OpenCloudOS-Kernel/tools/perf/scripts/python/mem-phys-addr.py

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perf script python: Add script to profile and resolve physical mem type There could be different types of memory in the system. E.g normal System Memory, Persistent Memory. To understand how the workload maps to those memories, it's important to know the I/O statistics of them. Perf can collect physical addresses, but those are raw data. It still needs extra work to resolve the physical addresses. Provide a script to facilitate the physical addresses resolving and I/O statistics. Profile with MEM_INST_RETIRED.ALL_LOADS or MEM_UOPS_RETIRED.ALL_LOADS event if any of them is available. Look up the /proc/iomem and resolve the physical address. Provide memory type summary. Here is an example output: # perf script report mem-phys-addr Event: mem_inst_retired.all_loads:P Memory type count percentage ---------------------------------------- ----------- ----------- System RAM 74 53.2% Persistent Memory 55 39.6% N/A --- Changes since V2: - Apply the new license rules. - Add comments for globals Changes since V1: - Do not mix DLA and Load Latency. Do not compare the loads and stores. Only profile the loads. - Use event name to replace the RAW event Signed-off-by: Kan Liang <Kan.liang@intel.com> Reviewed-by: Andi Kleen <ak@linux.intel.com> Cc: Dan Williams <dan.j.williams@intel.com> Cc: Jiri Olsa <jolsa@kernel.org> Cc: Peter Zijlstra <peterz@infradead.org> Cc: Philippe Ombredanne <pombredanne@nexb.com> Cc: Stephane Eranian <eranian@google.com> Link: https://lkml.kernel.org/r/1515099595-34770-1-git-send-email-kan.liang@intel.com Signed-off-by: Arnaldo Carvalho de Melo <acme@redhat.com>
2018-01-05 04:59:55 +08:00
# mem-phys-addr.py: Resolve physical address samples
# SPDX-License-Identifier: GPL-2.0
#
# Copyright (c) 2018, Intel Corporation.
from __future__ import division
from __future__ import print_function
perf script python: Add script to profile and resolve physical mem type There could be different types of memory in the system. E.g normal System Memory, Persistent Memory. To understand how the workload maps to those memories, it's important to know the I/O statistics of them. Perf can collect physical addresses, but those are raw data. It still needs extra work to resolve the physical addresses. Provide a script to facilitate the physical addresses resolving and I/O statistics. Profile with MEM_INST_RETIRED.ALL_LOADS or MEM_UOPS_RETIRED.ALL_LOADS event if any of them is available. Look up the /proc/iomem and resolve the physical address. Provide memory type summary. Here is an example output: # perf script report mem-phys-addr Event: mem_inst_retired.all_loads:P Memory type count percentage ---------------------------------------- ----------- ----------- System RAM 74 53.2% Persistent Memory 55 39.6% N/A --- Changes since V2: - Apply the new license rules. - Add comments for globals Changes since V1: - Do not mix DLA and Load Latency. Do not compare the loads and stores. Only profile the loads. - Use event name to replace the RAW event Signed-off-by: Kan Liang <Kan.liang@intel.com> Reviewed-by: Andi Kleen <ak@linux.intel.com> Cc: Dan Williams <dan.j.williams@intel.com> Cc: Jiri Olsa <jolsa@kernel.org> Cc: Peter Zijlstra <peterz@infradead.org> Cc: Philippe Ombredanne <pombredanne@nexb.com> Cc: Stephane Eranian <eranian@google.com> Link: https://lkml.kernel.org/r/1515099595-34770-1-git-send-email-kan.liang@intel.com Signed-off-by: Arnaldo Carvalho de Melo <acme@redhat.com>
2018-01-05 04:59:55 +08:00
import os
import sys
import struct
import re
import bisect
import collections
sys.path.append(os.environ['PERF_EXEC_PATH'] + \
'/scripts/python/Perf-Trace-Util/lib/Perf/Trace')
#physical address ranges for System RAM
system_ram = []
#physical address ranges for Persistent Memory
pmem = []
