The goal is to allow grafting an inline tree from Clang or GCC into a new compilation without affecting other functions. For GCC, we're doing this by extracting the inline tree from dwarf information and generating the equivalent remarks.
This allows easier side-by-side asm analysis and a trial way to see if a particular inlining setup provides benefits by itself.
Testing:
ninja check-all
Reviewed By: wenlei, mtrofin
Differential Revision: https://reviews.llvm.org/D110658
Fix a bug when getInlineCost incorrectly returns a
cost/threshold pair instead of an explicit never inline.
Reviewed By: mtrofin
Differential Revision: https://reviews.llvm.org/D111687
This patch continues unblocking optimizations that are blocked by pseudo probe instrumentation.
Not exactly like DbgIntrinsics, PseudoProbe intrinsic has other attributes (such as mayread, maywrite, mayhaveSideEffect) that can block optimizations. The issues fixed are:
- Flipped default param of getFirstNonPHIOrDbg API to skip pseudo probes
- Unblocked CSE by avoiding pseudo probe from clobbering memory SSA
- Unblocked induction variable simpliciation
- Allow empty loop deletion by treating probe intrinsic isDroppable
- Some refactoring.
Reviewed By: wenlei
Differential Revision: https://reviews.llvm.org/D110847
Currenlty PseudoProbeInserter is a pass conditioned on a target switch. It works well with a single clang invocation. It doesn't work so well when the backend is called separately (i.e, through the linker or llc), where user has always to pass -pseudo-probe-for-profiling explictly. I'm making the pass a default pass that requires no command line arg to trigger, but will be actually run depending on whether the CU comes with `llvm.pseudo_probe_desc` metadata.
Reviewed By: wenlei
Differential Revision: https://reviews.llvm.org/D110209
A couple tweaks to
1. allow more thinlto importing by excluding probe intrinsics from IR size in module summary
2. Allow general default attributes (nofree nosync nounwind) for pseudo probe intrinsic. Without those attributes, pseudo probes will be basically treated as unknown calls which will in turn block their containing functions from annotated with those attributes.
Reviewed By: wenlei
Differential Revision: https://reviews.llvm.org/D109976
When pre-inliner decision is used for CSSPGO, we should take that into account for ThinLTO importing as well, so post-link sample loader inliner can favor that decision. This is handled by a small tweak in this patch. It also includes a change to transfer preinliner decision when merging context.
Differential Revision: https://reviews.llvm.org/D109088
When pre-inliner decision is used for CSSPGO, we should take that into account for ThinLTO importing as well, so post-link sample loader inliner can favor that decision. This is handled by a small tweak in this patch. It also includes a change to transfer preinliner decision when merging context.
Differential Revision: https://reviews.llvm.org/D109088
For CSSPGO, turn on `sample-profile-use-preinliner` by default. This simplifies the use of llvm-profgen preinliner as it's now simply driven by ContextShouldBeInlined flag for each context profile without needing extra compiler switch.
Note that llvm-profgen's preinliner is still off by default, under switch `csspgo-preinliner`.
Differential Revision: https://reviews.llvm.org/D109111
With the context split work, the context-based (an array of strings) sorting performed at profile load time is way more expansive than single-string-based sorting. This is likely due to auxiliary operations done on each array element, such as indirect references, std::min operations, also likely cache misses. In this change I'm presorting profiles during profile generation time to avoid sorting at compile time.
Compared to the previous context-split work, this effectively cuts down compile time by 20% for one of our large services and brings us closer to non-CS build, with still a small gap in build time.
Reviewed By: wenlei, wmi
Differential Revision: https://reviews.llvm.org/D109036
Adding the compiler support of MD5 CS profile based on pervious context split work D107299. A MD5 CS profile is about 40% smaller than the string-based extbinary profile. As a result, the compilation is 15% faster.
There are a few conversion from real names to md5 names that have been made on the sample loader and context tracker side to get it work.
