llvm-project/llvm/lib/Analysis/LoopCacheAnalysis.cpp

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Title: Loop Cache Analysis Summary: Implement a new analysis to estimate the number of cache lines required by a loop nest. The analysis is largely based on the following paper: Compiler Optimizations for Improving Data Locality By: Steve Carr, Katherine S. McKinley, Chau-Wen Tseng http://www.cs.utexas.edu/users/mckinley/papers/asplos-1994.pdf The analysis considers temporal reuse (accesses to the same memory location) and spatial reuse (accesses to memory locations within a cache line). For simplicity the analysis considers memory accesses in the innermost loop in a loop nest, and thus determines the number of cache lines used when the loop L in loop nest LN is placed in the innermost position. The result of the analysis can be used to drive several transformations. As an example, loop interchange could use it determine which loops in a perfect loop nest should be interchanged to maximize cache reuse. Similarly, loop distribution could be enhanced to take into consideration cache reuse between arrays when distributing a loop to eliminate vectorization inhibiting dependencies. The general approach taken to estimate the number of cache lines used by the memory references in the inner loop of a loop nest is: Partition memory references that exhibit temporal or spatial reuse into reference groups. For each loop L in the a loop nest LN: a. Compute the cost of the reference group b. Compute the 'cache cost' of the loop nest by summing up the reference groups costs For further details of the algorithm please refer to the paper. Authored By: etiotto Reviewers: hfinkel, Meinersbur, jdoerfert, kbarton, bmahjour, anemet, fhahn Reviewed By: Meinersbur Subscribers: reames, nemanjai, MaskRay, wuzish, Hahnfeld, xusx595, venkataramanan.kumar.llvm, greened, dmgreen, steleman, fhahn, xblvaOO, Whitney, mgorny, hiraditya, mgrang, jsji, llvm-commits Tag: LLVM Differential Revision: https://reviews.llvm.org/D63459 llvm-svn: 368439
2019-08-09 21:56:29 +08:00
//===- LoopCacheAnalysis.cpp - Loop Cache Analysis -------------------------==//
//
// The LLVM Compiler Infrastructure
//
// 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
//
//===----------------------------------------------------------------------===//
///
/// \file
/// This file defines the implementation for the loop cache analysis.
/// The implementation is largely based on the following paper:
///
/// Compiler Optimizations for Improving Data Locality
/// By: Steve Carr, Katherine S. McKinley, Chau-Wen Tseng
/// http://www.cs.utexas.edu/users/mckinley/papers/asplos-1994.pdf
///
/// The general approach taken to estimate the number of cache lines used by the
/// memory references in an inner loop is:
/// 1. Partition memory references that exhibit temporal or spacial reuse
/// into reference groups.
/// 2. For each loop L in the a loop nest LN:
/// a. Compute the cost of the reference group
/// b. Compute the loop cost by summing up the reference groups costs
//===----------------------------------------------------------------------===//
#include "llvm/Analysis/LoopCacheAnalysis.h"
#include "llvm/ADT/BreadthFirstIterator.h"
#include "llvm/ADT/Sequence.h"
#include "llvm/ADT/SmallVector.h"
#include "llvm/Analysis/AliasAnalysis.h"
#include "llvm/Analysis/DependenceAnalysis.h"
#include "llvm/Analysis/LoopInfo.h"
#include "llvm/Analysis/ScalarEvolutionExpressions.h"
#include "llvm/Analysis/TargetTransformInfo.h"
#include "llvm/Support/CommandLine.h"
Title: Loop Cache Analysis Summary: Implement a new analysis to estimate the number of cache lines required by a loop nest. The analysis is largely based on the following paper: Compiler Optimizations for Improving Data Locality By: Steve Carr, Katherine S. McKinley, Chau-Wen Tseng http://www.cs.utexas.edu/users/mckinley/papers/asplos-1994.pdf The analysis considers temporal reuse (accesses to the same memory location) and spatial reuse (accesses to memory locations within a cache line). For simplicity the analysis considers memory accesses in the innermost loop in a loop nest, and thus determines the number of cache lines used when the loop L in loop nest LN is placed in the innermost position. The result of the analysis can be used to drive several transformations. As an example, loop interchange could use it determine which loops in a perfect loop nest should be interchanged to maximize cache reuse. Similarly, loop distribution could be enhanced to take into consideration cache reuse between arrays when distributing a loop to eliminate vectorization inhibiting dependencies. The general approach taken to estimate the number of cache lines used by the memory references in the inner loop of a loop nest is: Partition memory references that exhibit temporal or spatial reuse into reference groups. For each loop L in the a loop nest LN: a. Compute the cost of the reference group b. Compute the 'cache cost' of the loop nest by summing up the reference groups costs For further details of the algorithm please refer to the paper. Authored By: etiotto Reviewers: hfinkel, Meinersbur, jdoerfert, kbarton, bmahjour, anemet, fhahn Reviewed By: Meinersbur Subscribers: reames, nemanjai, MaskRay, wuzish, Hahnfeld, xusx595, venkataramanan.kumar.llvm, greened, dmgreen, steleman, fhahn, xblvaOO, Whitney, mgorny, hiraditya, mgrang, jsji, llvm-commits Tag: LLVM Differential Revision: https://reviews.llvm.org/D63459 llvm-svn: 368439
2019-08-09 21:56:29 +08:00
#include "llvm/Support/Debug.h"
using namespace llvm;
#define DEBUG_TYPE "loop-cache-cost"
static cl::opt<unsigned> DefaultTripCount(
"default-trip-count", cl::init(100), cl::Hidden,
cl::desc("Use this to specify the default trip count of a loop"));
// In this analysis two array references are considered to exhibit temporal
// reuse if they access either the same memory location, or a memory location
// with distance smaller than a configurable threshold.
static cl::opt<unsigned> TemporalReuseThreshold(
"temporal-reuse-threshold", cl::init(2), cl::Hidden,
cl::desc("Use this to specify the max. distance between array elements "
"accessed in a loop so that the elements are classified to have "
"temporal reuse"));
/// Retrieve the innermost loop in the given loop nest \p Loops. It returns a
/// nullptr if any loops in the loop vector supplied has more than one sibling.
/// The loop vector is expected to contain loops collected in breadth-first
/// order.