#file object for proc iomem
f = None
#Count for each type of memory
load_mem_type_cnt = collections.Counter()
#perf event name
event_name = None
def parse_iomem():
global f
f = open('/proc/iomem', 'r')
for i, j in enumerate(f):
m = re.split('-|:',j,2)
if m[2].strip() == 'System RAM':
system_ram.append(int(m[0], 16))
system_ram.append(int(m[1], 16))
perf script python: Add script to profile and resolve physical mem type There could be different types of memory in the system. E.g normal System Memory, Persistent Memory. To understand how the workload maps to those memories, it's important to know the I/O statistics of them. Perf can collect physical addresses, but those are raw data. It still needs extra work to resolve the physical addresses. Provide a script to facilitate the physical addresses resolving and I/O statistics. Profile with MEM_INST_RETIRED.ALL_LOADS or MEM_UOPS_RETIRED.ALL_LOADS event if any of them is available. Look up the /proc/iomem and resolve the physical address. Provide memory type summary. Here is an example output: # perf script report mem-phys-addr Event: mem_inst_retired.all_loads:P Memory type count percentage ---------------------------------------- ----------- ----------- System RAM 74 53.2% Persistent Memory 55 39.6% N/A --- Changes since V2: - Apply the new license rules. - Add comments for globals Changes since V1: - Do not mix DLA and Load Latency. Do not compare the loads and stores. Only profile the loads. - Use event name to replace the RAW event Signed-off-by: Kan Liang <Kan.liang@intel.com> Reviewed-by: Andi Kleen <ak@linux.intel.com> Cc: Dan Williams <dan.j.williams@intel.com> Cc: Jiri Olsa <jolsa@kernel.org> Cc: Peter Zijlstra <peterz@infradead.org> Cc: Philippe Ombredanne <pombredanne@nexb.com> Cc: Stephane Eranian <eranian@google.com> Link: https://lkml.kernel.org/r/1515099595-34770-1-git-send-email-kan.liang@intel.com Signed-off-by: Arnaldo Carvalho de Melo <acme@redhat.com>
2018-01-05 04:59:55 +08:00
if m[2].strip() == 'Persistent Memory':
pmem.append(int(m[0], 16))
pmem.append(int(m[1], 16))
perf script python: Add script to profile and resolve physical mem type There could be different types of memory in the system. E.g normal System Memory, Persistent Memory. To understand how the workload maps to those memories, it's important to know the I/O statistics of them. Perf can collect physical addresses, but those are raw data. It still needs extra work to resolve the physical addresses. Provide a script to facilitate the physical addresses resolving and I/O statistics. Profile with MEM_INST_RETIRED.ALL_LOADS or MEM_UOPS_RETIRED.ALL_LOADS event if any of them is available. Look up the /proc/iomem and resolve the physical address. Provide memory type summary. Here is an example output: # perf script report mem-phys-addr Event: mem_inst_retired.all_loads:P Memory type count percentage ---------------------------------------- ----------- ----------- System RAM 74 53.2% Persistent Memory 55 39.6% N/A --- Changes since V2: - Apply the new license rules. - Add comments for globals Changes since V1: - Do not mix DLA and Load Latency. Do not compare the loads and stores. Only profile the loads. - Use event name to replace the RAW event Signed-off-by: Kan Liang <Kan.liang@intel.com> Reviewed-by: Andi Kleen <ak@linux.intel.com> Cc: Dan Williams <dan.j.williams@intel.com> Cc: Jiri Olsa <jolsa@kernel.org> Cc: Peter Zijlstra <peterz@infradead.org> Cc: Philippe Ombredanne <pombredanne@nexb.com> Cc: Stephane Eranian <eranian@google.com> Link: https://lkml.kernel.org/r/1515099595-34770-1-git-send-email-kan.liang@intel.com Signed-off-by: Arnaldo Carvalho de Melo <acme@redhat.com>
2018-01-05 04:59:55 +08:00
def print_memory_type():
print("Event: %s" % (event_name))
print("%-40s %10s %10s\n" % ("Memory type", "count", "percentage"), end='')
