver@google.com> Cc: Markus Boehme <markubo@amazon.de> Cc: Maximilian Heyne <mheyne@amazon.de> Cc: Mel Gorman <mgorman@suse.de> Cc: Minchan Kim <minchan@kernel.org> Cc: Namhyung Kim <namhyung@kernel.org> Cc: Peter Zijlstra <peterz@infradead.org> Cc: Rik van Riel <riel@surriel.com> Cc: Shuah Khan <shuah@kernel.org> Cc: Steven Rostedt (VMware) <rostedt@goodmis.org> Cc: Vladimir Davydov <vdavydov.dev@gmail.com> Cc: Vlastimil Babka <vbabka@suse.cz> Signed-off-by: Andrew Morton <akpm@linux-foundation.org> Signed-off-by: Linus Torvalds <torvalds@linux-foundation.org>
2021-09-08 10:56:32 +08:00
void damon_add_region(struct damon_region *r, struct damon_target *t);
mm/damon: adaptively adjust regions Even somehow the initial monitoring target regions are well constructed to fulfill the assumption (pages in same region have similar access frequencies), the data access pattern can be dynamically changed. This will result in low monitoring quality. To keep the assumption as much as possible, DAMON adaptively merges and splits each region based on their access frequency. For each ``aggregation interval``, it compares the access frequencies of adjacent regions and merges those if the frequency difference is small. Then, after it reports and clears the aggregated access frequency of each region, it splits each region into two or three regions if the total number of regions will not exceed the user-specified maximum number of regions after the split. In this way, DAMON provides its best-effort quality and minimal overhead while keeping the upper-bound overhead that users set. Link: https://lkml.kernel.org/r/20210716081449.22187-4-sj38.park@gmail.com Signed-off-by: SeongJae Park <sjpark@amazon.de> Reviewed-by: Leonard Foerster <foersleo@amazon.de> Reviewed-by: Fernand Sieber <sieberf@amazon.com> Acked-by: Shakeel Butt <shakeelb@google.com> Cc: Alexander Shishkin <alexander.shishkin@linux.intel.com> Cc: Amit Shah <amit@kernel.org> Cc: Benjamin Herrenschmidt <benh@kernel.crashing.org> Cc: Brendan Higgins <brendanhiggins@google.com> Cc: David Hildenbrand <david@redhat.com> Cc: David Rientjes <rientjes@google.com> Cc: David Woodhouse <dwmw@amazon.com> Cc: Fan Du <fan.du@intel.com> Cc: Greg Kroah-Hartman <greg@kroah.com> Cc: Greg Thelen <gthelen@google.com> Cc: Ingo Molnar <mingo@redhat.com> Cc: Joe Perches <joe@perches.com> Cc: Jonathan Cameron <Jonathan.Cameron@huawei.com> Cc: Jonathan Corbet <corbet@lwn.net> Cc: Marco Elver <elver@google.com> Cc: Markus Boehme <markubo@amazon.de> Cc: Maximilian Heyne <mheyne@amazon.de> Cc: Mel Gorman <mgorman@suse.de> Cc: Minchan Kim <minchan@kernel.org> Cc: Namhyung Kim <namhyung@kernel.org> Cc: Peter Zijlstra <peterz@infradead.org> Cc: Rik van Riel <riel@surriel.com> Cc: Shuah Khan <shuah@kernel.org> Cc: Steven Rostedt (VMware) <rostedt@goodmis.org> Cc: Vladimir Davydov <vdavydov.dev@gmail.com> Cc: Vlastimil Babka <vbabka@suse.cz> Signed-off-by: Andrew Morton <akpm@linux-foundation.org> Signed-off-by: Linus Torvalds <torvalds@linux-foundation.org>
2021-09-08 10:56:36 +08:00
void damon_destroy_region(struct damon_region *r, struct damon_target *t);
int damon_set_regions(struct damon_target *t, struct damon_addr_range *ranges,
unsigned int nr_ranges);
mm/damon/core: implement region-based sampling To avoid the unbounded increase of the overhead, DAMON groups adjacent pages that are assumed to have the same access frequencies into a region. As long as the assumption (pages in a region have the same access frequencies) is kept, only one page in the region is required to be checked. Thus, for each ``sampling interval``, 1. the 'prepare_access_checks' primitive picks one page in each region, 2. waits for one ``sampling interval``, 3. checks whether the page is accessed meanwhile, and 4. increases the access count of the region if so. Therefore, the monitoring overhead is controllable by adjusting the number of regions. DAMON allows both the underlying primitives and user callbacks to adjust regions for the trade-off. In other words, this commit makes DAMON to use not only time-based sampling but also space-based sampling. This scheme, however, cannot preserve the quality of the output if the assumption is not guaranteed. Next commit will address this problem. Link: https://lkml.kernel.org/r/20210716081449.22187-3-sj38.park@gmail.com Signed-off-by: SeongJae Park <sjpark@amazon.de> Reviewed-by: Leonard Foerster <foersleo@amazon.de> Reviewed-by: Fernand Sieber <sieberf@amazon.com> Acked-by: Shakeel Butt <shakeelb@google.com> Cc: Alexander Shishkin <alexander.shishkin@linux.intel.com> Cc: Amit Shah <amit@kernel.org> Cc: Benjamin Herrenschmidt <benh@kernel.crashing.org> Cc: Brendan Higgins <brendanhiggins@google.com> Cc: David Hildenbrand <david@redhat.com> Cc: David Rientjes <rientjes@google.com> Cc: David Woodhouse <dwmw@amazon.com> Cc: Fan Du <fan.du@intel.com> Cc: Greg Kroah-Hartman <greg@kroah.com> Cc: Greg Thelen <gthelen@google.com> Cc: Ingo Molnar <mingo@redhat.com> Cc: Joe Perches <joe@perches.com> Cc: Jonathan Cameron <Jonathan.Cameron@huawei.com> Cc: Jonathan Corbet <corbet@lwn.net> Cc: Marco Elver <elver@google.com> Cc: Markus Boehme <markubo@amazon.de> Cc: Maximilian Heyne <mheyne@amazon.de> Cc: Mel Gorman <mgorman@suse.de> Cc: Minchan Kim <minchan@kernel.org> Cc: Namhyung Kim <namhyung@kernel.org> Cc: Peter Zijlstra <peterz@infradead.org> Cc: Rik van Riel <riel@surriel.com> Cc: Shuah Khan <shuah@kernel.org> Cc: Steven Rostedt (VMware) <rostedt@goodmis.org> Cc: Vladimir Davydov <vdavydov.dev@gmail.com> Cc: Vlastimil Babka <vbabka@suse.cz> Signed-off-by: Andrew Morton <akpm@linux-foundation.org> Signed-off-by: Linus Torvalds <torvalds@linux-foundation.org>
2021-09-08 10:56:32 +08:00
mm/damon/core: implement DAMON-based Operation Schemes (DAMOS) In many cases, users might use DAMON for simple data access aware memory management optimizations such as applying an operation scheme to a memory region of a specific size having a specific access frequency for a specific time. For example, "page out a memory region larger than 100 MiB but having a low access frequency more than 10 minutes", or "Use THP for a memory region larger than 2 MiB having a high access frequency for more than 2 seconds". Most simple form of the solution would be doing offline data access pattern profiling using DAMON and modifying the application source code or system configuration based on the profiling results. Or, developing a daemon constructed with two modules (one for access monitoring and the other for applying memory management actions via mlock(), madvise(), sysctl, etc) is imaginable. To avoid users spending their time for implementation of such simple data access monitoring-based operation schemes, this makes DAMON to handle such schemes directly. With this change, users can simply specify their desired schemes to DAMON. Then, DAMON will automatically apply the schemes to the user-specified target processes. Each of the schemes is composed with conditions for filtering of the target memory regions and desired memory management action for the target. Specifically, the format is:: <min/max size> <min/max access frequency> <min/max age> <action> The filtering conditions are size of memory region, number of accesses to the region monitored by DAMON, and the age of the region. The age of region is incremented periodically but reset when its addresses or access frequency has significantly changed or the action of a scheme was applied. For the action, current implementation supports a few of madvise()-like hints, ``WILLNEED``, ``COLD``, ``PAGEOUT``, ``HUGEPAGE``, and ``NOHUGEPAGE``. Because DAMON supports various address spaces and application of the actions to a monitoring target region is dependent to the type of the target address space, the application code should be implemented by each primitives and registered to the framework. Note that this only implements the framework part. Following commit will implement the action applications for virtual address spaces primitives. Link: https://lkml.kernel.org/r/20211001125604.29660-3-sj@kernel.org Signed-off-by: SeongJae Park <sj@kernel.org> Cc: Amit Shah <amit@kernel.org> Cc: Benjamin Herrenschmidt <benh@kernel.crashing.org> Cc: David Hildenbrand <david@redhat.com> Cc: David Rienjes <rientjes@google.com> Cc: David Woodhouse <dwmw@amazon.com> Cc: Greg Thelen <gthelen@google.com> Cc: Jonathan Cameron <Jonathan.Cameron@huawei.com> Cc: Jonathan Corbet <corbet@lwn.net> Cc: Leonard Foerster <foersleo@amazon.de> Cc: Marco Elver <elver@google.com> Cc: Markus Boehme <markubo@amazon.de> Cc: Shakeel Butt <shakeelb@google.com> Cc: Shuah Khan <shuah@kernel.org> Signed-off-by: Andrew Morton <akpm@linux-foundation.org> Signed-off-by: Linus Torvalds <torvalds@linux-foundation.org>
2021-11-06 04:46:22 +08:00
struct damos *damon_new_scheme(
unsigned long min_sz_region, unsigned long max_sz_region,
unsigned int min_nr_accesses, unsigned int max_nr_accesses,
unsigned int min_age_region, unsigned int max_age_region,
mm/damon/schemes: activate schemes based on a watermarks mechanism DAMON-based operation schemes need to be manually turned on and off. In some use cases, however, the condition for turning a scheme on and off would depend on the system's situation. For example, schemes for proactive pages reclamation would need to be turned on when some memory pressure is detected, and turned off when the system has enough free memory. For easier control of schemes activation based on the system situation, this introduces a watermarks-based mechanism. The client can describe the watermark metric (e.g., amount of free memory in the system), watermark check interval, and three watermarks, namely high, mid, and low. If the scheme is deactivated, it only gets the metric and compare that to the three watermarks for every check interval. If the metric is higher than the high watermark, the scheme is deactivated. If the metric is between the mid watermark and the low watermark, the scheme is activated. If the metric is lower than the low watermark, the scheme is deactivated again. This is to allow users fall back to traditional page-granularity mechanisms. Link: https://lkml.kernel.org/r/20211019150731.16699-12-sj@kernel.org Signed-off-by: SeongJae Park <sj@kernel.org> Cc: Amit Shah <amit@kernel.org> Cc: Benjamin Herrenschmidt <benh@kernel.crashing.org> Cc: David Hildenbrand <david@redhat.com> Cc: David Rientjes <rientjes@google.com> Cc: David Woodhouse <dwmw@amazon.com> Cc: Greg Thelen <gthelen@google.com> Cc: Jonathan Cameron <Jonathan.Cameron@huawei.com> Cc: Jonathan Corbet <corbet@lwn.net> Cc: Leonard Foerster <foersleo@amazon.de> Cc: Marco Elver <elver@google.com> Cc: Markus Boehme <markubo@amazon.de> Cc: Shakeel Butt <shakeelb@google.com> Cc: Shuah Khan <shuah@kernel.org> Signed-off-by: Andrew Morton <akpm@linux-foundation.org> Signed-off-by: Linus Torvalds <torvalds@linux-foundation.org>
2021-11-06 04:47:47 +08:00
enum damos_action action, struct damos_quota *quota,
struct damos_watermarks *wmarks);
mm/damon/core: implement DAMON-based Operation Schemes (DAMOS) In many cases, users might use DAMON for simple data access aware memory management optimizations such as applying an operation scheme to a memory region of a specific size having a specific access frequency for a specific time. For example, "page out a memory region larger than 100 MiB but having a low access frequency more than 10 minutes", or "Use THP for a memory region larger than 2 MiB having a high access frequency for more than 2 seconds". Most simple form of the solution would be doing offline data access pattern profiling using DAMON and modifying the application source code or system configuration based on the profiling results. Or, developing a daemon constructed with two modules (one for access monitoring and the other for applying memory management actions via mlock(), madvise(), sysctl, etc) is imaginable. To avoid users spending their time for implementation of such simple data access monitoring-based operation schemes, this makes DAMON to handle such schemes directly. With this change, users can simply specify their desired schemes to DAMON. Then, DAMON will automatically apply the schemes to the user-specified target processes. Each of the schemes is composed with conditions for filtering of the target memory regions and desired memory management action for the target. Specifically, the format is:: <min/max size> <min/max access frequency> <min/max age> <action> The filtering conditions are size of memory region, number of accesses to the region monitored by DAMON, and the age of the region. The age of region is incremented periodically but reset when its addresses or access frequency has significantly changed or the action of a scheme was applied. For the action, current implementation supports a few of madvise()-like hints, ``WILLNEED``, ``COLD``, ``PAGEOUT``, ``HUGEPAGE``, and ``NOHUGEPAGE``. Because DAMON supports various address spaces and application of the actions to a monitoring target region is dependent to the type of the target address space, the application code should be implemented by each primitives and registered to the framework. Note that this only implements the framework part. Following commit will implement the action applications for virtual address spaces primitives. Link: https://lkml.kernel.org/r/20211001125604.29660-3-sj@kernel.org Signed-off-by: SeongJae Park <sj@kernel.org> Cc: Amit Shah <amit@kernel.org> Cc: Benjamin Herrenschmidt <benh@kernel.crashing.org> Cc: David Hildenbrand <david@redhat.com> Cc: David Rienjes <rientjes@google.com> Cc: David Woodhouse <dwmw@amazon.com> Cc: Greg Thelen <gthelen@google.com> Cc: Jonathan Cameron <Jonathan.Cameron@huawei.com> Cc: Jonathan Corbet <corbet@lwn.net> Cc: Leonard Foerster <foersleo@amazon.de> Cc: Marco Elver <elver@google.com> Cc: Markus Boehme <markubo@amazon.de> Cc: Shakeel Butt <shakeelb@google.com> Cc: Shuah Khan <shuah@kernel.org> Signed-off-by: Andrew Morton <akpm@linux-foundation.org> Signed-off-by: Linus Torvalds <torvalds@linux-foundation.org>