static Loop *getInnerMostLoop(const LoopVectorTy &Loops) {
assert(!Loops.empty() && "Expecting a non-empy loop vector");
Loop *LastLoop = Loops.back();
Loop *ParentLoop = LastLoop->getParentLoop();
if (ParentLoop == nullptr) {
assert(Loops.size() == 1 && "Expecting a single loop");
return LastLoop;
}
return (llvm::is_sorted(Loops,
[](const Loop *L1, const Loop *L2) {
return L1->getLoopDepth() < L2->getLoopDepth();
}))
Title: Loop Cache Analysis Summary: Implement a new analysis to estimate the number of cache lines required by a loop nest. The analysis is largely based on the following paper: Compiler Optimizations for Improving Data Locality By: Steve Carr, Katherine S. McKinley, Chau-Wen Tseng http://www.cs.utexas.edu/users/mckinley/papers/asplos-1994.pdf The analysis considers temporal reuse (accesses to the same memory location) and spatial reuse (accesses to memory locations within a cache line). For simplicity the analysis considers memory accesses in the innermost loop in a loop nest, and thus determines the number of cache lines used when the loop L in loop nest LN is placed in the innermost position. The result of the analysis can be used to drive several transformations. As an example, loop interchange could use it determine which loops in a perfect loop nest should be interchanged to maximize cache reuse. Similarly, loop distribution could be enhanced to take into consideration cache reuse between arrays when distributing a loop to eliminate vectorization inhibiting dependencies. The general approach taken to estimate the number of cache lines used by the memory references in the inner loop of a loop nest is: Partition memory references that exhibit temporal or spatial reuse into reference groups. For each loop L in the a loop nest LN: a. Compute the cost of the reference group b. Compute the 'cache cost' of the loop nest by summing up the reference groups costs For further details of the algorithm please refer to the paper. Authored By: etiotto Reviewers: hfinkel, Meinersbur, jdoerfert, kbarton, bmahjour, anemet, fhahn Reviewed By: Meinersbur Subscribers: reames, nemanjai, MaskRay, wuzish, Hahnfeld, xusx595, venkataramanan.kumar.llvm, greened, dmgreen, steleman, fhahn, xblvaOO, Whitney, mgorny, hiraditya, mgrang, jsji, llvm-commits Tag: LLVM Differential Revision: https://reviews.llvm.org/D63459 llvm-svn: 368439
2019-08-09 21:56:29 +08:00
? LastLoop
: nullptr;
}
static bool isOneDimensionalArray(const SCEV &AccessFn, const SCEV &ElemSize,
const Loop &L, ScalarEvolution &SE) {
const SCEVAddRecExpr *AR = dyn_cast<SCEVAddRecExpr>(&AccessFn);
if (!AR || !AR->isAffine())
return false;
assert(AR->getLoop() && "AR should have a loop");
// Check that start and increment are not add recurrences.
const SCEV *Start = AR->getStart();
const SCEV *Step = AR->getStepRecurrence(SE);
if (isa<SCEVAddRecExpr>(Start) || isa<SCEVAddRecExpr>(Step))
return false;
// Check that start and increment are both invariant in the loop.
if (!SE.isLoopInvariant(Start, &L) || !SE.isLoopInvariant(Step, &L))
return false;
const SCEV *StepRec = AR->getStepRecurrence(SE);
if (StepRec && SE.isKnownNegative(StepRec))
StepRec = SE.getNegativeSCEV(StepRec);
return StepRec == &ElemSize;
Title: Loop Cache Analysis Summary: Implement a new analysis to estimate the number of cache lines required by a loop nest. The analysis is largely based on the following paper: Compiler Optimizations for Improving Data Locality By: Steve Carr, Katherine S. McKinley, Chau-Wen Tseng http://www.cs.utexas.edu/users/mckinley/papers/asplos-1994.pdf The analysis considers temporal reuse (accesses to the same memory location) and spatial reuse (accesses to memory locations within a cache line). For simplicity the analysis considers memory accesses in the innermost loop in a loop nest, and thus determines the number of cache lines used when the loop L in loop nest LN is placed in the innermost position. The result of the analysis can be used to drive several transformations. As an example, loop interchange could use it determine which loops in a perfect loop nest should be interchanged to maximize cache reuse. Similarly, loop distribution could be enhanced to take into consideration cache reuse between arrays when distributing a loop to eliminate vectorization inhibiting dependencies. The general approach taken to estimate the number of cache lines used by the memory references in the inner loop of a loop nest is: Partition memory references that exhibit temporal or spatial reuse into reference groups. For each loop L in the a loop nest LN: a. Compute the cost of the reference group b. Compute the 'cache cost' of the loop nest by summing up the reference groups costs For further details of the algorithm please refer to the paper. Authored By: etiotto Reviewers: hfinkel, Meinersbur, jdoerfert, kbarton, bmahjour, anemet, fhahn Reviewed By: Meinersbur Subscribers: reames, nemanjai, MaskRay, wuzish, Hahnfeld, xusx595, venkataramanan.kumar.llvm, greened, dmgreen, steleman, fhahn, xblvaOO, Whitney, mgorny, hiraditya, mgrang, jsji, llvm-commits Tag: LLVM Differential Revision: https://reviews.llvm.org/D63459 llvm-svn: 368439
2019-08-09 21:56:29 +08:00
}
/// Compute the trip count for the given loop \p L. Return the SCEV expression
/// for the trip count or nullptr if it cannot be computed.
static const SCEV *computeTripCount(const Loop &L, ScalarEvolution &SE) {
const SCEV *BackedgeTakenCount = SE.getBackedgeTakenCount(&L);
if (isa<SCEVCouldNotCompute>(BackedgeTakenCount) ||
!isa<SCEVConstant>(BackedgeTakenCount))
return nullptr;
return SE.getAddExpr(BackedgeTakenCount,
SE.getOne(BackedgeTakenCount->getType()));
}
//===----------------------------------------------------------------------===//
// IndexedReference implementation
//
raw_ostream &llvm::operator<<(raw_ostream &OS, const IndexedReference &R) {
if (!R.IsValid) {
OS << R.StoreOrLoadInst;
OS << ", IsValid=false.";
return OS;
}
OS << *R.BasePointer;
for (const SCEV *Subscript : R.Subscripts)
OS << "[" << *Subscript << "]";
OS << ", Sizes: ";
for (const SCEV *Size : R.Sizes)
OS << "[" << *Size << "]";
return OS;
}
IndexedReference::IndexedReference(Instruction &StoreOrLoadInst,
const LoopInfo &LI, ScalarEvolution &SE)
: StoreOrLoadInst(StoreOrLoadInst), SE(SE) {
assert((isa<StoreInst>(StoreOrLoadInst) || isa<LoadInst>(StoreOrLoadInst)) &&
"Expecting a load or store instruction");
IsValid = delinearize(LI);
if (IsValid)
LLVM_DEBUG(dbgs().indent(2) << "Succesfully delinearized: " << *this
<< "\n");
}
Optional<bool> IndexedReference::hasSpacialReuse(const IndexedReference &Other,
unsigned CLS,
AAResults &AA) const {
Title: Loop Cache Analysis Summary: Implement a new analysis to estimate the number of cache lines required by a loop nest. The analysis is largely based on the following paper: Compiler Optimizations for Improving Data Locality By: Steve Carr, Katherine S. McKinley, Chau-Wen Tseng http://www.cs.utexas.edu/users/mckinley/papers/asplos-1994.pdf The analysis considers temporal reuse (accesses to the same memory location) and spatial reuse (accesses to memory locations within a cache line). For simplicity the analysis considers memory accesses in the innermost loop in a loop nest, and thus determines the number of cache lines used when the loop L in loop nest LN is placed in the innermost position. The result of the analysis can be used to drive several transformations. As an example, loop interchange could use it determine which loops in a perfect loop nest should be interchanged to maximize cache reuse. Similarly, loop distribution could be enhanced to take into consideration cache reuse between arrays when distributing a loop to eliminate vectorization inhibiting dependencies. The general approach taken to estimate the number of cache lines used by the memory references in the inner loop of a loop nest is: Partition memory references that exhibit temporal or spatial reuse into reference groups. For each loop L in the a loop nest LN: a. Compute the cost of the reference group b. Compute the 'cache cost' of the loop nest by summing up the reference groups costs For further details of the algorithm please refer to the paper. Authored By: etiotto Reviewers: hfinkel, Meinersbur, jdoerfert, kbarton, bmahjour, anemet, fhahn Reviewed By: Meinersbur Subscribers: reames, nemanjai, MaskRay, wuzish, Hahnfeld, xusx595, venkataramanan.kumar.llvm, greened, dmgreen, steleman, fhahn, xblvaOO, Whitney, mgorny, hiraditya, mgrang, jsji, llvm-commits Tag: LLVM Differential Revision: https://reviews.llvm.org/D63459 llvm-svn: 368439
2019-08-09 21:56:29 +08:00
assert(IsValid && "Expecting a valid reference");
if (BasePointer != Other.getBasePointer() && !isAliased(Other, AA)) {
LLVM_DEBUG(dbgs().indent(2)
<< "No spacial reuse: different base pointers\n");
return false;
}
unsigned NumSubscripts = getNumSubscripts();
if (NumSubscripts != Other.getNumSubscripts()) {
LLVM_DEBUG(dbgs().indent(2)
<< "No spacial reuse: different number of subscripts\n");
return false;
}
// all subscripts must be equal, except the leftmost one (the last one).