Reviewed By: wenlei, wmi
Differential Revision: https://reviews.llvm.org/D108342
Currently context strings contain a lot of duplicated function names and that significantly increase the profile size. This change split the context into a series of {name, offset, discriminator} tuples so function names used in the context can be replaced by the index into the name table and that significantly reduce the size consumed by context.
A follow-up improvement made in the compiler and profiling tools is to avoid reconstructing full context strings which is time- and memory- consuming. Instead a context vector of `StringRef` is adopted to represent the full context in all scenarios. As a result, the previous prevalent profile map which was implemented as a `StringRef` is now engineered as an unordered map keyed by `SampleContext`. `SampleContext` is reshaped to using an `ArrayRef` to represent a full context for CS profile. For non-CS profile, it falls back to use `StringRef` to represent a contextless function name. Both the `ArrayRef` and `StringRef` objects are underpinned by real array and string objects that are stored in producer buffers. For compiler, they are maintained by the sample reader. For llvm-profgen, they are maintained in `ProfiledBinary` and `ProfileGenerator`. Full context strings can be generated only in those cases of debugging and printing.
When it comes to profile format, nothing has changed to the text format, though internally CS context is implemented as a vector. Extbinary format is only changed for CS profile, with an additional `SecCSNameTable` section which stores all full contexts logically in the form of `vector<int>`, which each element as an offset points to `SecNameTable`. All occurrences of contexts elsewhere are redirected to using the offset of `SecCSNameTable`.
Testing
This is no-diff change in terms of code quality and profile content (for text profile).
For our internal large service (aka ads), the profile generation is cut to half, with a 20x smaller string-based extbinary format generated.
The compile time of ads is dropped by 25%.
Differential Revision: https://reviews.llvm.org/D107299
The change adds a switch to allow sample loader to use global pre-inliner's decision instead. The pre-inliner in llvm-profgen makes inline decision globally based on whole program profile and function byte size as cost proxy.
Since pre-inliner also adjusts/merges context profile based on its inline decision, honoring its inline decision in sample loader would lead to better post-inline profile quality especially for thinlto where cross module profile merging isn't possible without pre-inliner.
Minor fix in profile reader is also included. When pre-inliner is use, we now also turn off the default merging and trimming logic unless it's explicitly asked.
Differential Revision: https://reviews.llvm.org/D108677
Previoulsy debug-info-for-profiling and pseudo-probe-for-profiling are mutual exclusive because they compete the dwarf discrimnator for callsites on the IR. This changes allows to use the two switches together. The side effect is that callsite discriminators will be taken by pseudo probe, while discriminators for other instructions are still available for AutoFDO use. This is less than ideal, however, it still allows us a chance to smoothly transition from AutoFDO to CSSPGO, by collecting both profiles from a CSSPGO binary.
Reviewed By: wenlei, wmi
Differential Revision: https://reviews.llvm.org/D107876
Clang diagnostics refer to identifier names in quotes.
This patch makes inline remarks conform to the convention.
New behavior:
```
% clang -O2 -Rpass=inline -Rpass-missed=inline -S a.c
a.c:4:25: remark: 'foo' inlined into 'bar' with (cost=-30, threshold=337) at callsite bar:0:25; [-Rpass=inline]
int bar(int a) { return foo(a); }
^
```
Reviewed By: hoy
Differential Revision: https://reviews.llvm.org/D107791
This change slightly relaxed the current ICP threshold in top-down inliner, specifically always allow one ICP for it. It shows some perf improvements on SPEC and our internal benchmarks. Also renamed the previous flag. We can also try to turn off PGO ICP in the future.
Reviewed By: wenlei, hoy, wmi
Differential Revision: https://reviews.llvm.org/D106588
This patch teaches the sample profile loader to merge function
attributes after inlining functions.
Without this patch, the compiler could inline a function requiring the
512-bit vector width into its caller without merging function
attributes, triggering a failure during instruction selection.
Differential Revision: https://reviews.llvm.org/D105729
As a follow-up to https://reviews.llvm.org/D104129, I'm cleaning up the danling probe related code in both the compiler and llvm-profgen.