print("%-40s %10s %10s\n" % ("----------------------------------------",
perf script python: Add script to profile and resolve physical mem type There could be different types of memory in the system. E.g normal System Memory, Persistent Memory. To understand how the workload maps to those memories, it's important to know the I/O statistics of them. Perf can collect physical addresses, but those are raw data. It still needs extra work to resolve the physical addresses. Provide a script to facilitate the physical addresses resolving and I/O statistics. Profile with MEM_INST_RETIRED.ALL_LOADS or MEM_UOPS_RETIRED.ALL_LOADS event if any of them is available. Look up the /proc/iomem and resolve the physical address. Provide memory type summary. Here is an example output: # perf script report mem-phys-addr Event: mem_inst_retired.all_loads:P Memory type count percentage ---------------------------------------- ----------- ----------- System RAM 74 53.2% Persistent Memory 55 39.6% N/A --- Changes since V2: - Apply the new license rules. - Add comments for globals Changes since V1: - Do not mix DLA and Load Latency. Do not compare the loads and stores. Only profile the loads. - Use event name to replace the RAW event Signed-off-by: Kan Liang <Kan.liang@intel.com> Reviewed-by: Andi Kleen <ak@linux.intel.com> Cc: Dan Williams <dan.j.williams@intel.com> Cc: Jiri Olsa <jolsa@kernel.org> Cc: Peter Zijlstra <peterz@infradead.org> Cc: Philippe Ombredanne <pombredanne@nexb.com> Cc: Stephane Eranian <eranian@google.com> Link: https://lkml.kernel.org/r/1515099595-34770-1-git-send-email-kan.liang@intel.com Signed-off-by: Arnaldo Carvalho de Melo <acme@redhat.com>
2018-01-05 04:59:55 +08:00
"-----------", "-----------"),
end='');
perf script python: Add script to profile and resolve physical mem type There could be different types of memory in the system. E.g normal System Memory, Persistent Memory. To understand how the workload maps to those memories, it's important to know the I/O statistics of them. Perf can collect physical addresses, but those are raw data. It still needs extra work to resolve the physical addresses. Provide a script to facilitate the physical addresses resolving and I/O statistics. Profile with MEM_INST_RETIRED.ALL_LOADS or MEM_UOPS_RETIRED.ALL_LOADS event if any of them is available. Look up the /proc/iomem and resolve the physical address. Provide memory type summary. Here is an example output: # perf script report mem-phys-addr Event: mem_inst_retired.all_loads:P Memory type count percentage ---------------------------------------- ----------- ----------- System RAM 74 53.2% Persistent Memory 55 39.6% N/A --- Changes since V2: - Apply the new license rules. - Add comments for globals Changes since V1: - Do not mix DLA and Load Latency. Do not compare the loads and stores. Only profile the loads. - Use event name to replace the RAW event Signed-off-by: Kan Liang <Kan.liang@intel.com> Reviewed-by: Andi Kleen <ak@linux.intel.com> Cc: Dan Williams <dan.j.williams@intel.com> Cc: Jiri Olsa <jolsa@kernel.org> Cc: Peter Zijlstra <peterz@infradead.org> Cc: Philippe Ombredanne <pombredanne@nexb.com> Cc: Stephane Eranian <eranian@google.com> Link: https://lkml.kernel.org/r/1515099595-34770-1-git-send-email-kan.liang@intel.com Signed-off-by: Arnaldo Carvalho de Melo <acme@redhat.com>
2018-01-05 04:59:55 +08:00
total = sum(load_mem_type_cnt.values())
for mem_type, count in sorted(load_mem_type_cnt.most_common(), \
key = lambda kv: (kv[1], kv[0]), reverse = True):
print("%-40s %10d %10.1f%%\n" %
(mem_type, count, 100 * count / total),
end='')