for (auto SubNum : seq<unsigned>(0, NumSubscripts - 1)) {
if (getSubscript(SubNum) != Other.getSubscript(SubNum)) {
LLVM_DEBUG(dbgs().indent(2) << "No spacial reuse, different subscripts: "
<< "\n\t" << *getSubscript(SubNum) << "\n\t"
<< *Other.getSubscript(SubNum) << "\n");
return false;
}
}
// the difference between the last subscripts must be less than the cache line
// size.
const SCEV *LastSubscript = getLastSubscript();
const SCEV *OtherLastSubscript = Other.getLastSubscript();
const SCEVConstant *Diff = dyn_cast<SCEVConstant>(
SE.getMinusSCEV(LastSubscript, OtherLastSubscript));
if (Diff == nullptr) {
LLVM_DEBUG(dbgs().indent(2)
<< "No spacial reuse, difference between subscript:\n\t"
<< *LastSubscript << "\n\t" << OtherLastSubscript
<< "\nis not constant.\n");
return None;
}
bool InSameCacheLine = (Diff->getValue()->getSExtValue() < CLS);
LLVM_DEBUG({
if (InSameCacheLine)
dbgs().indent(2) << "Found spacial reuse.\n";
else
dbgs().indent(2) << "No spacial reuse.\n";
});
return InSameCacheLine;
}
Optional<bool> IndexedReference::hasTemporalReuse(const IndexedReference &Other,
unsigned MaxDistance,
const Loop &L,
DependenceInfo &DI,
AAResults &AA) const {
Title: Loop Cache Analysis Summary: Implement a new analysis to estimate the number of cache lines required by a loop nest. The analysis is largely based on the following paper: Compiler Optimizations for Improving Data Locality By: Steve Carr, Katherine S. McKinley, Chau-Wen Tseng http://www.cs.utexas.edu/users/mckinley/papers/asplos-1994.pdf The analysis considers temporal reuse (accesses to the same memory location) and spatial reuse (accesses to memory locations within a cache line). For simplicity the analysis considers memory accesses in the innermost loop in a loop nest, and thus determines the number of cache lines used when the loop L in loop nest LN is placed in the innermost position. The result of the analysis can be used to drive several transformations. As an example, loop interchange could use it determine which loops in a perfect loop nest should be interchanged to maximize cache reuse. Similarly, loop distribution could be enhanced to take into consideration cache reuse between arrays when distributing a loop to eliminate vectorization inhibiting dependencies. The general approach taken to estimate the number of cache lines used by the memory references in the inner loop of a loop nest is: Partition memory references that exhibit temporal or spatial reuse into reference groups. For each loop L in the a loop nest LN: a. Compute the cost of the reference group b. Compute the 'cache cost' of the loop nest by summing up the reference groups costs For further details of the algorithm please refer to the paper. Authored By: etiotto Reviewers: hfinkel, Meinersbur, jdoerfert, kbarton, bmahjour, anemet, fhahn Reviewed By: Meinersbur Subscribers: reames, nemanjai, MaskRay, wuzish, Hahnfeld, xusx595, venkataramanan.kumar.llvm, greened, dmgreen, steleman, fhahn, xblvaOO, Whitney, mgorny, hiraditya, mgrang, jsji, llvm-commits Tag: LLVM Differential Revision: https://reviews.llvm.org/D63459 llvm-svn: 368439
2019-08-09 21:56:29 +08:00
assert(IsValid && "Expecting a valid reference");
if (BasePointer != Other.getBasePointer() && !isAliased(Other, AA)) {
LLVM_DEBUG(dbgs().indent(2)
<< "No temporal reuse: different base pointer\n");
return false;
}
std::unique_ptr<Dependence> D =
DI.depends(&StoreOrLoadInst, &Other.StoreOrLoadInst, true);
if (D == nullptr) {
LLVM_DEBUG(dbgs().indent(2) << "No temporal reuse: no dependence\n");
return false;
}
if (D->isLoopIndependent()) {
LLVM_DEBUG(dbgs().indent(2) << "Found temporal reuse\n");
return true;
}
// Check the dependence distance at every loop level. There is temporal reuse
// if the distance at the given loop's depth is small (|d| <= MaxDistance) and
// it is zero at every other loop level.
int LoopDepth = L.getLoopDepth();
int Levels = D->getLevels();
for (int Level = 1; Level <= Levels; ++Level) {
const SCEV *Distance = D->getDistance(Level);
const SCEVConstant *SCEVConst = dyn_cast_or_null<SCEVConstant>(Distance);
if (SCEVConst == nullptr) {
LLVM_DEBUG(dbgs().indent(2) << "No temporal reuse: distance unknown\n");
return None;
}
const ConstantInt &CI = *SCEVConst->getValue();
if (Level != LoopDepth && !CI.isZero()) {
LLVM_DEBUG(dbgs().indent(2)
<< "No temporal reuse: distance is not zero at depth=" << Level
<< "\n");
return false;
} else if (Level == LoopDepth && CI.getSExtValue() > MaxDistance) {
LLVM_DEBUG(
dbgs().indent(2)
<< "No temporal reuse: distance is greater than MaxDistance at depth="
<< Level << "\n");
return false;
}
}
LLVM_DEBUG(dbgs().indent(2) << "Found temporal reuse\n");
return true;
}
CacheCostTy IndexedReference::computeRefCost(const Loop &L,
unsigned CLS) const {
assert(IsValid && "Expecting a valid reference");
LLVM_DEBUG({
dbgs().indent(2) << "Computing cache cost for:\n";
dbgs().indent(4) << *this << "\n";
});
// If the indexed reference is loop invariant the cost is one.
if (isLoopInvariant(L)) {
LLVM_DEBUG(dbgs().indent(4) << "Reference is loop invariant: RefCost=1\n");
return 1;
}
const SCEV *TripCount = computeTripCount(L, SE);
if (!TripCount) {
LLVM_DEBUG(dbgs() << "Trip count of loop " << L.getName()
<< " could not be computed, using DefaultTripCount\n");
const SCEV *ElemSize = Sizes.back();
TripCount = SE.getConstant(ElemSize->getType(), DefaultTripCount);
}
LLVM_DEBUG(dbgs() << "TripCount=" << *TripCount << "\n");
// If the indexed reference is 'consecutive' the cost is
// (TripCount*Stride)/CLS, otherwise the cost is TripCount.
