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llvm-project/llvm/lib/CodeGen/SelectOptimize.cpp

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//===--- SelectOptimize.cpp - Convert select to branches if profitable ---===//
//
// 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 pass converts selects to conditional jumps when profitable.
//
//===----------------------------------------------------------------------===//
#include "llvm/ADT/Optional.h"
#include "llvm/ADT/SmallVector.h"
#include "llvm/ADT/Statistic.h"
#include "llvm/Analysis/BlockFrequencyInfo.h"
#include "llvm/Analysis/BranchProbabilityInfo.h"
#include "llvm/Analysis/LoopInfo.h"
#include "llvm/Analysis/OptimizationRemarkEmitter.h"
#include "llvm/Analysis/ProfileSummaryInfo.h"
#include "llvm/Analysis/TargetTransformInfo.h"
#include "llvm/CodeGen/Passes.h"
#include "llvm/CodeGen/TargetLowering.h"
#include "llvm/CodeGen/TargetPassConfig.h"
#include "llvm/CodeGen/TargetSchedule.h"
#include "llvm/CodeGen/TargetSubtargetInfo.h"
#include "llvm/IR/BasicBlock.h"
#include "llvm/IR/Dominators.h"
#include "llvm/IR/Function.h"
#include "llvm/IR/IRBuilder.h"
#include "llvm/IR/Instruction.h"
#include "llvm/InitializePasses.h"
#include "llvm/Pass.h"
#include "llvm/Support/ScaledNumber.h"
#include "llvm/Target/TargetMachine.h"
#include "llvm/Transforms/Utils/SizeOpts.h"
#include <algorithm>
#include <memory>
#include <queue>
#include <stack>
#include <string>
using namespace llvm;
#define DEBUG_TYPE "select-optimize"
STATISTIC(NumSelectOptAnalyzed,
"Number of select groups considered for conversion to branch");
STATISTIC(NumSelectConvertedExpColdOperand,
"Number of select groups converted due to expensive cold operand");
STATISTIC(NumSelectConvertedHighPred,
"Number of select groups converted due to high-predictability");
STATISTIC(NumSelectUnPred,
"Number of select groups not converted due to unpredictability");
STATISTIC(NumSelectColdBB,
"Number of select groups not converted due to cold basic block");
STATISTIC(NumSelectConvertedLoop,
"Number of select groups converted due to loop-level analysis");
STATISTIC(NumSelectsConverted, "Number of selects converted");
static cl::opt<unsigned> ColdOperandThreshold(
"cold-operand-threshold",
cl::desc("Maximum frequency of path for an operand to be considered cold."),
cl::init(20), cl::Hidden);
static cl::opt<unsigned> ColdOperandMaxCostMultiplier(
"cold-operand-max-cost-multiplier",
cl::desc("Maximum cost multiplier of TCC_expensive for the dependence "
"slice of a cold operand to be considered inexpensive."),
cl::init(1), cl::Hidden);
static cl::opt<unsigned>
GainGradientThreshold("select-opti-loop-gradient-gain-threshold",
cl::desc("Gradient gain threshold (%)."),
cl::init(25), cl::Hidden);
static cl::opt<unsigned>
GainCycleThreshold("select-opti-loop-cycle-gain-threshold",
cl::desc("Minimum gain per loop (in cycles) threshold."),
cl::init(4), cl::Hidden);
static cl::opt<unsigned> GainRelativeThreshold(
"select-opti-loop-relative-gain-threshold",
cl::desc(
"Minimum relative gain per loop threshold (1/X). Defaults to 12.5%"),
cl::init(8), cl::Hidden);
static cl::opt<unsigned> MispredictDefaultRate(
"mispredict-default-rate", cl::Hidden, cl::init(25),
cl::desc("Default mispredict rate (initialized to 25%)."));
static cl::opt<bool>
DisableLoopLevelHeuristics("disable-loop-level-heuristics", cl::Hidden,
cl::init(false),
cl::desc("Disable loop-level heuristics."));
namespace {
class SelectOptimize : public FunctionPass {
const TargetMachine *TM = nullptr;
const TargetSubtargetInfo *TSI;
const TargetLowering *TLI = nullptr;
const TargetTransformInfo *TTI = nullptr;
const LoopInfo *LI;
DominatorTree *DT;
std::unique_ptr<BlockFrequencyInfo> BFI;
std::unique_ptr<BranchProbabilityInfo> BPI;
ProfileSummaryInfo *PSI;
OptimizationRemarkEmitter *ORE;
TargetSchedModel TSchedModel;
public:
static char ID;
SelectOptimize() : FunctionPass(ID) {
initializeSelectOptimizePass(*PassRegistry::getPassRegistry());
}
bool runOnFunction(Function &F) override;
void getAnalysisUsage(AnalysisUsage &AU) const override {
AU.addRequired<ProfileSummaryInfoWrapperPass>();
AU.addRequired<TargetPassConfig>();
AU.addRequired<TargetTransformInfoWrapperPass>();
AU.addRequired<DominatorTreeWrapperPass>();
AU.addRequired<LoopInfoWrapperPass>();
AU.addRequired<OptimizationRemarkEmitterWrapperPass>();
}
private:
// Select groups consist of consecutive select instructions with the same
// condition.
using SelectGroup = SmallVector<SelectInst *, 2>;
using SelectGroups = SmallVector<SelectGroup, 2>;
using Scaled64 = ScaledNumber<uint64_t>;
struct CostInfo {
/// Predicated cost (with selects as conditional moves).
