dependence analysis
Patch from Preston Briggs <preston.briggs@gmail.com>.
This is an updated version of the dependence-analysis patch, including an MIV
test based on Banerjee's inequalities.
It's a fairly complete implementation of the paper
Practical Dependence Testing
Gina Goff, Ken Kennedy, and Chau-Wen Tseng
PLDI 1991
It cannot yet propagate constraints between coupled RDIV subscripts (discussed
in Section 5.3.2 of the paper).
It's organized as a FunctionPass with a single entry point that supports testing
for dependence between two instructions in a function. If there's no dependence,
it returns null. If there's a dependence, it returns a pointer to a Dependence
which can be queried about details (what kind of dependence, is it loop
independent, direction and distance vector entries, etc). I haven't included
every imaginable feature, but there's a good selection that should be adequate
for supporting many loop transformations. Of course, it can be extended as
necessary.
Included in the patch file are many test cases, commented with C code showing
the loops and array references.
git-svn-id: https://llvm.org/svn/llvm-project/llvm/trunk@165708 91177308-0d34-0410-b5e6-96231b3b80d8
2012-10-11 07:32:34 +00:00
|
|
|
//===-- DependenceAnalysis.cpp - DA Implementation --------------*- C++ -*-===//
|
|
|
|
//
|
|
|
|
// The LLVM Compiler Infrastructure
|
|
|
|
//
|
|
|
|
// This file is distributed under the University of Illinois Open Source
|
|
|
|
// License. See LICENSE.TXT for details.
|
|
|
|
//
|
|
|
|
//===----------------------------------------------------------------------===//
|
|
|
|
//
|
|
|
|
// DependenceAnalysis is an LLVM pass that analyses dependences between memory
|
|
|
|
// accesses. Currently, it is an (incomplete) implementation of the approach
|
|
|
|
// described in
|
|
|
|
//
|
|
|
|
// Practical Dependence Testing
|
|
|
|
// Goff, Kennedy, Tseng
|
|
|
|
// PLDI 1991
|
|
|
|
//
|
|
|
|
// There's a single entry point that analyzes the dependence between a pair
|
|
|
|
// of memory references in a function, returning either NULL, for no dependence,
|
|
|
|
// or a more-or-less detailed description of the dependence between them.
|
|
|
|
//
|
|
|
|
// Currently, the implementation cannot propagate constraints between
|
|
|
|
// coupled RDIV subscripts and lacks a multi-subscript MIV test.
|
|
|
|
// Both of these are conservative weaknesses;
|
|
|
|
// that is, not a source of correctness problems.
|
|
|
|
//
|
|
|
|
// The implementation depends on the GEP instruction to
|
|
|
|
// differentiate subscripts. Since Clang linearizes subscripts
|
|
|
|
// for most arrays, we give up some precision (though the existing MIV tests
|
|
|
|
// will help). We trust that the GEP instruction will eventually be extended.
|
|
|
|
// In the meantime, we should explore Maslov's ideas about delinearization.
|
|
|
|
//
|
|
|
|
// We should pay some careful attention to the possibility of integer overflow
|
|
|
|
// in the implementation of the various tests. This could happen with Add,
|
|
|
|
// Subtract, or Multiply, with both APInt's and SCEV's.
|
|
|
|
//
|
|
|
|
// Some non-linear subscript pairs can be handled by the GCD test
|
|
|
|
// (and perhaps other tests).
|
|
|
|
// Should explore how often these things occur.
|
|
|
|
//
|
|
|
|
// Finally, it seems like certain test cases expose weaknesses in the SCEV
|
|
|
|
// simplification, especially in the handling of sign and zero extensions.
|
|
|
|
// It could be useful to spend time exploring these.
|
|
|
|
//
|
|
|
|
// Please note that this is work in progress and the interface is subject to
|
|
|
|
// change.
|
|
|
|
//
|
|
|
|
//===----------------------------------------------------------------------===//
|
|
|
|
// //
|
|
|
|
// In memory of Ken Kennedy, 1945 - 2007 //
|
|
|
|
// //
|
|
|
|
//===----------------------------------------------------------------------===//
|
|
|
|
|
|
|
|
#define DEBUG_TYPE "da"
|
|
|
|
|
|
|
|
#include "llvm/Analysis/DependenceAnalysis.h"
|
|
|
|
#include "llvm/ADT/Statistic.h"
|
2012-10-25 16:15:22 +00:00
|
|
|
#include "llvm/Analysis/AliasAnalysis.h"
|
|
|
|
#include "llvm/Analysis/LoopInfo.h"
|
|
|
|
#include "llvm/Analysis/ScalarEvolution.h"
|
|
|
|
#include "llvm/Analysis/ScalarEvolutionExpressions.h"
|
2012-12-03 16:50:05 +00:00
|
|
|
#include "llvm/Analysis/ValueTracking.h"
|
2013-01-02 11:36:10 +00:00
|
|
|
#include "llvm/IR/Operator.h"
|
dependence analysis
Patch from Preston Briggs <preston.briggs@gmail.com>.
This is an updated version of the dependence-analysis patch, including an MIV
test based on Banerjee's inequalities.
It's a fairly complete implementation of the paper
Practical Dependence Testing
Gina Goff, Ken Kennedy, and Chau-Wen Tseng
PLDI 1991
It cannot yet propagate constraints between coupled RDIV subscripts (discussed
in Section 5.3.2 of the paper).
It's organized as a FunctionPass with a single entry point that supports testing
for dependence between two instructions in a function. If there's no dependence,
it returns null. If there's a dependence, it returns a pointer to a Dependence
which can be queried about details (what kind of dependence, is it loop
independent, direction and distance vector entries, etc). I haven't included
every imaginable feature, but there's a good selection that should be adequate
for supporting many loop transformations. Of course, it can be extended as
necessary.
Included in the patch file are many test cases, commented with C code showing
the loops and array references.
git-svn-id: https://llvm.org/svn/llvm-project/llvm/trunk@165708 91177308-0d34-0410-b5e6-96231b3b80d8
2012-10-11 07:32:34 +00:00
|
|
|
#include "llvm/Support/Debug.h"
|
|
|
|
#include "llvm/Support/ErrorHandling.h"
|
|
|
|
#include "llvm/Support/InstIterator.h"
|
2012-10-25 16:15:22 +00:00
|
|
|
#include "llvm/Support/raw_ostream.h"
|
dependence analysis
Patch from Preston Briggs <preston.briggs@gmail.com>.
This is an updated version of the dependence-analysis patch, including an MIV
test based on Banerjee's inequalities.
It's a fairly complete implementation of the paper
Practical Dependence Testing
Gina Goff, Ken Kennedy, and Chau-Wen Tseng
PLDI 1991
It cannot yet propagate constraints between coupled RDIV subscripts (discussed
in Section 5.3.2 of the paper).
It's organized as a FunctionPass with a single entry point that supports testing
for dependence between two instructions in a function. If there's no dependence,
it returns null. If there's a dependence, it returns a pointer to a Dependence
which can be queried about details (what kind of dependence, is it loop
independent, direction and distance vector entries, etc). I haven't included
every imaginable feature, but there's a good selection that should be adequate
for supporting many loop transformations. Of course, it can be extended as
necessary.