2021-11-06 04:46:22 +08:00
void damon_add_scheme(struct damon_ctx *ctx, struct damos *s);
void damon_destroy_scheme(struct damos *s);
struct damon_target *damon_new_target(void);
mm/damon/core: implement region-based sampling To avoid the unbounded increase of the overhead, DAMON groups adjacent pages that are assumed to have the same access frequencies into a region. As long as the assumption (pages in a region have the same access frequencies) is kept, only one page in the region is required to be checked. Thus, for each ``sampling interval``, 1. the 'prepare_access_checks' primitive picks one page in each region, 2. waits for one ``sampling interval``, 3. checks whether the page is accessed meanwhile, and 4. increases the access count of the region if so. Therefore, the monitoring overhead is controllable by adjusting the number of regions. DAMON allows both the underlying primitives and user callbacks to adjust regions for the trade-off. In other words, this commit makes DAMON to use not only time-based sampling but also space-based sampling. This scheme, however, cannot preserve the quality of the output if the assumption is not guaranteed. Next commit will address this problem. Link: https://lkml.kernel.org/r/20210716081449.22187-3-sj38.park@gmail.com Signed-off-by: SeongJae Park <sjpark@amazon.de> Reviewed-by: Leonard Foerster <foersleo@amazon.de> Reviewed-by: Fernand Sieber <sieberf@amazon.com> Acked-by: Shakeel Butt <shakeelb@google.com> Cc: Alexander Shishkin <alexander.shishkin@linux.intel.com> Cc: Amit Shah <amit@kernel.org> Cc: Benjamin Herrenschmidt <benh@kernel.crashing.org> Cc: Brendan Higgins <brendanhiggins@google.com> Cc: David Hildenbrand <david@redhat.com> Cc: David Rientjes <rientjes@google.com> Cc: David Woodhouse <dwmw@amazon.com> Cc: Fan Du <fan.du@intel.com> Cc: Greg Kroah-Hartman <greg@kroah.com> Cc: Greg Thelen <gthelen@google.com> Cc: Ingo Molnar <mingo@redhat.com> Cc: Joe Perches <joe@perches.com> Cc: Jonathan Cameron <Jonathan.Cameron@huawei.com> Cc: Jonathan Corbet <corbet@lwn.net> Cc: Marco Elver <elver@google.com> Cc: Markus Boehme <markubo@amazon.de> Cc: Maximilian Heyne <mheyne@amazon.de> Cc: Mel Gorman <mgorman@suse.de> Cc: Minchan Kim <minchan@kernel.org> Cc: Namhyung Kim <namhyung@kernel.org> Cc: Peter Zijlstra <peterz@infradead.org> Cc: Rik van Riel <riel@surriel.com> Cc: Shuah Khan <shuah@kernel.org> Cc: Steven Rostedt (VMware) <rostedt@goodmis.org> Cc: Vladimir Davydov <vdavydov.dev@gmail.com> Cc: Vlastimil Babka <vbabka@suse.cz> Signed-off-by: Andrew Morton <akpm@linux-foundation.org> Signed-off-by: Linus Torvalds <torvalds@linux-foundation.org>
2021-09-08 10:56:32 +08:00
void damon_add_target(struct damon_ctx *ctx, struct damon_target *t);
bool damon_targets_empty(struct damon_ctx *ctx);
mm/damon/core: implement region-based sampling To avoid the unbounded increase of the overhead, DAMON groups adjacent pages that are assumed to have the same access frequencies into a region. As long as the assumption (pages in a region have the same access frequencies) is kept, only one page in the region is required to be checked. Thus, for each ``sampling interval``, 1. the 'prepare_access_checks' primitive picks one page in each region, 2. waits for one ``sampling interval``, 3. checks whether the page is accessed meanwhile, and 4. increases the access count of the region if so. Therefore, the monitoring overhead is controllable by adjusting the number of regions. DAMON allows both the underlying primitives and user callbacks to adjust regions for the trade-off. In other words, this commit makes DAMON to use not only time-based sampling but also space-based sampling. This scheme, however, cannot preserve the quality of the output if the assumption is not guaranteed. Next commit will address this problem. Link: https://lkml.kernel.org/r/20210716081449.22187-3-sj38.park@gmail.com Signed-off-by: SeongJae Park <sjpark@amazon.de> Reviewed-by: Leonard Foerster <foersleo@amazon.de> Reviewed-by: Fernand Sieber <sieberf@amazon.com> Acked-by: Shakeel Butt <shakeelb@google.com> Cc: Alexander Shishkin <alexander.shishkin@linux.intel.com> Cc: Amit Shah <amit@kernel.org> Cc: Benjamin Herrenschmidt <benh@kernel.crashing.org> Cc: Brendan Higgins <brendanhiggins@google.com> Cc: David Hildenbrand <david@redhat.com> Cc: David Rientjes <rientjes@google.com> Cc: David Woodhouse <dwmw@amazon.com> Cc: Fan Du <fan.du@intel.com> Cc: Greg Kroah-Hartman <greg@kroah.com> Cc: Greg Thelen <gthelen@google.com> Cc: Ingo Molnar <mingo@redhat.com> Cc: Joe Perches <joe@perches.com> Cc: Jonathan Cameron <Jonathan.Cameron@huawei.com> Cc: Jonathan Corbet <corbet@lwn.net> Cc: Marco Elver <elver@google.com> Cc: Markus Boehme <markubo@amazon.de> Cc: Maximilian Heyne <mheyne@amazon.de> Cc: Mel Gorman <mgorman@suse.de> Cc: Minchan Kim <minchan@kernel.org> Cc: Namhyung Kim <namhyung@kernel.org> Cc: Peter Zijlstra <peterz@infradead.org> Cc: Rik van Riel <riel@surriel.com> Cc: Shuah Khan <shuah@kernel.org> Cc: Steven Rostedt (VMware) <rostedt@goodmis.org> Cc: Vladimir Davydov <vdavydov.dev@gmail.com> Cc: Vlastimil Babka <vbabka@suse.cz> Signed-off-by: Andrew Morton <akpm@linux-foundation.org> Signed-off-by: Linus Torvalds <torvalds@linux-foundation.org>
2021-09-08 10:56:32 +08:00
void damon_free_target(struct damon_target *t);
void damon_destroy_target(struct damon_target *t);
mm/damon: adaptively adjust regions Even somehow the initial monitoring target regions are well constructed to fulfill the assumption (pages in same region have similar access frequencies), the data access pattern can be dynamically changed. This will result in low monitoring quality. To keep the assumption as much as possible, DAMON adaptively merges and splits each region based on their access frequency. For each ``aggregation interval``, it compares the access frequencies of adjacent regions and merges those if the frequency difference is small. Then, after it reports and clears the aggregated access frequency of each region, it splits each region into two or three regions if the total number of regions will not exceed the user-specified maximum number of regions after the split. In this way, DAMON provides its best-effort quality and minimal overhead while keeping the upper-bound overhead that users set. Link: https://lkml.kernel.org/r/20210716081449.22187-4-sj38.park@gmail.com Signed-off-by: SeongJae Park <sjpark@amazon.de> Reviewed-by: Leonard Foerster <foersleo@amazon.de> Reviewed-by: Fernand Sieber <sieberf@amazon.com> Acked-by: Shakeel Butt <shakeelb@google.com> Cc: Alexander Shishkin <alexander.shishkin@linux.intel.com> Cc: Amit Shah <amit@kernel.org> Cc: Benjamin Herrenschmidt <benh@kernel.crashing.org> Cc: Brendan Higgins <brendanhiggins@google.com> Cc: David Hildenbrand <david@redhat.com> Cc: David Rientjes <rientjes@google.com> Cc: David Woodhouse <dwmw@amazon.com> Cc: Fan Du <fan.du@intel.com> Cc: Greg Kroah-Hartman <greg@kroah.com> Cc: Greg Thelen <gthelen@google.com> Cc: Ingo Molnar <mingo@redhat.com> Cc: Joe Perches <joe@perches.com> Cc: Jonathan Cameron <Jonathan.Cameron@huawei.com> Cc: Jonathan Corbet <corbet@lwn.net> Cc: Marco Elver <elver@google.com> Cc: Markus Boehme <markubo@amazon.de> Cc: Maximilian Heyne <mheyne@amazon.de> Cc: Mel Gorman <mgorman@suse.de> Cc: Minchan Kim <minchan@kernel.org> Cc: Namhyung Kim <namhyung@kernel.org> Cc: Peter Zijlstra <peterz@infradead.org> Cc: Rik van Riel <riel@surriel.com> Cc: Shuah Khan <shuah@kernel.org> Cc: Steven Rostedt (VMware) <rostedt@goodmis.org> Cc: Vladimir Davydov <vdavydov.dev@gmail.com> Cc: Vlastimil Babka <vbabka@suse.cz> Signed-off-by: Andrew Morton <akpm@linux-foundation.org> Signed-off-by: Linus Torvalds <torvalds@linux-foundation.org>
2021-09-08 10:56:36 +08:00
unsigned int damon_nr_regions(struct damon_target *t);
mm/damon/core: implement region-based sampling To avoid the unbounded increase of the overhead, DAMON groups adjacent pages that are assumed to have the same access frequencies into a region. As long as the assumption (pages in a region have the same access frequencies) is kept, only one page in the region is required to be checked. Thus, for each ``sampling interval``, 1. the 'prepare_access_checks' primitive picks one page in each region, 2. waits for one ``sampling interval``, 3. checks whether the page is accessed meanwhile, and 4. increases the access count of the region if so. Therefore, the monitoring overhead is controllable by adjusting the number of regions. DAMON allows both the underlying primitives and user callbacks to adjust regions for the trade-off. In other words, this commit makes DAMON to use not only time-based sampling but also space-based sampling. This scheme, however, cannot preserve the quality of the output if the assumption is not guaranteed. Next commit will address this problem. Link: https://lkml.kernel.org/r/20210716081449.22187-3-sj38.park@gmail.com Signed-off-by: SeongJae Park <sjpark@amazon.de> Reviewed-by: Leonard Foerster <foersleo@amazon.de> Reviewed-by: Fernand Sieber <sieberf@amazon.com> Acked-by: Shakeel Butt <shakeelb@google.com> Cc: Alexander Shishkin <alexander.shishkin@linux.intel.com> Cc: Amit Shah <amit@kernel.org> Cc: Benjamin Herrenschmidt <benh@kernel.crashing.org> Cc: Brendan Higgins <brendanhiggins@google.com> Cc: David Hildenbrand <david@redhat.com> Cc: David Rientjes <rientjes@google.com> Cc: David Woodhouse <dwmw@amazon.com> Cc: Fan Du <fan.du@intel.com> Cc: Greg Kroah-Hartman <greg@kroah.com> Cc: Greg Thelen <gthelen@google.com> Cc: Ingo Molnar <mingo@redhat.com> Cc: Joe Perches <joe@perches.com> Cc: Jonathan Cameron <Jonathan.Cameron@huawei.com> Cc: Jonathan Corbet <corbet@lwn.net> Cc: Marco Elver <elver@google.com> Cc: Markus Boehme <markubo@amazon.de> Cc: Maximilian Heyne <mheyne@amazon.de> Cc: Mel Gorman <mgorman@suse.de> Cc: Minchan Kim <minchan@kernel.org> Cc: Namhyung Kim <namhyung@kernel.org> Cc: Peter Zijlstra <peterz@infradead.org> Cc: Rik van Riel <riel@surriel.com> Cc: Shuah Khan <shuah@kernel.org> Cc: Steven Rostedt (VMware) <rostedt@goodmis.org> Cc: Vladimir Davydov <vdavydov.dev@gmail.com> Cc: Vlastimil Babka <vbabka@suse.cz> Signed-off-by: Andrew Morton <akpm@linux-foundation.org> Signed-off-by: Linus Torvalds <torvalds@linux-foundation.org>
2021-09-08 10:56:32 +08:00
mm: introduce Data Access MONitor (DAMON) Patch series "Introduce Data Access MONitor (DAMON)", v34. Introduction ============ DAMON is a data access monitoring framework for the Linux kernel. The core mechanisms of DAMON called 'region based sampling' and 'adaptive regions adjustment' (refer to 'mechanisms.rst' in the 11th patch of this patchset for the detail) make it - accurate (The monitored information is useful for DRAM level memory management. It might not appropriate for Cache-level accuracy, though.), - light-weight (The monitoring overhead is low enough to be applied online while making no impact on the performance of the target workloads.), and - scalable (the upper-bound of the instrumentation overhead is controllable regardless of the size of target workloads.). Using this framework, therefore, several memory management mechanisms such as reclamation and THP can be optimized to aware real data access patterns. Experimental access pattern aware memory management optimization works that incurring high instrumentation overhead will be able to have another try. Though DAMON is for kernel subsystems, it can be easily exposed to the user space by writing a DAMON-wrapper kernel subsystem. Then, user space users who have some special workloads will be able to write personalized tools or applications for deeper understanding and specialized optimizations of their systems. DAMON is also merged in two public Amazon Linux kernel trees that based on v5.4.y[1] and v5.10.y[2]. [1] https://github.com/amazonlinux/linux/tree/amazon-5.4.y/master/mm/damon [2] https://github.com/amazonlinux/linux/tree/amazon-5.10.y/master/mm/damon The userspace tool[1] is available, released under GPLv2, and actively being maintained. I am also planning to implement another basic user interface in perf[2]. Also, the basic test suite for DAMON is available under GPLv2[3]. [1] https://github.com/awslabs/damo [2] https://lore.kernel.org/linux-mm/20210107120729.22328-1-sjpark@amazon.com/ [3] https://github.com/awslabs/damon-tests Long-term Plan -------------- DAMON is a part of a project called Data Access-aware Operating System (DAOS). As the name implies, I want to improve the performance and efficiency of systems using fine-grained data access patterns. The optimizations are for both kernel and user spaces. I will therefore modify or create kernel subsystems, export some of those to user space and implement user space library / tools. Below shows the layers and components for the project. --------------------------------------------------------------------------- Primitives: PTE Accessed bit, PG_idle, rmap, (Intel CMT), ... Framework: DAMON Features: DAMOS, virtual addr, physical addr, ... Applications: DAMON-debugfs, (DARC), ... ^^^^^^^^^^^^^^^^^^^^^^^ KERNEL SPACE ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ Raw Interface: debugfs, (sysfs), (damonfs), tracepoints, (sys_damon), ... vvvvvvvvvvvvvvvvvvvvvvv USER SPACE vvvvvvvvvvvvvvvvvvvvvvvvvvvvvvvv Library: (libdamon), ... Tools: DAMO, (perf), ... --------------------------------------------------------------------------- The components in parentheses or marked as '...' are not implemented yet but in the future plan. IOW, those are the TODO tasks of DAOS project. For more detail, please refer to the plans: https://lore.kernel.org/linux-mm/20201202082731.24828-1-sjpark@amazon.com/ Evaluations =========== We evaluated DAMON's