I'm seeing a 5% size win for the pseudo_probe section for SPEC2017 and 10% for Ciner. Certain benchmark such as 602.gcc has a 20% size win. No obvious difference seen on build time for SPEC2017 and Cinder.
Reviewed By: wenlei
Differential Revision: https://reviews.llvm.org/D104477
Previously dangling samples were represented by INT64_MAX in sample profile while probes never executed were not reported. This was based on an observation that dangling probes were only at a smaller portion than zero-count probes. However, with compiler optimizations, dangling probes end up becoming at large portion of all probes in general and reporting them does not make sense from profile size point of view. This change flips sample reporting by reporting zero-count probes instead. This enabled dangling probe to be represented by none (missing entry in profile). This has a couple benefits:
1. Reducing sample profile size in optimize mode, even when the number of non-executed probes outperform the number of dangling probes, since INT64_MAX takes more space over 0 to encode.
2. Binary size savings. No need to encode dangling probe anymore, since missing probes are treated as dangling in the profile reader.
3. Reducing compiler work to track dangling probes. However, for probes that are real dead and removed, we still need the compiler to identify them so that they can be reported as zero-count, instead of mistreated as dangling probes.
4. Improving counts quality by respecting the counts already collected on the non-dangling copy of a probe. A probe, when duplicated, gets two copies at runtime. If one of them is dangling while the other is not, merging the two probes at profile generation time will cause the real samples collected on the non-dangling one to be discarded. Not reporting the dangling counterpart will keep the real samples.
5. Better readability.
6. Be consistent with non-CS dwarf line number based profile. Zero counts are trusted by the compiler counts inferencer while missing counts will be inferred by the compiler.
Note that the current patch does include any work for #3. There will be follow-up changes.
For #1, I've seen for a large Facebook service, the text profile is reduced by 7%. For extbinary profile, the size of LBRProfileSection is reduced by 35%.
For #4, I have seen general counts quality for SPEC2017 is improved by 10%.
Reviewed By: wenlei, wlei, wmi
Differential Revision: https://reviews.llvm.org/D104129
The current implementation for computing relative block frequencies does
not handle correctly control-flow graphs containing irreducible loops. This
results in suboptimally generated binaries, whose perf can be up to 5%
worse than optimal.
To resolve the problem, we apply a post-processing step, which iteratively
updates block frequencies based on the frequencies of their predesessors.
This corresponds to finding the stationary point of the Markov chain by
an iterative method aka "PageRank computation". The algorithm takes at
most O(|E| * IterativeBFIMaxIterations) steps but typically converges faster.
It is turned on by passing option `use-iterative-bfi-inference`
and applied only for functions containing profile data and irreducible loops.
Tested on SPEC06/17, where it is helping to get correct profile counts for one of
the binaries (403.gcc). In prod binaries, we've seen a speedup of up to 2%-5%
for binaries containing functions with hot irreducible loops.
Reviewed By: hoy, wenlei, davidxl
Differential Revision: https://reviews.llvm.org/D103289
This patch was split from https://reviews.llvm.org/D102246
[SampleFDO] New hierarchical discriminator for Flow Sensitive SampleFDO
This is mainly for ProfileData part of change. It will load
FS Profile when such profile is detected. For an extbinary format profile,
create_llvm_prof tool will add a flag to profile summary section.
For other format profiles, the users need to use an internal option
(-profile-isfs) to tell the compiler that the profile uses FS discriminators.
This patch also simplified the bit API used by FS discriminators.
Differential Revision: https://reviews.llvm.org/D103041
This change tries to fix a place missing `moveAndDanglePseudoProbes `. In FoldValueComparisonIntoPredecessors, it folds the BB into predecessors and then marked the BB unreachable. However, the original logic from the BB is still alive, deleting the probe will mislead the SampleLoader mark it as zero count sample.
Reviewed By: hoy, wenlei
Differential Revision: https://reviews.llvm.org/D102721
Sample profile loader can be run in both LTO prelink and postlink. Currently the counts annoation in postilnk doesn't fully overwrite what's done in prelink. I'm adding a switch (`-overwrite-existing-weights=1`) to enable a full overwrite, which includes:
1. Clear old metadata for calls when their parent block has a zero count. This could be caused by prelink code duplication.