const SCEV *RefCost = TripCount;
if (isConsecutive(L, CLS)) {
const SCEV *Coeff = getLastCoefficient();
const SCEV *ElemSize = Sizes.back();
const SCEV *Stride = SE.getMulExpr(Coeff, ElemSize);
const SCEV *CacheLineSize = SE.getConstant(Stride->getType(), CLS);
Type *WiderType = SE.getWiderType(Stride->getType(), TripCount->getType());
if (SE.isKnownNegative(Stride))
Stride = SE.getNegativeSCEV(Stride);
Stride = SE.getNoopOrAnyExtend(Stride, WiderType);
TripCount = SE.getNoopOrAnyExtend(TripCount, WiderType);
Title: Loop Cache Analysis Summary: Implement a new analysis to estimate the number of cache lines required by a loop nest. The analysis is largely based on the following paper: Compiler Optimizations for Improving Data Locality By: Steve Carr, Katherine S. McKinley, Chau-Wen Tseng http://www.cs.utexas.edu/users/mckinley/papers/asplos-1994.pdf The analysis considers temporal reuse (accesses to the same memory location) and spatial reuse (accesses to memory locations within a cache line). For simplicity the analysis considers memory accesses in the innermost loop in a loop nest, and thus determines the number of cache lines used when the loop L in loop nest LN is placed in the innermost position. The result of the analysis can be used to drive several transformations. As an example, loop interchange could use it determine which loops in a perfect loop nest should be interchanged to maximize cache reuse. Similarly, loop distribution could be enhanced to take into consideration cache reuse between arrays when distributing a loop to eliminate vectorization inhibiting dependencies. The general approach taken to estimate the number of cache lines used by the memory references in the inner loop of a loop nest is: Partition memory references that exhibit temporal or spatial reuse into reference groups. For each loop L in the a loop nest LN: a. Compute the cost of the reference group b. Compute the 'cache cost' of the loop nest by summing up the reference groups costs For further details of the algorithm please refer to the paper. Authored By: etiotto Reviewers: hfinkel, Meinersbur, jdoerfert, kbarton, bmahjour, anemet, fhahn Reviewed By: Meinersbur Subscribers: reames, nemanjai, MaskRay, wuzish, Hahnfeld, xusx595, venkataramanan.kumar.llvm, greened, dmgreen, steleman, fhahn, xblvaOO, Whitney, mgorny, hiraditya, mgrang, jsji, llvm-commits Tag: LLVM Differential Revision: https://reviews.llvm.org/D63459 llvm-svn: 368439
2019-08-09 21:56:29 +08:00
const SCEV *Numerator = SE.getMulExpr(Stride, TripCount);
RefCost = SE.getUDivExpr(Numerator, CacheLineSize);
Title: Loop Cache Analysis Summary: Implement a new analysis to estimate the number of cache lines required by a loop nest. The analysis is largely based on the following paper: Compiler Optimizations for Improving Data Locality By: Steve Carr, Katherine S. McKinley, Chau-Wen Tseng http://www.cs.utexas.edu/users/mckinley/papers/asplos-1994.pdf The analysis considers temporal reuse (accesses to the same memory location) and spatial reuse (accesses to memory locations within a cache line). For simplicity the analysis considers memory accesses in the innermost loop in a loop nest, and thus determines the number of cache lines used when the loop L in loop nest LN is placed in the innermost position. The result of the analysis can be used to drive several transformations. As an example, loop interchange could use it determine which loops in a perfect loop nest should be interchanged to maximize cache reuse. Similarly, loop distribution could be enhanced to take into consideration cache reuse between arrays when distributing a loop to eliminate vectorization inhibiting dependencies. The general approach taken to estimate the number of cache lines used by the memory references in the inner loop of a loop nest is: Partition memory references that exhibit temporal or spatial reuse into reference groups. For each loop L in the a loop nest LN: a. Compute the cost of the reference group b. Compute the 'cache cost' of the loop nest by summing up the reference groups costs For further details of the algorithm please refer to the paper. Authored By: etiotto Reviewers: hfinkel, Meinersbur, jdoerfert, kbarton, bmahjour, anemet, fhahn Reviewed By: Meinersbur Subscribers: reames, nemanjai, MaskRay, wuzish, Hahnfeld, xusx595, venkataramanan.kumar.llvm, greened, dmgreen, steleman, fhahn, xblvaOO, Whitney, mgorny, hiraditya, mgrang, jsji, llvm-commits Tag: LLVM Differential Revision: https://reviews.llvm.org/D63459 llvm-svn: 368439
2019-08-09 21:56:29 +08:00
LLVM_DEBUG(dbgs().indent(4)
<< "Access is consecutive: RefCost=(TripCount*Stride)/CLS="
<< *RefCost << "\n");
} else
LLVM_DEBUG(dbgs().indent(4)
<< "Access is not consecutive: RefCost=TripCount=" << *RefCost
<< "\n");
// Attempt to fold RefCost into a constant.
if (auto ConstantCost = dyn_cast<SCEVConstant>(RefCost))
return ConstantCost->getValue()->getSExtValue();
LLVM_DEBUG(dbgs().indent(4)
<< "RefCost is not a constant! Setting to RefCost=InvalidCost "
"(invalid value).\n");
return CacheCost::InvalidCost;
}
bool IndexedReference::delinearize(const LoopInfo &LI) {
assert(Subscripts.empty() && "Subscripts should be empty");
assert(Sizes.empty() && "Sizes should be empty");
assert(!IsValid && "Should be called once from the constructor");
LLVM_DEBUG(dbgs() << "Delinearizing: " << StoreOrLoadInst << "\n");
const SCEV *ElemSize = SE.getElementSize(&StoreOrLoadInst);
const BasicBlock *BB = StoreOrLoadInst.getParent();
if (Loop *L = LI.getLoopFor(BB)) {
Title: Loop Cache Analysis Summary: Implement a new analysis to estimate the number of cache lines required by a loop nest. The analysis is largely based on the following paper: Compiler Optimizations for Improving Data Locality By: Steve Carr, Katherine S. McKinley, Chau-Wen Tseng http://www.cs.utexas.edu/users/mckinley/papers/asplos-1994.pdf The analysis considers temporal reuse (accesses to the same memory location) and spatial reuse (accesses to memory locations within a cache line). For simplicity the analysis considers memory accesses in the innermost loop in a loop nest, and thus determines the number of cache lines used when the loop L in loop nest LN is placed in the innermost position. The result of the analysis can be used to drive several transformations. As an example, loop interchange could use it determine which loops in a perfect loop nest should be interchanged to maximize cache reuse. Similarly, loop distribution could be enhanced to take into consideration cache reuse between arrays when distributing a loop to eliminate vectorization inhibiting dependencies. The general approach taken to estimate the number of cache lines used by the memory references in the inner loop of a loop nest is: Partition memory references that exhibit temporal or spatial reuse into reference groups. For each loop L in the a loop nest LN: a. Compute the cost of the reference group b. Compute the 'cache cost' of the loop nest by summing up the reference groups costs For further details of the algorithm please refer to the paper. Authored By: etiotto Reviewers: hfinkel, Meinersbur, jdoerfert, kbarton, bmahjour, anemet, fhahn Reviewed By: Meinersbur Subscribers: reames, nemanjai, MaskRay, wuzish, Hahnfeld, xusx595, venkataramanan.kumar.llvm, greened, dmgreen, steleman, fhahn, xblvaOO, Whitney, mgorny, hiraditya, mgrang, jsji, llvm-commits Tag: LLVM Differential Revision: https://reviews.llvm.org/D63459 llvm-svn: 368439
2019-08-09 21:56:29 +08:00
const SCEV *AccessFn =
SE.getSCEVAtScope(getPointerOperand(&StoreOrLoadInst), L);
BasePointer = dyn_cast<SCEVUnknown>(SE.getPointerBase(AccessFn));
if (BasePointer == nullptr) {
LLVM_DEBUG(
dbgs().indent(2)
<< "ERROR: failed to delinearize, can't identify base pointer\n");
return false;
}
AccessFn = SE.getMinusSCEV(AccessFn, BasePointer);
LLVM_DEBUG(dbgs().indent(2) << "In Loop '" << L->getName()
<< "', AccessFn: " << *AccessFn << "\n");
SE.delinearize(AccessFn, Subscripts, Sizes,
SE.getElementSize(&StoreOrLoadInst));
if (Subscripts.empty() || Sizes.empty() ||
Subscripts.size() != Sizes.size()) {
// Attempt to determine whether we have a single dimensional array access.
