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android/toolchain/llvm-project/f142f8afe21bceb00fb495468aa0b5043e98c419/./llvm/lib/CodeGen/SelectOptimize.cpp
blob: 61341e1f2d04ce8015cab8ceee2dabbe53f2c1e2 [file]
//===--- 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/CodeGen/SelectOptimize.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/IR/PatternMatch.h"
#include "llvm/IR/ProfDataUtils.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>
using namespace llvm;
using namespace llvm::PatternMatch;
#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 SelectOptimizeImpl {
const TargetMachine *TM = nullptr;
const TargetSubtargetInfo *TSI = nullptr;
const TargetLowering *TLI = nullptr;
const TargetTransformInfo *TTI = nullptr;
const LoopInfo *LI = nullptr;
BlockFrequencyInfo *BFI;
ProfileSummaryInfo *PSI = nullptr;
OptimizationRemarkEmitter *ORE = nullptr;
TargetSchedModel TSchedModel;
public:
SelectOptimizeImpl() = default;
SelectOptimizeImpl(const TargetMachine *TM) : TM(TM){};
PreservedAnalyses run(Function &F, FunctionAnalysisManager &FAM);
bool runOnFunction(Function &F, Pass &P);
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;
};
/// SelectLike is an abstraction over SelectInst and other operations that can
/// act like selects. For example Or(Zext(icmp), X) can be treated like
/// select(icmp, X|1, X).
class SelectLike {
SelectLike(Instruction *I) : I(I) {}
/// The select (/or) instruction.
Instruction *I;
/// Whether this select is inverted, "not(cond), FalseVal, TrueVal", as
/// opposed to the original condition.
bool Inverted = false;
public:
/// Match a select or select-like instruction, returning a SelectLike.
static SelectLike match(Instruction *I) {
// Select instruction are what we are usually looking for.
if (isa<SelectInst>(I))
return SelectLike(I);
// An Or(zext(i1 X), Y) can also be treated like a select, with condition
// C and values Y|1 and Y.
Value *X;
if (PatternMatch::match(
I, m_c_Or(m_OneUse(m_ZExt(m_Value(X))), m_Value())) &&
X->getType()->isIntegerTy(1))
return SelectLike(I);
return SelectLike(nullptr);
}
bool isValid() { return I; }
operator bool() { return isValid(); }
/// Invert the select by inverting the condition and switching the operands.
void setInverted() {
assert(!Inverted && "Trying to invert an inverted SelectLike");
assert(isa<Instruction>(getCondition()) &&
cast<Instruction>(getCondition())->getOpcode() ==
Instruction::Xor);
Inverted = true;
}
bool isInverted() const { return Inverted; }
Instruction *getI() { return I; }
const Instruction *getI() const { return I; }
Type *getType() const { return I->getType(); }
Value *getNonInvertedCondition() const {
if (auto *Sel = dyn_cast<SelectInst>(I))
return Sel->getCondition();
// Or(zext) case
if (auto *BO = dyn_cast<BinaryOperator>(I)) {
Value *X;
if (PatternMatch::match(BO->getOperand(0),
m_OneUse(m_ZExt(m_Value(X)))))
return X;
if (PatternMatch::match(BO->getOperand(1),
m_OneUse(m_ZExt(m_Value(X)))))
return X;
}
llvm_unreachable("Unhandled case in getCondition");
}
/// Return the condition for the SelectLike instruction. For example the
/// condition of a select or c in `or(zext(c), x)`
Value *getCondition() const {
Value *CC = getNonInvertedCondition();
// For inverted conditions the CC is checked when created to be a not
// (xor) instruction.
if (Inverted)
return cast<Instruction>(CC)->getOperand(0);
return CC;
}
/// Return the true value for the SelectLike instruction. Note this may not
/// exist for all SelectLike instructions. For example, for `or(zext(c), x)`
/// the true value would be `or(x,1)`. As this value does not exist, nullptr
/// is returned.