2. Clear indirect call metadata if somehow all the rest targets have a sum of zero count.
3. Overwrite branch weight for basic blocks.
With a CS profile, I was seeing #1 and #2 help reduce code size by preventing post-sample ICP and CGSCC inliner working on obsolete metadata, which come from a partial global inlining in prelink. It's not expected to work well for non-CS case with a less-accurate post-inline count quality.
It's worth calling out that some prelink optimizations can damage counts quality in an irreversible way. One example is the loop rotate optimization. Due to lack of exact loop entry count (profiling can only give loop iteration count and loop exit count), moving one iteration out of the loop body leaves the rest iteration count unknown. We had to turn off prelink loop rotate to achieve a better postlink counts quality. A even better postlink counts quality can be archived by turning off prelink CGSCC inlining which is not context-sensitive.
Reviewed By: wenlei, wmi
Differential Revision: https://reviews.llvm.org/D102537
With prelink inlining, pseudo probes with same ID can come from different inline contexts. Such probes should not share samples and their factors should be fixed up separately.
I'm seeing 0.3% speedup for SPEC2017 overall. Benchmark 631.deepsjeng_s benefits the most, about 4%.
Reviewed By: wenlei, wmi
Differential Revision: https://reviews.llvm.org/D102429
As a follow-up to D95982, this patch continues unblocking optimizations that are blocked by pseudu probe instrumention.
The optimizations unblocked are:
- In-block load propagation.
- In-block dead store elimination
- Memory copy optimization that turns stores to consecutive memories into a memset.
These optimizations are local to a block, so they shouldn't affect the profile quality.
Reviewed By: wmi
Differential Revision: https://reviews.llvm.org/D100075
While doing speculative execution opt, it conservatively drops all insn's debug info in the merged `ThenBB`(see the loop at line 2384) including the dangling probe. The missing debug info of the dangling probe will cause the wrong inference computation.
So we should avoid dropping the debug info from pseudo probe, this change try to fix this by moving the to-be dangling probe to the merging target BB before the debug info is dropped.
Reviewed By: hoy, wenlei
Differential Revision: https://reviews.llvm.org/D101195
Pseudo probe distribution factor is used to scale down profile samples to avoid misleading the counts inference due to the usage of "maximum" in `getBlockWeight`. For callsites, the scaling down can come from code duplication prior to the sample profile loader (prelink or postlink), or due to the indirect call promotion in sample loader inliner. This patch fixes an issue in sample loader ICP where the leftover indirect callsite scaling down causes the loss of non-promoted call target samples unexpectedly. While the scaling down is to favor BFI/BPI with accurate an callsite count, it doesn't fit in the current distribution factor that represents code duplication changes. Ideally, we would need two factors, one is for code duplication, the other is for ICP. However this seems over complicated. I'm going to trade one usage (callsite counts) for the other (call target counts).
Seeing perf win on one benchmark (mcf) of SPEC2017 with others unchanged.
Reviewed By: wenlei
Differential Revision: https://reviews.llvm.org/D100993
Pseudo probe are currently given a slot index like other regular instructions. This affects register pressure and lifetime weight computation because of enlarged lifetime length with pseudo probe instructions. As a consequence, program could get different code generated w/ and w/o pseudo probes. I'm closing the gap by excluding pseudo probes from stack index and downstream register allocation related passes.
Reviewed By: wmi
Differential Revision: https://reviews.llvm.org/D100334
As a follow-up to D99815, this patch enables the test by using a DAG order instead of a sequential order to mitigate the platform portability issue due to `std:: _Hash_bytes`.
Reviewed By: wenlei, jsji
Differential Revision: https://reviews.llvm.org/D100351
Pseudo probes, when scattered in a block, can be chained dependencies of other regular DAG nodes and block DAG combine optimizations. To fix this, scattered probes in a block are grouped and placed at the beginning of the block. This shouldn't affect the profile quality.