Scaled64 PredCost;
/// Non-predicated cost (with selects converted to branches).
Scaled64 NonPredCost;
};
// Converts select instructions of a function to conditional jumps when deemed
// profitable. Returns true if at least one select was converted.
bool optimizeSelects(Function &F);
// Heuristics for determining which select instructions can be profitably
// conveted to branches. Separate heuristics for selects in inner-most loops
// and the rest of code regions (base heuristics for non-inner-most loop
// regions).
void optimizeSelectsBase(Function &F, SelectGroups &ProfSIGroups);
void optimizeSelectsInnerLoops(Function &F, SelectGroups &ProfSIGroups);
// Converts to branches the select groups that were deemed
// profitable-to-convert.
void convertProfitableSIGroups(SelectGroups &ProfSIGroups);
// Splits selects of a given basic block into select groups.
void collectSelectGroups(BasicBlock &BB, SelectGroups &SIGroups);
// Determines for which select groups it is profitable converting to branches
// (base and inner-most-loop heuristics).
void findProfitableSIGroupsBase(SelectGroups &SIGroups,
SelectGroups &ProfSIGroups);
void findProfitableSIGroupsInnerLoops(const Loop *L, SelectGroups &SIGroups,
SelectGroups &ProfSIGroups);
// Determines if a select group should be converted to a branch (base
// heuristics).
bool isConvertToBranchProfitableBase(const SmallVector<SelectInst *, 2> &ASI);
// Returns true if there are expensive instructions in the cold value
// operand's (if any) dependence slice of any of the selects of the given
// group.
bool hasExpensiveColdOperand(const SmallVector<SelectInst *, 2> &ASI);
// For a given source instruction, collect its backwards dependence slice
// consisting of instructions exclusively computed for producing the operands
// of the source instruction.
void getExclBackwardsSlice(Instruction *I, std::stack<Instruction *> &Slice,
bool ForSinking = false);
// Returns true if the condition of the select is highly predictable.
bool isSelectHighlyPredictable(const SelectInst *SI);
// Loop-level checks to determine if a non-predicated version (with branches)
// of the given loop is more profitable than its predicated version.
bool checkLoopHeuristics(const Loop *L, const CostInfo LoopDepth[2]);
// Computes instruction and loop-critical-path costs for both the predicated
// and non-predicated version of the given loop.
bool computeLoopCosts(const Loop *L, const SelectGroups &SIGroups,
DenseMap<const Instruction *, CostInfo> &InstCostMap,
CostInfo *LoopCost);
// Returns a set of all the select instructions in the given select groups.
SmallPtrSet<const Instruction *, 2> getSIset(const SelectGroups &SIGroups);
// Returns the latency cost of a given instruction.
Optional<uint64_t> computeInstCost(const Instruction *I);
// Returns the misprediction cost of a given select when converted to branch.
Scaled64 getMispredictionCost(const SelectInst *SI, const Scaled64 CondCost);
// Returns the cost of a branch when the prediction is correct.
Scaled64 getPredictedPathCost(Scaled64 TrueCost, Scaled64 FalseCost,
const SelectInst *SI);
// Returns true if the target architecture supports lowering a given select.
bool isSelectKindSupported(SelectInst *SI);
};
} // namespace
char SelectOptimize::ID = 0;
INITIALIZE_PASS_BEGIN(SelectOptimize, DEBUG_TYPE, "Optimize selects", false,
false)
INITIALIZE_PASS_DEPENDENCY(LoopInfoWrapperPass)
INITIALIZE_PASS_DEPENDENCY(DominatorTreeWrapperPass)
INITIALIZE_PASS_DEPENDENCY(ProfileSummaryInfoWrapperPass)
INITIALIZE_PASS_DEPENDENCY(TargetPassConfig)
INITIALIZE_PASS_DEPENDENCY(TargetTransformInfoWrapperPass)
INITIALIZE_PASS_DEPENDENCY(OptimizationRemarkEmitterWrapperPass)
INITIALIZE_PASS_END(SelectOptimize, DEBUG_TYPE, "Optimize selects", false,
false)
FunctionPass *llvm::createSelectOptimizePass() { return new SelectOptimize(); }
bool SelectOptimize::runOnFunction(Function &F) {
TM = &getAnalysis<TargetPassConfig>().getTM<TargetMachine>();
TSI = TM->getSubtargetImpl(F);
TLI = TSI->getTargetLowering();
// If none of the select types is supported then skip this pass.