Value *getTrueValue(bool HonorInverts = true) const {
if (Inverted && HonorInverts)
return getFalseValue(/*HonorInverts=*/false);
if (auto *Sel = dyn_cast<SelectInst>(I))
return Sel->getTrueValue();
// Or(zext) case - The true value is Or(X), so return nullptr as the value
// does not yet exist.
if (isa<BinaryOperator>(I))
return nullptr;
llvm_unreachable("Unhandled case in getTrueValue");
}
/// Return the false value for the SelectLike instruction. For example the
/// getFalseValue of a select or `x` in `or(zext(c), x)` (which is
/// `select(c, x|1, x)`)
Value *getFalseValue(bool HonorInverts = true) const {
if (Inverted && HonorInverts)
return getTrueValue(/*HonorInverts=*/false);
if (auto *Sel = dyn_cast<SelectInst>(I))
return Sel->getFalseValue();
// Or(zext) case - return the operand which is not the zext.
if (auto *BO = dyn_cast<BinaryOperator>(I)) {
Value *X;
if (PatternMatch::match(BO->getOperand(0),
m_OneUse(m_ZExt(m_Value(X)))))
return BO->getOperand(1);
if (PatternMatch::match(BO->getOperand(1),
m_OneUse(m_ZExt(m_Value(X)))))
return BO->getOperand(0);
}
llvm_unreachable("Unhandled case in getFalseValue");
}
/// Return the NonPredCost cost of the true op, given the costs in
/// InstCostMap. This may need to be generated for select-like instructions.
Scaled64 getTrueOpCost(DenseMap<const Instruction *, CostInfo> &InstCostMap,
const TargetTransformInfo *TTI) {
if (isa<SelectInst>(I))
if (auto *I = dyn_cast<Instruction>(getTrueValue()))
return InstCostMap.contains(I) ? InstCostMap[I].NonPredCost
: Scaled64::getZero();
// Or case - add the cost of an extra Or to the cost of the False case.
if (isa<BinaryOperator>(I))
if (auto I = dyn_cast<Instruction>(getFalseValue()))
if (InstCostMap.contains(I)) {
InstructionCost OrCost = TTI->getArithmeticInstrCost(
Instruction::Or, I->getType(), TargetTransformInfo::TCK_Latency,
{TargetTransformInfo::OK_AnyValue,
TargetTransformInfo::OP_None},
{TTI::OK_UniformConstantValue, TTI::OP_PowerOf2});
return InstCostMap[I].NonPredCost +
Scaled64::get(*OrCost.getValue());
}
return Scaled64::getZero();
}
/// Return the NonPredCost cost of the false op, given the costs in
/// InstCostMap. This may need to be generated for select-like instructions.
Scaled64
getFalseOpCost(DenseMap<const Instruction *, CostInfo> &InstCostMap,
const TargetTransformInfo *TTI) {
if (isa<SelectInst>(I))
if (auto *I = dyn_cast<Instruction>(getFalseValue()))
return InstCostMap.contains(I) ? InstCostMap[I].NonPredCost
: Scaled64::getZero();
// Or case - return the cost of the false case
if (isa<BinaryOperator>(I))
if (auto I = dyn_cast<Instruction>(getFalseValue()))
if (InstCostMap.contains(I))
return InstCostMap[I].NonPredCost;
return Scaled64::getZero();
}
};
private:
// Select groups consist of consecutive select instructions with the same
// condition.
using SelectGroup = SmallVector<SelectLike, 2>;
using SelectGroups = SmallVector<SelectGroup, 2>;
// 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 SelectGroup &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 SelectGroup &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,
Instruction *SI, bool ForSinking = false);
// Returns true if the condition of the select is highly predictable.
bool isSelectHighlyPredictable(const SelectLike 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.
SmallDenseMap<const Instruction *, SelectLike, 2>
getSImap(const SelectGroups &SIGroups);
// Returns the latency cost of a given instruction.
std::optional<uint64_t> computeInstCost(const Instruction *I);
// Returns the misprediction cost of a given select when converted to branch.
Scaled64 getMispredictionCost(const SelectLike SI, const Scaled64 CondCost);
// Returns the cost of a branch when the prediction is correct.