Test Plan:
Reviewed By: wenlei, wmi
Differential Revision: https://reviews.llvm.org/D100002
We see a regression related to low probe factor(0.01) which prevents some callsites being promoted in ICPPass and later cause the missing inline in CGSCC inliner. The root cause is due to redundant(the second) multiplication of the probe factor and this change try to fix it.
`Sum` does multiply a factor right after findCallSamples but later when using as the parameter in setProbeDistributionFactor, it multiplies one again.
This change could get ~2% perf back on mcf benchmark. In mcf, previously the corresponding factor is 1 and it's the recent feature introducing the <1 factor then trigger this bug.
Reviewed By: hoy, wenlei
Differential Revision: https://reviews.llvm.org/D99787
The case start to fail since https://reviews.llvm.org/D99351.
Looks like to me that the node order within Context Profile Tree depends
on the implmementation of std::hash<std::string>.
Unfortunately, the current clang implementation generate different values on
AIX (or for all big-endian systems?)
On Linux:
main: 2408804140(0x8f936f2c)
external: 896680882(0x357243b2)
externalA: 620231129(0x24f7f9d9)
On AIX:
main: 994322777(0x3b442959)
external: 3548191215(0xd37d19ef)
externalA: 1390365101(0x52df49ad)
XFAIL it first while we discuss and seek for a fix.
Reviewed By: wenlei
Differential Revision: https://reviews.llvm.org/D99815
Currently prof metadata with branch counts is added only for BranchInst and SwitchInst, but not for IndirectBrInst. As a result, BPI/BFI make incorrect inferences for indirect branches, which can be very hot.
This diff adds metadata for IndirectBrInst, in addition to BranchInst and SwitchInst.
Reviewed By: wmi, wenlei
Differential Revision: https://reviews.llvm.org/D99550
Use profiled call edges to augment the top-down order. There are cases that the top-down order computed based on the static call graph doesn't reflect real execution order. For example:
1. Incomplete static call graph due to unknown indirect call targets. Adjusting the order by considering indirect call edges from the profile can enable the inlining of indirect call targets by allowing the caller processed before them.
2. Mutual call edges in an SCC. The static processing order computed for an SCC may not reflect the call contexts in the context-sensitive profile, thus may cause potential inlining to be overlooked. The function order in one SCC is being adjusted to a top-down order based on the profile to favor more inlining.
3. Transitive indirect call edges due to inlining. When a callee function is inlined into into a caller function in LTO prelink, every call edge originated from the callee will be transferred to the caller. If any of the transferred edges is indirect, the original profiled indirect edge, even if considered, would not enforce a top-down order from the caller to the potential indirect call target in LTO postlink since the inlined callee is gone from the static call graph.
4. #3 can happen even for direct call targets, due to functions defined in header files. Header functions, when included into source files, are defined multiple times but only one definition survives due to ODR. Therefore, the LTO prelink inlining done on those dropped definitions can be useless based on a local file scope. More importantly, the inlinee, once fully inlined to a to-be-dropped inliner, will have no profile to consume when its outlined version is compiled. This can lead to a profile-less prelink compilation for the outlined version of the inlinee function which may be called from external modules. while this isn't easy to fix, we rely on the postlink AutoFDO pipeline to optimize the inlinee. Since the survived copy of the inliner (defined in headers) can be inlined in its local scope in prelink, it may not exist in the merged IR in postlink, and we'll need the profiled call edges to enforce a top-down order for the rest of the functions.
Considering those cases, a profiled call graph completely independent of the static call graph is constructed based on profile data, where function objects are not even needed to handle case #3 and case 4.
I'm seeing an average 0.4% perf win out of SPEC2017. For certain benchmark such as Xalanbmk and GCC, the win is bigger, above 2%.
The change is an enhancement to https://reviews.llvm.org/D95988.
Reviewed By: wmi, wenlei
Differential Revision: https://reviews.llvm.org/D99351
during profile update.