Included in the patch file are many test cases, commented with C code showing
the loops and array references.
git-svn-id: https://llvm.org/svn/llvm-project/llvm/trunk@165708 91177308-0d34-0410-b5e6-96231b3b80d8
2012-10-11 07:32:34 +00:00
|
|
|
|
|
|
|
using namespace llvm;
|
|
|
|
|
|
|
|
//===----------------------------------------------------------------------===//
|
|
|
|
// statistics
|
|
|
|
|
|
|
|
STATISTIC(TotalArrayPairs, "Array pairs tested");
|
|
|
|
STATISTIC(SeparableSubscriptPairs, "Separable subscript pairs");
|
|
|
|
STATISTIC(CoupledSubscriptPairs, "Coupled subscript pairs");
|
|
|
|
STATISTIC(NonlinearSubscriptPairs, "Nonlinear subscript pairs");
|
|
|
|
STATISTIC(ZIVapplications, "ZIV applications");
|
|
|
|
STATISTIC(ZIVindependence, "ZIV independence");
|
|
|
|
STATISTIC(StrongSIVapplications, "Strong SIV applications");
|
|
|
|
STATISTIC(StrongSIVsuccesses, "Strong SIV successes");
|
|
|
|
STATISTIC(StrongSIVindependence, "Strong SIV independence");
|
|
|
|
STATISTIC(WeakCrossingSIVapplications, "Weak-Crossing SIV applications");
|
|
|
|
STATISTIC(WeakCrossingSIVsuccesses, "Weak-Crossing SIV successes");
|
|
|
|
STATISTIC(WeakCrossingSIVindependence, "Weak-Crossing SIV independence");
|
|
|
|
STATISTIC(ExactSIVapplications, "Exact SIV applications");
|
|
|
|
STATISTIC(ExactSIVsuccesses, "Exact SIV successes");
|
|
|
|
STATISTIC(ExactSIVindependence, "Exact SIV independence");
|
|
|
|
STATISTIC(WeakZeroSIVapplications, "Weak-Zero SIV applications");
|
|
|
|
STATISTIC(WeakZeroSIVsuccesses, "Weak-Zero SIV successes");
|
|
|
|
STATISTIC(WeakZeroSIVindependence, "Weak-Zero SIV independence");
|
|
|
|
STATISTIC(ExactRDIVapplications, "Exact RDIV applications");
|
|
|
|
STATISTIC(ExactRDIVindependence, "Exact RDIV independence");
|
|
|
|
STATISTIC(SymbolicRDIVapplications, "Symbolic RDIV applications");
|
|
|
|
STATISTIC(SymbolicRDIVindependence, "Symbolic RDIV independence");
|
|
|
|
STATISTIC(DeltaApplications, "Delta applications");
|
|
|
|
STATISTIC(DeltaSuccesses, "Delta successes");
|
|
|
|
STATISTIC(DeltaIndependence, "Delta independence");
|
|
|
|
STATISTIC(DeltaPropagations, "Delta propagations");
|
|
|
|
STATISTIC(GCDapplications, "GCD applications");
|
|
|
|
STATISTIC(GCDsuccesses, "GCD successes");
|
|
|
|
STATISTIC(GCDindependence, "GCD independence");
|
|
|
|
STATISTIC(BanerjeeApplications, "Banerjee applications");
|
|
|
|
STATISTIC(BanerjeeIndependence, "Banerjee independence");
|
|
|
|
STATISTIC(BanerjeeSuccesses, "Banerjee successes");
|
|
|
|
|
|
|
|
//===----------------------------------------------------------------------===//
|
|
|
|
// basics
|
|
|
|
|
|
|
|
INITIALIZE_PASS_BEGIN(DependenceAnalysis, "da",
|
|
|
|
"Dependence Analysis", true, true)
|
|
|
|
INITIALIZE_PASS_DEPENDENCY(LoopInfo)
|
|
|
|
INITIALIZE_PASS_DEPENDENCY(ScalarEvolution)
|
|
|
|
INITIALIZE_AG_DEPENDENCY(AliasAnalysis)
|
|
|
|
INITIALIZE_PASS_END(DependenceAnalysis, "da",
|
|
|
|
"Dependence Analysis", true, true)
|
|
|
|
|
|
|
|
char DependenceAnalysis::ID = 0;
|
|
|
|
|
|
|
|
|
|
|
|
FunctionPass *llvm::createDependenceAnalysisPass() {
|
|
|
|
return new DependenceAnalysis();
|
|
|
|
}
|
|
|
|
|
|
|
|
|
|
|
|
bool DependenceAnalysis::runOnFunction(Function &F) {
|
|
|
|
this->F = &F;
|
|
|
|
AA = &getAnalysis<AliasAnalysis>();
|
|
|
|
SE = &getAnalysis<ScalarEvolution>();
|
|
|
|
LI = &getAnalysis<LoopInfo>();
|
|
|
|
return false;
|
|
|
|
}
|
|
|
|
|
|
|
|
|
|
|
|
void DependenceAnalysis::releaseMemory() {
|
|
|
|
}
|
|
|
|
|
|
|
|
|
|
|
|
void DependenceAnalysis::getAnalysisUsage(AnalysisUsage &AU) const {
|
|
|
|
AU.setPreservesAll();
|
|
|
|
AU.addRequiredTransitive<AliasAnalysis>();
|
|
|
|
AU.addRequiredTransitive<ScalarEvolution>();
|
|
|
|
AU.addRequiredTransitive<LoopInfo>();
|
|
|
|
}
|
|
|
|
|
|
|
|
|
|
|
|
// Used to test the dependence analyzer.
|
2012-11-13 12:12:02 +00:00
|
|
|
// Looks through the function, noting loads and stores.
|
|
|
|
// Calls depends() on every possible pair and prints out the result.
|
dependence analysis
Patch from Preston Briggs <preston.briggs@gmail.com>.
This is an updated version of the dependence-analysis patch, including an MIV
test based on Banerjee's inequalities.
It's a fairly complete implementation of the paper
Practical Dependence Testing
Gina Goff, Ken Kennedy, and Chau-Wen Tseng
PLDI 1991
It cannot yet propagate constraints between coupled RDIV subscripts (discussed
in Section 5.3.2 of the paper).
It's organized as a FunctionPass with a single entry point that supports testing
for dependence between two instructions in a function. If there's no dependence,
it returns null. If there's a dependence, it returns a pointer to a Dependence
which can be queried about details (what kind of dependence, is it loop
independent, direction and distance vector entries, etc). I haven't included
every imaginable feature, but there's a good selection that should be adequate
for supporting many loop transformations. Of course, it can be extended as
necessary.
Included in the patch file are many test cases, commented with C code showing
the loops and array references.
git-svn-id: https://llvm.org/svn/llvm-project/llvm/trunk@165708 91177308-0d34-0410-b5e6-96231b3b80d8
2012-10-11 07:32:34 +00:00
|
|
|
// Ignores all other instructions.
|
|
|
|
static
|
|
|
|
void dumpExampleDependence(raw_ostream &OS, Function *F,
|
|
|
|
DependenceAnalysis *DA) {
|
|
|
|
for (inst_iterator SrcI = inst_begin(F), SrcE = inst_end(F);
|
|
|
|
SrcI != SrcE; ++SrcI) {
|
2012-11-13 12:12:02 +00:00
|
|
|
if (isa<StoreInst>(*SrcI) || isa<LoadInst>(*SrcI)) {
|
dependence analysis
Patch from Preston Briggs <preston.briggs@gmail.com>.
This is an updated version of the dependence-analysis patch, including an MIV
test based on Banerjee's inequalities.
It's a fairly complete implementation of the paper
Practical Dependence Testing
Gina Goff, Ken Kennedy, and Chau-Wen Tseng
PLDI 1991
It cannot yet propagate constraints between coupled RDIV subscripts (discussed
in Section 5.3.2 of the paper).
It's organized as a FunctionPass with a single entry point that supports testing
for dependence between two instructions in a function. If there's no dependence,
it returns null. If there's a dependence, it returns a pointer to a Dependence
which can be queried about details (what kind of dependence, is it loop
independent, direction and distance vector entries, etc). I haven't included
every imaginable feature, but there's a good selection that should be adequate
for supporting many loop transformations. Of course, it can be extended as
necessary.