Scaled64 getPredictedPathCost(Scaled64 TrueCost, Scaled64 FalseCost,
const SelectLike SI);
// Returns true if the target architecture supports lowering a given select.
bool isSelectKindSupported(const SelectLike SI);
};
class SelectOptimize : public FunctionPass {
SelectOptimizeImpl Impl;
public:
static char ID;
SelectOptimize() : FunctionPass(ID) {
initializeSelectOptimizePass(*PassRegistry::getPassRegistry());
}
bool runOnFunction(Function &F) override {
return Impl.runOnFunction(F, *this);
}
void getAnalysisUsage(AnalysisUsage &AU) const override {
AU.addRequired<ProfileSummaryInfoWrapperPass>();
AU.addRequired<TargetPassConfig>();
AU.addRequired<TargetTransformInfoWrapperPass>();
AU.addRequired<LoopInfoWrapperPass>();
AU.addRequired<BlockFrequencyInfoWrapperPass>();
AU.addRequired<OptimizationRemarkEmitterWrapperPass>();
}
};
} // namespace
PreservedAnalyses SelectOptimizePass::run(Function &F,
FunctionAnalysisManager &FAM) {
SelectOptimizeImpl Impl(TM);
return Impl.run(F, FAM);
}
char SelectOptimize::ID = 0;
INITIALIZE_PASS_BEGIN(SelectOptimize, DEBUG_TYPE, "Optimize selects", false,
false)
INITIALIZE_PASS_DEPENDENCY(LoopInfoWrapperPass)
INITIALIZE_PASS_DEPENDENCY(ProfileSummaryInfoWrapperPass)
INITIALIZE_PASS_DEPENDENCY(TargetPassConfig)
INITIALIZE_PASS_DEPENDENCY(TargetTransformInfoWrapperPass)
INITIALIZE_PASS_DEPENDENCY(BlockFrequencyInfoWrapperPass)
INITIALIZE_PASS_DEPENDENCY(OptimizationRemarkEmitterWrapperPass)
INITIALIZE_PASS_END(SelectOptimize, DEBUG_TYPE, "Optimize selects", false,
false)
FunctionPass *llvm::createSelectOptimizePass() { return new SelectOptimize(); }
PreservedAnalyses SelectOptimizeImpl::run(Function &F,
FunctionAnalysisManager &FAM) {
TSI = TM->getSubtargetImpl(F);
TLI = TSI->getTargetLowering();
// If none of the select types are 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 PreservedAnalyses::all();
TTI = &FAM.getResult<TargetIRAnalysis>(F);
if (!TTI->enableSelectOptimize())
return PreservedAnalyses::all();
PSI = FAM.getResult<ModuleAnalysisManagerFunctionProxy>(F)
.getCachedResult<ProfileSummaryAnalysis>(*F.getParent());
assert(PSI && "This pass requires module analysis pass `profile-summary`!");
BFI = &FAM.getResult<BlockFrequencyAnalysis>(F);
// When optimizing for size, selects are preferable over branches.
if (F.hasOptSize() || llvm::shouldOptimizeForSize(&F, PSI, BFI))
return PreservedAnalyses::all();
LI = &FAM.getResult<LoopAnalysis>(F);
ORE = &FAM.getResult<OptimizationRemarkEmitterAnalysis>(F);
TSchedModel.init(TSI);
bool Changed = optimizeSelects(F);
return Changed ? PreservedAnalyses::none() : PreservedAnalyses::all();
}
bool SelectOptimizeImpl::runOnFunction(Function &F, Pass &P) {
TM = &P.getAnalysis<TargetPassConfig>().getTM<TargetMachine>();
TSI = TM->getSubtargetImpl(F);
TLI = TSI->getTargetLowering();
// If none of the select types are 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 = &P.getAnalysis<TargetTransformInfoWrapperPass>().getTTI(F);
if (!TTI->enableSelectOptimize())
return false;
LI = &P.getAnalysis<LoopInfoWrapperPass>().getLoopInfo();
BFI = &P.getAnalysis<BlockFrequencyInfoWrapperPass>().getBFI();
PSI = &P.getAnalysis<ProfileSummaryInfoWrapperPass>().getPSI();
ORE = &P.getAnalysis<OptimizationRemarkEmitterWrapperPass>().getORE();
TSchedModel.init(TSI);
// When optimizing for size, selects are preferable over branches.