// This is an optimization pass. Legality issues will be handled by
// instruction selection.
if (!TLI->isSelectSupported(TargetLowering::ScalarValSelect) &&
!TLI->isSelectSupported(TargetLowering::ScalarCondVectorVal) &&
!TLI->isSelectSupported(TargetLowering::VectorMaskSelect))
return false;
TTI = &getAnalysis<TargetTransformInfoWrapperPass>().getTTI(F);
DT = &getAnalysis<DominatorTreeWrapperPass>().getDomTree();
LI = &getAnalysis<LoopInfoWrapperPass>().getLoopInfo();
BPI.reset(new BranchProbabilityInfo(F, *LI));
BFI.reset(new BlockFrequencyInfo(F, *BPI, *LI));
PSI = &getAnalysis<ProfileSummaryInfoWrapperPass>().getPSI();
ORE = &getAnalysis<OptimizationRemarkEmitterWrapperPass>().getORE();
TSchedModel.init(TSI);
// When optimizing for size, selects are preferable over branches.
if (F.hasOptSize() || llvm::shouldOptimizeForSize(&F, PSI, BFI.get()))
return false;
return optimizeSelects(F);
}
bool SelectOptimize::optimizeSelects(Function &F) {
// Determine for which select groups it is profitable converting to branches.
SelectGroups ProfSIGroups;
// Base heuristics apply only to non-loops and outer loops.
optimizeSelectsBase(F, ProfSIGroups);
// Separate heuristics for inner-most loops.
optimizeSelectsInnerLoops(F, ProfSIGroups);
// Convert to branches the select groups that were deemed
// profitable-to-convert.
convertProfitableSIGroups(ProfSIGroups);
// Code modified if at least one select group was converted.
return !ProfSIGroups.empty();
}
void SelectOptimize::optimizeSelectsBase(Function &F,
SelectGroups &ProfSIGroups) {
// Collect all the select groups.
SelectGroups SIGroups;
for (BasicBlock &BB : F) {
// Base heuristics apply only to non-loops and outer loops.
Loop *L = LI->getLoopFor(&BB);
if (L && L->isInnermost())
continue;
collectSelectGroups(BB, SIGroups);
}
// Determine for which select groups it is profitable converting to branches.
findProfitableSIGroupsBase(SIGroups, ProfSIGroups);
}
void SelectOptimize::optimizeSelectsInnerLoops(Function &F,
SelectGroups &ProfSIGroups) {
SmallVector<Loop *, 4> Loops(LI->begin(), LI->end());
// Need to check size on each iteration as we accumulate child loops.
for (unsigned long i = 0; i < Loops.size(); ++i)
for (Loop *ChildL : Loops[i]->getSubLoops())
Loops.push_back(ChildL);
for (Loop *L : Loops) {
if (!L->isInnermost())
continue;
SelectGroups SIGroups;
for (BasicBlock *BB : L->getBlocks())
collectSelectGroups(*BB, SIGroups);
findProfitableSIGroupsInnerLoops(L, SIGroups, ProfSIGroups);
}
}
/// If \p isTrue is true, return the true value of \p SI, otherwise return
/// false value of \p SI. If the true/false value of \p SI is defined by any
/// select instructions in \p Selects, look through the defining select
/// instruction until the true/false value is not defined in \p Selects.
static Value *
getTrueOrFalseValue(SelectInst *SI, bool isTrue,
const SmallPtrSet<const Instruction *, 2> &Selects) {
Value *V = nullptr;
for (SelectInst *DefSI = SI; DefSI != nullptr && Selects.count(DefSI);
DefSI = dyn_cast<SelectInst>(V)) {
assert(DefSI->getCondition() == SI->getCondition() &&
"The condition of DefSI does not match with SI");
V = (isTrue ? DefSI->getTrueValue() : DefSI->getFalseValue());
}
assert(V && "Failed to get select true/false value");
return V;
}
void SelectOptimize::convertProfitableSIGroups(SelectGroups &ProfSIGroups) {
for (SelectGroup &ASI : ProfSIGroups) {
// The code transformation here is a modified version of the sinking
// transformation in CodeGenPrepare::optimizeSelectInst with a more
// aggressive strategy of which instructions to sink.
//
// TODO: eliminate the redundancy of logic transforming selects to branches
// by removing CodeGenPrepare::optimizeSelectInst and optimizing here
// selects for all cases (with and without profile information).
// Transform a sequence like this:
// start:
// %cmp = cmp uge i32 %a, %b
// %sel = select i1 %cmp, i32 %c, i32 %d
//
// Into:
// start:
// %cmp = cmp uge i32 %a, %b
// %cmp.frozen = freeze %cmp
// br i1 %cmp.frozen, label %select.true, label %select.false
// select.true:
// br label %select.end
// select.false:
// br label %select.end
// select.end:
// %sel = phi i32 [ %c, %select.true ], [ %d, %select.false ]
//
// %cmp should be frozen, otherwise it may introduce undefined behavior.
// In addition, we may sink instructions that produce %c or %d into the
// destination(s) of the new branch.
// If the true or false blocks do not contain a sunken instruction, that
// block and its branch may be optimized away. In that case, one side of the
// first branch will point directly to select.end, and the corresponding PHI
// predecessor block will be the start block.
// Find all the instructions that can be soundly sunk to the true/false
// blocks. These are instructions that are computed solely for producing the
// operands of the select instructions in the group and can be sunk without
// breaking the semantics of the LLVM IR (e.g., cannot sink instructions
// with side effects).