Included in the patch file are many test cases, commented with C code showing
the loops and array references.
git-svn-id: https://llvm.org/svn/llvm-project/llvm/trunk@165708 91177308-0d34-0410-b5e6-96231b3b80d8
2012-10-11 07:32:34 +00:00
|
|
|
for (inst_iterator DstI = SrcI, DstE = inst_end(F);
|
|
|
|
DstI != DstE; ++DstI) {
|
2012-11-13 12:12:02 +00:00
|
|
|
if (isa<StoreInst>(*DstI) || isa<LoadInst>(*DstI)) {
|
dependence analysis
Patch from Preston Briggs <preston.briggs@gmail.com>.
This is an updated version of the dependence-analysis patch, including an MIV
test based on Banerjee's inequalities.
It's a fairly complete implementation of the paper
Practical Dependence Testing
Gina Goff, Ken Kennedy, and Chau-Wen Tseng
PLDI 1991
It cannot yet propagate constraints between coupled RDIV subscripts (discussed
in Section 5.3.2 of the paper).
It's organized as a FunctionPass with a single entry point that supports testing
for dependence between two instructions in a function. If there's no dependence,
it returns null. If there's a dependence, it returns a pointer to a Dependence
which can be queried about details (what kind of dependence, is it loop
independent, direction and distance vector entries, etc). I haven't included
every imaginable feature, but there's a good selection that should be adequate
for supporting many loop transformations. Of course, it can be extended as
necessary.
Included in the patch file are many test cases, commented with C code showing
the loops and array references.
git-svn-id: https://llvm.org/svn/llvm-project/llvm/trunk@165708 91177308-0d34-0410-b5e6-96231b3b80d8
2012-10-11 07:32:34 +00:00
|
|
|
OS << "da analyze - ";
|
2012-11-13 12:12:02 +00:00
|
|
|
if (Dependence *D = DA->depends(&*SrcI, &*DstI, true)) {
|
dependence analysis
Patch from Preston Briggs <preston.briggs@gmail.com>.
This is an updated version of the dependence-analysis patch, including an MIV
test based on Banerjee's inequalities.
It's a fairly complete implementation of the paper
Practical Dependence Testing
Gina Goff, Ken Kennedy, and Chau-Wen Tseng
PLDI 1991
It cannot yet propagate constraints between coupled RDIV subscripts (discussed
in Section 5.3.2 of the paper).
It's organized as a FunctionPass with a single entry point that supports testing
for dependence between two instructions in a function. If there's no dependence,
it returns null. If there's a dependence, it returns a pointer to a Dependence
which can be queried about details (what kind of dependence, is it loop
independent, direction and distance vector entries, etc). I haven't included
every imaginable feature, but there's a good selection that should be adequate
for supporting many loop transformations. Of course, it can be extended as
necessary.
Included in the patch file are many test cases, commented with C code showing
the loops and array references.
git-svn-id: https://llvm.org/svn/llvm-project/llvm/trunk@165708 91177308-0d34-0410-b5e6-96231b3b80d8
2012-10-11 07:32:34 +00:00
|
|
|
D->dump(OS);
|
|
|
|
for (unsigned Level = 1; Level <= D->getLevels(); Level++) {
|
|
|
|
if (D->isSplitable(Level)) {
|
|
|
|
OS << "da analyze - split level = " << Level;
|
|
|
|
OS << ", iteration = " << *DA->getSplitIteration(D, Level);
|
|
|
|
OS << "!\n";
|
|
|
|
}
|
|
|
|
}
|
|
|
|
delete D;
|
|
|
|
}
|
|
|
|
else
|
|
|
|
OS << "none!\n";
|
|
|
|
}
|
|
|
|
}
|
|
|
|
}
|
|
|
|
}
|
|
|
|
}
|
|
|
|
|
|
|
|
|
|
|
|
void DependenceAnalysis::print(raw_ostream &OS, const Module*) const {
|
|
|
|
dumpExampleDependence(OS, F, const_cast<DependenceAnalysis *>(this));
|
|
|
|
}
|
|
|
|
|
|
|
|
//===----------------------------------------------------------------------===//
|
|
|
|
// Dependence methods
|
|
|
|
|
|
|
|
// Returns true if this is an input dependence.
|
|
|
|
bool Dependence::isInput() const {
|
|
|
|
return Src->mayReadFromMemory() && Dst->mayReadFromMemory();
|
|
|
|
}
|
|
|
|
|
|
|
|
|
|
|
|
// Returns true if this is an output dependence.
|
|
|
|
bool Dependence::isOutput() const {
|
|
|
|
return Src->mayWriteToMemory() && Dst->mayWriteToMemory();
|
|
|
|
}
|
|
|
|
|
|
|
|
|
|
|
|
// Returns true if this is an flow (aka true) dependence.
|
|
|
|
bool Dependence::isFlow() const {
|
|
|
|
return Src->mayWriteToMemory() && Dst->mayReadFromMemory();
|
|
|
|
}
|
|
|
|
|
|
|
|
|
|
|
|
// Returns true if this is an anti dependence.
|
|
|
|
bool Dependence::isAnti() const {
|
|
|
|
return Src->mayReadFromMemory() && Dst->mayWriteToMemory();
|
|
|
|
}
|
|
|
|
|
|
|
|
|
|
|
|
// Returns true if a particular level is scalar; that is,
|
|
|
|
// if no subscript in the source or destination mention the induction
|
|
|
|
// variable associated with the loop at this level.
|
|
|
|
// Leave this out of line, so it will serve as a virtual method anchor
|
|
|
|
bool Dependence::isScalar(unsigned level) const {
|
|
|
|
return false;
|
|
|
|
}
|
|
|
|
|
|
|
|
|
|
|
|
//===----------------------------------------------------------------------===//
|
|
|
|
// FullDependence methods
|
|
|
|
|
2012-11-20 22:28:04 +00:00
|
|
|
FullDependence::FullDependence(Instruction *Source,
|
|
|
|
Instruction *Destination,
|
dependence analysis
Patch from Preston Briggs <preston.briggs@gmail.com>.
This is an updated version of the dependence-analysis patch, including an MIV
test based on Banerjee's inequalities.
It's a fairly complete implementation of the paper
Practical Dependence Testing
Gina Goff, Ken Kennedy, and Chau-Wen Tseng
PLDI 1991
It cannot yet propagate constraints between coupled RDIV subscripts (discussed
in Section 5.3.2 of the paper).
It's organized as a FunctionPass with a single entry point that supports testing
for dependence between two instructions in a function. If there's no dependence,
it returns null. If there's a dependence, it returns a pointer to a Dependence
which can be queried about details (what kind of dependence, is it loop
independent, direction and distance vector entries, etc). I haven't included
every imaginable feature, but there's a good selection that should be adequate
for supporting many loop transformations. Of course, it can be extended as
necessary.