// before giving up.
if (!isOneDimensionalArray(*AccessFn, *ElemSize, *L, SE)) {
LLVM_DEBUG(dbgs().indent(2)
<< "ERROR: failed to delinearize reference\n");
Subscripts.clear();
Sizes.clear();
return false;
Title: Loop Cache Analysis Summary: Implement a new analysis to estimate the number of cache lines required by a loop nest. The analysis is largely based on the following paper: Compiler Optimizations for Improving Data Locality By: Steve Carr, Katherine S. McKinley, Chau-Wen Tseng http://www.cs.utexas.edu/users/mckinley/papers/asplos-1994.pdf The analysis considers temporal reuse (accesses to the same memory location) and spatial reuse (accesses to memory locations within a cache line). For simplicity the analysis considers memory accesses in the innermost loop in a loop nest, and thus determines the number of cache lines used when the loop L in loop nest LN is placed in the innermost position. The result of the analysis can be used to drive several transformations. As an example, loop interchange could use it determine which loops in a perfect loop nest should be interchanged to maximize cache reuse. Similarly, loop distribution could be enhanced to take into consideration cache reuse between arrays when distributing a loop to eliminate vectorization inhibiting dependencies. The general approach taken to estimate the number of cache lines used by the memory references in the inner loop of a loop nest is: Partition memory references that exhibit temporal or spatial reuse into reference groups. For each loop L in the a loop nest LN: a. Compute the cost of the reference group b. Compute the 'cache cost' of the loop nest by summing up the reference groups costs For further details of the algorithm please refer to the paper. Authored By: etiotto Reviewers: hfinkel, Meinersbur, jdoerfert, kbarton, bmahjour, anemet, fhahn Reviewed By: Meinersbur Subscribers: reames, nemanjai, MaskRay, wuzish, Hahnfeld, xusx595, venkataramanan.kumar.llvm, greened, dmgreen, steleman, fhahn, xblvaOO, Whitney, mgorny, hiraditya, mgrang, jsji, llvm-commits Tag: LLVM Differential Revision: https://reviews.llvm.org/D63459 llvm-svn: 368439
2019-08-09 21:56:29 +08:00
}
// The array may be accessed in reverse, for example:
// for (i = N; i > 0; i--)
// A[i] = 0;
// In this case, reconstruct the access function using the absolute value
// of the step recurrence.
const SCEVAddRecExpr *AccessFnAR = dyn_cast<SCEVAddRecExpr>(AccessFn);
const SCEV *StepRec = AccessFnAR ? AccessFnAR->getStepRecurrence(SE) : nullptr;
if (StepRec && SE.isKnownNegative(StepRec))
AccessFn = SE.getAddRecExpr(AccessFnAR->getStart(),
SE.getNegativeSCEV(StepRec),
AccessFnAR->getLoop(),
AccessFnAR->getNoWrapFlags());
Title: Loop Cache Analysis Summary: Implement a new analysis to estimate the number of cache lines required by a loop nest. The analysis is largely based on the following paper: Compiler Optimizations for Improving Data Locality By: Steve Carr, Katherine S. McKinley, Chau-Wen Tseng http://www.cs.utexas.edu/users/mckinley/papers/asplos-1994.pdf The analysis considers temporal reuse (accesses to the same memory location) and spatial reuse (accesses to memory locations within a cache line). For simplicity the analysis considers memory accesses in the innermost loop in a loop nest, and thus determines the number of cache lines used when the loop L in loop nest LN is placed in the innermost position. The result of the analysis can be used to drive several transformations. As an example, loop interchange could use it determine which loops in a perfect loop nest should be interchanged to maximize cache reuse. Similarly, loop distribution could be enhanced to take into consideration cache reuse between arrays when distributing a loop to eliminate vectorization inhibiting dependencies. The general approach taken to estimate the number of cache lines used by the memory references in the inner loop of a loop nest is: Partition memory references that exhibit temporal or spatial reuse into reference groups. For each loop L in the a loop nest LN: a. Compute the cost of the reference group b. Compute the 'cache cost' of the loop nest by summing up the reference groups costs For further details of the algorithm please refer to the paper. Authored By: etiotto Reviewers: hfinkel, Meinersbur, jdoerfert, kbarton, bmahjour, anemet, fhahn Reviewed By: Meinersbur Subscribers: reames, nemanjai, MaskRay, wuzish, Hahnfeld, xusx595, venkataramanan.kumar.llvm, greened, dmgreen, steleman, fhahn, xblvaOO, Whitney, mgorny, hiraditya, mgrang, jsji, llvm-commits Tag: LLVM Differential Revision: https://reviews.llvm.org/D63459 llvm-svn: 368439
2019-08-09 21:56:29 +08:00
const SCEV *Div = SE.getUDivExactExpr(AccessFn, ElemSize);
Subscripts.push_back(Div);
Sizes.push_back(ElemSize);
}
return all_of(Subscripts, [&](const SCEV *Subscript) {
return isSimpleAddRecurrence(*Subscript, *L);
});
}
return false;
}
bool IndexedReference::isLoopInvariant(const Loop &L) const {
Value *Addr = getPointerOperand(&StoreOrLoadInst);
assert(Addr != nullptr && "Expecting either a load or a store instruction");
assert(SE.isSCEVable(Addr->getType()) && "Addr should be SCEVable");
if (SE.isLoopInvariant(SE.getSCEV(Addr), &L))
return true;
// The indexed reference is loop invariant if none of the coefficients use
// the loop induction variable.
bool allCoeffForLoopAreZero = all_of(Subscripts, [&](const SCEV *Subscript) {
return isCoeffForLoopZeroOrInvariant(*Subscript, L);
});
return allCoeffForLoopAreZero;
}
bool IndexedReference::isConsecutive(const Loop &L, unsigned CLS) const {
// The indexed reference is 'consecutive' if the only coefficient that uses
// the loop induction variable is the last one...
const SCEV *LastSubscript = Subscripts.back();
for (const SCEV *Subscript : Subscripts) {
if (Subscript == LastSubscript)
continue;
if (!isCoeffForLoopZeroOrInvariant(*Subscript, L))
return false;
}
// ...and the access stride is less than the cache line size.