SmallVector<std::stack<Instruction *>, 2> TrueSlices, FalseSlices;
typedef std::stack<Instruction *>::size_type StackSizeType;
StackSizeType maxTrueSliceLen = 0, maxFalseSliceLen = 0;
for (SelectInst *SI : ASI) {
// For each select, compute the sinkable dependence chains of the true and
// false operands.
if (auto *TI = dyn_cast<Instruction>(SI->getTrueValue())) {
std::stack<Instruction *> TrueSlice;
getExclBackwardsSlice(TI, TrueSlice, true);
maxTrueSliceLen = std::max(maxTrueSliceLen, TrueSlice.size());
TrueSlices.push_back(TrueSlice);
}
if (auto *FI = dyn_cast<Instruction>(SI->getFalseValue())) {
std::stack<Instruction *> FalseSlice;
getExclBackwardsSlice(FI, FalseSlice, true);
maxFalseSliceLen = std::max(maxFalseSliceLen, FalseSlice.size());
FalseSlices.push_back(FalseSlice);
}
}
// In the case of multiple select instructions in the same group, the order
// of non-dependent instructions (instructions of different dependence
// slices) in the true/false blocks appears to affect performance.
// Interleaving the slices seems to experimentally be the optimal approach.
// This interleaving scheduling allows for more ILP (with a natural downside
// of increasing a bit register pressure) compared to a simple ordering of
// one whole chain after another. One would expect that this ordering would
// not matter since the scheduling in the backend of the compiler would
// take care of it, but apparently the scheduler fails to deliver optimal
// ILP with a naive ordering here.
SmallVector<Instruction *, 2> TrueSlicesInterleaved, FalseSlicesInterleaved;
for (StackSizeType IS = 0; IS < maxTrueSliceLen; ++IS) {
for (auto &S : TrueSlices) {
if (!S.empty()) {
TrueSlicesInterleaved.push_back(S.top());
S.pop();
}
}
}
for (StackSizeType IS = 0; IS < maxFalseSliceLen; ++IS) {
for (auto &S : FalseSlices) {
if (!S.empty()) {
FalseSlicesInterleaved.push_back(S.top());
S.pop();
}
}
}
// We split the block containing the select(s) into two blocks.
SelectInst *SI = ASI.front();
SelectInst *LastSI = ASI.back();
BasicBlock *StartBlock = SI->getParent();
BasicBlock::iterator SplitPt = ++(BasicBlock::iterator(LastSI));
BasicBlock *EndBlock = StartBlock->splitBasicBlock(SplitPt, "select.end");
BFI->setBlockFreq(EndBlock, BFI->getBlockFreq(StartBlock).getFrequency());
// Delete the unconditional branch that was just created by the split.
StartBlock->getTerminator()->eraseFromParent();
// Move any debug/pseudo instructions that were in-between the select
// group to the newly-created end block.
SmallVector<Instruction *, 2> DebugPseudoINS;
auto DIt = SI->getIterator();
while (&*DIt != LastSI) {
if (DIt->isDebugOrPseudoInst())
DebugPseudoINS.push_back(&*DIt);
DIt++;
}
for (auto DI : DebugPseudoINS) {
DI->moveBefore(&*EndBlock->getFirstInsertionPt());
}
// These are the new basic blocks for the conditional branch.
// At least one will become an actual new basic block.
BasicBlock *TrueBlock = nullptr, *FalseBlock = nullptr;
BranchInst *TrueBranch = nullptr, *FalseBranch = nullptr;
if (!TrueSlicesInterleaved.empty()) {
TrueBlock = BasicBlock::Create(LastSI->getContext(), "select.true.sink",
EndBlock->getParent(), EndBlock);
TrueBranch = BranchInst::Create(EndBlock, TrueBlock);
TrueBranch->setDebugLoc(LastSI->getDebugLoc());
for (Instruction *TrueInst : TrueSlicesInterleaved)
TrueInst->moveBefore(TrueBranch);
}
if (!FalseSlicesInterleaved.empty()) {
FalseBlock = BasicBlock::Create(LastSI->getContext(), "select.false.sink",
EndBlock->getParent(), EndBlock);
FalseBranch = BranchInst::Create(EndBlock, FalseBlock);
FalseBranch->setDebugLoc(LastSI->getDebugLoc());
for (Instruction *FalseInst : FalseSlicesInterleaved)
FalseInst->moveBefore(FalseBranch);
}
// If there was nothing to sink, then arbitrarily choose the 'false' side
// for a new input value to the PHI.
if (TrueBlock == FalseBlock) {
assert(TrueBlock == nullptr &&
"Unexpected basic block transform while optimizing select");
FalseBlock = BasicBlock::Create(SI->getContext(), "select.false",
EndBlock->getParent(), EndBlock);
auto *FalseBranch = BranchInst::Create(EndBlock, FalseBlock);
FalseBranch->setDebugLoc(SI->getDebugLoc());
}
// Insert the real conditional branch based on the original condition.
// If we did not create a new block for one of the 'true' or 'false' paths
// of the condition, it means that side of the branch goes to the end block
// directly and the path originates from the start block from the point of
// view of the new PHI.