overhead, monitoring quality and usefulness using 24 realistic workloads on my QEMU/KVM based virtual machine running a kernel that v24 DAMON patchset is applied. DAMON is lightweight. It increases system memory usage by 0.39% and slows target workloads down by 1.16%. DAMON is accurate and useful for memory management optimizations. An experimental DAMON-based operation scheme for THP, namely 'ethp', removes 76.15% of THP memory overheads while preserving 51.25% of THP speedup. Another experimental DAMON-based 'proactive reclamation' implementation, 'prcl', reduces 93.38% of residential sets and 23.63% of system memory footprint while incurring only 1.22% runtime overhead in the best case (parsec3/freqmine). NOTE that the experimental THP optimization and proactive reclamation are not for production but only for proof of concepts. Please refer to the official document[1] or "Documentation/admin-guide/mm: Add a document for DAMON" patch in this patchset for detailed evaluation setup and results. [1] https://damonitor.github.io/doc/html/latest-damon/admin-guide/mm/damon/eval.html Real-world User Story ===================== In summary, DAMON has used on production systems and proved its usefulness. DAMON as a profiler ------------------- We analyzed characteristics of a large scale production systems of our customers using DAMON. The systems utilize 70GB DRAM and 36 CPUs. From this, we were able to find interesting things below. There were obviously different access pattern under idle workload and active workload. Under the idle workload, it accessed large memory regions with low frequency, while the active workload accessed small memory regions with high freuqnecy. DAMON found a 7GB memory region that showing obviously high access frequency under the active workload. We believe this is the performance-effective working set and need to be protected. There was a 4KB memory region that showing highest access frequency under not only active but also idle workloads. We think this must be a hottest code section like thing that should never be paged out. For this analysis, DAMON used only 0.3-1% of single CPU time. Because we used recording-based analysis, it consumed about 3-12 MB of disk space per 20 minutes. This is only small amount of disk space, but we can further reduce the disk usage by using non-recording-based DAMON features. I'd like to argue that only DAMON can do such detailed analysis (finding 4KB highest region in 70GB memory) with the light overhead. DAMON as a system optimization tool ----------------------------------- We also found below potential performance problems on the systems and made DAMON-based solutions. The system doesn't want to make the workload suffer from the page reclamation and thus it utilizes enough DRAM but no swap device. However, we found the system is actively reclaiming file-backed pages, because the system has intensive file IO. The file IO turned out to be not performance critical for the workload, but the customer wanted to ensure performance critical file-backed pages like code section to not mistakenly be evicted. Using direct IO should or `mlock()` would be a straightforward solution, but modifying the user space code is not easy for the customer. Alternatively, we could use DAMON-based operation scheme[1]. By using it, we can ask DAMON to track access frequency of each region and make 'process_madvise(MADV_WILLNEED)[2]' call for regions having specific size and access frequency for a time interval. We also found the system is having high number of TLB misses. We tried 'always' THP enabled policy and it greatly reduced TLB misses, but the page reclamation also been more frequent due to the THP internal fragmentation caused memory bloat. We could try another DAMON-based operation scheme that applies 'MADV_HUGEPAGE' to memory regions having >=2MB size and high access frequency, while applying 'MADV_NOHUGEPAGE' to regions having <2MB size and low access frequency. We do not own the systems so we only reported the analysis results and possible optimization solutions to the customers. The customers satisfied about the analysis results and promised to try the optimization guides. [1] https://lore.kernel.org/linux-mm/20201006123931.5847-1-sjpark@amazon.com/ [2] https://lore.kernel.org/linux-api/20200622192900.22757-4-minchan@kernel.org/ Comparison with Idle Page Tracking ================================== Idle Page Tracking allows users to set and read idleness of pages using a bitmap file which represents each page with each bit of the file. One recommended usage of it is working set size detection. Users can do that by 1. find PFN of each page for workloads in interest, 2. set all the pages as idle by doing writes to the bitmap file, 3. wait until the workload accesses its working set, and 4. read the idleness of the pages again and count pages became not idle. NOTE: While Idle Page Tracking is for user space users, DAMON is primarily designed for kernel subsystems though it can easily exposed to the user space. Hence, this section only assumes such user space use of DAMON. For what use cases Idle Page Tracking would be better? ------------------------------------------------------ 1. Flexible usecases other than hotness monitoring. Because Idle Page Tracking allows users to control the primitive (Page idleness) by themselves, Idle Page Tracking users can do anything they want. Meanwhile, DAMON is primarily designed to monitor the hotness of each memory region. For this, DAMON asks users to provide sampling interval and aggregation interval. For the reason, there could be some use case that using Idle Page Tracking is simpler. 2. Physical memory monitoring. Idle Page Tracking receives PFN range as input, so natively supports physical memory monitoring. DAMON is designed to be extensible for multiple address spaces and use cases by implementing and using primitives for the given use case. Therefore, by theory, DAMON has no limitation in the type of target address space as long as primitives for the given address space exists. However, the default primitives introduced by this patchset supports only virtual address spaces. Therefore, for physical memory monitoring, you should implement your own primitives and use it, or simply use Idle Page Tracking. Nonetheless, RFC patchsets[1] for the physical memory address space primitives is already available. It also supports user memory same to Idle Page Tracking. [1] https://lore.kernel.org/linux-mm/20200831104730.28970-1-sjpark@amazon.com/ For what use cases DAMON is better? ----------------------------------- 1. Hotness Monitoring. Idle Page Tracking let users know only if a page frame is accessed or not. For hotness check, the user should write more code and use more memory. DAMON do that by itself. 2. Low Monitoring Overhead DAMON receives user's monitoring request with one step and then provide the results. So, roughly speaking, DAMON require only O(1) user/kernel context switches. In case of Idle Page Tracking, however, because the interface receives contiguous page frames, the number of user/kernel context switches increases as the monitoring target becomes complex and huge. As a result, the context switch overhead could be not negligible. Moreover, DAMON is born to handle with the monitoring overhead. Because the core mechanism is pure logical, Idle Page Tracking users might be able to implement the mechanism on their own, but it would be time consuming and the user/kernel context switching will still more frequent than that of DAMON. Also, the kernel subsystems cannot use the logic in this case. 3. Page granularity working set size detection. Until v22 of this patchset, this was categorized as the thing Idle Page Tracking could do better, because DAMON basically maintains additional metadata for each of the monitoring target regions. So, in the page granularity working set size detection use case, DAMON would incur (number of monitoring target pages * size of metadata) memory overhead. Size of the single metadata item is about 54 bytes, so assuming 4KB pages, about 1.3% of monitoring target pages will be additionally used. All essential metadata for Idle Page Tracking are embedded in 'struct page' and page table entries. Therefore, in this use case, only one counter variable for working set size accounting is required if Idle Page Tracking is used. There are more details to consider, but roughly speaking, this is true in most cases. However, the situation changed from v23. Now DAMON supports arbitrary types of monitoring targets, which don't use the metadata. Using that, DAMON can do the working set size detection with no additional space overhead but less user-kernel context switch. A first draft for the implementation of monitoring primitives for this usage is available in a DAMON development tree[1]. An RFC patchset for it based on this patchset will also be available soon. Since v24, the arbitrary type support is dropped from this patchset because this patchset doesn't introduce real use of the type. You can still get it from the DAMON development tree[2], though. [1] https://github.com/sjp38/linux/tree/damon/pgidle_hack [2] https://github.com/sjp38/linux/tree/damon/master 4. More future usecases While Idle Page Tracking has tight coupling with base primitives (PG_Idle and page table Accessed bits), DAMON is designed to be extensible for many use cases and address spaces. If you need some special address type or want to use special h/w access check primitives, you can write your own primitives for that and configure DAMON to use those. Therefore, if your use case could be changed a lot in future, using DAMON could be better. Can I use both Idle Page Tracking and DAMON? -------------------------------------------- Yes, though using them concurrently for overlapping memory regions could result in interference to each other. Nevertheless, such use case would be rare or makes no sense at all. Even in the case, the noise would bot be really significant. So, you can choose whatever you want depending on the characteristics of your use cases. More Information ================ We prepared a showcase web site[1] that you can get more information. There are - the official documentations[2], - the heatmap format dynamic access pattern of various realistic workloads for heap area[3], mmap()-ed area[4], and stack[5] area, - the dynamic working set size distribution[6] and chronological working set size changes[7], and - the latest performance test results[8]. [1] https://damonitor.github.io/_index [2] https://damonitor.github.io/doc/html/latest-damon [3] https://damonitor.github.io/test/result/visual/latest/rec.heatmap.0.png.html [4] https://damonitor.github.io/test/result/visual/latest/rec.heatmap.1.png.html [5] https://damonitor.github.io/test/result/visual/latest/rec.heatmap.2.png.html [6] https://damonitor.github.io/test/result/visual/latest/rec.wss_sz.png.html [7] https://damonitor.github.io/test/result/visual/latest/rec.wss_time.png.html [8] https://damonitor.github.io/test/result/perf/latest/html/index.html Baseline and Complete Git Trees =============================== The patches are based on the latest -mm tree, specifically v5.14-rc1-mmots-2021-07-15-18-47 of https://github.com/hnaz/linux-mm. You can also clone the complete git tree: $ git clone git://github.com/sjp38/linux -b damon/patches/v34 The web is also available: https://github.com/sjp38/linux/releases/tag/damon/patches/v34 Development Trees ----------------- There are a couple of trees for entire DAMON patchset series and features for future release. - For latest release: https://github.com/sjp38/linux/tree/damon/master - For next release: https://github.com/sjp38/linux/tree/damon/next Long-term Support Trees ----------------------- For people who want to test DAMON but using LTS kernels, there are another couple of trees based on two latest LTS kernels respectively and containing the 'damon/master' backports. - For v5.4.y: https://github.com/sjp38/linux/tree/damon/for-v5.4.y - For v5.10.y: https://github.com/sjp38/linux/tree/damon/for-v5.10.y Amazon Linux Kernel Trees ------------------------- DAMON is also merged in two public Amazon Linux kernel trees that based on v5.4.y[1] and v5.10.y[2]. [1] https://github.com/amazonlinux/linux/tree/amazon-5.4.y/master/mm/damon [2] https://github.com/amazonlinux/linux/tree/amazon-5.10.y/master/mm/damon Git Tree for Diff of Patches ============================ For easy review of diff between different versions of each patch, I prepared a git tree containing all versions of the DAMON patchset series: https://github.com/sjp38/damon-patches You can clone it and use 'diff' for easy review of changes between different versions of the patchset. For example: $ git clone https://github.com/sjp38/damon-patches && cd damon-patches $ diff -u damon/v33 damon/v34 Sequence Of Patches =================== First three patches implement the core logics of DAMON. The 1st patch introduces basic sampling based hotness monitoring for arbitrary types of targets. Following two patches implement the core mechanisms for control of overhead and accuracy, namely regions based sampling (patch 2) and adaptive regions adjustment (patch 3). Now the essential parts of DAMON is complete, but it cannot work unless someone provides monitoring primitives for a specific use case. The following two patches make it just work for virtual address spaces monitoring. The 4th patch makes 'PG_idle' can be used by DAMON and the 5th patch implements the virtual memory address space specific monitoring primitives using page table Accessed bits and the 'PG_idle' page flag. Now DAMON just works for virtual address space monitoring via the kernel space api. To let the user space users can use DAMON, following four patches add interfaces for them. The 6th patch adds a tracepoint for monitoring results. The 