perf script python: Add script to profile and resolve physical mem type There could be different types of memory in the system. E.g normal System Memory, Persistent Memory. To understand how the workload maps to those memories, it's important to know the I/O statistics of them. Perf can collect physical addresses, but those are raw data. It still needs extra work to resolve the physical addresses. Provide a script to facilitate the physical addresses resolving and I/O statistics. Profile with MEM_INST_RETIRED.ALL_LOADS or MEM_UOPS_RETIRED.ALL_LOADS event if any of them is available. Look up the /proc/iomem and resolve the physical address. Provide memory type summary. Here is an example output: # perf script report mem-phys-addr Event: mem_inst_retired.all_loads:P Memory type count percentage ---------------------------------------- ----------- ----------- System RAM 74 53.2% Persistent Memory 55 39.6% N/A --- Changes since V2: - Apply the new license rules. - Add comments for globals Changes since V1: - Do not mix DLA and Load Latency. Do not compare the loads and stores. Only profile the loads. - Use event name to replace the RAW event Signed-off-by: Kan Liang <Kan.liang@intel.com> Reviewed-by: Andi Kleen <ak@linux.intel.com> Cc: Dan Williams <dan.j.williams@intel.com> Cc: Jiri Olsa <jolsa@kernel.org> Cc: Peter Zijlstra <peterz@infradead.org> Cc: Philippe Ombredanne <pombredanne@nexb.com> Cc: Stephane Eranian <eranian@google.com> Link: https://lkml.kernel.org/r/1515099595-34770-1-git-send-email-kan.liang@intel.com Signed-off-by: Arnaldo Carvalho de Melo <acme@redhat.com>
2018-01-05 04:59:55 +08:00
def trace_begin():
parse_iomem()
def trace_end():
print_memory_type()
f.close()
def is_system_ram(phys_addr):
#/proc/iomem is sorted
position = bisect.bisect(system_ram, phys_addr)
if position % 2 == 0:
return False
return True
def is_persistent_mem(phys_addr):
position = bisect.bisect(pmem, phys_addr)
if position % 2 == 0:
return False
return True
def find_memory_type(phys_addr):
if phys_addr == 0:
return "N/A"
if is_system_ram(phys_addr):
return "System RAM"
if is_persistent_mem(phys_addr):
return "Persistent Memory"
#slow path, search all
f.seek(0, 0)
for j in f:
m = re.split('-|:',j,2)
if int(m[0], 16) <= phys_addr <= int(m[1], 16):
perf script python: Add script to profile and resolve physical mem type There could be different types of memory in the system. E.g normal System Memory, Persistent Memory. To understand how the workload maps to those memories, it's important to know the I/O statistics of them. Perf can collect physical addresses, but those are raw data. It still needs extra work to resolve the physical addresses. Provide a script to facilitate the physical addresses resolving and I/O statistics. Profile with MEM_INST_RETIRED.ALL_LOADS or MEM_UOPS_RETIRED.ALL_LOADS event if any of them is available. Look up the /proc/iomem and resolve the physical address. Provide memory type summary. Here is an example output: # perf script report mem-phys-addr Event: mem_inst_retired.all_loads:P Memory type count percentage ---------------------------------------- ----------- ----------- System RAM 74 53.2% Persistent Memory 55 39.6% N/A --- Changes since V2: - Apply the new license rules. - Add comments for globals Changes since V1: - Do not mix DLA and Load Latency. Do not compare the loads and stores. Only profile the loads. - Use event name to replace the RAW event Signed-off-by: Kan Liang <Kan.liang@intel.com> Reviewed-by: Andi Kleen <ak@linux.intel.com> Cc: Dan Williams <dan.j.williams@intel.com> Cc: Jiri Olsa <jolsa@kernel.org> Cc: Peter Zijlstra <peterz@infradead.org> Cc: Philippe Ombredanne <pombredanne@nexb.com> Cc: Stephane Eranian <eranian@google.com> Link: https://lkml.kernel.org/r/1515099595-34770-1-git-send-email-kan.liang@intel.com Signed-off-by: Arnaldo Carvalho de Melo <acme@redhat.com>
2018-01-05 04:59:55 +08:00
return m[2]
return "N/A"
def process_event(param_dict):
name = param_dict["ev_name"]
sample = param_dict["sample"]
phys_addr = sample["phys_addr"]
global event_name
if event_name == None:
event_name = name
load_mem_type_cnt[find_memory_type(phys_addr)] += 1