if (F.hasOptSize() || llvm::shouldOptimizeForSize(&F, PSI, BFI))
return false;
return optimizeSelects(F);
}
bool SelectOptimizeImpl::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 SelectOptimizeImpl::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 SelectOptimizeImpl::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(SelectOptimizeImpl::SelectLike SI, bool isTrue,
const SmallPtrSet<const Instruction *, 2> &Selects,
IRBuilder<> &IB) {
Value *V = nullptr;
for (SelectInst *DefSI = dyn_cast<SelectInst>(SI.getI());
DefSI != nullptr && Selects.count(DefSI);
DefSI = dyn_cast<SelectInst>(V)) {
if (DefSI->getCondition() == SI.getCondition())
V = (isTrue ? DefSI->getTrueValue() : DefSI->getFalseValue());
else // Handle inverted SI
V = (!isTrue ? DefSI->getTrueValue() : DefSI->getFalseValue());
}
if (isa<BinaryOperator>(SI.getI())) {
assert(SI.getI()->getOpcode() == Instruction::Or &&
"Only currently handling Or instructions.");
V = SI.getFalseValue();
if (isTrue)
V = IB.CreateOr(V, ConstantInt::get(V->getType(), 1));
}
assert(V && "Failed to get select true/false value");
return V;
}
void SelectOptimizeImpl::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 (SelectLike SI : ASI) {
// For each select, compute the sinkable dependence chains of the true and
// false operands.
if (auto *TI = dyn_cast_or_null<Instruction>(SI.getTrueValue())) {
std::stack<Instruction *> TrueSlice;
getExclBackwardsSlice(TI, TrueSlice, SI.getI(), true);
maxTrueSliceLen = std::max(maxTrueSliceLen, TrueSlice.size());
TrueSlices.push_back(TrueSlice);
}
if (auto *FI = dyn_cast_or_null<Instruction>(SI.getFalseValue())) {
if (isa<SelectInst>(SI.getI()) || !FI->hasOneUse()) {
std::stack<Instruction *> FalseSlice;
getExclBackwardsSlice(FI, FalseSlice, SI.getI(), 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.
SelectLike SI = ASI.front();
SelectLike LastSI = ASI.back();
BasicBlock *StartBlock = SI.getI()->getParent();
BasicBlock::iterator SplitPt = ++(BasicBlock::iterator(LastSI.getI()));
// With RemoveDIs turned off, SplitPt can be a dbg.* intrinsic. With
// RemoveDIs turned on, SplitPt would instead point to the next
// instruction. To match existing dbg.* intrinsic behaviour with RemoveDIs,
// tell splitBasicBlock that we want to include any DbgVariableRecords
// attached to SplitPt in the splice.
SplitPt.setHeadBit(true);
BasicBlock *EndBlock = StartBlock->splitBasicBlock(SplitPt, "select.end");
BFI->setBlockFreq(EndBlock, BFI->getBlockFreq(StartBlock));
// Delete the unconditional branch that was just created by the split.
StartBlock->getTerminator()->eraseFromParent();
// Move any debug/pseudo instructions and not's that were in-between the
// select group to the newly-created end block.
SmallVector<Instruction *, 2> SinkInstrs;
auto DIt = SI.getI()->getIterator();
while (&*DIt != LastSI.getI()) {
if (DIt->isDebugOrPseudoInst())
SinkInstrs.push_back(&*DIt);
if (match(&*DIt, m_Not(m_Specific(SI.getCondition()))))
SinkInstrs.push_back(&*DIt);
DIt++;
}
for (auto *DI : SinkInstrs)
DI->moveBeforePreserving(&*EndBlock->getFirstInsertionPt());
// Duplicate implementation for DbgRecords, the non-instruction debug-info
// format. Helper lambda for moving DbgRecords to the end block.
auto TransferDbgRecords = [&](Instruction &I) {
for (auto &DbgRecord :
llvm::make_early_inc_range(I.getDbgRecordRange())) {
DbgRecord.removeFromParent();
EndBlock->insertDbgRecordBefore(&DbgRecord,
EndBlock->getFirstInsertionPt());
}
};
// Iterate over all instructions in between SI and LastSI, not including
// SI itself. These are all the variable assignments that happen "in the
// middle" of the select group.