const SCEV *Coeff = getLastCoefficient();
const SCEV *ElemSize = Sizes.back();
const SCEV *Stride = SE.getMulExpr(Coeff, ElemSize);
const SCEV *CacheLineSize = SE.getConstant(Stride->getType(), CLS);
Stride = SE.isKnownNegative(Stride) ? SE.getNegativeSCEV(Stride) : Stride;
Title: Loop Cache Analysis Summary: Implement a new analysis to estimate the number of cache lines required by a loop nest. The analysis is largely based on the following paper: Compiler Optimizations for Improving Data Locality By: Steve Carr, Katherine S. McKinley, Chau-Wen Tseng http://www.cs.utexas.edu/users/mckinley/papers/asplos-1994.pdf The analysis considers temporal reuse (accesses to the same memory location) and spatial reuse (accesses to memory locations within a cache line). For simplicity the analysis considers memory accesses in the innermost loop in a loop nest, and thus determines the number of cache lines used when the loop L in loop nest LN is placed in the innermost position. The result of the analysis can be used to drive several transformations. As an example, loop interchange could use it determine which loops in a perfect loop nest should be interchanged to maximize cache reuse. Similarly, loop distribution could be enhanced to take into consideration cache reuse between arrays when distributing a loop to eliminate vectorization inhibiting dependencies. The general approach taken to estimate the number of cache lines used by the memory references in the inner loop of a loop nest is: Partition memory references that exhibit temporal or spatial reuse into reference groups. For each loop L in the a loop nest LN: a. Compute the cost of the reference group b. Compute the 'cache cost' of the loop nest by summing up the reference groups costs For further details of the algorithm please refer to the paper. Authored By: etiotto Reviewers: hfinkel, Meinersbur, jdoerfert, kbarton, bmahjour, anemet, fhahn Reviewed By: Meinersbur Subscribers: reames, nemanjai, MaskRay, wuzish, Hahnfeld, xusx595, venkataramanan.kumar.llvm, greened, dmgreen, steleman, fhahn, xblvaOO, Whitney, mgorny, hiraditya, mgrang, jsji, llvm-commits Tag: LLVM Differential Revision: https://reviews.llvm.org/D63459 llvm-svn: 368439
2019-08-09 21:56:29 +08:00
return SE.isKnownPredicate(ICmpInst::ICMP_ULT, Stride, CacheLineSize);
}
const SCEV *IndexedReference::getLastCoefficient() const {
const SCEV *LastSubscript = getLastSubscript();
assert(isa<SCEVAddRecExpr>(LastSubscript) &&
"Expecting a SCEV add recurrence expression");
const SCEVAddRecExpr *AR = dyn_cast<SCEVAddRecExpr>(LastSubscript);
return AR->getStepRecurrence(SE);
}
bool IndexedReference::isCoeffForLoopZeroOrInvariant(const SCEV &Subscript,
const Loop &L) const {
const SCEVAddRecExpr *AR = dyn_cast<SCEVAddRecExpr>(&Subscript);
return (AR != nullptr) ? AR->getLoop() != &L
: SE.isLoopInvariant(&Subscript, &L);
}
bool IndexedReference::isSimpleAddRecurrence(const SCEV &Subscript,
const Loop &L) const {
if (!isa<SCEVAddRecExpr>(Subscript))
return false;
const SCEVAddRecExpr *AR = cast<SCEVAddRecExpr>(&Subscript);
assert(AR->getLoop() && "AR should have a loop");
if (!AR->isAffine())
return false;
const SCEV *Start = AR->getStart();
const SCEV *Step = AR->getStepRecurrence(SE);
if (!SE.isLoopInvariant(Start, &L) || !SE.isLoopInvariant(Step, &L))
return false;
return true;
}
bool IndexedReference::isAliased(const IndexedReference &Other,
AAResults &AA) const {
Title: Loop Cache Analysis Summary: Implement a new analysis to estimate the number of cache lines required by a loop nest. The analysis is largely based on the following paper: Compiler Optimizations for Improving Data Locality By: Steve Carr, Katherine S. McKinley, Chau-Wen Tseng http://www.cs.utexas.edu/users/mckinley/papers/asplos-1994.pdf The analysis considers temporal reuse (accesses to the same memory location) and spatial reuse (accesses to memory locations within a cache line). For simplicity the analysis considers memory accesses in the innermost loop in a loop nest, and thus determines the number of cache lines used when the loop L in loop nest LN is placed in the innermost position. The result of the analysis can be used to drive several transformations. As an example, loop interchange could use it determine which loops in a perfect loop nest should be interchanged to maximize cache reuse. Similarly, loop distribution could be enhanced to take into consideration cache reuse between arrays when distributing a loop to eliminate vectorization inhibiting dependencies. The general approach taken to estimate the number of cache lines used by the memory references in the inner loop of a loop nest is: Partition memory references that exhibit temporal or spatial reuse into reference groups. For each loop L in the a loop nest LN: a. Compute the cost of the reference group b. Compute the 'cache cost' of the loop nest by summing up the reference groups costs For further details of the algorithm please refer to the paper. Authored By: etiotto Reviewers: hfinkel, Meinersbur, jdoerfert, kbarton, bmahjour, anemet, fhahn Reviewed By: Meinersbur Subscribers: reames, nemanjai, MaskRay, wuzish, Hahnfeld, xusx595, venkataramanan.kumar.llvm, greened, dmgreen, steleman, fhahn, xblvaOO, Whitney, mgorny, hiraditya, mgrang, jsji, llvm-commits Tag: LLVM Differential Revision: https://reviews.llvm.org/D63459 llvm-svn: 368439
2019-08-09 21:56:29 +08:00
const auto &Loc1 = MemoryLocation::get(&StoreOrLoadInst);
const auto &Loc2 = MemoryLocation::get(&Other.StoreOrLoadInst);
return AA.isMustAlias(Loc1, Loc2);
}
//===----------------------------------------------------------------------===//
// CacheCost implementation
//
raw_ostream &llvm::operator<<(raw_ostream &OS, const CacheCost &CC) {
for (const auto &LC : CC.LoopCosts) {
const Loop *L = LC.first;
OS << "Loop '" << L->getName() << "' has cost = " << LC.second << "\n";
}
return OS;
}
CacheCost::CacheCost(const LoopVectorTy &Loops, const LoopInfo &LI,
ScalarEvolution &SE, TargetTransformInfo &TTI,
AAResults &AA, DependenceInfo &DI,
Title: Loop Cache Analysis Summary: Implement a new analysis to estimate the number of cache lines required by a loop nest. The analysis is largely based on the following paper: Compiler Optimizations for Improving Data Locality By: Steve Carr, Katherine S. McKinley, Chau-Wen Tseng http://www.cs.utexas.edu/users/mckinley/papers/asplos-1994.pdf The analysis considers temporal reuse (accesses to the same memory location) and spatial reuse (accesses to memory locations within a cache line). For simplicity the analysis considers memory accesses in the innermost loop in a loop nest, and thus determines the number of cache lines used when the loop L in loop nest LN is placed in the innermost position. The result of the analysis can be used to drive several transformations. As an example, loop interchange could use it determine which loops in a perfect loop nest should be interchanged to maximize cache reuse. Similarly, loop distribution could be enhanced to take into consideration cache reuse between arrays when distributing a loop to eliminate vectorization inhibiting dependencies. The general approach taken to estimate the number of cache lines used by the memory references in the inner loop of a loop nest is: Partition memory references that exhibit temporal or spatial reuse into reference groups. For each loop L in the a loop nest LN: a. Compute the cost of the reference group b. Compute the 'cache cost' of the loop nest by summing up the reference groups costs For further details of the algorithm please refer to the paper. Authored By: etiotto Reviewers: hfinkel, Meinersbur, jdoerfert, kbarton, bmahjour, anemet, fhahn Reviewed By: Meinersbur Subscribers: reames, nemanjai, MaskRay, wuzish, Hahnfeld, xusx595, venkataramanan.kumar.llvm, greened, dmgreen, steleman, fhahn, xblvaOO, Whitney, mgorny, hiraditya, mgrang, jsji, llvm-commits Tag: LLVM Differential Revision: https://reviews.llvm.org/D63459 llvm-svn: 368439