BasicBlock *TT, *FT;
if (TrueBlock == nullptr) {
TT = EndBlock;
FT = FalseBlock;
TrueBlock = StartBlock;
} else if (FalseBlock == nullptr) {
TT = TrueBlock;
FT = EndBlock;
FalseBlock = StartBlock;
} else {
TT = TrueBlock;
FT = FalseBlock;
}
IRBuilder<> IB(SI);
auto *CondFr =
IB.CreateFreeze(SI->getCondition(), SI->getName() + ".frozen");
IB.CreateCondBr(CondFr, TT, FT, SI);
SmallPtrSet<const Instruction *, 2> INS;
INS.insert(ASI.begin(), ASI.end());
// Use reverse iterator because later select may use the value of the
// earlier select, and we need to propagate value through earlier select
// to get the PHI operand.
for (auto It = ASI.rbegin(); It != ASI.rend(); ++It) {
SelectInst *SI = *It;
// The select itself is replaced with a PHI Node.
PHINode *PN = PHINode::Create(SI->getType(), 2, "", &EndBlock->front());
PN->takeName(SI);
PN->addIncoming(getTrueOrFalseValue(SI, true, INS), TrueBlock);
PN->addIncoming(getTrueOrFalseValue(SI, false, INS), FalseBlock);
PN->setDebugLoc(SI->getDebugLoc());
SI->replaceAllUsesWith(PN);
SI->eraseFromParent();
INS.erase(SI);
++NumSelectsConverted;
}
}
}
void SelectOptimize::collectSelectGroups(BasicBlock &BB,
SelectGroups &SIGroups) {
BasicBlock::iterator BBIt = BB.begin();
while (BBIt != BB.end()) {
Instruction *I = &*BBIt++;
if (SelectInst *SI = dyn_cast<SelectInst>(I)) {
SelectGroup SIGroup;
SIGroup.push_back(SI);
while (BBIt != BB.end()) {
Instruction *NI = &*BBIt;
SelectInst *NSI = dyn_cast<SelectInst>(NI);
if (NSI && SI->getCondition() == NSI->getCondition()) {
SIGroup.push_back(NSI);
} else if (!NI->isDebugOrPseudoInst()) {
// Debug/pseudo instructions should be skipped and not prevent the
// formation of a select group.
break;
}
++BBIt;
}
// If the select type is not supported, no point optimizing it.
// Instruction selection will take care of it.
if (!isSelectKindSupported(SI))
continue;
SIGroups.push_back(SIGroup);
}
}
}
void SelectOptimize::findProfitableSIGroupsBase(SelectGroups &SIGroups,
SelectGroups &ProfSIGroups) {
for (SelectGroup &ASI : SIGroups) {
++NumSelectOptAnalyzed;
if (isConvertToBranchProfitableBase(ASI))
ProfSIGroups.push_back(ASI);
}
}
void SelectOptimize::findProfitableSIGroupsInnerLoops(
const Loop *L, SelectGroups &SIGroups, SelectGroups &ProfSIGroups) {
NumSelectOptAnalyzed += SIGroups.size();
// For each select group in an inner-most loop,
// a branch is more preferable than a select/conditional-move if:
// i) conversion to branches for all the select groups of the loop satisfies
// loop-level heuristics including reducing the loop's critical path by
// some threshold (see SelectOptimize::checkLoopHeuristics); and
// ii) the total cost of the select group is cheaper with a branch compared
// to its predicated version. The cost is in terms of latency and the cost
// of a select group is the cost of its most expensive select instruction
// (assuming infinite resources and thus fully leveraging available ILP).
DenseMap<const Instruction *, CostInfo> InstCostMap;
CostInfo LoopCost[2] = {{Scaled64::getZero(), Scaled64::getZero()},
{Scaled64::getZero(), Scaled64::getZero()}};
if (!computeLoopCosts(L, SIGroups, InstCostMap, LoopCost) ||
!checkLoopHeuristics(L, LoopCost)) {
return;
}
for (SelectGroup &ASI : SIGroups) {
// Assuming infinite resources, the cost of a group of instructions is the
// cost of the most expensive instruction of the group.
Scaled64 SelectCost = Scaled64::getZero(), BranchCost = Scaled64::getZero();
for (SelectInst *SI : ASI) {
SelectCost = std::max(SelectCost, InstCostMap[SI].PredCost);
BranchCost = std::max(BranchCost, InstCostMap[SI].NonPredCost);
}
if (BranchCost < SelectCost) {
OptimizationRemark OR(DEBUG_TYPE, "SelectOpti", ASI.front());
OR << "Profitable to convert to branch (loop analysis). BranchCost="
<< BranchCost.toString() << ", SelectCost=" << SelectCost.toString()
<< ". ";
ORE->emit(OR);
++NumSelectConvertedLoop;
ProfSIGroups.push_back(ASI);
} else {
OptimizationRemarkMissed ORmiss(DEBUG_TYPE, "SelectOpti", ASI.front());
ORmiss << "Select is more profitable (loop analysis). BranchCost="
<< BranchCost.toString()
<< ", SelectCost=" << SelectCost.toString() << ". ";
ORE->emit(ORmiss);
}
}
}
bool SelectOptimize::isConvertToBranchProfitableBase(
const SmallVector<SelectInst *, 2> &ASI) {
SelectInst *SI = ASI.front();
OptimizationRemark OR(DEBUG_TYPE, "SelectOpti", SI);
OptimizationRemarkMissed ORmiss(DEBUG_TYPE, "SelectOpti", SI);
// Skip cold basic blocks. Better to optimize for size for cold blocks.