When we inline a function and update the profile, the value profiles of the
indirect call in the inliner and inlinee will be scaled. In
https://reviews.llvm.org/D96806 and https://reviews.llvm.org/D97350, we start
using the magic number NOMORE_ICP_MAGICNUM (-1) to mark targets which have
been promoted. The magic number shouldn't be scaled during the profile update.
Although the problem has been suppressed by https://reviews.llvm.org/D98187
for SampleFDO, which stops profile update for inlining in sampleFDO, the patch
is still wanted since it will be more consistent to handle the magic number
properly in profile update.
Differential Revision: https://reviews.llvm.org/D99394
value profile annotated after inlining.
In https://reviews.llvm.org/D96806 and https://reviews.llvm.org/D97350, we
use the magic number -1 in the value profile to avoid repeated indirect call
promotion to the same target for an indirect call. Function updateIDTMetaData
is used to mark an target as being promoted in the value profile with the
magic number. updateIDTMetaData is also used to update the value profile
when an indirect call is inlined and new inline instance profile should be
applied. For the second case, currently updateIDTMetaData mixes up the
existing value profile of the indirect call with the new profile, leading
to the problematic senario that a target count is larger than the total count
in the value profile.
The patch fixes the problem. When updateIDTMetaData is used to update the
value profile after inlining, all the values in the existing value profile
will be dropped except the values with the magic number counts.
Differential Revision: https://reviews.llvm.org/D98835
For ThinLTO's prelink compilation, we need to put external inline candidates into an import list attached to function's entry count metadata. This enables ThinLink to treat such cross module callee as hot in summary index, and later helps postlink to import them for profile guided cross module inlining.
For AutoFDO, the import list is retrieved by traversing the nested inlinee functions. For CSSPGO, since profile is flatterned, a few things need to happen for it to work:
- When loading input profile in extended binary format, we need to load all child context profile whose parent is in current module, so context trie for current module includes potential cross module inlinee.
- In order to make the above happen, we need to know whether input profile is CSSPGO profile before start reading function profile, hence a flag for profile summary section is added.
- When searching for cross module inline candidate, we need to walk through the context trie instead of nested inlinee profile (callsite sample of AutoFDO profile).
- Now that we have more accurate counts with CSSPGO, we swtiched to use entry count instead of total count to decided if an external callee is potentially beneficial to inline. This make it consistent with how we determine whether call tagert is potential inline candidate.
Differential Revision: https://reviews.llvm.org/D98590
now -funique-internal-linkage-name flag is available, and we want to flip
it on by default since it is beneficial to have separate sample profiles
for different internal symbols with the same name. As a preparation, we
want to avoid regression caused by the flip.
When we flip -funique-internal-linkage-name on, the profile is collected
from binary built without -funique-internal-linkage-name so it has no uniq
suffix, but the IR in the optimized build contains the suffix. This kind of
mismatch may introduce transient regression.
To avoid such mismatch, we introduce a NameTable section flag indicating
whether there is any name in the profile containing uniq suffix. Compiler
will decide whether to keep uniq suffix during name canonicalization
depending on the NameTable section flag. The flag is only available for
extbinary format. For other formats, by default compiler will keep uniq
suffix so they will only experience transient regression when
-funique-internal-linkage-name is just flipped.
Another type of regression is caused by places where we miss to call
getCanonicalFnName. Those places are fixed.
Differential Revision: https://reviews.llvm.org/D96932
sample loader pass.
In https://reviews.llvm.org/rG5fb65c02ca5e91e7e1a00e0efdb8edc899f3e4b9,
to prevent repeated indirect call promotion for the same indirect call
and the same target, we used zero-count value profile to indicate an
indirect call has been promoted for a certain target. We removed
PromotedInsns cache in the same patch. However, there was a problem in
that patch described below, and that problem led me to add PromotedInsns
back as a mitigation in
https://reviews.llvm.org/rG4ffad1fb489f691825d6c7d78e1626de142f26cf.
When we get value profile from metadata by calling getValueProfDataFromInst,
we need to specify the maximum possible number of values we expect to read.
