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llvm-mirror/lib/ProfileData/ProfileSummaryBuilder.cpp

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//=-- ProfilesummaryBuilder.cpp - Profile summary computation ---------------=//
//
// Part of the LLVM Project, under the Apache License v2.0 with LLVM Exceptions.
// See https://llvm.org/LICENSE.txt for license information.
// SPDX-License-Identifier: Apache-2.0 WITH LLVM-exception
//
//===----------------------------------------------------------------------===//
//
// This file contains support for computing profile summary data.
//
//===----------------------------------------------------------------------===//
#include "llvm/IR/Attributes.h"
#include "llvm/IR/Function.h"
#include "llvm/IR/Metadata.h"
#include "llvm/IR/Type.h"
#include "llvm/ProfileData/InstrProf.h"
#include "llvm/ProfileData/ProfileCommon.h"
#include "llvm/ProfileData/SampleProf.h"
#include "llvm/Support/Casting.h"
using namespace llvm;
// A set of cutoff values. Each value, when divided by ProfileSummary::Scale
// (which is 1000000) is a desired percentile of total counts.
static const uint32_t DefaultCutoffsData[] = {
10000, /* 1% */
100000, /* 10% */
200000, 300000, 400000, 500000, 600000, 700000, 800000,
900000, 950000, 990000, 999000, 999900, 999990, 999999};
const ArrayRef<uint32_t> ProfileSummaryBuilder::DefaultCutoffs =
DefaultCutoffsData;
const ProfileSummaryEntry &
ProfileSummaryBuilder::getEntryForPercentile(SummaryEntryVector &DS,
uint64_t Percentile) {
auto It = partition_point(DS, [=](const ProfileSummaryEntry &Entry) {
return Entry.Cutoff < Percentile;
});
// The required percentile has to be <= one of the percentiles in the
// detailed summary.
if (It == DS.end())
report_fatal_error("Desired percentile exceeds the maximum cutoff");
return *It;
}
void InstrProfSummaryBuilder::addRecord(const InstrProfRecord &R) {
// The first counter is not necessarily an entry count for IR
// instrumentation profiles.
// Eventually MaxFunctionCount will become obsolete and this can be
// removed.
addEntryCount(R.Counts[0]);
for (size_t I = 1, E = R.Counts.size(); I < E; ++I)
addInternalCount(R.Counts[I]);
}
// To compute the detailed summary, we consider each line containing samples as
// equivalent to a block with a count in the instrumented profile.
void SampleProfileSummaryBuilder::addRecord(
const sampleprof::FunctionSamples &FS, bool isCallsiteSample) {
if (!isCallsiteSample) {
NumFunctions++;
if (FS.getHeadSamples() > MaxFunctionCount)
MaxFunctionCount = FS.getHeadSamples();
}
for (const auto &I : FS.getBodySamples())
addCount(I.second.getSamples());
for (const auto &I : FS.getCallsiteSamples())
for (const auto &CS : I.second)
addRecord(CS.second, true);
}
// The argument to this method is a vector of cutoff percentages and the return
// value is a vector of (Cutoff, MinCount, NumCounts) triplets.