if (PSI->isColdBlock(SI->getParent(), BFI.get())) {
++NumSelectColdBB;
ORmiss << "Not converted to branch because of cold basic block. ";
ORE->emit(ORmiss);
return false;
}
// If unpredictable, branch form is less profitable.
if (SI->getMetadata(LLVMContext::MD_unpredictable)) {
++NumSelectUnPred;
ORmiss << "Not converted to branch because of unpredictable branch. ";
ORE->emit(ORmiss);
return false;
}
// If highly predictable, branch form is more profitable, unless a
// predictable select is inexpensive in the target architecture.
if (isSelectHighlyPredictable(SI) && TLI->isPredictableSelectExpensive()) {
++NumSelectConvertedHighPred;
OR << "Converted to branch because of highly predictable branch. ";
ORE->emit(OR);
return true;
}
// Look for expensive instructions in the cold operand's (if any) dependence
// slice of any of the selects in the group.
if (hasExpensiveColdOperand(ASI)) {
++NumSelectConvertedExpColdOperand;
OR << "Converted to branch because of expensive cold operand.";
ORE->emit(OR);
return true;
}
ORmiss << "Not profitable to convert to branch (base heuristic).";
ORE->emit(ORmiss);
return false;
}
static InstructionCost divideNearest(InstructionCost Numerator,
uint64_t Denominator) {
return (Numerator + (Denominator / 2)) / Denominator;
}
bool SelectOptimize::hasExpensiveColdOperand(
const SmallVector<SelectInst *, 2> &ASI) {
bool ColdOperand = false;
uint64_t TrueWeight, FalseWeight, TotalWeight;
if (ASI.front()->extractProfMetadata(TrueWeight, FalseWeight)) {
uint64_t MinWeight = std::min(TrueWeight, FalseWeight);
TotalWeight = TrueWeight + FalseWeight;
// Is there a path with frequency <ColdOperandThreshold% (default:20%) ?
ColdOperand = TotalWeight * ColdOperandThreshold > 100 * MinWeight;
} else if (PSI->hasProfileSummary()) {
OptimizationRemarkMissed ORmiss(DEBUG_TYPE, "SelectOpti", ASI.front());
ORmiss << "Profile data available but missing branch-weights metadata for "
"select instruction. ";
ORE->emit(ORmiss);
}
if (!ColdOperand)
return false;
// Check if the cold path's dependence slice is expensive for any of the
// selects of the group.
for (SelectInst *SI : ASI) {
Instruction *ColdI = nullptr;
uint64_t HotWeight;
if (TrueWeight < FalseWeight) {
ColdI = dyn_cast<Instruction>(SI->getTrueValue());
HotWeight = FalseWeight;
} else {
ColdI = dyn_cast<Instruction>(SI->getFalseValue());
HotWeight = TrueWeight;
}
if (ColdI) {
std::stack<Instruction *> ColdSlice;
getExclBackwardsSlice(ColdI, ColdSlice);
InstructionCost SliceCost = 0;
while (!ColdSlice.empty()) {
SliceCost += TTI->getInstructionCost(ColdSlice.top(),
TargetTransformInfo::TCK_Latency);
ColdSlice.pop();
}
// The colder the cold value operand of the select is the more expensive
// the cmov becomes for computing the cold value operand every time. Thus,
// the colder the cold operand is the more its cost counts.
// Get nearest integer cost adjusted for coldness.
InstructionCost AdjSliceCost =
divideNearest(SliceCost * HotWeight, TotalWeight);
if (AdjSliceCost >=
ColdOperandMaxCostMultiplier * TargetTransformInfo::TCC_Expensive)
return true;
}
}
return false;
}
// For a given source instruction, collect its backwards dependence slice
// consisting of instructions exclusively computed for the purpose of producing
// the operands of the source instruction. As an approximation
// (sufficiently-accurate in practice), we populate this set with the
// instructions of the backwards dependence slice that only have one-use and
// form an one-use chain that leads to the source instruction.
void SelectOptimize::getExclBackwardsSlice(Instruction *I,
std::stack<Instruction *> &Slice,
bool ForSinking) {
SmallPtrSet<Instruction *, 2> Visited;
std::queue<Instruction *> Worklist;
Worklist.push(I);
while (!Worklist.empty()) {
Instruction *II = Worklist.front();
Worklist.pop();
// Avoid cycles.
if (Visited.count(II))
continue;
Visited.insert(II);
if (!II->hasOneUse())
continue;
// Cannot soundly sink instructions with side-effects.
// Terminator or phi instructions cannot be sunk.
// Avoid sinking other select instructions (should be handled separetely).
if (ForSinking && (II->isTerminator() || II->mayHaveSideEffects() ||
isa<SelectInst>(II) || isa<PHINode>(II)))
continue;
// Avoid considering instructions with less frequency than the source
// instruction (i.e., avoid colder code regions of the dependence slice).
if (BFI->getBlockFreq(II->getParent()) < BFI->getBlockFreq(I->getParent()))
continue;
// Eligible one-use instruction added to the dependence slice.