auto R = make_range(std::next(SI.getI()->getIterator()),
std::next(LastSI.getI()->getIterator()));
llvm::for_each(R, TransferDbgRecords);
// 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(EndBlock->getContext(), "select.true.sink",
EndBlock->getParent(), EndBlock);
TrueBranch = BranchInst::Create(EndBlock, TrueBlock);
TrueBranch->setDebugLoc(LastSI.getI()->getDebugLoc());
for (Instruction *TrueInst : TrueSlicesInterleaved)
TrueInst->moveBefore(TrueBranch);
}
if (!FalseSlicesInterleaved.empty()) {
FalseBlock =
BasicBlock::Create(EndBlock->getContext(), "select.false.sink",
EndBlock->getParent(), EndBlock);
FalseBranch = BranchInst::Create(EndBlock, FalseBlock);
FalseBranch->setDebugLoc(LastSI.getI()->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(StartBlock->getContext(), "select.false",
EndBlock->getParent(), EndBlock);
auto *FalseBranch = BranchInst::Create(EndBlock, FalseBlock);
FalseBranch->setDebugLoc(SI.getI()->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.getI());
auto *CondFr = IB.CreateFreeze(SI.getCondition(),
SI.getCondition()->getName() + ".frozen");
SmallPtrSet<const Instruction *, 2> INS;
for (auto SI : ASI)
INS.insert(SI.getI());
// 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) {
SelectLike SI = *It;
// The select itself is replaced with a PHI Node.
PHINode *PN = PHINode::Create(SI.getType(), 2, "");
PN->insertBefore(EndBlock->begin());
PN->takeName(SI.getI());
PN->addIncoming(getTrueOrFalseValue(SI, true, INS, IB), TrueBlock);
PN->addIncoming(getTrueOrFalseValue(SI, false, INS, IB), FalseBlock);
PN->setDebugLoc(SI.getI()->getDebugLoc());
SI.getI()->replaceAllUsesWith(PN);
INS.erase(SI.getI());
++NumSelectsConverted;
}
IB.CreateCondBr(CondFr, TT, FT, SI.getI());
// Remove the old select instructions, now that they are not longer used.
for (auto SI : ASI)
SI.getI()->eraseFromParent();
}
}
void SelectOptimizeImpl::collectSelectGroups(BasicBlock &BB,
SelectGroups &SIGroups) {
BasicBlock::iterator BBIt = BB.begin();
while (BBIt != BB.end()) {
Instruction *I = &*BBIt++;
if (SelectLike SI = SelectLike::match(I)) {
if (!TTI->shouldTreatInstructionLikeSelect(I))
continue;
SelectGroup SIGroup;
SIGroup.push_back(SI);
while (BBIt != BB.end()) {
Instruction *NI = &*BBIt;
// Debug/pseudo instructions should be skipped and not prevent the
// formation of a select group.
if (NI->isDebugOrPseudoInst()) {
++BBIt;
continue;
}
// Skip not(select(..)), if the not is part of the same select group
if (match(NI, m_Not(m_Specific(SI.getCondition())))) {
++BBIt;
continue;
}
// We only allow selects in the same group, not other select-like
// instructions.
if (!isa<SelectInst>(NI))
break;
SelectLike NSI = SelectLike::match(NI);
if (NSI && SI.getCondition() == NSI.getCondition()) {
SIGroup.push_back(NSI);
} else if (NSI && match(NSI.getCondition(),
m_Not(m_Specific(SI.getCondition())))) {
NSI.setInverted();
SIGroup.push_back(NSI);
} else
break;
++BBIt;
}
// If the select type is not supported, no point optimizing it.
// Instruction selection will take care of it.