2019-08-09 21:56:29 +08:00
Optional<unsigned> TRT)
: Loops(Loops), TripCounts(), LoopCosts(),
TRT((TRT == None) ? Optional<unsigned>(TemporalReuseThreshold) : TRT),
Title: Loop Cache Analysis Summary: Implement a new analysis to estimate the number of cache lines required by a loop nest. The analysis is largely based on the following paper: Compiler Optimizations for Improving Data Locality By: Steve Carr, Katherine S. McKinley, Chau-Wen Tseng http://www.cs.utexas.edu/users/mckinley/papers/asplos-1994.pdf The analysis considers temporal reuse (accesses to the same memory location) and spatial reuse (accesses to memory locations within a cache line). For simplicity the analysis considers memory accesses in the innermost loop in a loop nest, and thus determines the number of cache lines used when the loop L in loop nest LN is placed in the innermost position. The result of the analysis can be used to drive several transformations. As an example, loop interchange could use it determine which loops in a perfect loop nest should be interchanged to maximize cache reuse. Similarly, loop distribution could be enhanced to take into consideration cache reuse between arrays when distributing a loop to eliminate vectorization inhibiting dependencies. The general approach taken to estimate the number of cache lines used by the memory references in the inner loop of a loop nest is: Partition memory references that exhibit temporal or spatial reuse into reference groups. For each loop L in the a loop nest LN: a. Compute the cost of the reference group b. Compute the 'cache cost' of the loop nest by summing up the reference groups costs For further details of the algorithm please refer to the paper. Authored By: etiotto Reviewers: hfinkel, Meinersbur, jdoerfert, kbarton, bmahjour, anemet, fhahn Reviewed By: Meinersbur Subscribers: reames, nemanjai, MaskRay, wuzish, Hahnfeld, xusx595, venkataramanan.kumar.llvm, greened, dmgreen, steleman, fhahn, xblvaOO, Whitney, mgorny, hiraditya, mgrang, jsji, llvm-commits Tag: LLVM Differential Revision: https://reviews.llvm.org/D63459 llvm-svn: 368439
2019-08-09 21:56:29 +08:00
LI(LI), SE(SE), TTI(TTI), AA(AA), DI(DI) {
assert(!Loops.empty() && "Expecting a non-empty loop vector.");
for (const Loop *L : Loops) {
unsigned TripCount = SE.getSmallConstantTripCount(L);
TripCount = (TripCount == 0) ? DefaultTripCount : TripCount;
TripCounts.push_back({L, TripCount});
}
calculateCacheFootprint();
}
std::unique_ptr<CacheCost>
CacheCost::getCacheCost(Loop &Root, LoopStandardAnalysisResults &AR,
DependenceInfo &DI, Optional<unsigned> TRT) {
if (!Root.isOutermost()) {
Title: Loop Cache Analysis Summary: Implement a new analysis to estimate the number of cache lines required by a loop nest. The analysis is largely based on the following paper: Compiler Optimizations for Improving Data Locality By: Steve Carr, Katherine S. McKinley, Chau-Wen Tseng http://www.cs.utexas.edu/users/mckinley/papers/asplos-1994.pdf The analysis considers temporal reuse (accesses to the same memory location) and spatial reuse (accesses to memory locations within a cache line). For simplicity the analysis considers memory accesses in the innermost loop in a loop nest, and thus determines the number of cache lines used when the loop L in loop nest LN is placed in the innermost position. The result of the analysis can be used to drive several transformations. As an example, loop interchange could use it determine which loops in a perfect loop nest should be interchanged to maximize cache reuse. Similarly, loop distribution could be enhanced to take into consideration cache reuse between arrays when distributing a loop to eliminate vectorization inhibiting dependencies. The general approach taken to estimate the number of cache lines used by the memory references in the inner loop of a loop nest is: Partition memory references that exhibit temporal or spatial reuse into reference groups. For each loop L in the a loop nest LN: a. Compute the cost of the reference group b. Compute the 'cache cost' of the loop nest by summing up the reference groups costs For further details of the algorithm please refer to the paper. Authored By: etiotto Reviewers: hfinkel, Meinersbur, jdoerfert, kbarton, bmahjour, anemet, fhahn Reviewed By: Meinersbur Subscribers: reames, nemanjai, MaskRay, wuzish, Hahnfeld, xusx595, venkataramanan.kumar.llvm, greened, dmgreen, steleman, fhahn, xblvaOO, Whitney, mgorny, hiraditya, mgrang, jsji, llvm-commits Tag: LLVM Differential Revision: https://reviews.llvm.org/D63459 llvm-svn: 368439
2019-08-09 21:56:29 +08:00
LLVM_DEBUG(dbgs() << "Expecting the outermost loop in a loop nest\n");
return nullptr;
}
LoopVectorTy Loops;
for (Loop *L : breadth_first(&Root))
Loops.push_back(L);
if (!getInnerMostLoop(Loops)) {
LLVM_DEBUG(dbgs() << "Cannot compute cache cost of loop nest with more "
"than one innermost loop\n");
return nullptr;
}
return std::make_unique<CacheCost>(Loops, AR.LI, AR.SE, AR.TTI, AR.AA, DI, TRT);
Title: Loop Cache Analysis Summary: Implement a new analysis to estimate the number of cache lines required by a loop nest. The analysis is largely based on the following paper: Compiler Optimizations for Improving Data Locality By: Steve Carr, Katherine S. McKinley, Chau-Wen Tseng http://www.cs.utexas.edu/users/mckinley/papers/asplos-1994.pdf The analysis considers temporal reuse (accesses to the same memory location) and spatial reuse (accesses to memory locations within a cache line). For simplicity the analysis considers memory accesses in the innermost loop in a loop nest, and thus determines the number of cache lines used when the loop L in loop nest LN is placed in the innermost position. The result of the analysis can be used to drive several transformations. As an example, loop interchange could use it determine which loops in a perfect loop nest should be interchanged to maximize cache reuse. Similarly, loop distribution could be enhanced to take into consideration cache reuse between arrays when distributing a loop to eliminate vectorization inhibiting dependencies. The general approach taken to estimate the number of cache lines used by the memory references in the inner loop of a loop nest is: Partition memory references that exhibit temporal or spatial reuse into reference groups. For each loop L in the a loop nest LN: a. Compute the cost of the reference group b. Compute the 'cache cost' of the loop nest by summing up the reference groups costs For further details of the algorithm please refer to the paper. Authored By: etiotto Reviewers: hfinkel, Meinersbur, jdoerfert, kbarton, bmahjour, anemet, fhahn Reviewed By: Meinersbur Subscribers: reames, nemanjai, MaskRay, wuzish, Hahnfeld, xusx595, venkataramanan.kumar.llvm, greened, dmgreen, steleman, fhahn, xblvaOO, Whitney, mgorny, hiraditya, mgrang, jsji, llvm-commits Tag: LLVM Differential Revision: https://reviews.llvm.org/D63459 llvm-svn: 368439