7th patch implements a DAMON application kernel module, namely damon-dbgfs, that simply wraps DAMON and exposes DAMON interface to the user space via the debugfs interface. The 8th patch further exports pid of monitoring thread (kdamond) to user space for easier cpu usage accounting, and the 9th patch makes the debugfs interface to support multiple contexts. Three patches for maintainability follows. The 10th patch adds documentations for both the user space and the kernel space. The 11th patch provides unit tests (based on the kunit) while the 12th patch adds user space tests (based on the kselftest). Finally, the last patch (13th) updates the MAINTAINERS file. This patch (of 13): DAMON is a data access monitoring framework for the Linux kernel. The core mechanisms of DAMON make it - accurate (the monitoring output is useful enough for DRAM level performance-centric memory management; It might be inappropriate for CPU cache levels, though), - light-weight (the monitoring overhead is normally low enough to be applied online), and - scalable (the upper-bound of the overhead is in constant range regardless of the size of target workloads). Using this framework, hence, we can easily write efficient kernel space data access monitoring applications. For example, the kernel's memory management mechanisms can make advanced decisions using this. Experimental data access aware optimization works that incurring high access monitoring overhead could again be implemented on top of this. Due to its simple and flexible interface, providing user space interface would be also easy. Then, user space users who have some special workloads can write personalized applications for better understanding and optimizations of their workloads and systems. === Nevertheless, this commit is defining and implementing only basic access check part without the overhead-accuracy handling core logic. The basic access check is as below. The output of DAMON says what memory regions are how frequently accessed for a given duration. The resolution of the access frequency is controlled by setting ``sampling interval`` and ``aggregation interval``. In detail, DAMON checks access to each page per ``sampling interval`` and aggregates the results. In other words, counts the number of the accesses to each region. After each ``aggregation interval`` passes, DAMON calls callback functions that previously registered by users so that users can read the aggregated results and then clears the results. This can be described in below simple pseudo-code:: init() while monitoring_on: for page in monitoring_target: if accessed(page): nr_accesses[page] += 1 if time() % aggregation_interval == 0: for callback in user_registered_callbacks: callback(monitoring_target, nr_accesses) for page in monitoring_target: nr_accesses[page] = 0 if time() % update_interval == 0: update() sleep(sampling interval) The target regions constructed at the beginning of the monitoring and updated after each ``regions_update_interval``, because the target regions could be dynamically changed (e.g., mmap() or memory hotplug). The monitoring overhead of this mechanism will arbitrarily increase as the size of the target workload grows. The basic monitoring primitives for actual access check and dynamic target regions construction aren't in the core part of DAMON. Instead, it allows users to implement their own primitives that are optimized for their use case and configure DAMON to use those. In other words, users cannot use current version of DAMON without some additional works. Following commits will implement the core mechanisms for the overhead-accuracy control and default primitives implementations. Link: https://lkml.kernel.org/r/20210716081449.22187-1-sj38.park@gmail.com Link: https://lkml.kernel.org/r/20210716081449.22187-2-sj38.park@gmail.com Signed-off-by: SeongJae Park <sjpark@amazon.de> Reviewed-by: Leonard Foerster <foersleo@amazon.de> Reviewed-by: Fernand Sieber <sieberf@amazon.com> Acked-by: Shakeel Butt <shakeelb@google.com> Cc: Jonathan Cameron <Jonathan.Cameron@huawei.com> Cc: Alexander Shishkin <alexander.shishkin@linux.intel.com> Cc: Amit Shah <amit@kernel.org> Cc: Benjamin Herrenschmidt <benh@kernel.crashing.org> Cc: Jonathan Corbet <corbet@lwn.net> Cc: David Hildenbrand <david@redhat.com> Cc: David Woodhouse <dwmw@amazon.com> Cc: Marco Elver <elver@google.com> Cc: Fan Du <fan.du@intel.com> Cc: Greg Kroah-Hartman <greg@kroah.com> Cc: Greg Thelen <gthelen@google.com> Cc: Joe Perches <joe@perches.com> Cc: Mel Gorman <mgorman@suse.de> Cc: Maximilian Heyne <mheyne@amazon.de> Cc: Minchan Kim <minchan@kernel.org> Cc: Ingo Molnar <mingo@redhat.com> Cc: Namhyung Kim <namhyung@kernel.org> Cc: Peter Zijlstra <peterz@infradead.org> Cc: Rik van Riel <riel@surriel.com> Cc: David Rientjes <rientjes@google.com> Cc: Steven Rostedt (VMware) <rostedt@goodmis.org> Cc: Shuah Khan <shuah@kernel.org> Cc: Vlastimil Babka <vbabka@suse.cz> Cc: Vladimir Davydov <vdavydov.dev@gmail.com> Cc: Brendan Higgins <brendanhiggins@google.com> Cc: Markus Boehme <markubo@amazon.de> Signed-off-by: Andrew Morton <akpm@linux-foundation.org> Signed-off-by: Linus Torvalds <torvalds@linux-foundation.org>
2021-09-08 10:56:28 +08:00
struct damon_ctx *damon_new_ctx(void);
void damon_destroy_ctx(struct damon_ctx *ctx);
int damon_set_attrs(struct damon_ctx *ctx, unsigned long sample_int,
mm/damon: rename damon_primitives to damon_operations Patch series "Allow DAMON user code independent of monitoring primitives". In-kernel DAMON user code is required to configure the monitoring context (struct damon_ctx) with proper monitoring primitives (struct damon_primitive). This makes the user code dependent to all supporting monitoring primitives. For example, DAMON debugfs interface depends on both DAMON_VADDR and DAMON_PADDR, though some users have interest in only one use case. As more monitoring primitives are introduced, the problem will be bigger. To minimize such unnecessary dependency, this patchset makes monitoring primitives can be registered by the implemnting code and later dynamically searched and selected by the user code. In addition to that, this patchset renames monitoring primitives to monitoring operations, which is more easy to intuitively understand what it means and how it would be structed. This patch (of 8): DAMON has a set of callback functions called monitoring primitives and let it can be configured with various implementations for easy extension for different address spaces and usages. However, the word 'primitive' is not so explicit. Meanwhile, many other structs resembles similar purpose calls themselves 'operations'. To make the code easier to be understood, this commit renames 'damon_primitives' to 'damon_operations' before it is too late to rename. Link: https://lkml.kernel.org/r/20220215184603.1479-1-sj@kernel.org Link: https://lkml.kernel.org/r/20220215184603.1479-2-sj@kernel.org Signed-off-by: SeongJae Park <sj@kernel.org> Cc: Xin Hao <xhao@linux.alibaba.com> Cc: David Rientjes <rientjes@google.com> Signed-off-by: Andrew Morton <akpm@linux-foundation.org> Signed-off-by: Linus Torvalds <torvalds@linux-foundation.org>
2022-03-23 05:48:46 +08:00
unsigned long aggr_int, unsigned long ops_upd_int,
mm/damon: adaptively adjust regions Even somehow the initial monitoring target regions are well constructed to fulfill the assumption (pages in same region have similar access frequencies), the data access pattern can be dynamically changed. This will result in low monitoring quality. To keep the assumption as much as possible, DAMON adaptively merges and splits each region based on their access frequency. For each ``aggregation interval``, it compares the access frequencies of adjacent regions and merges those if the frequency difference is small. Then, after it reports and clears the aggregated access frequency of each region, it splits each region into two or three regions if the total number of regions will not exceed the user-specified maximum number of regions after the split. In this way, DAMON provides its best-effort quality and minimal overhead while keeping the upper-bound overhead that users set. Link: https://lkml.kernel.org/r/20210716081449.22187-4-sj38.park@gmail.com Signed-off-by: SeongJae Park <sjpark@amazon.de> Reviewed-by: Leonard Foerster <foersleo@amazon.de> Reviewed-by: Fernand Sieber <sieberf@amazon.com> Acked-by: Shakeel Butt <shakeelb@google.com> Cc: Alexander Shishkin <alexander.shishkin@linux.intel.com> Cc: Amit Shah <amit@kernel.org> Cc: Benjamin Herrenschmidt <benh@kernel.crashing.org> Cc: Brendan Higgins <brendanhiggins@google.com> Cc: David Hildenbrand <david@redhat.com> Cc: David Rientjes <rientjes@google.com> Cc: David Woodhouse <dwmw@amazon.com> Cc: Fan Du <fan.du@intel.com> Cc: Greg Kroah-Hartman <greg@kroah.com> Cc: Greg Thelen <gthelen@google.com> Cc: Ingo Molnar <mingo@redhat.com> Cc: Joe Perches <joe@perches.com> Cc: Jonathan Cameron <Jonathan.Cameron@huawei.com> Cc: Jonathan Corbet <corbet@lwn.net> Cc: Marco Elver <elver@google.com> Cc: Markus Boehme <markubo@amazon.de> Cc: Maximilian Heyne <mheyne@amazon.de> Cc: Mel Gorman <mgorman@suse.de> Cc: Minchan Kim <minchan@kernel.org> Cc: Namhyung Kim <namhyung@kernel.org> Cc: Peter Zijlstra <peterz@infradead.org> Cc: Rik van Riel <riel@surriel.com> Cc: Shuah Khan <shuah@kernel.org> Cc: Steven Rostedt (VMware) <rostedt@goodmis.org> Cc: Vladimir Davydov <vdavydov.dev@gmail.com> Cc: Vlastimil Babka <vbabka@suse.cz> Signed-off-by: Andrew Morton <akpm@linux-foundation.org> Signed-off-by: Linus Torvalds <torvalds@linux-foundation.org>
2021-09-08 10:56:36 +08:00
unsigned long min_nr_reg, unsigned long max_nr_reg);
mm/damon/core: implement DAMON-based Operation Schemes (DAMOS) In many cases, users might use DAMON for simple data access aware memory management optimizations such as applying an operation scheme to a memory region of a specific size having a specific access frequency for a specific time. For example, "page out a memory region larger than 100 MiB but having a low access frequency more than 10 minutes", or "Use THP for a memory region larger than 2 MiB having a high access frequency for more than 2 seconds". Most simple form of the solution would be doing offline data access pattern profiling using DAMON and modifying the application source code or system configuration based on the profiling results. Or, developing a daemon constructed with two modules (one for access monitoring and the other for applying memory management actions via mlock(), madvise(), sysctl, etc) is imaginable. To avoid users spending their time for implementation of such simple data access monitoring-based operation schemes, this makes DAMON to handle such schemes directly. With this change, users can simply specify their desired schemes to DAMON. Then, DAMON will automatically apply the schemes to the user-specified target processes. Each of the schemes is composed with conditions for filtering of the target memory regions and desired memory management action for the target. Specifically, the format is:: <min/max size> <min/max access frequency> <min/max age> <action> The filtering conditions are size of memory region, number of accesses to the region monitored by DAMON, and the age of the region. The age of region is incremented periodically but reset when its addresses or access frequency has significantly changed or the action of a scheme was applied. For the action, current implementation supports a few of madvise()-like hints, ``WILLNEED``, ``COLD``, ``PAGEOUT``, ``HUGEPAGE``, and ``NOHUGEPAGE``. Because DAMON supports various address spaces and application of the actions to a monitoring target region is dependent to the type of the target address space, the application code should be implemented by each primitives and registered to the framework. Note that this only implements the framework part. Following commit will implement the action applications for virtual address spaces primitives. Link: https://lkml.kernel.org/r/20211001125604.29660-3-sj@kernel.org Signed-off-by: SeongJae Park <sj@kernel.org> Cc: Amit Shah <amit@kernel.org> Cc: Benjamin Herrenschmidt <benh@kernel.crashing.org> Cc: David Hildenbrand <david@redhat.com> Cc: David Rienjes <rientjes@google.com> Cc: David Woodhouse <dwmw@amazon.com> Cc: Greg Thelen <gthelen@google.com> Cc: Jonathan Cameron <Jonathan.Cameron@huawei.com> Cc: Jonathan Corbet <corbet@lwn.net> Cc: Leonard Foerster <foersleo@amazon.de> Cc: Marco Elver <elver@google.com> Cc: Markus Boehme <markubo@amazon.de> Cc: Shakeel Butt <shakeelb@google.com> Cc: Shuah Khan <shuah@kernel.org> Signed-off-by: Andrew Morton <akpm@linux-foundation.org> Signed-off-by: Linus Torvalds <torvalds@linux-foundation.org>
2021-11-06 04:46:22 +08:00
int damon_set_schemes(struct damon_ctx *ctx,
struct damos **schemes, ssize_t nr_schemes);