Included in the patch file are many test cases, commented with C code showing
the loops and array references.
git-svn-id: https://llvm.org/svn/llvm-project/llvm/trunk@165708 91177308-0d34-0410-b5e6-96231b3b80d8
2012-10-11 07:32:34 +00:00
|
|
|
bool PossiblyLoopIndependent,
|
|
|
|
unsigned CommonLevels) :
|
|
|
|
Dependence(Source, Destination),
|
|
|
|
Levels(CommonLevels),
|
|
|
|
LoopIndependent(PossiblyLoopIndependent) {
|
|
|
|
Consistent = true;
|
|
|
|
DV = CommonLevels ? new DVEntry[CommonLevels] : NULL;
|
|
|
|
}
|
|
|
|
|
|
|
|
// The rest are simple getters that hide the implementation.
|
|
|
|
|
|
|
|
// getDirection - Returns the direction associated with a particular level.
|
|
|
|
unsigned FullDependence::getDirection(unsigned Level) const {
|
|
|
|
assert(0 < Level && Level <= Levels && "Level out of range");
|
|
|
|
return DV[Level - 1].Direction;
|
|
|
|
}
|
|
|
|
|
|
|
|
|
|
|
|
// Returns the distance (or NULL) associated with a particular level.
|
|
|
|
const SCEV *FullDependence::getDistance(unsigned Level) const {
|
|
|
|
assert(0 < Level && Level <= Levels && "Level out of range");
|
|
|
|
return DV[Level - 1].Distance;
|
|
|
|
}
|
|
|
|
|
|
|
|
|
|
|
|
// Returns true if a particular level is scalar; that is,
|
|
|
|
// if no subscript in the source or destination mention the induction
|
|
|
|
// variable associated with the loop at this level.
|
|
|
|
bool FullDependence::isScalar(unsigned Level) const {
|
|
|
|
assert(0 < Level && Level <= Levels && "Level out of range");
|
|
|
|
return DV[Level - 1].Scalar;
|
|
|
|
}
|
|
|
|
|
|
|
|
|
|
|
|
// Returns true if peeling the first iteration from this loop
|
|
|
|
// will break this dependence.
|
|
|
|
bool FullDependence::isPeelFirst(unsigned Level) const {
|
|
|
|
assert(0 < Level && Level <= Levels && "Level out of range");
|
|
|
|
return DV[Level - 1].PeelFirst;
|
|
|
|
}
|
|
|
|
|
|
|
|
|
|
|
|
// Returns true if peeling the last iteration from this loop
|
|
|
|
// will break this dependence.
|
|
|
|
bool FullDependence::isPeelLast(unsigned Level) const {
|
|
|
|
assert(0 < Level && Level <= Levels && "Level out of range");
|
|
|
|
return DV[Level - 1].PeelLast;
|
|
|
|
}
|
|
|
|
|
|
|
|
|
|
|
|
// Returns true if splitting this loop will break the dependence.
|
|
|
|
bool FullDependence::isSplitable(unsigned Level) const {
|
|
|
|
assert(0 < Level && Level <= Levels && "Level out of range");
|
|
|
|
return DV[Level - 1].Splitable;
|
|
|
|
}
|
|
|
|
|
|
|
|
|
|
|
|
//===----------------------------------------------------------------------===//
|
|
|
|
// DependenceAnalysis::Constraint methods
|
|
|
|
|
|
|
|
// If constraint is a point <X, Y>, returns X.
|
|
|
|
// Otherwise assert.
|
|
|
|
const SCEV *DependenceAnalysis::Constraint::getX() const {
|
|
|
|
assert(Kind == Point && "Kind should be Point");
|
|
|
|
return A;
|
|
|
|
}
|
|
|
|
|
|
|
|
|
|
|
|
// If constraint is a point <X, Y>, returns Y.
|
|
|
|
// Otherwise assert.
|
|
|
|
const SCEV *DependenceAnalysis::Constraint::getY() const {
|
|
|
|
assert(Kind == Point && "Kind should be Point");
|
|
|
|
return B;
|
|
|
|
}
|
|
|
|
|
|
|
|
|
|
|
|
// If constraint is a line AX + BY = C, returns A.
|
|
|
|
// Otherwise assert.
|
|
|
|
const SCEV *DependenceAnalysis::Constraint::getA() const {
|
|
|
|
assert((Kind == Line || Kind == Distance) &&
|
|
|
|
"Kind should be Line (or Distance)");
|
|
|
|
return A;
|
|
|
|
}
|
|
|
|
|
|
|
|
|
|
|
|
// If constraint is a line AX + BY = C, returns B.
|
|
|
|
// Otherwise assert.
|
|
|
|
const SCEV *DependenceAnalysis::Constraint::getB() const {
|
|
|
|
assert((Kind == Line || Kind == Distance) &&
|
|
|
|
"Kind should be Line (or Distance)");
|
|
|
|
return B;
|
|
|
|
}
|
|
|
|
|
|
|
|
|
|
|
|
// If constraint is a line AX + BY = C, returns C.
|
|
|
|
// Otherwise assert.
|
|
|
|
const SCEV *DependenceAnalysis::Constraint::getC() const {
|
|
|
|
assert((Kind == Line || Kind == Distance) &&
|
|
|
|
"Kind should be Line (or Distance)");
|
|
|
|
return C;
|
|
|
|
}
|
|
|
|
|
|
|
|
|
|
|
|
// If constraint is a distance, returns D.
|
|
|
|
// Otherwise assert.
|
|
|
|
const SCEV *DependenceAnalysis::Constraint::getD() const {
|
|
|
|
assert(Kind == Distance && "Kind should be Distance");
|
|
|
|
return SE->getNegativeSCEV(C);
|
|
|
|
}
|
|
|
|
|
|
|
|
|
|
|
|
// Returns the loop associated with this constraint.
|
|
|
|
const Loop *DependenceAnalysis::Constraint::getAssociatedLoop() const {
|
|
|
|
assert((Kind == Distance || Kind == Line || Kind == Point) &&
|
|
|
|
"Kind should be Distance, Line, or Point");
|
|
|
|
return AssociatedLoop;
|
|
|
|
}
|
|
|
|
|
|
|
|
|
|
|
|
void DependenceAnalysis::Constraint::setPoint(const SCEV *X,
|
|
|
|
const SCEV *Y,
|
|
|
|
const Loop *CurLoop) {
|
|
|
|
Kind = Point;
|
|
|
|
A = X;
|
|
|
|
B = Y;
|
|
|
|
AssociatedLoop = CurLoop;
|
|
|
|
}
|
|
|
|
|
|
|
|
|
|
|
|
void DependenceAnalysis::Constraint::setLine(const SCEV *AA,
|
|
|
|
const SCEV *BB,
|
|
|
|
const SCEV *CC,
|
|
|
|
const Loop *CurLoop) {
|
|
|
|
Kind = Line;
|
|
|
|
A = AA;
|
|
|
|
B = BB;
|
|
|
|
C = CC;
|
|
|
|
AssociatedLoop = CurLoop;
|
|
|
|
}
|
|
|
|
|
|
|
|
|
|
|
|
void DependenceAnalysis::Constraint::setDistance(const SCEV *D,
|
|
|
|
const Loop *CurLoop) {
|
|
|
|
Kind = Distance;
|
|
|
|
A = SE->getConstant(D->getType(), 1);
|
|
|
|
B = SE->getNegativeSCEV(A);
|
|
|
|
C = SE->getNegativeSCEV(D);
|
|
|
|
AssociatedLoop = CurLoop;
|
|
|
|
}
|
|
|
|
|
|
|
|
|
|
|
|
void DependenceAnalysis::Constraint::setEmpty() {
|
|
|
|
Kind = Empty;
|
|
|
|
}
|
|
|
|
|
|
|
|
|
|
|
|
void DependenceAnalysis::Constraint::setAny(ScalarEvolution *NewSE) {
|
|
|
|
SE = NewSE;
|
|
|
|
Kind = Any;
|
|
|
|
}
|
|
|
|
|
|
|
|
|
|
|
|
// For debugging purposes. Dumps the constraint out to OS.