2019-08-09 21:56:29 +08:00
}
void CacheCost::calculateCacheFootprint() {
LLVM_DEBUG(dbgs() << "POPULATING REFERENCE GROUPS\n");
ReferenceGroupsTy RefGroups;
if (!populateReferenceGroups(RefGroups))
return;
LLVM_DEBUG(dbgs() << "COMPUTING LOOP CACHE COSTS\n");
for (const Loop *L : Loops) {
assert((std::find_if(LoopCosts.begin(), LoopCosts.end(),
[L](const LoopCacheCostTy &LCC) {
return LCC.first == L;
}) == LoopCosts.end()) &&
"Should not add duplicate element");
CacheCostTy LoopCost = computeLoopCacheCost(*L, RefGroups);
LoopCosts.push_back(std::make_pair(L, LoopCost));
}
sortLoopCosts();
RefGroups.clear();
}
bool CacheCost::populateReferenceGroups(ReferenceGroupsTy &RefGroups) const {
assert(RefGroups.empty() && "Reference groups should be empty");
unsigned CLS = TTI.getCacheLineSize();
Loop *InnerMostLoop = getInnerMostLoop(Loops);
assert(InnerMostLoop != nullptr && "Expecting a valid innermost loop");
for (BasicBlock *BB : InnerMostLoop->getBlocks()) {
for (Instruction &I : *BB) {
if (!isa<StoreInst>(I) && !isa<LoadInst>(I))
continue;
std::unique_ptr<IndexedReference> R(new IndexedReference(I, LI, SE));
if (!R->isValid())
continue;
bool Added = false;
for (ReferenceGroupTy &RefGroup : RefGroups) {
const IndexedReference &Representative = *RefGroup.front().get();
LLVM_DEBUG({
dbgs() << "References:\n";
dbgs().indent(2) << *R << "\n";
dbgs().indent(2) << Representative << "\n";
});
// FIXME: Both positive and negative access functions will be placed
// into the same reference group, resulting in a bi-directional array
// access such as:
// for (i = N; i > 0; i--)
// A[i] = A[N - i];
// having the same cost calculation as a single dimention access pattern
// for (i = 0; i < N; i++)
// A[i] = A[i];
// when in actuality, depending on the array size, the first example
// should have a cost closer to 2x the second due to the two cache
// access per iteration from opposite ends of the array
Title: Loop Cache Analysis Summary: Implement a new analysis to estimate the number of cache lines required by a loop nest. The analysis is largely based on the following paper: Compiler Optimizations for Improving Data Locality By: Steve Carr, Katherine S. McKinley, Chau-Wen Tseng http://www.cs.utexas.edu/users/mckinley/papers/asplos-1994.pdf The analysis considers temporal reuse (accesses to the same memory location) and spatial reuse (accesses to memory locations within a cache line). For simplicity the analysis considers memory accesses in the innermost loop in a loop nest, and thus determines the number of cache lines used when the loop L in loop nest LN is placed in the innermost position. The result of the analysis can be used to drive several transformations. As an example, loop interchange could use it determine which loops in a perfect loop nest should be interchanged to maximize cache reuse. Similarly, loop distribution could be enhanced to take into consideration cache reuse between arrays when distributing a loop to eliminate vectorization inhibiting dependencies. The general approach taken to estimate the number of cache lines used by the memory references in the inner loop of a loop nest is: Partition memory references that exhibit temporal or spatial reuse into reference groups. For each loop L in the a loop nest LN: a. Compute the cost of the reference group b. Compute the 'cache cost' of the loop nest by summing up the reference groups costs For further details of the algorithm please refer to the paper. Authored By: etiotto Reviewers: hfinkel, Meinersbur, jdoerfert, kbarton, bmahjour, anemet, fhahn Reviewed By: Meinersbur Subscribers: reames, nemanjai, MaskRay, wuzish, Hahnfeld, xusx595, venkataramanan.kumar.llvm, greened, dmgreen, steleman, fhahn, xblvaOO, Whitney, mgorny, hiraditya, mgrang, jsji, llvm-commits Tag: LLVM Differential Revision: https://reviews.llvm.org/D63459 llvm-svn: 368439
2019-08-09 21:56:29 +08:00
Optional<bool> HasTemporalReuse =
R->hasTemporalReuse(Representative, *TRT, *InnerMostLoop, DI, AA);
Optional<bool> HasSpacialReuse =
R->hasSpacialReuse(Representative, CLS, AA);
if ((HasTemporalReuse.hasValue() && *HasTemporalReuse) ||
(HasSpacialReuse.hasValue() && *HasSpacialReuse)) {
RefGroup.push_back(std::move(R));
Added = true;
break;
}
}
if (!Added) {
ReferenceGroupTy RG;
RG.push_back(std::move(R));
RefGroups.push_back(std::move(RG));
}
}
}
if (RefGroups.empty())
return false;
LLVM_DEBUG({
dbgs() << "\nIDENTIFIED REFERENCE GROUPS:\n";
int n = 1;
for (const ReferenceGroupTy &RG : RefGroups) {
dbgs().indent(2) << "RefGroup " << n << ":\n";
for (const auto &IR : RG)
dbgs().indent(4) << *IR << "\n";
n++;
}
dbgs() << "\n";
});
return true;
}
CacheCostTy
CacheCost::computeLoopCacheCost(const Loop &L,
const ReferenceGroupsTy &RefGroups) const {
if (!L.isLoopSimplifyForm())
return InvalidCost;
LLVM_DEBUG(dbgs() << "Considering loop '" << L.getName()
<< "' as innermost loop.\n");
// Compute the product of the trip counts of each other loop in the nest.
CacheCostTy TripCountsProduct = 1;
for (const auto &TC : TripCounts) {
if (TC.first == &L)
continue;
TripCountsProduct *= TC.second;
}
CacheCostTy LoopCost = 0;
for (const ReferenceGroupTy &RG : RefGroups) {
CacheCostTy RefGroupCost = computeRefGroupCacheCost(RG, L);
LoopCost += RefGroupCost * TripCountsProduct;
}
LLVM_DEBUG(dbgs().indent(2) << "Loop '" << L.getName()
<< "' has cost=" << LoopCost << "\n");
return LoopCost;
}
CacheCostTy CacheCost::computeRefGroupCacheCost(const ReferenceGroupTy &RG,
const Loop &L) const {
assert(!RG.empty() && "Reference group should have at least one member.");
const IndexedReference *Representative = RG.front().get();
return Representative->computeRefCost(L, TTI.getCacheLineSize());
}
//===----------------------------------------------------------------------===//
// LoopCachePrinterPass implementation
//
PreservedAnalyses LoopCachePrinterPass::run(Loop &L, LoopAnalysisManager &AM,
LoopStandardAnalysisResults &AR,
LPMUpdater &U) {
Function *F = L.getHeader()->getParent();
DependenceInfo DI(F, &AR.AA, &AR.SE, &AR.LI);
if (auto CC = CacheCost::getCacheCost(L, AR, DI))
OS << *CC;
return PreservedAnalyses::all();
}