mm/damon: implement a debugfs-based user space interface DAMON is designed to be used by kernel space code such as the memory management subsystems, and therefore it provides only kernel space API. That said, letting the user space control DAMON could provide some benefits to them. For example, it will allow user space to analyze their specific workloads and make their own special optimizations. For such cases, this commit implements a simple DAMON application kernel module, namely 'damon-dbgfs', which merely wraps the DAMON api and exports those to the user space via the debugfs. 'damon-dbgfs' exports three files, ``attrs``, ``target_ids``, and ``monitor_on`` under its debugfs directory, ``<debugfs>/damon/``. Attributes ---------- Users can read and write the ``sampling interval``, ``aggregation interval``, ``regions update interval``, and min/max number of monitoring target regions by reading from and writing to the ``attrs`` file. For example, below commands set those values to 5 ms, 100 ms, 1,000 ms, 10, 1000 and check it again:: # cd <debugfs>/damon # echo 5000 100000 1000000 10 1000 > attrs # cat attrs 5000 100000 1000000 10 1000 Target IDs ---------- Some types of address spaces supports multiple monitoring target. For example, the virtual memory address spaces monitoring can have multiple processes as the monitoring targets. Users can set the targets by writing relevant id values of the targets to, and get the ids of the current targets by reading from the ``target_ids`` file. In case of the virtual address spaces monitoring, the values should be pids of the monitoring target processes. For example, below commands set processes having pids 42 and 4242 as the monitoring targets and check it again:: # cd <debugfs>/damon # echo 42 4242 > target_ids # cat target_ids 42 4242 Note that setting the target ids doesn't start the monitoring. Turning On/Off -------------- Setting the files as described above doesn't incur effect unless you explicitly start the monitoring. You can start, stop, and check the current status of the monitoring by writing to and reading from the ``monitor_on`` file. Writing ``on`` to the file starts the monitoring of the targets with the attributes. Writing ``off`` to the file stops those. DAMON also stops if every targets are invalidated (in case of the virtual memory monitoring, target processes are invalidated when terminated). Below example commands turn on, off, and check the status of DAMON:: # cd <debugfs>/damon # echo on > monitor_on # echo off > monitor_on # cat monitor_on off Please note that you cannot write to the above-mentioned debugfs files while the monitoring is turned on. If you write to the files while DAMON is running, an error code such as ``-EBUSY`` will be returned. [akpm@linux-foundation.org: remove unneeded "alloc failed" printks] [akpm@linux-foundation.org: replace macro with static inline] Link: https://lkml.kernel.org/r/20210716081449.22187-8-sj38.park@gmail.com Signed-off-by: SeongJae Park <sjpark@amazon.de> Reviewed-by: Leonard Foerster <foersleo@amazon.de> Reviewed-by: Fernand Sieber <sieberf@amazon.com> Cc: Alexander Shishkin <alexander.shishkin@linux.intel.com> Cc: Amit Shah <amit@kernel.org> Cc: Benjamin Herrenschmidt <benh@kernel.crashing.org> Cc: Brendan Higgins <brendanhiggins@google.com> Cc: David Hildenbrand <david@redhat.com> Cc: David Rientjes <rientjes@google.com> Cc: David Woodhouse <dwmw@amazon.com> Cc: Fan Du <fan.du@intel.com> Cc: Greg Kroah-Hartman <greg@kroah.com> Cc: Greg Thelen <gthelen@google.com> Cc: Ingo Molnar <mingo@redhat.com> Cc: Joe Perches <joe@perches.com> Cc: Jonathan Cameron <Jonathan.Cameron@huawei.com> Cc: Jonathan Corbet <corbet@lwn.net> Cc: Marco Elver <elver@google.com> Cc: Markus Boehme <markubo@amazon.de> Cc: Maximilian Heyne <mheyne@amazon.de> Cc: Mel Gorman <mgorman@suse.de> Cc: Minchan Kim <minchan@kernel.org> Cc: Namhyung Kim <namhyung@kernel.org> Cc: Peter Zijlstra <peterz@infradead.org> Cc: Rik van Riel <riel@surriel.com> Cc: Shakeel Butt <shakeelb@google.com> Cc: Shuah Khan <shuah@kernel.org> Cc: Steven Rostedt (VMware) <rostedt@goodmis.org> Cc: Vladimir Davydov <vdavydov.dev@gmail.com> Cc: Vlastimil Babka <vbabka@suse.cz> Signed-off-by: Andrew Morton <akpm@linux-foundation.org> Signed-off-by: Linus Torvalds <torvalds@linux-foundation.org>
2021-09-08 10:56:53 +08:00
int damon_nr_running_ctxs(void);
bool damon_is_registered_ops(enum damon_ops_id id);
int damon_register_ops(struct damon_operations *ops);
int damon_select_ops(struct damon_ctx *ctx, enum damon_ops_id id);
mm: introduce Data Access MONitor (DAMON) Patch series "Introduce Data Access MONitor (DAMON)", v34. Introduction ============ DAMON is a data access monitoring framework for the Linux kernel. The core mechanisms of DAMON called 'region based sampling' and 'adaptive regions adjustment' (refer to 'mechanisms.rst' in the 11th patch of this patchset for the detail) make it - accurate (The monitored information is useful for DRAM level memory management. It might not appropriate for Cache-level accuracy, though.), - light-weight (The monitoring overhead is low enough to be applied online while making no impact on the performance of the target workloads.), and - scalable (the upper-bound of the instrumentation overhead is controllable regardless of the size of target workloads.). Using this framework, therefore, several memory management mechanisms such as reclamation and THP can be optimized to aware real data access patterns. Experimental access pattern aware memory management optimization works that incurring high instrumentation overhead will be able to have another try. Though DAMON is for kernel subsystems, it can be easily exposed to the user space by writing a DAMON-wrapper kernel subsystem. Then, user space users who have some special workloads will be able to write personalized tools or applications for deeper understanding and specialized optimizations of their systems. DAMON is also merged in two public Amazon Linux kernel trees that based on v5.4.y[1] and v5.10.y[2]. [1] https://github.com/amazonlinux/linux/tree/amazon-5.4.y/master/mm/damon [2] https://github.com/amazonlinux/linux/tree/amazon-5.10.y/master/mm/damon The userspace tool[1] is available, released under GPLv2, and actively being maintained. I am also planning to implement another basic user interface in perf[2]. Also, the basic test suite for DAMON is available under GPLv2[3]. [1] https://github.com/awslabs/damo [2] https://lore.kernel.org/linux-mm/20210107120729.22328-1-sjpark@amazon.com/ [3] https://github.com/awslabs/damon-tests Long-term Plan -------------- DAMON is a part of a project called Data Access-aware Operating System (DAOS). As the name implies, I want to improve the performance and efficiency of systems using fine-grained data access patterns. The optimizations are for both kernel and user spaces. I will therefore modify or create kernel subsystems, export some of those to user space and implement user space library / tools. Below shows the layers and components for the project. --------------------------------------------------------------------------- Primitives: PTE Accessed bit, PG_idle, rmap, (Intel CMT), ... Framework: DAMON Features: DAMOS, virtual addr, physical addr, ... Applications: DAMON-debugfs, (DARC), ... ^^^^^^^^^^^^^^^^^^^^^^^ KERNEL SPACE ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ Raw Interface: debugfs, (sysfs), (damonfs), tracepoints, (sys_damon), ... vvvvvvvvvvvvvvvvvvvvvvv USER SPACE vvvvvvvvvvvvvvvvvvvvvvvvvvvvvvvv Library: (libdamon), ... Tools: DAMO, (perf), ... --------------------------------------------------------------------------- The components in parentheses or marked as '...' are not implemented yet but in the future plan. IOW, those are the TODO tasks of DAOS project. For more detail, please refer to the plans: https://lore.kernel.org/linux-mm/20201202082731.24828-1-sjpark@amazon.com/ Evaluations =========== We evaluated DAMON's overhead, monitoring quality and usefulness using 24 realistic workloads on my QEMU/KVM based virtual machine running a kernel that v24 DAMON patchset is applied. DAMON is lightweight. It increases system memory usage by 0.39% and slows target workloads down by 1.16%. DAMON is accurate and useful for memory management optimizations. An experimental DAMON-based operation scheme for THP, namely 'ethp', removes 76.15% of THP memory overheads while preserving 51.25% of THP speedup. Another experimental DAMON-based 'proactive reclamation' implementation, 'prcl', reduces 93.38% of residential sets and 23.63% of system memory footprint while incurring only 1.22% runtime overhead in the best case (parsec3/freqmine). NOTE that the experimental THP optimization and proactive reclamation are not for production but only for proof of concepts. Please refer to the official document[1] or "Documentation/admin-guide/mm: Add a document for DAMON" patch in this patchset for detailed evaluation setup and results. [1] https://damonitor.github.io/doc/html/latest-damon/admin-guide/mm/damon/eval.html Real-world User Story ===================== In summary, DAMON has used on production systems and proved its usefulness. DAMON as a profiler ------------------- We analyzed characteristics of a large scale production systems of our customers using DAMON. The systems utilize 70GB DRAM and 36 CPUs. From this, we were able to find interesting things below. There were obviously different access pattern under idle workload and active workload. Under the idle workload, it accessed large memory regions with low frequency, while the active workload accessed small memory regions with high freuqnecy. DAMON found a 7GB memory region that showing obviously high access frequency under the active workload. We believe this is the performance-effective working set and need to be protected. There was a 4KB memory region that showing highest access frequency under not only active but also idle workloads. We think this must be a hottest code section like thing that should never be paged out. For this analysis, DAMON used only 0.3-1% of single CPU time. Because we used recording-based analysis, it consumed about 3-12 MB of disk space per 20 minutes. This is only small amount of disk space, but we can further reduce the disk usage by using non-recording-based DAMON features. I'd like to argue that only DAMON can do such detailed analysis (finding 4KB highest region in 70GB memory) with the light overhead. DAMON as a system optimization tool ----------------------------------- We also found below potential performance problems on the systems and made DAMON-based solutions. The system doesn't want to make the workload suffer from the page reclamation and thus it utilizes enough DRAM but no swap device. However, we found the system is actively reclaiming file-backed pages, because the system has intensive file IO. The file IO turned out to be not performance critical for the workload, but the customer wanted to ensure performance critical file-backed pages like code section to not mistakenly be evicted. Using direct IO should or `mlock()` would be a straightforward solution, but modifying the user space code is not easy for the customer. Alternatively, we could use DAMON-based operation scheme[1]. By using it, we can ask DAMON to track access frequency of each region and make 'process_madvise(MADV_WILLNEED)[2]' call for regions having specific size and access frequency for a time interval. We also found the system is having high number of TLB misses. We tried 'always' THP enabled policy and it greatly reduced TLB misses, but the page reclamation also been more frequent due to the THP internal fragmentation caused memory bloat. We could try another DAMON-based operation scheme that applies 'MADV_HUGEPAGE' to memory regions having >=2MB size and high access frequency, while applying 'MADV_NOHUGEPAGE' to regions having <2MB size and low access frequency. We do not own the systems so we only reported the analysis results and possible optimization solutions to the customers. The customers satisfied about the analysis results and promised to try the optimization guides. [1] https://lore.kernel.org/linux-mm/20201006123931.5847-1-sjpark@amazon.com/ [2] https://lore.kernel.org/linux-api/20200622192900.22757-4-minchan@kernel.org/ Comparison with Idle Page Tracking ================================== Idle Page Tracking allows users to set and read idleness of pages using a bitmap file which represents each page with each bit of the file. One recommended usage of it is working set size detection. Users can do that by 1. find PFN of each page for workloads in interest, 2. set all the pages as idle by doing writes to the bitmap file, 3. wait until the workload accesses its working set, and 4. read the idleness of the pages again and count pages became not idle. NOTE: While Idle Page Tracking is for user space users, DAMON is primarily designed for kernel subsystems though it can easily exposed to the user space. Hence, this section only assumes such user space use of DAMON. For what use cases Idle Page Tracking would be better? ------------------------------------------------------ 1. Flexible usecases other than hotness monitoring. Because Idle Page Tracking allows users to control the primitive (Page idleness) by themselves, Idle Page Tracking users can do anything they want. Meanwhile, DAMON is primarily designed to monitor the hotness of each memory region. For this, DAMON asks users to provide sampling interval and aggregation interval. For the reason, there could be some use case that using Idle Page Tracking is simpler. 2. Physical memory monitoring. Idle Page Tracking receives PFN range as input, so natively supports physical memory monitoring. DAMON is designed to be extensible for multiple address spaces and use cases by implementing and using primitives for the given use case. Therefore, by theory, DAMON has no limitation in the type of target address space as long as primitives for the given address space exists. However, the default primitives introduced by this patchset supports only virtual address spaces. Therefore, for physical memory monitoring, you should implement your own primitives and use it, or simply use Idle Page Tracking. Nonetheless, RFC patchsets[1] for the physical memory address space primitives is already available. It also supports user memory same to Idle Page Tracking. [1] https://lore.kernel.org/linux-mm/20200831104730.28970-1-sjpark@amazon.com/ For what use cases DAMON is better? ----------------------------------- 1. Hotness Monitoring. Idle Page Tracking let users know only if a page frame is accessed or not. For hotness check, the user should write more code and use more memory. DAMON do that by itself. 