void ProfileSummaryBuilder::computeDetailedSummary() {
if (DetailedSummaryCutoffs.empty())
return;
llvm::sort(DetailedSummaryCutoffs);
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auto Iter = CountFrequencies.begin();
const auto End = CountFrequencies.end();
uint32_t CountsSeen = 0;
uint64_t CurrSum = 0, Count = 0;
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for (const uint32_t Cutoff : DetailedSummaryCutoffs) {
assert(Cutoff <= 999999);
APInt Temp(128, TotalCount);
APInt N(128, Cutoff);
APInt D(128, ProfileSummary::Scale);
Temp *= N;
Temp = Temp.sdiv(D);
uint64_t DesiredCount = Temp.getZExtValue();
assert(DesiredCount <= TotalCount);
while (CurrSum < DesiredCount && Iter != End) {
Count = Iter->first;
uint32_t Freq = Iter->second;
CurrSum += (Count * Freq);
CountsSeen += Freq;
Iter++;
}
assert(CurrSum >= DesiredCount);
ProfileSummaryEntry PSE = {Cutoff, Count, CountsSeen};
DetailedSummary.push_back(PSE);
}
}
std::unique_ptr<ProfileSummary> SampleProfileSummaryBuilder::getSummary() {
computeDetailedSummary();
return std::make_unique<ProfileSummary>(
ProfileSummary::PSK_Sample, DetailedSummary, TotalCount, MaxCount, 0,
MaxFunctionCount, NumCounts, NumFunctions);
}
std::unique_ptr<ProfileSummary> InstrProfSummaryBuilder::getSummary() {
computeDetailedSummary();
return std::make_unique<ProfileSummary>(
ProfileSummary::PSK_Instr, DetailedSummary, TotalCount, MaxCount,
MaxInternalBlockCount, MaxFunctionCount, NumCounts, NumFunctions);
}
void InstrProfSummaryBuilder::addEntryCount(uint64_t Count) {
NumFunctions++;
Supplement instr profile with sample profile. PGO profile is usually more precise than sample profile. However, PGO profile needs to be collected from loadtest and loadtest may not be representative enough to the production workload. Sample profile collected from production can be used as a supplement -- for functions cold in loadtest but warm/hot in production, we can scale up the related function in PGO profile if the function is warm or hot in sample profile. The implementation contains changes in compiler side and llvm-profdata side. Given an instr profile and a sample profile, for a function cold in PGO profile but warm/hot in sample profile, llvm-profdata will either mark all the counters in the profile to be -1 or scale up the max count in the function to be above hot threshold, depending on the zero counter ratio in the profile. The assumption is if there are too many counters being zero in the function profile, the profile is more likely to cause harm than good, then llvm-profdata will mark all the counters to be -1 indicating the function is hot but the profile is unaccountable. In compiler side, if a function profile with all -1 counters is seen, the function entry count will be set to be above hot threshold but its internal profile will be dropped. In the long run, it may be useful to let compiler support using PGO profile and sample profile at the same time, but that requires more careful design and more substantial changes to make two profiles work seamlessly. The patch here serves as a simple intermediate solution. Differential Revision: https://reviews.llvm.org/D81981
2020-07-09 00:19:44 +02:00
// Skip invalid count.
if (Count == (uint64_t)-1)
return;
addCount(Count);
if (Count > MaxFunctionCount)
MaxFunctionCount = Count;
}
void InstrProfSummaryBuilder::addInternalCount(uint64_t Count) {
Supplement instr profile with sample profile. PGO profile is usually more precise than sample profile. However, PGO profile needs to be collected from loadtest and loadtest may not be representative enough to the production workload. Sample profile collected from production can be used as a supplement -- for functions cold in loadtest but warm/hot in production, we can scale up the related function in PGO profile if the function is warm or hot in sample profile. The implementation contains changes in compiler side and llvm-profdata side. Given an instr profile and a sample profile, for a function cold in PGO profile but warm/hot in sample profile, llvm-profdata will either mark all the counters in the profile to be -1 or scale up the max count in the function to be above hot threshold, depending on the zero counter ratio in the profile. The assumption is if there are too many counters being zero in the function profile, the profile is more likely to cause harm than good, then llvm-profdata will mark all the counters to be -1 indicating the function is hot but the profile is unaccountable. In compiler side, if a function profile with all -1 counters is seen, the function entry count will be set to be above hot threshold but its internal profile will be dropped. In the long run, it may be useful to let compiler support using PGO profile and sample profile at the same time, but that requires more careful design and more substantial changes to make two profiles work seamlessly. The patch here serves as a simple intermediate solution. Differential Revision: https://reviews.llvm.org/D81981
2020-07-09 00:19:44 +02:00
// Skip invalid count.
if (Count == (uint64_t)-1)
return;
addCount(Count);
if (Count > MaxInternalBlockCount)
MaxInternalBlockCount = Count;
}