We uses MaxNumPromotions in the last patch so the maximum number of value
information extracted from metadata is MaxNumPromotions. If we have many
values including zero-count values when we write the metadata, some of them
will be dropped when we read them because we only read MaxNumPromotions
values. It will allow repeated indirect call promotion again. We need to
make sure if there are values indicating promoted targets, those values need
to be saved in metadata with higher priority than other values.
The patch fixed that problem. We change to use -1 to represent the count
of a promoted target instead of 0 so it is easier to sort the values.
When we prepare to update the metadata in updateIDTMetaData, we will sort
the values in the descending count order and extract only MaxNumPromotions
values to write into metadata. Since -1 is the max uint64_t number, if we
have equal to or less than MaxNumPromotions of -1 count values, they will
all be kept in metadata. If we have more than MaxNumPromotions of -1 count
values, we will only save MaxNumPromotions such values maximally. In such
case, we have logic in place in doesHistoryAllowICP to guarantee no more
promotion in sample loader pass will happen for the indirect call, because
it has been promoted enough.
With this change, now we can remove PromotedInsns without problem.
Differential Revision: https://reviews.llvm.org/D97350
Same dangling probes are redundant since they all have the same semantic that is to rely on the counts inference tool to get reasonable count for the same original block. Therefore, there's no need to keep multiple copies of them. I've seen jump threading created tons of redundant dangling probes that slowed down the compiler dramatically. Other optimization passes can also result in redundant probes though without an observed impact so far.
This change removes block-wise redundant dangling probes specifically introduced by jump threading. To support removing redundant dangling probes caused by all other passes, a final function-wise deduplication is also added.
An 18% size win of the .pseudo_probe section was seen for SPEC2017. No performance difference was observed.
Differential Revision: https://reviews.llvm.org/D97482
This change fixes a couple places where the pseudo probe intrinsic blocks optimizations because they are not naturally removable. To unblock those optimizations, the blocking pseudo probes are moved out of the original blocks and tagged dangling, instead of allowing pseudo probes to be literally removed. The reason is that when the original block is removed, we won't be able to sample it. Instead of assigning it a zero weight, moving all its pseudo probes into another block and marking them dangling should allow the counts inference a chance to assign them a more reasonable weight. We have not seen counts quality degradation from our experiments.
The optimizations being unblocked are:
1. Removing conditional probes for if-converted branches. Conditional probes are tagged dangling when their homing branch arms are folded so that they will not be over-counted.
2. Unblocking jump threading from removing empty blocks. Pseudo probe prevents jump threading from removing logically empty blocks that only has one unconditional jump instructions.
3. Unblocking SimplifyCFG and MIR tail duplicate to thread empty blocks and blocks with redundant branch checks.
Since dangling probes are logically deleted, they should not consume any samples in LTO postLink. This can be achieved by setting their distribution factors to zero when dangled.
Reviewed By: wmi
Differential Revision: https://reviews.llvm.org/D97481
Dangling probes are the probes associated to an empty block. This usually happens when all real instructions are optimized away from the block. There is a problem with dangling probes during the offline counts processing. The way the sample profiler works is that samples collected on the first physical instruction following a probe will be counted towards the probe. This logically equals to treating the instruction next to a probe as if it is from the same block of the probe. In the dangling probe case, the real instruction following a dangling probe actually starts a new block, and samples collected on the new block may cause issues when counted towards the empty block.
To mitigate this issue, we first try to move around a dangling probe inside its owning block. If there are still native instructions preceding the probe in the same block, we can then use them as a place holder to collect samples for the probe. A pass is added to walk each block backwards looking for probes not followed by any real instruction and moving them before the first real instruction. This is done right before the object emission.
If we are unlucky to find such in-block preceding instructions for a probe, the solution we are taking is to tag such probe as dangling so that the samples reported for them will not be trusted by the compiler. We leave it up to the counts inference algorithm to get such probes a reasonable count. The number `UINT64_MAX` is used to mark sample count as collected for a dangling probe.
Reviewed By: wmi
Differential Revision: https://reviews.llvm.org/D95962
Bjorn Pettersson