2. Low Monitoring Overhead DAMON receives user's monitoring request with one step and then provide the results. So, roughly speaking, DAMON require only O(1) user/kernel context switches. In case of Idle Page Tracking, however, because the interface receives contiguous page frames, the number of user/kernel context switches increases as the monitoring target becomes complex and huge. As a result, the context switch overhead could be not negligible. Moreover, DAMON is born to handle with the monitoring overhead. Because the core mechanism is pure logical, Idle Page Tracking users might be able to implement the mechanism on their own, but it would be time consuming and the user/kernel context switching will still more frequent than that of DAMON. Also, the kernel subsystems cannot use the logic in this case. 3. Page granularity working set size detection. Until v22 of this patchset, this was categorized as the thing Idle Page Tracking could do better, because DAMON basically maintains additional metadata for each of the monitoring target regions. So, in the page granularity working set size detection use case, DAMON would incur (number of monitoring target pages * size of metadata) memory overhead. Size of the single metadata item is about 54 bytes, so assuming 4KB pages, about 1.3% of monitoring target pages will be additionally used. All essential metadata for Idle Page Tracking are embedded in 'struct page' and page table entries. Therefore, in this use case, only one counter variable for working set size accounting is required if Idle Page Tracking is used. There are more details to consider, but roughly speaking, this is true in most cases. However, the situation changed from v23. Now DAMON supports arbitrary types of monitoring targets, which don't use the metadata. Using that, DAMON can do the working set size detection with no additional space overhead but less user-kernel context switch. A first draft for the implementation of monitoring primitives for this usage is available in a DAMON development tree[1]. An RFC patchset for it based on this patchset will also be available soon. Since v24, the arbitrary type support is dropped from this patchset because this patchset doesn't introduce real use of the type. You can still get it from the DAMON development tree[2], though. [1] https://github.com/sjp38/linux/tree/damon/pgidle_hack [2] https://github.com/sjp38/linux/tree/damon/master 4. More future usecases While Idle Page Tracking has tight coupling with base primitives (PG_Idle and page table Accessed bits), DAMON is designed to be extensible for many use cases and address spaces. If you need some special address type or want to use special h/w access check primitives, you can write your own primitives for that and configure DAMON to use those. Therefore, if your use case could be changed a lot in future, using DAMON could be better. Can I use both Idle Page Tracking and DAMON? -------------------------------------------- Yes, though using them concurrently for overlapping memory regions could result in interference to each other. Nevertheless, such use case would be rare or makes no sense at all. Even in the case, the noise would bot be really significant. So, you can choose whatever you want depending on the characteristics of your use cases. More Information ================ We prepared a showcase web site[1] that you can get more information. There are - the official documentations[2], - the heatmap format dynamic access pattern of various realistic workloads for heap area[3], mmap()-ed area[4], and stack[5] area, - the dynamic working set size distribution[6] and chronological working set size changes[7], and - the latest performance test results[8]. [1] https://damonitor.github.io/_index [2] https://damonitor.github.io/doc/html/latest-damon [3] https://damonitor.github.io/test/result/visual/latest/rec.heatmap.0.png.html [4] https://damonitor.github.io/test/result/visual/latest/rec.heatmap.1.png.html [5] https://damonitor.github.io/test/result/visual/latest/rec.heatmap.2.png.html [6] https://damonitor.github.io/test/result/visual/latest/rec.wss_sz.png.html [7] https://damonitor.github.io/test/result/visual/latest/rec.wss_time.png.html [8] https://damonitor.github.io/test/result/perf/latest/html/index.html Baseline and Complete Git Trees =============================== The patches are based on the latest -mm tree, specifically v5.14-rc1-mmots-2021-07-15-18-47 of https://github.com/hnaz/linux-mm. You can also clone the complete git tree: $ git clone git://github.com/sjp38/linux -b damon/patches/v34 The web is also available: https://github.com/sjp38/linux/releases/tag/damon/patches/v34 Development Trees ----------------- There are a couple of trees for entire DAMON patchset series and features for future release. - For latest release: https://github.com/sjp38/linux/tree/damon/master - For next release: https://github.com/sjp38/linux/tree/damon/next Long-term Support Trees ----------------------- For people who want to test DAMON but using LTS kernels, there are another couple of trees based on two latest LTS kernels respectively and containing the 'damon/master' backports. - For v5.4.y: https://github.com/sjp38/linux/tree/damon/for-v5.4.y - For v5.10.y: https://github.com/sjp38/linux/tree/damon/for-v5.10.y Amazon Linux Kernel Trees ------------------------- DAMON is also merged in two public Amazon Linux kernel trees that based on v5.4.y[1] and v5.10.y[2]. [1] https://github.com/amazonlinux/linux/tree/amazon-5.4.y/master/mm/damon [2] https://github.com/amazonlinux/linux/tree/amazon-5.10.y/master/mm/damon Git Tree for Diff of Patches ============================ For easy review of diff between different versions of each patch, I prepared a git tree containing all versions of the DAMON patchset series: https://github.com/sjp38/damon-patches You can clone it and use 'diff' for easy review of changes between different versions of the patchset. For example: $ git clone https://github.com/sjp38/damon-patches && cd damon-patches $ diff -u damon/v33 damon/v34 Sequence Of Patches =================== First three patches implement the core logics of DAMON. The 1st patch introduces basic sampling based hotness monitoring for arbitrary types of targets. Following two patches implement the core mechanisms for control of overhead and accuracy, namely regions based sampling (patch 2) and adaptive regions adjustment (patch 3). Now the essential parts of DAMON is complete, but it cannot work unless someone provides monitoring primitives for a specific use case. The following two patches make it just work for virtual address spaces monitoring. The 4th patch makes 'PG_idle' can be used by DAMON and the 5th patch implements the virtual memory address space specific monitoring primitives using page table Accessed bits and the 'PG_idle' page flag. Now DAMON just works for virtual address space monitoring via the kernel space api. To let the user space users can use DAMON, following four patches add interfaces for them. The 6th patch adds a tracepoint for monitoring results. The 7th patch implements a DAMON application kernel module, namely damon-dbgfs, that simply wraps DAMON and exposes DAMON interface to the user space via the debugfs interface. The 8th patch further exports pid of monitoring thread (kdamond) to user space for easier cpu usage accounting, and the 9th patch makes the debugfs interface to support multiple contexts. Three patches for maintainability follows. The 10th patch adds documentations for both the user space and the kernel space. The 11th patch provides unit tests (based on the kunit) while the 12th patch adds user space tests (based on the kselftest). Finally, the last patch (13th) updates the MAINTAINERS file. This patch (of 13): DAMON is a data access monitoring framework for the Linux kernel. The core mechanisms of DAMON make it - accurate (the monitoring output is useful enough for DRAM level performance-centric memory management; It might be inappropriate for CPU cache levels, though), - light-weight (the monitoring overhead is normally low enough to be applied online), and - scalable (the upper-bound of the overhead is in constant range regardless of the size of target workloads). Using this framework, hence, we can easily write efficient kernel space data access monitoring applications. For example, the kernel's memory management mechanisms can make advanced decisions using this. Experimental data access aware optimization works that incurring high access monitoring overhead could again be implemented on top of this. Due to its simple and flexible interface, providing user space interface would be also easy. Then, user space users who have some special workloads can write personalized applications for better understanding and optimizations of their workloads and systems. === Nevertheless, this commit is defining and implementing only basic access check part without the overhead-accuracy handling core logic. The basic access check is as below. The output of DAMON says what memory regions are how frequently accessed for a given duration. The resolution of the access frequency is controlled by setting ``sampling interval`` and ``aggregation interval``. In detail, DAMON checks access to each page per ``sampling interval`` and aggregates the results. In other words, counts the number of the accesses to each region. After each ``aggregation interval`` passes, DAMON calls callback functions that previously registered by users so that users can read the aggregated results and then clears the results. This can be described in below simple pseudo-code:: init() while monitoring_on: for page in monitoring_target: if accessed(page): nr_accesses[page] += 1 if time() % aggregation_interval == 0: for callback in user_registered_callbacks: callback(monitoring_target, nr_accesses) for page in monitoring_target: nr_accesses[page] = 0 if time() % update_interval == 0: update() sleep(sampling interval) The target regions constructed at the beginning of the monitoring and updated after each ``regions_update_interval``, because the target regions could be dynamically changed (e.g., mmap() or memory hotplug). The monitoring overhead of this mechanism will arbitrarily increase as the size of the target workload grows. The basic monitoring primitives for actual access check and dynamic target regions construction aren't in the core part of DAMON. Instead, it allows users to implement their own primitives that are optimized for their use case and configure DAMON to use those. In other words, users cannot use current version of DAMON without some additional works. Following commits will implement the core mechanisms for the overhead-accuracy control and default primitives implementations. Link: https://lkml.kernel.org/r/20210716081449.22187-1-sj38.park@gmail.com Link: https://lkml.kernel.org/r/20210716081449.22187-2-sj38.park@gmail.com Signed-off-by: SeongJae Park <sjpark@amazon.de> Reviewed-by: Leonard Foerster <foersleo@amazon.de> Reviewed-by: Fernand Sieber <sieberf@amazon.com> Acked-by: Shakeel Butt <shakeelb@google.com> Cc: Jonathan Cameron <Jonathan.Cameron@huawei.com> Cc: Alexander Shishkin <alexander.shishkin@linux.intel.com> Cc: Amit Shah <amit@kernel.org> Cc: Benjamin Herrenschmidt <benh@kernel.crashing.org> Cc: Jonathan Corbet <corbet@lwn.net> Cc: David Hildenbrand <david@redhat.com> Cc: David Woodhouse <dwmw@amazon.com> Cc: Marco Elver <elver@google.com> Cc: Fan Du <fan.du@intel.com> Cc: Greg Kroah-Hartman <greg@kroah.com> Cc: Greg Thelen <gthelen@google.com> Cc: Joe Perches <joe@perches.com> Cc: Mel Gorman <mgorman@suse.de> Cc: Maximilian Heyne <mheyne@amazon.de> Cc: Minchan Kim <minchan@kernel.org> Cc: Ingo Molnar <mingo@redhat.com> Cc: Namhyung Kim <namhyung@kernel.org> Cc: Peter Zijlstra <peterz@infradead.org> Cc: Rik van Riel <riel@surriel.com> Cc: David Rientjes <rientjes@google.com> Cc: Steven Rostedt (VMware) <rostedt@goodmis.org> Cc: Shuah Khan <shuah@kernel.org> Cc: Vlastimil Babka <vbabka@suse.cz> Cc: Vladimir Davydov <vdavydov.dev@gmail.com> Cc: Brendan Higgins <brendanhiggins@google.com> Cc: Markus Boehme <markubo@amazon.de> Signed-off-by: Andrew Morton <akpm@linux-foundation.org> Signed-off-by: Linus Torvalds <torvalds@linux-foundation.org>
2021-09-08 10:56:28 +08:00