|
|
|
|
void DependenceAnalysis::Constraint::dump(raw_ostream &OS) const {
|
|
|
|
if (isEmpty())
|
|
|
|
OS << " Empty\n";
|
|
|
|
else if (isAny())
|
|
|
|
OS << " Any\n";
|
|
|
|
else if (isPoint())
|
|
|
|
OS << " Point is <" << *getX() << ", " << *getY() << ">\n";
|
|
|
|
else if (isDistance())
|
|
|
|
OS << " Distance is " << *getD() <<
|
|
|
|
" (" << *getA() << "*X + " << *getB() << "*Y = " << *getC() << ")\n";
|
|
|
|
else if (isLine())
|
|
|
|
OS << " Line is " << *getA() << "*X + " <<
|
|
|
|
*getB() << "*Y = " << *getC() << "\n";
|
|
|
|
else
|
|
|
|
llvm_unreachable("unknown constraint type in Constraint::dump");
|
|
|
|
}
|
|
|
|
|
|
|
|
|
|
|
|
// Updates X with the intersection
|
|
|
|
// of the Constraints X and Y. Returns true if X has changed.
|
|
|
|
// Corresponds to Figure 4 from the paper
|
|
|
|
//
|
|
|
|
// Practical Dependence Testing
|
|
|
|
// Goff, Kennedy, Tseng
|
|
|
|
// PLDI 1991
|
|
|
|
bool DependenceAnalysis::intersectConstraints(Constraint *X,
|
|
|
|
const Constraint *Y) {
|
|
|
|
++DeltaApplications;
|
|
|
|
DEBUG(dbgs() << "\tintersect constraints\n");
|
|
|
|
DEBUG(dbgs() << "\t X ="; X->dump(dbgs()));
|
|
|
|
DEBUG(dbgs() << "\t Y ="; Y->dump(dbgs()));
|
|
|
|
assert(!Y->isPoint() && "Y must not be a Point");
|
|
|
|
if (X->isAny()) {
|
|
|
|
if (Y->isAny())
|
|
|
|
return false;
|
|
|
|
*X = *Y;
|
|
|
|
return true;
|
|
|
|
}
|
|
|
|
if (X->isEmpty())
|
|
|
|
return false;
|
|
|
|
if (Y->isEmpty()) {
|
|
|
|
X->setEmpty();
|
|
|
|
return true;
|
|
|
|
}
|
|
|
|
|
|
|
|
if (X->isDistance() && Y->isDistance()) {
|
|
|
|
DEBUG(dbgs() << "\t intersect 2 distances\n");
|
|
|
|
if (isKnownPredicate(CmpInst::ICMP_EQ, X->getD(), Y->getD()))
|
|
|
|
return false;
|
|
|
|
if (isKnownPredicate(CmpInst::ICMP_NE, X->getD(), Y->getD())) {
|
|
|
|
X->setEmpty();
|
|
|
|
++DeltaSuccesses;
|
|
|
|
return true;
|
|
|
|
}
|
|
|
|
// Hmmm, interesting situation.
|
|
|
|
// I guess if either is constant, keep it and ignore the other.
|
|
|
|
if (isa<SCEVConstant>(Y->getD())) {
|
|
|
|
*X = *Y;
|
|
|
|
return true;
|
|
|
|
}
|
|
|
|
return false;
|
|
|
|
}
|
|
|
|
|
|
|
|
// At this point, the pseudo-code in Figure 4 of the paper
|
|
|
|
// checks if (X->isPoint() && Y->isPoint()).
|
|
|
|
// This case can't occur in our implementation,
|
|
|
|
// since a Point can only arise as the result of intersecting
|
|
|
|
// two Line constraints, and the right-hand value, Y, is never
|
|
|
|
// the result of an intersection.
|
|
|
|
assert(!(X->isPoint() && Y->isPoint()) &&
|
|
|
|
"We shouldn't ever see X->isPoint() && Y->isPoint()");
|
|
|
|
|
|
|
|
if (X->isLine() && Y->isLine()) {
|
|
|
|
DEBUG(dbgs() << "\t intersect 2 lines\n");
|
|
|
|
const SCEV *Prod1 = SE->getMulExpr(X->getA(), Y->getB());
|
|
|
|
const SCEV *Prod2 = SE->getMulExpr(X->getB(), Y->getA());
|
|
|
|
if (isKnownPredicate(CmpInst::ICMP_EQ, Prod1, Prod2)) {
|
|
|
|
// slopes are equal, so lines are parallel
|
|
|
|
DEBUG(dbgs() << "\t\tsame slope\n");
|
|
|
|
Prod1 = SE->getMulExpr(X->getC(), Y->getB());
|
|
|
|
Prod2 = SE->getMulExpr(X->getB(), Y->getC());
|
|
|
|
if (isKnownPredicate(CmpInst::ICMP_EQ, Prod1, Prod2))
|
|
|
|
return false;
|
|
|
|
if (isKnownPredicate(CmpInst::ICMP_NE, Prod1, Prod2)) {
|
|
|
|
X->setEmpty();
|
|
|
|
++DeltaSuccesses;
|
|
|
|
return true;
|
|
|
|
}
|
|
|
|
return false;
|
|
|
|
}
|
|
|
|
if (isKnownPredicate(CmpInst::ICMP_NE, Prod1, Prod2)) {
|
|
|
|
// slopes differ, so lines intersect
|
|
|
|
DEBUG(dbgs() << "\t\tdifferent slopes\n");
|
|
|
|
const SCEV *C1B2 = SE->getMulExpr(X->getC(), Y->getB());
|
|
|
|
const SCEV *C1A2 = SE->getMulExpr(X->getC(), Y->getA());
|
|
|
|
const SCEV *C2B1 = SE->getMulExpr(Y->getC(), X->getB());
|
|
|
|
const SCEV *C2A1 = SE->getMulExpr(Y->getC(), X->getA());
|
|
|
|
const SCEV *A1B2 = SE->getMulExpr(X->getA(), Y->getB());
|
|
|
|
const SCEV *A2B1 = SE->getMulExpr(Y->getA(), X->getB());
|
|
|
|
const SCEVConstant *C1A2_C2A1 =
|
|
|
|
dyn_cast<SCEVConstant>(SE->getMinusSCEV(C1A2, C2A1));
|
|
|
|
const SCEVConstant *C1B2_C2B1 =
|
|
|
|
dyn_cast<SCEVConstant>(SE->getMinusSCEV(C1B2, C2B1));
|
|
|
|
const SCEVConstant *A1B2_A2B1 =
|
|
|
|
dyn_cast<SCEVConstant>(SE->getMinusSCEV(A1B2, A2B1));
|
|
|
|
const SCEVConstant *A2B1_A1B2 =
|
|
|
|
dyn_cast<SCEVConstant>(SE->getMinusSCEV(A2B1, A1B2));
|
|
|
|
if (!C1B2_C2B1 || !C1A2_C2A1 ||
|
|
|
|
!A1B2_A2B1 || !A2B1_A1B2)
|
|
|
|
return false;
|
|
|
|
APInt Xtop = C1B2_C2B1->getValue()->getValue();
|
|
|
|
APInt Xbot = A1B2_A2B1->getValue()->getValue();
|
|
|
|
APInt Ytop = C1A2_C2A1->getValue()->getValue();
|
|
|
|
APInt Ybot = A2B1_A1B2->getValue()->getValue();
|
|
|
|
DEBUG(dbgs() << "\t\tXtop = " << Xtop << "\n");
|
|
|
|
DEBUG(dbgs() << "\t\tXbot = " << Xbot << "\n");
|
|
|
|
DEBUG(dbgs() << "\t\tYtop = " << Ytop << "\n");
|
|
|
|
DEBUG(dbgs() << "\t\tYbot = " << Ybot << "\n");
|
|
|
|
APInt Xq = Xtop; // these need to be initialized, even
|
|
|
|
APInt Xr = Xtop; // though they're just going to be overwritten
|
|
|
|
APInt::sdivrem(Xtop, Xbot, Xq, Xr);
|
|
|
|
APInt Yq = Ytop;
|
|
|
|
APInt Yr = Ytop;;
|
|
|
|
APInt::sdivrem(Ytop, Ybot, Yq, Yr);
|
|
|
|
if (Xr != 0 || Yr != 0) {
|
|
|
|
X->setEmpty();
|
|
|
|
++DeltaSuccesses;
|
|
|
|
return true;
|
|
|
|
}
|
|
|
|
DEBUG(dbgs() << "\t\tX = " << Xq << ", Y = " << Yq << "\n");
|
|
|
|
if (Xq.slt(0) || Yq.slt(0)) {
|
|
|
|
X->setEmpty();
|
|
|
|
++DeltaSuccesses;
|
|
|
|
return true;
|
|
|
|
}
|
|
|
|
if (const SCEVConstant *CUB =
|
|
|
|
collectConstantUpperBound(X->getAssociatedLoop(), Prod1->getType())) {
|
|
|
|
APInt UpperBound = CUB->getValue()->getValue();
|
|
|
|
DEBUG(dbgs() << "\t\tupper bound = " << UpperBound << "\n");
|
|
|
|
if (Xq.sgt(UpperBound) || Yq.sgt(UpperBound)) {
|
|
|
|
X->setEmpty();
|
|
|
|
++DeltaSuccesses;
|
|
|
|
return true;
|
|
|
|
}
|
|
|
|
}
|
|
|
|
X->setPoint(SE->getConstant(Xq),
|
|
|
|
SE->getConstant(Yq),
|
|
|
|
X->getAssociatedLoop());
|
|
|
|
++DeltaSuccesses;
|
|
|
|
return true;
|
|
|
|
}
|
|
|
|
return false;
|
|
|
|
}
|
|
|
|
|
|
|
|
// if (X->isLine() && Y->isPoint()) This case can't occur.