Slice.push(II);
// Explore all the operands of the current instruction to expand the slice.
for (unsigned k = 0; k < II->getNumOperands(); ++k)
if (auto *OpI = dyn_cast<Instruction>(II->getOperand(k)))
Worklist.push(OpI);
}
}
bool SelectOptimize::isSelectHighlyPredictable(const SelectInst *SI) {
uint64_t TrueWeight, FalseWeight;
if (SI->extractProfMetadata(TrueWeight, FalseWeight)) {
uint64_t Max = std::max(TrueWeight, FalseWeight);
uint64_t Sum = TrueWeight + FalseWeight;
if (Sum != 0) {
auto Probability = BranchProbability::getBranchProbability(Max, Sum);
if (Probability > TTI->getPredictableBranchThreshold())
return true;
}
}
return false;
}
bool SelectOptimize::checkLoopHeuristics(const Loop *L,
const CostInfo LoopCost[2]) {
// Loop-level checks to determine if a non-predicated version (with branches)
// of the loop is more profitable than its predicated version.
if (DisableLoopLevelHeuristics)
return true;
OptimizationRemarkMissed ORmissL(DEBUG_TYPE, "SelectOpti",
L->getHeader()->getFirstNonPHI());
if (LoopCost[0].NonPredCost > LoopCost[0].PredCost ||
LoopCost[1].NonPredCost >= LoopCost[1].PredCost) {
ORmissL << "No select conversion in the loop due to no reduction of loop's "
"critical path. ";
ORE->emit(ORmissL);
return false;
}
Scaled64 Gain[2] = {LoopCost[0].PredCost - LoopCost[0].NonPredCost,
LoopCost[1].PredCost - LoopCost[1].NonPredCost};
// Profitably converting to branches need to reduce the loop's critical path
// by at least some threshold (absolute gain of GainCycleThreshold cycles and
// relative gain of 12.5%).
if (Gain[1] < Scaled64::get(GainCycleThreshold) ||
Gain[1] * Scaled64::get(GainRelativeThreshold) < LoopCost[1].PredCost) {
Scaled64 RelativeGain = Scaled64::get(100) * Gain[1] / LoopCost[1].PredCost;
ORmissL << "No select conversion in the loop due to small reduction of "
"loop's critical path. Gain="
<< Gain[1].toString()
<< ", RelativeGain=" << RelativeGain.toString() << "%. ";
ORE->emit(ORmissL);
return false;
}
// If the loop's critical path involves loop-carried dependences, the gradient
// of the gain needs to be at least GainGradientThreshold% (defaults to 25%).
// This check ensures that the latency reduction for the loop's critical path
// keeps decreasing with sufficient rate beyond the two analyzed loop
// iterations.
if (Gain[1] > Gain[0]) {
Scaled64 GradientGain = Scaled64::get(100) * (Gain[1] - Gain[0]) /
(LoopCost[1].PredCost - LoopCost[0].PredCost);
if (GradientGain < Scaled64::get(GainGradientThreshold)) {
ORmissL << "No select conversion in the loop due to small gradient gain. "
"GradientGain="
<< GradientGain.toString() << "%. ";
ORE->emit(ORmissL);
return false;
}
}
// If the gain decreases it is not profitable to convert.
else if (Gain[1] < Gain[0]) {
ORmissL
<< "No select conversion in the loop due to negative gradient gain. ";
ORE->emit(ORmissL);
return false;
}
// Non-predicated version of the loop is more profitable than its
// predicated version.
return true;
}
// Computes instruction and loop-critical-path costs for both the predicated
// and non-predicated version of the given loop.
// Returns false if unable to compute these costs due to invalid cost of loop
// instruction(s).
bool SelectOptimize::computeLoopCosts(
const Loop *L, const SelectGroups &SIGroups,
DenseMap<const Instruction *, CostInfo> &InstCostMap, CostInfo *LoopCost) {
const auto &SIset = getSIset(SIGroups);
// Compute instruction and loop-critical-path costs across two iterations for
// both predicated and non-predicated version.
const unsigned Iterations = 2;
for (unsigned Iter = 0; Iter < Iterations; ++Iter) {
// Cost of the loop's critical path.
CostInfo &MaxCost = LoopCost[Iter];
for (BasicBlock *BB : L->getBlocks()) {
for (const Instruction &I : *BB) {
if (I.isDebugOrPseudoInst())
continue;
// Compute the predicated and non-predicated cost of the instruction.
Scaled64 IPredCost = Scaled64::getZero(),
INonPredCost = Scaled64::getZero();
// Assume infinite resources that allow to fully exploit the available
// instruction-level parallelism.
// InstCost = InstLatency + max(Op1Cost, Op2Cost, … OpNCost)
for (const Use &U : I.operands()) {
auto UI = dyn_cast<Instruction>(U.get());
if (!UI)
continue;
if (InstCostMap.count(UI)) {
IPredCost = std::max(IPredCost, InstCostMap[UI].PredCost);
INonPredCost = std::max(INonPredCost, InstCostMap[UI].NonPredCost);
}
}
auto ILatency = computeInstCost(&I);
if (!ILatency.hasValue()) {
OptimizationRemarkMissed ORmissL(DEBUG_TYPE, "SelectOpti", &I);
ORmissL << "Invalid instruction cost preventing analysis and "
"optimization of the inner-most loop containing this "
"instruction. ";
ORE->emit(ORmissL);
return false;
}
IPredCost += Scaled64::get(ILatency.getValue());
INonPredCost += Scaled64::get(ILatency.getValue());
// For a select that can be converted to branch,
// compute its cost as a branch (non-predicated cost).
