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ac4e5a38c8
Context-sensitive profile effectively split a function profile into many copies each representing the CFG profile of a particular calling context. That makes the count distribution looks more flat as we now have more function profiles each with lower counts, which in turn leads to lower hot thresholds. Now we tells threshold computation to merge context profile first before calculating percentile based cutoffs to compensate for seemingly flat context profile. This can be controlled by swtich `sample-profile-contextless-threshold`. Earlier measurement showed ~0.4% perf boost with this tuning on spec2k6 for CSSPGO (with pseudo-probe and new inliner). Differential Revision: https://reviews.llvm.org/D95980
176 lines
6.2 KiB
C++
176 lines
6.2 KiB
C++
//=-- ProfilesummaryBuilder.cpp - Profile summary computation ---------------=//
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//
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// Part of the LLVM Project, under the Apache License v2.0 with LLVM Exceptions.
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// See https://llvm.org/LICENSE.txt for license information.
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// SPDX-License-Identifier: Apache-2.0 WITH LLVM-exception
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//
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//===----------------------------------------------------------------------===//
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//
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// This file contains support for computing profile summary data.
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//
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//===----------------------------------------------------------------------===//
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#include "llvm/IR/Attributes.h"
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#include "llvm/IR/Function.h"
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#include "llvm/IR/Metadata.h"
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#include "llvm/IR/Type.h"
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#include "llvm/ProfileData/InstrProf.h"
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#include "llvm/ProfileData/ProfileCommon.h"
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#include "llvm/ProfileData/SampleProf.h"
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#include "llvm/Support/Casting.h"
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#include "llvm/Support/CommandLine.h"
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using namespace llvm;
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cl::opt<bool> UseContextLessSummary(
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"profile-summary-contextless", cl::Hidden, cl::init(false), cl::ZeroOrMore,
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cl::desc("Merge context profiles before calculating thresholds."));
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// A set of cutoff values. Each value, when divided by ProfileSummary::Scale
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// (which is 1000000) is a desired percentile of total counts.
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static const uint32_t DefaultCutoffsData[] = {
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10000, /* 1% */
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100000, /* 10% */
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200000, 300000, 400000, 500000, 600000, 700000, 800000,
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900000, 950000, 990000, 999000, 999900, 999990, 999999};
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const ArrayRef<uint32_t> ProfileSummaryBuilder::DefaultCutoffs =
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DefaultCutoffsData;
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const ProfileSummaryEntry &
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ProfileSummaryBuilder::getEntryForPercentile(SummaryEntryVector &DS,
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uint64_t Percentile) {
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auto It = partition_point(DS, [=](const ProfileSummaryEntry &Entry) {
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return Entry.Cutoff < Percentile;
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});
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// The required percentile has to be <= one of the percentiles in the
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// detailed summary.
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if (It == DS.end())
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report_fatal_error("Desired percentile exceeds the maximum cutoff");
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return *It;
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}
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void InstrProfSummaryBuilder::addRecord(const InstrProfRecord &R) {
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// The first counter is not necessarily an entry count for IR
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// instrumentation profiles.
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// Eventually MaxFunctionCount will become obsolete and this can be
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// removed.
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addEntryCount(R.Counts[0]);
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for (size_t I = 1, E = R.Counts.size(); I < E; ++I)
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addInternalCount(R.Counts[I]);
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}
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// To compute the detailed summary, we consider each line containing samples as
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// equivalent to a block with a count in the instrumented profile.
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void SampleProfileSummaryBuilder::addRecord(
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const sampleprof::FunctionSamples &FS, bool isCallsiteSample) {
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if (!isCallsiteSample) {
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NumFunctions++;
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if (FS.getHeadSamples() > MaxFunctionCount)
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MaxFunctionCount = FS.getHeadSamples();
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}
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for (const auto &I : FS.getBodySamples())
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addCount(I.second.getSamples());
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for (const auto &I : FS.getCallsiteSamples())
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for (const auto &CS : I.second)
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addRecord(CS.second, true);
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}
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// The argument to this method is a vector of cutoff percentages and the return
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// value is a vector of (Cutoff, MinCount, NumCounts) triplets.