|
|
|
|
assert(!(X->isLine() && Y->isPoint()) && "This case should never occur");
|
|
|
|
|
|
|
|
if (X->isPoint() && Y->isLine()) {
|
|
|
|
DEBUG(dbgs() << "\t intersect Point and Line\n");
|
|
|
|
const SCEV *A1X1 = SE->getMulExpr(Y->getA(), X->getX());
|
|
|
|
const SCEV *B1Y1 = SE->getMulExpr(Y->getB(), X->getY());
|
|
|
|
const SCEV *Sum = SE->getAddExpr(A1X1, B1Y1);
|
|
|
|
if (isKnownPredicate(CmpInst::ICMP_EQ, Sum, Y->getC()))
|
|
|
|
return false;
|
|
|
|
if (isKnownPredicate(CmpInst::ICMP_NE, Sum, Y->getC())) {
|
|
|
|
X->setEmpty();
|
|
|
|
++DeltaSuccesses;
|
|
|
|
return true;
|
|
|
|
}
|
|
|
|
return false;
|
|
|
|
}
|
|
|
|
|
|
|
|
llvm_unreachable("shouldn't reach the end of Constraint intersection");
|
|
|
|
return false;
|
|
|
|
}
|
|
|
|
|
|
|
|
|
|
|
|
//===----------------------------------------------------------------------===//
|
|
|
|
// DependenceAnalysis methods
|
|
|
|
|
|
|
|
// For debugging purposes. Dumps a dependence to OS.
|
|
|
|
void Dependence::dump(raw_ostream &OS) const {
|
|
|
|
bool Splitable = false;
|
|
|
|
if (isConfused())
|
|
|
|
OS << "confused";
|
|
|
|
else {
|
|
|
|
if (isConsistent())
|
|
|
|
OS << "consistent ";
|
|
|
|
if (isFlow())
|
|
|
|
OS << "flow";
|
|
|
|
else if (isOutput())
|
|
|
|
OS << "output";
|
|
|
|
else if (isAnti())
|
|
|
|
OS << "anti";
|
|
|
|
else if (isInput())
|
|
|
|
OS << "input";
|
|
|
|
unsigned Levels = getLevels();
|
2012-11-30 00:44:47 +00:00
|
|
|
OS << " [";
|
|
|
|
for (unsigned II = 1; II <= Levels; ++II) {
|
|
|
|
if (isSplitable(II))
|
|
|
|
Splitable = true;
|
|
|
|
if (isPeelFirst(II))
|
|
|
|
OS << 'p';
|
|
|
|
const SCEV *Distance = getDistance(II);
|
|
|
|
if (Distance)
|
|
|
|
OS << *Distance;
|
|
|
|
else if (isScalar(II))
|
|
|
|
OS << "S";
|
|
|
|
else {
|
|
|
|
unsigned Direction = getDirection(II);
|
|
|
|
if (Direction == DVEntry::ALL)
|
|
|
|
OS << "*";
|
dependence analysis
Patch from Preston Briggs <preston.briggs@gmail.com>.
This is an updated version of the dependence-analysis patch, including an MIV
test based on Banerjee's inequalities.
It's a fairly complete implementation of the paper
Practical Dependence Testing
Gina Goff, Ken Kennedy, and Chau-Wen Tseng
PLDI 1991
It cannot yet propagate constraints between coupled RDIV subscripts (discussed
in Section 5.3.2 of the paper).
It's organized as a FunctionPass with a single entry point that supports testing
for dependence between two instructions in a function. If there's no dependence,
it returns null. If there's a dependence, it returns a pointer to a Dependence
which can be queried about details (what kind of dependence, is it loop
independent, direction and distance vector entries, etc). I haven't included
every imaginable feature, but there's a good selection that should be adequate
for supporting many loop transformations. Of course, it can be extended as
necessary.
Included in the patch file are many test cases, commented with C code showing
the loops and array references.
git-svn-id: https://llvm.org/svn/llvm-project/llvm/trunk@165708 91177308-0d34-0410-b5e6-96231b3b80d8
2012-10-11 07:32:34 +00:00
|
|
|
else {
|
2012-11-30 00:44:47 +00:00
|
|
|
if (Direction & DVEntry::LT)
|
|
|
|
OS << "<";
|
|
|
|
if (Direction & DVEntry::EQ)
|
|
|
|
OS << "=";
|
|
|
|
if (Direction & DVEntry::GT)
|
|
|
|
OS << ">";
|
dependence analysis
Patch from Preston Briggs <preston.briggs@gmail.com>.
This is an updated version of the dependence-analysis patch, including an MIV
test based on Banerjee's inequalities.
It's a fairly complete implementation of the paper
Practical Dependence Testing
Gina Goff, Ken Kennedy, and Chau-Wen Tseng
PLDI 1991
It cannot yet propagate constraints between coupled RDIV subscripts (discussed
in Section 5.3.2 of the paper).
It's organized as a FunctionPass with a single entry point that supports testing
for dependence between two instructions in a function. If there's no dependence,
it returns null. If there's a dependence, it returns a pointer to a Dependence
which can be queried about details (what kind of dependence, is it loop
independent, direction and distance vector entries, etc). I haven't included
every imaginable feature, but there's a good selection that should be adequate
for supporting many loop transformations. Of course, it can be extended as
necessary.
Included in the patch file are many test cases, commented with C code showing
the loops and array references.
git-svn-id: https://llvm.org/svn/llvm-project/llvm/trunk@165708 91177308-0d34-0410-b5e6-96231b3b80d8
2012-10-11 07:32:34 +00:00
|
|
|
}
|
|
|
|
}
|
2012-11-30 00:44:47 +00:00
|
|
|
if (isPeelLast(II))
|
|
|
|
OS << 'p';
|
|
|
|
if (II < Levels)
|
|
|
|
OS << " ";
|
dependence analysis
Patch from Preston Briggs <preston.briggs@gmail.com>.