mm/damon/core: allow non-exclusive DAMON start/stop Patch series "Introduce DAMON sysfs interface", v3. Introduction ============ DAMON's debugfs-based user interface (DAMON_DBGFS) served very well, so far. However, it unnecessarily depends on debugfs, while DAMON is not aimed to be used for only debugging. Also, the interface receives multiple values via one file. For example, schemes file receives 18 values. As a result, it is inefficient, hard to be used, and difficult to be extended. Especially, keeping backward compatibility of user space tools is getting only challenging. It would be better to implement another reliable and flexible interface and deprecate DAMON_DBGFS in long term. For the reason, this patchset introduces a sysfs-based new user interface of DAMON. The idea of the new interface is, using directory hierarchies and having one dedicated file for each value. For a short example, users can do the virtual address monitoring via the interface as below: # cd /sys/kernel/mm/damon/admin/ # echo 1 > kdamonds/nr_kdamonds # echo 1 > kdamonds/0/contexts/nr_contexts # echo vaddr > kdamonds/0/contexts/0/operations # echo 1 > kdamonds/0/contexts/0/targets/nr_targets # echo $(pidof <workload>) > kdamonds/0/contexts/0/targets/0/pid_target # echo on > kdamonds/0/state A brief representation of the files hierarchy of DAMON sysfs interface is as below. Childs are represented with indentation, directories are having '/' suffix, and files in each directory are separated by comma. /sys/kernel/mm/damon/admin │ kdamonds/nr_kdamonds │ │ 0/state,pid │ │ │ contexts/nr_contexts │ │ │ │ 0/operations │ │ │ │ │ monitoring_attrs/ │ │ │ │ │ │ intervals/sample_us,aggr_us,update_us │ │ │ │ │ │ nr_regions/min,max │ │ │ │ │ targets/nr_targets │ │ │ │ │ │ 0/pid_target │ │ │ │ │ │ │ regions/nr_regions │ │ │ │ │ │ │ │ 0/start,end │ │ │ │ │ │ │ │ ... │ │ │ │ │ │ ... │ │ │ │ │ schemes/nr_schemes │ │ │ │ │ │ 0/action │ │ │ │ │ │ │ access_pattern/ │ │ │ │ │ │ │ │ sz/min,max │ │ │ │ │ │ │ │ nr_accesses/min,max │ │ │ │ │ │ │ │ age/min,max │ │ │ │ │ │ │ quotas/ms,bytes,reset_interval_ms │ │ │ │ │ │ │ │ weights/sz_permil,nr_accesses_permil,age_permil │ │ │ │ │ │ │ watermarks/metric,interval_us,high,mid,low │ │ │ │ │ │ │ stats/nr_tried,sz_tried,nr_applied,sz_applied,qt_exceeds │ │ │ │ │ │ ... │ │ │ │ ... │ │ ... Detailed usage of the files will be described in the final Documentation patch of this patchset. Main Difference Between DAMON_DBGFS and DAMON_SYSFS --------------------------------------------------- At the moment, DAMON_DBGFS and DAMON_SYSFS provides same features. One important difference between them is their exclusiveness. DAMON_DBGFS works in an exclusive manner, so that no DAMON worker thread (kdamond) in the system can run concurrently and interfere somehow. For the reason, DAMON_DBGFS asks users to construct all monitoring contexts and start them at once. It's not a big problem but makes the operation a little bit complex and unflexible. For more flexible usage, DAMON_SYSFS moves the responsibility of preventing any possible interference to the admins and work in a non-exclusive manner. That is, users can configure and start contexts one by one. Note that DAMON respects both exclusive groups and non-exclusive groups of contexts, in a manner similar to that of reader-writer locks. That is, if any exclusive monitoring contexts (e.g., contexts that started via DAMON_DBGFS) are running, DAMON_SYSFS does not start new contexts, and vice versa. Future Plan of DAMON_DBGFS Deprecation ====================================== Once this patchset is merged, DAMON_DBGFS development will be frozen. That is, we will maintain it to work as is now so that no users will be break. But, it will not be extended to provide any new feature of DAMON. The support will be continued only until next LTS release. After that, we will drop DAMON_DBGFS. User-space Tooling Compatibility -------------------------------- As DAMON_SYSFS provides all features of DAMON_DBGFS, all user space tooling can move to DAMON_SYSFS. As we will continue supporting DAMON_DBGFS until next LTS kernel release, user space tools would have enough time to move to DAMON_SYSFS. The official user space tool, damo[1], is already supporting both DAMON_SYSFS and DAMON_DBGFS. Both correctness tests[2] and performance tests[3] of DAMON using DAMON_SYSFS also passed. [1] https://github.com/awslabs/damo [2] https://github.com/awslabs/damon-tests/tree/master/corr [3] https://github.com/awslabs/damon-tests/tree/master/perf Sequence of Patches =================== First two patches (patches 1-2) make core changes for DAMON_SYSFS. The first one (patch 1) allows non-exclusive DAMON contexts so that DAMON_SYSFS can work in non-exclusive mode, while the second one (patch 2) adds size of DAMON enum types so that DAMON API users can safely iterate the enums. Third patch (patch 3) implements basic sysfs stub for virtual address spaces monitoring. Note that this implements only sysfs files and DAMON is not linked. Fourth patch (patch 4) links the DAMON_SYSFS to DAMON so that users can control DAMON using the sysfs files. Following six patches (patches 5-10) implements other DAMON features that DAMON_DBGFS supports one by one (physical address space monitoring, DAMON-based operation schemes, schemes quotas, schemes prioritization weights, schemes watermarks, and schemes stats). Following patch (patch 11) adds a simple selftest for DAMON_SYSFS, and the final one (patch 12) documents DAMON_SYSFS. This patch (of 13): To avoid interference between DAMON contexts monitoring overlapping memory regions, damon_start() works in an exclusive manner. That is, damon_start() does nothing bug fails if any context that started by another instance of the function is still running. This makes its usage a little bit restrictive. However, admins could aware each DAMON usage and address such interferences on their own in some cases. This commit hence implements non-exclusive mode of the function and allows the callers to select the mode. Note that the exclusive groups and non-exclusive groups of contexts will respect each other in a manner similar to that of reader-writer locks. Therefore, this commit will not cause any behavioral change to the exclusive groups. Link: https://lkml.kernel.org/r/20220228081314.5770-1-sj@kernel.org Link: https://lkml.kernel.org/r/20220228081314.5770-2-sj@kernel.org Signed-off-by: SeongJae Park <sj@kernel.org> Cc: Jonathan Corbet <corbet@lwn.net> Cc: Shuah Khan <skhan@linuxfoundation.org> Cc: David Rientjes <rientjes@google.com> Cc: Xin Hao <xhao@linux.alibaba.com> Cc: Greg Kroah-Hartman <gregkh@linuxfoundation.org> Signed-off-by: Andrew Morton <akpm@linux-foundation.org> Signed-off-by: Linus Torvalds <torvalds@linux-foundation.org>
2022-03-23 05:49:21 +08:00
int damon_start(struct damon_ctx **ctxs, int nr_ctxs, bool exclusive);
mm: introduce Data Access MONitor (DAMON) Patch series "Introduce Data Access MONitor (DAMON)", v34. Introduction ============ DAMON is a data access monitoring framework for the Linux kernel. The core mechanisms of DAMON called 'region based sampling' and 'adaptive regions adjustment' (refer to 'mechanisms.rst' in the 11th patch of this patchset for the detail) make it - accurate (The monitored information is useful for DRAM level memory management. It might not appropriate for Cache-level accuracy, though.), - light-weight (The monitoring overhead is low enough to be applied online while making no impact on the performance of the target workloads.), and - scalable (the upper-bound of the instrumentation overhead is controllable regardless of the size of target workloads.). Using this framework, therefore, several memory management mechanisms such as reclamation and THP can be optimized to aware real data access patterns. Experimental access pattern aware memory management optimization works that incurring high instrumentation overhead will be able to have another try. Though DAMON is for kernel subsystems, it can be easily exposed to the user space by writing a DAMON-wrapper kernel subsystem. Then, user space users who have some special workloads will be able to write personalized tools or applications for deeper understanding and specialized optimizations of their systems. DAMON is also merged in two public Amazon Linux kernel trees that based on v5.4.y[1] and v5.10.y[2]. [1] https://github.com/amazonlinux/linux/tree/amazon-5.4.y/master/mm/damon [2] https://github.com/amazonlinux/linux/tree/amazon-5.10.y/master/mm/damon The userspace tool[1] is available, released under GPLv2, and actively being maintained. I am also planning to implement another basic user interface in perf[2]. Also, the basic test suite for DAMON is available under GPLv2[3]. [1] https://github.com/awslabs/damo [2] https://lore.kernel.org/linux-mm/20210107120729.22328-1-sjpark@amazon.com/ [3] https://github.com/awslabs/damon-tests Long-term Plan -------------- DAMON is a part of a project called Data Access-aware Operating System (DAOS). As the name implies, I want to improve the performance and efficiency of systems using fine-grained data access patterns. The optimizations are for both kernel and user spaces. I will therefore modify or create kernel subsystems, export some of those to user space and implement user space library / tools. Below shows the layers and components for the project. --------------------------------------------------------------------------- Primitives: PTE Accessed bit, PG_idle, rmap, (Intel CMT), ... Framework: DAMON Features: DAMOS, virtual addr, physical addr, ... Applications: DAMON-debugfs, (DARC), ... ^^^^^^^^^^^^^^^^^^^^^^^ KERNEL SPACE ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ Raw Interface: debugfs, (sysfs), (damonfs), tracepoints, (sys_damon), ... vvvvvvvvvvvvvvvvvvvvvvv USER SPACE vvvvvvvvvvvvvvvvvvvvvvvvvvvvvvvv Library: (libdamon), ... Tools: DAMO, (perf), ... --------------------------------------------------------------------------- The components in parentheses or marked as '...' are not implemented yet but in the future plan. IOW, those are the TODO tasks of DAOS project. For more detail, please refer to the plans: https://lore.kernel.org/linux-mm/20201202082731.24828-1-sjpark@amazon.com/ Evaluations =========== We evaluated DAMON's overhead, monitoring quality and usefulness using 24 realistic workloads on my QEMU/KVM based virtual machine running a kernel that v24 DAMON patchset is applied. DAMON is lightweight. It increases system memory usage by 0.39% and slows target workloads down by 1.16%. DAMON is accurate and useful for memory management optimizations. An experimental DAMON-based operation scheme for THP, namely 'ethp', removes 76.15% of THP memory overheads while preserving 51.25% of THP speedup. Another experimental DAMON-based 'proactive reclamation' implementation, 'prcl', reduces 93.38% of residential sets and 23.63% of system memory footprint while incurring only 1.22% runtime overhead in the best case (parsec3/freqmine). NOTE that the experimental THP optimization and proactive reclamation are not for production but only for proof of concepts. Please refer to the official document[1] or "Documentation/admin-guide/mm: Add a document for DAMON" patch in this patchset for detailed evaluation setup and results. [1] https://damonitor.github.io/doc/html/latest-damon/admin-guide/mm/damon/eval.html Real-world User Story ===================== In summary, DAMON has used on production systems and proved its usefulness. DAMON as a profiler ------------------- We analyzed characteristics of a large scale production systems of our customers using DAMON. The systems utilize 70GB DRAM and 36 CPUs. From this, we were able to find interesting things below. There were obviously different access pattern under idle workload and active workload. Under the idle workload, it accessed large memory regions with low frequency, while the active workload accessed small memory regions with high freuqnecy. DAMON found a 7GB memory region that showing obviously high access frequency under the active workload. We believe this is the performance-effective working set and need to be protected. There was a 4KB memory region that showing highest access frequency under not only active but also idle workloads. We think this must be a hottest code section like thing that should never be paged out. For this analysis, DAMON used only 0.3-1% of single CPU time. Because we used recording-based analysis, it consumed about 3-12 MB of disk space per 20 minutes. This is only small amount of disk space, but we can further reduce the disk usage by using non-recording-based DAMON features. I'd like to argue that only DAMON can do such detailed analysis (finding 4KB highest region in 70GB memory) with the light overhead. DAMON as a system optimization tool ----------------------------------- We also found below potential performance problems on the systems and made DAMON-based solutions. The system doesn't want to make the workload suffer from the page reclamation and thus it utilizes enough DRAM but no swap device. However, we found the system is actively reclaiming file-backed pages, because the system has intensive file IO. The file IO turned out to be not performance critical for the workload, but the customer wanted to ensure performance critical file-backed pages like code section to not mistakenly be evicted. Using direct IO should or `mlock()` would be a straightforward solution, but modifying the user space code is not easy for the customer. Alternatively, we could use DAMON-based operation scheme[1]. By using it, we can ask DAMON to track access frequency of each region and make 'process_madvise(MADV_WILLNEED)[2]' call for regions having specific size and access frequency for a time interval. We also found the system is having high number of TLB misses. We tried 'always' THP enabled policy and it greatly reduced TLB misses, but the page reclamation also been more frequent due to the THP internal fragmentation caused memory bloat. We could try another DAMON-based operation scheme that applies 'MADV_HUGEPAGE' to memory regions having >=2MB size and high access frequency, while applying 'MADV_NOHUGEPAGE' to regions having <2MB size and low access frequency. We do not own the systems so we only reported the analysis results and possible optimization solutions to the customers. The customers satisfied about the analysis results and promised to try the optimization guides. [1] https://lore.kernel.org/linux-mm/20201006123931.5847-1-sjpark@amazon.com/ [2] https://lore.kernel.org/linux-api/20200622192900.22757-4-minchan@kernel.org/ Comparison with Idle Page Tracking ================================== Idle Page Tracking allows users to set and read idleness of pages using a bitmap file which represents each page with each bit of the file. One recommended usage of it is working set size detection. Users can do that by 1. find PFN of each page for workloads in interest, 2. set all the pages as idle by doing writes to the bitmap file, 3. wait until the workload accesses its working set, and 4. read the idleness of the pages again and count pages became not idle. NOTE: While Idle Page Tracking is for user space users, DAMON is primarily designed for kernel subsystems though it can easily exposed to the user space. Hence, this section only assumes such user space use of DAMON. For what use cases Idle Page Tracking would be better? ------------------------------------------------------ 1. Flexible usecases other than hotness monitoring. Because Idle Page Tracking allows users to control the primitive (Page idleness) by themselves, Idle Page Tracking users can do anything they want. Meanwhile, DAMON is primarily designed to monitor the hotness of each memory region. For this, DAMON asks users to provide sampling interval and aggregation interval. For the reason, there could be some use case that using Idle Page Tracking is simpler. 