if (!isSelectKindSupported(SI))
continue;
LLVM_DEBUG({
dbgs() << "New Select group with\n";
for (auto SI : SIGroup)
dbgs() << " " << *SI.getI() << "\n";
});
SIGroups.push_back(SIGroup);
}
}
}
void SelectOptimizeImpl::findProfitableSIGroupsBase(
SelectGroups &SIGroups, SelectGroups &ProfSIGroups) {
for (SelectGroup &ASI : SIGroups) {
++NumSelectOptAnalyzed;
if (isConvertToBranchProfitableBase(ASI))
ProfSIGroups.push_back(ASI);
}
}
static void EmitAndPrintRemark(OptimizationRemarkEmitter *ORE,
DiagnosticInfoOptimizationBase &Rem) {
LLVM_DEBUG(dbgs() << Rem.getMsg() << "\n");
ORE->emit(Rem);
}
void SelectOptimizeImpl::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 SelectOptimizeImpl::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 (SelectLike SI : ASI) {
SelectCost = std::max(SelectCost, InstCostMap[SI.getI()].PredCost);
BranchCost = std::max(BranchCost, InstCostMap[SI.getI()].NonPredCost);
}
if (BranchCost < SelectCost) {
OptimizationRemark OR(DEBUG_TYPE, "SelectOpti", ASI.front().getI());
OR << "Profitable to convert to branch (loop analysis). BranchCost="
<< BranchCost.toString() << ", SelectCost=" << SelectCost.toString()
<< ". ";
EmitAndPrintRemark(ORE, OR);
++NumSelectConvertedLoop;
ProfSIGroups.push_back(ASI);
} else {
OptimizationRemarkMissed ORmiss(DEBUG_TYPE, "SelectOpti",
ASI.front().getI());
ORmiss << "Select is more profitable (loop analysis). BranchCost="
<< BranchCost.toString()
<< ", SelectCost=" << SelectCost.toString() << ". ";
EmitAndPrintRemark(ORE, ORmiss);
}
}
}
bool SelectOptimizeImpl::isConvertToBranchProfitableBase(
const SelectGroup &ASI) {
SelectLike SI = ASI.front();
LLVM_DEBUG(dbgs() << "Analyzing select group containing " << *SI.getI()
<< "\n");
OptimizationRemark OR(DEBUG_TYPE, "SelectOpti", SI.getI());
OptimizationRemarkMissed ORmiss(DEBUG_TYPE, "SelectOpti", SI.getI());
// Skip cold basic blocks. Better to optimize for size for cold blocks.
if (PSI->isColdBlock(SI.getI()->getParent(), BFI)) {
++NumSelectColdBB;
ORmiss << "Not converted to branch because of cold basic block. ";
EmitAndPrintRemark(ORE, ORmiss);
return false;
}
// If unpredictable, branch form is less profitable.
if (SI.getI()->getMetadata(LLVMContext::MD_unpredictable)) {
++NumSelectUnPred;
ORmiss << "Not converted to branch because of unpredictable branch. ";
EmitAndPrintRemark(ORE, 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. ";
EmitAndPrintRemark(ORE, 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.";
EmitAndPrintRemark(ORE, OR);
return true;
}
ORmiss << "Not profitable to convert to branch (base heuristic).";
EmitAndPrintRemark(ORE, ORmiss);
return false;
}
static InstructionCost divideNearest(InstructionCost Numerator,
uint64_t Denominator) {
return (Numerator + (Denominator / 2)) / Denominator;
}
static bool extractBranchWeights(const SelectOptimizeImpl::SelectLike SI,
uint64_t &TrueVal, uint64_t &FalseVal) {
if (isa<SelectInst>(SI.getI()))
return extractBranchWeights(*SI.getI(), TrueVal, FalseVal);
return false;
}
bool SelectOptimizeImpl::hasExpensiveColdOperand(const SelectGroup &ASI) {
bool ColdOperand = false;
uint64_t TrueWeight, FalseWeight, TotalWeight;
if (extractBranchWeights(ASI.front(), 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().getI());
ORmiss << "Profile data available but missing branch-weights metadata for "
"select instruction. ";
EmitAndPrintRemark(ORE, ORmiss);
}
if (!ColdOperand)
return false;
// Check if the cold path's dependence slice is expensive for any of the
// selects of the group.
for (SelectLike SI : ASI) {
Instruction *ColdI = nullptr;
uint64_t HotWeight;
if (TrueWeight < FalseWeight) {
ColdI = dyn_cast_or_null<Instruction>(SI.getTrueValue());
HotWeight = FalseWeight;
} else {
ColdI = dyn_cast_or_null<Instruction>(SI.getFalseValue());
HotWeight = TrueWeight;
}
if (ColdI) {
std::stack<Instruction *> ColdSlice;
getExclBackwardsSlice(ColdI, ColdSlice, SI.getI());
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;
}
// Check if it is safe to move LoadI next to the SI.