This is an updated version of the dependence-analysis patch, including an MIV
test based on Banerjee's inequalities.
It's a fairly complete implementation of the paper
Practical Dependence Testing
Gina Goff, Ken Kennedy, and Chau-Wen Tseng
PLDI 1991
It cannot yet propagate constraints between coupled RDIV subscripts (discussed
in Section 5.3.2 of the paper).
It's organized as a FunctionPass with a single entry point that supports testing
for dependence between two instructions in a function. If there's no dependence,
it returns null. If there's a dependence, it returns a pointer to a Dependence
which can be queried about details (what kind of dependence, is it loop
independent, direction and distance vector entries, etc). I haven't included
every imaginable feature, but there's a good selection that should be adequate
for supporting many loop transformations. Of course, it can be extended as
necessary.
Included in the patch file are many test cases, commented with C code showing
the loops and array references.
git-svn-id: https://llvm.org/svn/llvm-project/llvm/trunk@165708 91177308-0d34-0410-b5e6-96231b3b80d8
2012-10-11 07:32:34 +00:00
|
|
|
}
|
2012-11-30 00:44:47 +00:00
|
|
|
if (isLoopIndependent())
|
|
|
|
OS << "|<";
|
|
|
|
OS << "]";
|
|
|
|
if (Splitable)
|
|
|
|
OS << " splitable";
|
dependence analysis
Patch from Preston Briggs <preston.briggs@gmail.com>.
This is an updated version of the dependence-analysis patch, including an MIV
test based on Banerjee's inequalities.
It's a fairly complete implementation of the paper
Practical Dependence Testing
Gina Goff, Ken Kennedy, and Chau-Wen Tseng
PLDI 1991
It cannot yet propagate constraints between coupled RDIV subscripts (discussed
in Section 5.3.2 of the paper).
It's organized as a FunctionPass with a single entry point that supports testing
for dependence between two instructions in a function. If there's no dependence,
it returns null. If there's a dependence, it returns a pointer to a Dependence
which can be queried about details (what kind of dependence, is it loop
independent, direction and distance vector entries, etc). I haven't included
every imaginable feature, but there's a good selection that should be adequate
for supporting many loop transformations. Of course, it can be extended as
necessary.
Included in the patch file are many test cases, commented with C code showing
the loops and array references.
git-svn-id: https://llvm.org/svn/llvm-project/llvm/trunk@165708 91177308-0d34-0410-b5e6-96231b3b80d8
2012-10-11 07:32:34 +00:00
|
|
|
}
|
|
|
|
OS << "!\n";
|
|
|
|
}
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
static
|
|
|
|
AliasAnalysis::AliasResult underlyingObjectsAlias(AliasAnalysis *AA,
|
|
|
|
const Value *A,
|
|
|
|
const Value *B) {
|
|
|
|
const Value *AObj = GetUnderlyingObject(A);
|
|
|
|
const Value *BObj = GetUnderlyingObject(B);
|
|
|
|
return AA->alias(AObj, AA->getTypeStoreSize(AObj->getType()),
|
|
|
|
BObj, AA->getTypeStoreSize(BObj->getType()));
|
|
|
|
}
|
|
|
|
|
|
|
|
|
|
|
|
// Returns true if the load or store can be analyzed. Atomic and volatile
|
|
|
|
// operations have properties which this analysis does not understand.
|
|
|
|
static
|
|
|
|
bool isLoadOrStore(const Instruction *I) {
|
|
|
|
if (const LoadInst *LI = dyn_cast<LoadInst>(I))
|
|
|
|
return LI->isUnordered();
|
|
|
|
else if (const StoreInst *SI = dyn_cast<StoreInst>(I))
|
|
|
|
return SI->isUnordered();
|
|
|
|
return false;
|
|
|
|
}
|
|
|
|
|
|
|
|
|
|
|
|
static
|
2012-11-20 22:28:04 +00:00
|
|
|
Value *getPointerOperand(Instruction *I) {
|
|
|
|
if (LoadInst *LI = dyn_cast<LoadInst>(I))
|
dependence analysis
Patch from Preston Briggs <preston.briggs@gmail.com>.
This is an updated version of the dependence-analysis patch, including an MIV
test based on Banerjee's inequalities.
It's a fairly complete implementation of the paper
Practical Dependence Testing
Gina Goff, Ken Kennedy, and Chau-Wen Tseng
PLDI 1991
It cannot yet propagate constraints between coupled RDIV subscripts (discussed
in Section 5.3.2 of the paper).
It's organized as a FunctionPass with a single entry point that supports testing
for dependence between two instructions in a function. If there's no dependence,
it returns null. If there's a dependence, it returns a pointer to a Dependence
which can be queried about details (what kind of dependence, is it loop
independent, direction and distance vector entries, etc). I haven't included
every imaginable feature, but there's a good selection that should be adequate
for supporting many loop transformations. Of course, it can be extended as
necessary.
Included in the patch file are many test cases, commented with C code showing
the loops and array references.
git-svn-id: https://llvm.org/svn/llvm-project/llvm/trunk@165708 91177308-0d34-0410-b5e6-96231b3b80d8
2012-10-11 07:32:34 +00:00
|
|
|
return LI->getPointerOperand();
|
2012-11-20 22:28:04 +00:00
|
|
|
if (StoreInst *SI = dyn_cast<StoreInst>(I))
|
dependence analysis
Patch from Preston Briggs <preston.briggs@gmail.com>.
This is an updated version of the dependence-analysis patch, including an MIV
test based on Banerjee's inequalities.
It's a fairly complete implementation of the paper
Practical Dependence Testing
Gina Goff, Ken Kennedy, and Chau-Wen Tseng
PLDI 1991
It cannot yet propagate constraints between coupled RDIV subscripts (discussed
in Section 5.3.2 of the paper).
It's organized as a FunctionPass with a single entry point that supports testing
for dependence between two instructions in a function. If there's no dependence,
it returns null. If there's a dependence, it returns a pointer to a Dependence
which can be queried about details (what kind of dependence, is it loop
independent, direction and distance vector entries, etc). I haven't included
every imaginable feature, but there's a good selection that should be adequate
for supporting many loop transformations. Of course, it can be extended as
necessary.
Included in the patch file are many test cases, commented with C code showing
the loops and array references.
git-svn-id: https://llvm.org/svn/llvm-project/llvm/trunk@165708 91177308-0d34-0410-b5e6-96231b3b80d8
2012-10-11 07:32:34 +00:00
|
|
|
return SI->getPointerOperand();
|
|
|
|
llvm_unreachable("Value is not load or store instruction");
|
|
|
|
return 0;
|
|
|
|
}
|
|
|
|
|
|
|
|
|
|
|
|
// Examines the loop nesting of the Src and Dst
|
|
|
|
// instructions and establishes their shared loops. Sets the variables
|
|
|
|
// CommonLevels, SrcLevels, and MaxLevels.
|
|
|
|
// The source and destination instructions needn't be contained in the same
|
|
|
|
// loop. The routine establishNestingLevels finds the level of most deeply
|
|
|
|
// nested loop that contains them both, CommonLevels. An instruction that's
|
|
|
|
// not contained in a loop is at level = 0. MaxLevels is equal to the level
|
|
|
|
// of the source plus the level of the destination, minus CommonLevels.
|
|
|
|
// This lets us allocate vectors MaxLevels in length, with room for every
|
|
|
|
// distinct loop referenced in both the source and destination subscripts.
|
|
|
|
// The variable SrcLevels is the nesting depth of the source instruction.
|
|
|
|
// It's used to help calculate distinct loops referenced by the destination.