2. Physical memory monitoring. Idle Page Tracking receives PFN range as input, so natively supports physical memory monitoring. DAMON is designed to be extensible for multiple address spaces and use cases by implementing and using primitives for the given use case. Therefore, by theory, DAMON has no limitation in the type of target address space as long as primitives for the given address space exists. However, the default primitives introduced by this patchset supports only virtual address spaces. Therefore, for physical memory monitoring, you should implement your own primitives and use it, or simply use Idle Page Tracking. Nonetheless, RFC patchsets[1] for the physical memory address space primitives is already available. It also supports user memory same to Idle Page Tracking. [1] https://lore.kernel.org/linux-mm/20200831104730.28970-1-sjpark@amazon.com/ For what use cases DAMON is better? ----------------------------------- 1. Hotness Monitoring. Idle Page Tracking let users know only if a page frame is accessed or not. For hotness check, the user should write more code and use more memory. DAMON do that by itself. 2. Low Monitoring Overhead DAMON receives user's monitoring request with one step and then provide the results. So, roughly speaking, DAMON require only O(1) user/kernel context switches. In case of Idle Page Tracking, however, because the interface receives contiguous page frames, the number of user/kernel context switches increases as the monitoring target becomes complex and huge. As a result, the context switch overhead could be not negligible. Moreover, DAMON is born to handle with the monitoring overhead. Because the core mechanism is pure logical, Idle Page Tracking users might be able to implement the mechanism on their own, but it would be time consuming and the user/kernel context switching will still more frequent than that of DAMON. Also, the kernel subsystems cannot use the logic in this case. 3. Page granularity working set size detection. Until v22 of this patchset, this was categorized as the thing Idle Page Tracking could do better, because DAMON basically maintains additional metadata for each of the monitoring target regions. So, in the page granularity working set size detection use case, DAMON would incur (number of monitoring target pages * size of metadata) memory overhead. Size of the single metadata item is about 54 bytes, so assuming 4KB pages, about 1.3% of monitoring target pages will be additionally used. All essential metadata for Idle Page Tracking are embedded in 'struct page' and page table entries. Therefore, in this use case, only one counter variable for working set size accounting is required if Idle Page Tracking is used. There are more details to consider, but roughly speaking, this is true in most cases. However, the situation changed from v23. Now DAMON supports arbitrary types of monitoring targets, which don't use the metadata. Using that, DAMON can do the working set size detection with no additional space overhead but less user-kernel context switch. A first draft for the implementation of monitoring primitives for this usage is available in a DAMON development tree[1]. An RFC patchset for it based on this patchset will also be available soon. Since v24, the arbitrary type support is dropped from this patchset because this patchset doesn't introduce real use of the type. You can still get it from the DAMON development tree[2], though. [1] https://github.com/sjp38/linux/tree/damon/pgidle_hack [2] https://github.com/sjp38/linux/tree/damon/master 4. More future usecases While Idle Page Tracking has tight coupling with base primitives (PG_Idle and page table Accessed bits), DAMON is designed to be extensible for many use cases and address spaces. If you need some special address type or want to use special h/w access check primitives, you can write your own primitives for that and configure DAMON to use those. Therefore, if your use case could be changed a lot in future, using DAMON could be better. Can I use both Idle Page Tracking and DAMON? -------------------------------------------- Yes, though using them concurrently for overlapping memory regions could result in interference to each other. Nevertheless, such use case would be rare or makes no sense at all. Even in the case, the noise would bot be really significant. So, you can choose whatever you want depending on the characteristics of your use cases. More Information ================ We prepared a showcase web site[1] that you can get more information. There are - the official documentations[2], - the heatmap format dynamic access pattern of various realistic workloads for heap area[3], mmap()-ed area[4], and stack[5] area, - the dynamic working set size distribution[6] and chronological working set size changes[7], and - the latest performance test results[8]. [1] https://damonitor.github.io/_index [2] https://damonitor.github.io/doc/html/latest-damon [3] https://damonitor.github.io/test/result/visual/latest/rec.heatmap.0.png.html [4] https://damonitor.github.io/test/result/visual/latest/rec.heatmap.1.png.html [5] https://damonitor.github.io/test/result/visual/latest/rec.heatmap.2.png.html [6] https://damonitor.github.io/test/result/visual/latest/rec.wss_sz.png.html [7] https://damonitor.github.io/test/result/visual/latest/rec.wss_time.png.html [8] https://damonitor.github.io/test/result/perf/latest/html/index.html Baseline and Complete Git Trees =============================== The patches are based on the latest -mm tree, specifically v5.14-rc1-mmots-2021-07-15-18-47 of https://github.com/hnaz/linux-mm. You can also clone the complete git tree: $ git clone git://github.com/sjp38/linux -b damon/patches/v34 The web is also available: https://github.com/sjp38/linux/releases/tag/damon/patches/v34 Development Trees ----------------- There are a couple of trees for entire DAMON patchset series and features for future release. - For latest release: https://github.com/sjp38/linux/tree/damon/master - For next release: https://github.com/sjp38/linux/tree/damon/next Long-term Support Trees ----------------------- For people who want to test DAMON but using LTS kernels, there are another couple of trees based on two latest LTS kernels respectively and containing the 'damon/master' backports. - For v5.4.y: https://github.com/sjp38/linux/tree/damon/for-v5.4.y - For v5.10.y: https://github.com/sjp38/linux/tree/damon/for-v5.10.y Amazon Linux Kernel Trees ------------------------- DAMON is also merged in two public Amazon Linux kernel trees that based on v5.4.y[1] and v5.10.y[2]. [1] https://github.com/amazonlinux/linux/tree/amazon-5.4.y/master/mm/damon [2] https://github.com/amazonlinux/linux/tree/amazon-5.10.y/master/mm/damon Git Tree for Diff of Patches ============================ For easy review of diff between different versions of each patch, I prepared a git tree containing all versions of the DAMON patchset series: https://github.com/sjp38/damon-patches You can clone it and use 'diff' for easy review of changes between different versions of the patchset. For example: $ git clone https://github.com/sjp38/damon-patches && cd damon-patches $ diff -u damon/v33 damon/v34 Sequence Of Patches =================== First three patches implement the core logics of DAMON. The 1st patch introduces basic sampling based hotness monitoring for arbitrary types of targets. Following two patches implement the core mechanisms for control of overhead and accuracy, namely regions based sampling (patch 2) and adaptive regions adjustment (patch 3). Now the essential parts of DAMON is complete, but it cannot work unless someone provides monitoring primitives for a specific use case. The following two patches make it just work for virtual address spaces monitoring. The 4th patch makes 'PG_idle' can be used by DAMON and the 5th patch implements the virtual memory address space specific monitoring primitives using page table Accessed bits and the 'PG_idle' page flag. Now DAMON just works for virtual address space monitoring via the kernel space api. To let the user space users can use DAMON, following four patches add interfaces for them. The 6th patch adds a tracepoint for monitoring results. The 7th patch implements a DAMON application kernel module, namely damon-dbgfs, that simply wraps DAMON and exposes DAMON interface to the user space via the debugfs interface. The 8th patch further exports pid of monitoring thread (kdamond) to user space for easier cpu usage accounting, and the 9th patch makes the debugfs interface to support multiple contexts. Three patches for maintainability follows. The 10th patch adds documentations for both the user space and the kernel space. The 11th patch provides unit tests (based on the kunit) while the 12th patch adds user space tests (based on the kselftest). Finally, the last patch (13th) updates the MAINTAINERS file. This patch (of 13): DAMON is a data access monitoring framework for the Linux kernel. The core mechanisms of DAMON make it - accurate (the monitoring output is useful enough for DRAM level performance-centric memory management; It might be inappropriate for CPU cache levels, though), - light-weight (the monitoring overhead is normally low enough to be applied online), and - scalable (the upper-bound of the overhead is in constant range regardless of the size of target workloads). Using this framework, hence, we can easily write efficient kernel space data access monitoring applications. For example, the kernel's memory management mechanisms can make advanced decisions using this. Experimental data access aware optimization works that incurring high access monitoring overhead could again be implemented on top of this. Due to its simple and flexible interface, providing user space interface would be also easy. Then, user space users who have some special workloads can write personalized applications for better understanding and optimizations of their workloads and systems. === Nevertheless, this commit is defining and implementing only basic access check part without the overhead-accuracy handling core logic. The basic access check is as below. The output of DAMON says what memory regions are how frequently accessed for a given duration. The resolution of the access frequency is controlled by setting ``sampling interval`` and ``aggregation interval``. In detail, DAMON checks access to each page per ``sampling interval`` and aggregates the results. In other words, counts the number of the accesses to each region. After each ``aggregation interval`` passes, DAMON calls callback functions that previously registered by users so that users can read the aggregated results and then clears the results. This can be described in below simple pseudo-code:: init() while monitoring_on: for page in monitoring_target: if accessed(page): nr_accesses[page] += 1 if time() % aggregation_interval == 0: for callback in user_registered_callbacks: callback(monitoring_target, nr_accesses) for page in monitoring_target: nr_accesses[page] = 0 if time() % update_interval == 0: update() sleep(sampling interval) The target regions constructed at the beginning of the monitoring and updated after each ``regions_update_interval``, because the target regions could be dynamically changed (e.g., mmap() or memory hotplug). The monitoring overhead of this mechanism will arbitrarily increase as the size of the target workload grows. The basic monitoring primitives for actual access check and dynamic target regions construction aren't in the core part of DAMON. Instead, it allows users to implement their own primitives that are optimized for their use case and configure DAMON to use those. In other words, users cannot use current version of DAMON without some additional works. Following commits will implement the core mechanisms for the overhead-accuracy control and default primitives implementations. Link: https://lkml.kernel.org/r/20210716081449.22187-1-sj38.park@gmail.com Link: https://lkml.kernel.org/r/20210716081449.22187-2-sj38.park@gmail.com Signed-off-by: SeongJae Park <sjpark@amazon.de> Reviewed-by: Leonard Foerster <foersleo@amazon.de> Reviewed-by: Fernand Sieber <sieberf@amazon.com> Acked-by: Shakeel Butt <shakeelb@google.com> Cc: Jonathan Cameron <Jonathan.Cameron@huawei.com> Cc: Alexander Shishkin <alexander.shishkin@linux.intel.com> Cc: Amit Shah <amit@kernel.org> Cc: Benjamin Herrenschmidt <benh@kernel.crashing.org> Cc: Jonathan Corbet <corbet@lwn.net> Cc: David Hildenbrand <david@redhat.com> Cc: David Woodhouse <dwmw@amazon.com> Cc: Marco Elver <elver@google.com> Cc: Fan Du <fan.du@intel.com> Cc: Greg Kroah-Hartman <greg@kroah.com> Cc: Greg Thelen <gthelen@google.com> Cc: Joe Perches <joe@perches.com> Cc: Mel Gorman <mgorman@suse.de> Cc: Maximilian Heyne <mheyne@amazon.de> Cc: Minchan Kim <minchan@kernel.org> Cc: Ingo Molnar <mingo@redhat.com> Cc: Namhyung Kim <namhyung@kernel.org> Cc: Peter Zijlstra <peterz@infradead.org> Cc: Rik van Riel <riel@surriel.com> Cc: David Rientjes <rientjes@google.com> Cc: Steven Rostedt (VMware) <rostedt@goodmis.org> Cc: Shuah Khan <shuah@kernel.org> Cc: Vlastimil Babka <vbabka@suse.cz> Cc: Vladimir Davydov <vdavydov.dev@gmail.com> Cc: Brendan Higgins <brendanhiggins@google.com> Cc: Markus Boehme <markubo@amazon.de> Signed-off-by: Andrew Morton <akpm@linux-foundation.org> Signed-off-by: Linus Torvalds <torvalds@linux-foundation.org>
2021-09-08 10:56:28 +08:00
int damon_stop(struct damon_ctx **ctxs, int nr_ctxs);
#endif /* CONFIG_DAMON */
#endif /* _DAMON_H */