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void ProfileSummaryBuilder::computeDetailedSummary() {
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if (DetailedSummaryCutoffs.empty())
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return;
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llvm::sort(DetailedSummaryCutoffs);
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auto Iter = CountFrequencies.begin();
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const auto End = CountFrequencies.end();
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uint32_t CountsSeen = 0;
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uint64_t CurrSum = 0, Count = 0;
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for (const uint32_t Cutoff : DetailedSummaryCutoffs) {
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assert(Cutoff <= 999999);
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APInt Temp(128, TotalCount);
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APInt N(128, Cutoff);
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APInt D(128, ProfileSummary::Scale);
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Temp *= N;
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Temp = Temp.sdiv(D);
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uint64_t DesiredCount = Temp.getZExtValue();
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assert(DesiredCount <= TotalCount);
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while (CurrSum < DesiredCount && Iter != End) {
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Count = Iter->first;
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uint32_t Freq = Iter->second;
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CurrSum += (Count * Freq);
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CountsSeen += Freq;
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Iter++;
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}
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assert(CurrSum >= DesiredCount);
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ProfileSummaryEntry PSE = {Cutoff, Count, CountsSeen};
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DetailedSummary.push_back(PSE);
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}
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}
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std::unique_ptr<ProfileSummary> SampleProfileSummaryBuilder::getSummary() {
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computeDetailedSummary();
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return std::make_unique<ProfileSummary>(
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ProfileSummary::PSK_Sample, DetailedSummary, TotalCount, MaxCount, 0,
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MaxFunctionCount, NumCounts, NumFunctions);
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}
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std::unique_ptr<ProfileSummary>
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SampleProfileSummaryBuilder::computeSummaryForProfiles(
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const StringMap<sampleprof::FunctionSamples> &Profiles) {
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assert(NumFunctions == 0 &&
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"This can only be called on an empty summary builder");
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StringMap<sampleprof::FunctionSamples> ContextLessProfiles;
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const StringMap<sampleprof::FunctionSamples> *ProfilesToUse = &Profiles;
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// For CSSPGO, context-sensitive profile effectively split a function profile
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// into many copies each representing the CFG profile of a particular calling
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// context. That makes the count distribution looks more flat as we now have
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// more function profiles each with lower counts, which in turn leads to lower
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// hot thresholds. To compensate for that, by defauly we merge context
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// profiles before coumputing profile summary.
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if (UseContextLessSummary || (sampleprof::FunctionSamples::ProfileIsCS &&
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!UseContextLessSummary.getNumOccurrences())) {
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for (const auto &I : Profiles) {
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ContextLessProfiles[I.second.getName()].merge(I.second);
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}
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ProfilesToUse = &ContextLessProfiles;
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}
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for (const auto &I : *ProfilesToUse) {
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const sampleprof::FunctionSamples &Profile = I.second;
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addRecord(Profile);
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}
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return getSummary();
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}
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std::unique_ptr<ProfileSummary> InstrProfSummaryBuilder::getSummary() {
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computeDetailedSummary();
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return std::make_unique<ProfileSummary>(
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ProfileSummary::PSK_Instr, DetailedSummary, TotalCount, MaxCount,
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MaxInternalBlockCount, MaxFunctionCount, NumCounts, NumFunctions);
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}
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void InstrProfSummaryBuilder::addEntryCount(uint64_t Count) {
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NumFunctions++;
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// Skip invalid count.
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if (Count == (uint64_t)-1)
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return;
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addCount(Count);
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if (Count > MaxFunctionCount)
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MaxFunctionCount = Count;
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}
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void InstrProfSummaryBuilder::addInternalCount(uint64_t Count) {
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// Skip invalid count.
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if (Count == (uint64_t)-1)
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return;
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addCount(Count);
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if (Count > MaxInternalBlockCount)
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MaxInternalBlockCount = Count;
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}
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