// Conservatively assume it is safe only if there is no instruction
// modifying memory in-between the load and the select instruction.
static bool isSafeToSinkLoad(Instruction *LoadI, Instruction *SI) {
// Assume loads from different basic blocks are unsafe to move.
if (LoadI->getParent() != SI->getParent())
return false;
auto It = LoadI->getIterator();
while (&*It != SI) {
if (It->mayWriteToMemory())
return false;
It++;
}
return true;
}
// 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 SelectOptimizeImpl::getExclBackwardsSlice(Instruction *I,
std::stack<Instruction *> &Slice,
Instruction *SI,
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.insert(II).second)
continue;
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 sinking loads in order not to skip state-modifying instructions,
// that may alias with the loaded address.
// Only allow sinking of loads within the same basic block that are
// conservatively proven to be safe.
if (ForSinking && II->mayReadFromMemory() && !isSafeToSinkLoad(II, SI))
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 (Value *Op : II->operand_values())
if (auto *OpI = dyn_cast<Instruction>(Op))
Worklist.push(OpI);
}
}
bool SelectOptimizeImpl::isSelectHighlyPredictable(const SelectLike SI) {
uint64_t TrueWeight, FalseWeight;
if (extractBranchWeights(SI, 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 SelectOptimizeImpl::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. ";
EmitAndPrintRemark(ORE, 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() << "%. ";
EmitAndPrintRemark(ORE, 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() << "%. ";
EmitAndPrintRemark(ORE, 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. ";
EmitAndPrintRemark(ORE, 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 SelectOptimizeImpl::computeLoopCosts(
const Loop *L, const SelectGroups &SIGroups,
DenseMap<const Instruction *, CostInfo> &InstCostMap, CostInfo *LoopCost) {
LLVM_DEBUG(dbgs() << "Calculating Latency / IPredCost / INonPredCost of loop "
<< L->getHeader()->getName() << "\n");
const auto &SImap = getSImap(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) {
OptimizationRemarkMissed ORmissL(DEBUG_TYPE, "SelectOpti", &I);
ORmissL << "Invalid instruction cost preventing analysis and "
"optimization of the inner-most loop containing this "
"instruction. ";
EmitAndPrintRemark(ORE, ORmissL);
return false;
}
IPredCost += Scaled64::get(*ILatency);
INonPredCost += Scaled64::get(*ILatency);
// 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 (SImap.contains(&I)) {
auto SI = SImap.at(&I);
Scaled64 TrueOpCost = SI.getTrueOpCost(InstCostMap, TTI);
Scaled64 FalseOpCost = SI.getFalseOpCost(InstCostMap, TTI);
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;
}
LLVM_DEBUG(dbgs() << " " << ILatency << "/" << IPredCost << "/"
<< INonPredCost << " for " << I << "\n");
InstCostMap[&I] = {IPredCost, INonPredCost};
MaxCost.PredCost = std::max(MaxCost.PredCost, IPredCost);
MaxCost.NonPredCost = std::max(MaxCost.NonPredCost, INonPredCost);
}
}
LLVM_DEBUG(dbgs() << "Iteration " << Iter + 1
<< " MaxCost = " << MaxCost.PredCost << " "
<< MaxCost.NonPredCost << "\n");
}
return true;
}
SmallDenseMap<const Instruction *, SelectOptimizeImpl::SelectLike, 2>
SelectOptimizeImpl::getSImap(const SelectGroups &SIGroups) {
SmallDenseMap<const Instruction *, SelectLike, 2> SImap;
for (const SelectGroup &ASI : SIGroups)
for (SelectLike SI : ASI)
SImap.try_emplace(SI.getI(), SI);
return SImap;
}
std::optional<uint64_t>
SelectOptimizeImpl::computeInstCost(const Instruction *I) {
InstructionCost ICost =
TTI->getInstructionCost(I, TargetTransformInfo::TCK_Latency);
if (auto OC = ICost.getValue())
return std::optional<uint64_t>(*OC);
return std::nullopt;
}
ScaledNumber<uint64_t>
SelectOptimizeImpl::getMispredictionCost(const SelectLike 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>
SelectOptimizeImpl::getPredictedPathCost(Scaled64 TrueCost, Scaled64 FalseCost,
const SelectLike SI) {
Scaled64 PredPathCost;
uint64_t TrueWeight, FalseWeight;
if (extractBranchWeights(SI, 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 SelectOptimizeImpl::isSelectKindSupported(const SelectLike 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);
}