//
// BranchCost = PredictedPathCost + MispredictCost
// PredictedPathCost = TrueOpCost * TrueProb + FalseOpCost * FalseProb
// MispredictCost = max(MispredictPenalty, CondCost) * MispredictRate
if (SIset.contains(&I)) {
auto SI = dyn_cast<SelectInst>(&I);
Scaled64 TrueOpCost = Scaled64::getZero(),
FalseOpCost = Scaled64::getZero();
if (auto *TI = dyn_cast<Instruction>(SI->getTrueValue()))
if (InstCostMap.count(TI))
TrueOpCost = InstCostMap[TI].NonPredCost;
if (auto *FI = dyn_cast<Instruction>(SI->getFalseValue()))
if (InstCostMap.count(FI))
FalseOpCost = InstCostMap[FI].NonPredCost;
Scaled64 PredictedPathCost =
getPredictedPathCost(TrueOpCost, FalseOpCost, SI);
Scaled64 CondCost = Scaled64::getZero();
if (auto *CI = dyn_cast<Instruction>(SI->getCondition()))
if (InstCostMap.count(CI))
CondCost = InstCostMap[CI].NonPredCost;
Scaled64 MispredictCost = getMispredictionCost(SI, CondCost);
INonPredCost = PredictedPathCost + MispredictCost;
}
InstCostMap[&I] = {IPredCost, INonPredCost};
MaxCost.PredCost = std::max(MaxCost.PredCost, IPredCost);
MaxCost.NonPredCost = std::max(MaxCost.NonPredCost, INonPredCost);
}
}
}
return true;
}
SmallPtrSet<const Instruction *, 2>
SelectOptimize::getSIset(const SelectGroups &SIGroups) {
SmallPtrSet<const Instruction *, 2> SIset;
for (const SelectGroup &ASI : SIGroups)
for (const SelectInst *SI : ASI)
SIset.insert(SI);
return SIset;
}
Optional<uint64_t> SelectOptimize::computeInstCost(const Instruction *I) {
InstructionCost ICost =
TTI->getInstructionCost(I, TargetTransformInfo::TCK_Latency);
if (auto OC = ICost.getValue())
return Optional<uint64_t>(OC.getValue());
return Optional<uint64_t>(None);
}
ScaledNumber<uint64_t>
SelectOptimize::getMispredictionCost(const SelectInst *SI,
const Scaled64 CondCost) {
uint64_t MispredictPenalty = TSchedModel.getMCSchedModel()->MispredictPenalty;
// Account for the default misprediction rate when using a branch
// (conservatively set to 25% by default).
uint64_t MispredictRate = MispredictDefaultRate;
// If the select condition is obviously predictable, then the misprediction
// rate is zero.
if (isSelectHighlyPredictable(SI))
MispredictRate = 0;
// CondCost is included to account for cases where the computation of the
// condition is part of a long dependence chain (potentially loop-carried)
// that would delay detection of a misprediction and increase its cost.
Scaled64 MispredictCost =
std::max(Scaled64::get(MispredictPenalty), CondCost) *
Scaled64::get(MispredictRate);
MispredictCost /= Scaled64::get(100);
return MispredictCost;
}
// Returns the cost of a branch when the prediction is correct.
// TrueCost * TrueProbability + FalseCost * FalseProbability.
ScaledNumber<uint64_t>
SelectOptimize::getPredictedPathCost(Scaled64 TrueCost, Scaled64 FalseCost,
const SelectInst *SI) {
Scaled64 PredPathCost;
uint64_t TrueWeight, FalseWeight;
if (SI->extractProfMetadata(TrueWeight, FalseWeight)) {
uint64_t SumWeight = TrueWeight + FalseWeight;
if (SumWeight != 0) {
PredPathCost = TrueCost * Scaled64::get(TrueWeight) +
FalseCost * Scaled64::get(FalseWeight);
PredPathCost /= Scaled64::get(SumWeight);
return PredPathCost;
}
}
// Without branch weight metadata, we assume 75% for the one path and 25% for
// the other, and pick the result with the biggest cost.
PredPathCost = std::max(TrueCost * Scaled64::get(3) + FalseCost,
FalseCost * Scaled64::get(3) + TrueCost);
PredPathCost /= Scaled64::get(4);
return PredPathCost;
}
bool SelectOptimize::isSelectKindSupported(SelectInst *SI) {
bool VectorCond = !SI->getCondition()->getType()->isIntegerTy(1);
if (VectorCond)
return false;
TargetLowering::SelectSupportKind SelectKind;
if (SI->getType()->isVectorTy())
SelectKind = TargetLowering::ScalarCondVectorVal;
else
SelectKind = TargetLowering::ScalarValSelect;
return TLI->isSelectSupported(SelectKind);
}
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