|
|
|
|
// Here's the map from loops to levels:
|
|
|
|
// 0 - unused
|
|
|
|
// 1 - outermost common loop
|
|
|
|
// ... - other common loops
|
|
|
|
// CommonLevels - innermost common loop
|
|
|
|
// ... - loops containing Src but not Dst
|
|
|
|
// SrcLevels - innermost loop containing Src but not Dst
|
|
|
|
// ... - loops containing Dst but not Src
|
|
|
|
// MaxLevels - innermost loops containing Dst but not Src
|
|
|
|
// Consider the follow code fragment:
|
|
|
|
// for (a = ...) {
|
|
|
|
// for (b = ...) {
|
|
|
|
// for (c = ...) {
|
|
|
|
// for (d = ...) {
|
|
|
|
// A[] = ...;
|
|
|
|
// }
|
|
|
|
// }
|
|
|
|
// for (e = ...) {
|
|
|
|
// for (f = ...) {
|
|
|
|
// for (g = ...) {
|
|
|
|
// ... = A[];
|
|
|
|
// }
|
|
|
|
// }
|
|
|
|
// }
|
|
|
|
// }
|
|
|
|
// }
|
|
|
|
// If we're looking at the possibility of a dependence between the store
|
|
|
|
// to A (the Src) and the load from A (the Dst), we'll note that they
|
|
|
|
// have 2 loops in common, so CommonLevels will equal 2 and the direction
|
|
|
|
// vector for Result will have 2 entries. SrcLevels = 4 and MaxLevels = 7.
|
|
|
|
// A map from loop names to loop numbers would look like
|
|
|
|
// a - 1
|
|
|
|
// b - 2 = CommonLevels
|
|
|
|
// c - 3
|
|
|
|
// d - 4 = SrcLevels
|
|
|
|
// e - 5
|
|
|
|
// f - 6
|
|
|
|
// g - 7 = MaxLevels
|
|
|
|
void DependenceAnalysis::establishNestingLevels(const Instruction *Src,
|
|
|
|
const Instruction *Dst) {
|
|
|
|
const BasicBlock *SrcBlock = Src->getParent();
|
|
|
|
const BasicBlock *DstBlock = Dst->getParent();
|
|
|
|
unsigned SrcLevel = LI->getLoopDepth(SrcBlock);
|
|
|
|
unsigned DstLevel = LI->getLoopDepth(DstBlock);
|
|
|
|
const Loop *SrcLoop = LI->getLoopFor(SrcBlock);
|
|
|
|
const Loop *DstLoop = LI->getLoopFor(DstBlock);
|
|
|
|
SrcLevels = SrcLevel;
|
|
|
|
MaxLevels = SrcLevel + DstLevel;
|
|
|
|
while (SrcLevel > DstLevel) {
|
|
|
|
SrcLoop = SrcLoop->getParentLoop();
|
|
|
|
SrcLevel--;
|
|
|
|
}
|
|
|
|
while (DstLevel > SrcLevel) {
|
|
|
|
DstLoop = DstLoop->getParentLoop();
|
|
|
|
DstLevel--;
|
|
|
|
}
|
|
|
|
while (SrcLoop != DstLoop) {
|
|
|
|
SrcLoop = SrcLoop->getParentLoop();
|
|
|
|
DstLoop = DstLoop->getParentLoop();
|
|
|
|
SrcLevel--;
|
|
|
|
}
|
|
|
|
CommonLevels = SrcLevel;
|
|
|
|
MaxLevels -= CommonLevels;
|
|
|
|
}
|
|
|
|
|
|
|
|
|
|
|
|
// Given one of the loops containing the source, return
|
|
|
|
// its level index in our numbering scheme.
|
|
|
|
unsigned DependenceAnalysis::mapSrcLoop(const Loop *SrcLoop) const {
|
|
|
|
return SrcLoop->getLoopDepth();
|
|
|
|
}
|
|
|
|
|
|
|
|
|
|
|
|
// Given one of the loops containing the destination,
|
|
|
|
// return its level index in our numbering scheme.
|
|
|
|
unsigned DependenceAnalysis::mapDstLoop(const Loop *DstLoop) const {
|
|
|
|
unsigned D = DstLoop->getLoopDepth();
|
|
|
|
if (D > CommonLevels)
|
|
|
|
return D - CommonLevels + SrcLevels;
|
|
|
|
else
|
|
|
|
return D;
|
|
|
|
}
|
|
|
|
|
|
|
|
|
|
|
|
// Returns true if Expression is loop invariant in LoopNest.
|
|
|
|
bool DependenceAnalysis::isLoopInvariant(const SCEV *Expression,
|
|
|
|
const Loop *LoopNest) const {
|
|
|
|
if (!LoopNest)
|
|
|
|
return true;
|
|
|
|
return SE->isLoopInvariant(Expression, LoopNest) &&
|
|
|
|
isLoopInvariant(Expression, LoopNest->getParentLoop());
|
|
|
|
}
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
// Finds the set of loops from the LoopNest that
|
|
|
|
// have a level <= CommonLevels and are referred to by the SCEV Expression.
|
|
|
|
void DependenceAnalysis::collectCommonLoops(const SCEV *Expression,
|
|
|
|
const Loop *LoopNest,
|
|
|
|
SmallBitVector &Loops) const {
|
|
|
|
while (LoopNest) {
|
|
|
|
unsigned Level = LoopNest->getLoopDepth();
|
|
|
|
if (Level <= CommonLevels && !SE->isLoopInvariant(Expression, LoopNest))
|
|
|
|
Loops.set(Level);
|
|
|
|
LoopNest = LoopNest->getParentLoop();
|
|
|
|
}
|
|
|
|
}
|
|
|
|
|
|
|
|
|
|
|
|
// removeMatchingExtensions - Examines a subscript pair.
|
|
|
|
// If the source and destination are identically sign (or zero)
|
|
|
|
// extended, it strips off the extension in an effect to simplify
|
|
|
|
// the actual analysis.
|
|
|
|
void DependenceAnalysis::removeMatchingExtensions(Subscript *Pair) {
|
|
|
|
const SCEV *Src = Pair->Src;
|
|
|
|
const SCEV *Dst = Pair->Dst;
|
|
|
|
if ((isa<SCEVZeroExtendExpr>(Src) && isa<SCEVZeroExtendExpr>(Dst)) ||
|
|
|
|
(isa<SCEVSignExtendExpr>(Src) && isa<SCEVSignExtendExpr>(Dst))) {
|
|
|
|
const SCEVCastExpr *SrcCast = cast<SCEVCastExpr>(Src);
|
|
|
|
const SCEVCastExpr *DstCast = cast<SCEVCastExpr>(Dst);
|
|
|
|
if (SrcCast->getType() == DstCast->getType()) {
|
|
|
|
Pair->Src = SrcCast->getOperand();
|
|
|
|
Pair->Dst = DstCast->getOperand();
|
|
|
|
}
|
|
|
|
}
|
|
|
|
}
|
|
|
|
|
|
|
|
|
|
|
|
// Examine the scev and return true iff it's linear.
|
|
|
|
// Collect any loops mentioned in the set of "Loops".
|
|
|
|
bool DependenceAnalysis::checkSrcSubscript(const SCEV *Src,
|
|
|
|
const Loop *LoopNest,
|
|
|
|
SmallBitVector &Loops) {
|
|
|
|
const SCEVAddRecExpr *AddRec = dyn_cast<SCEVAddRecExpr>(Src);
|
|
|
|
if (!AddRec)
|
|
|
|
return isLoopInvariant(Src, LoopNest);
|
|
|
|
const SCEV *Start = AddRec->getStart();
|
|
|
|
const SCEV *Step = AddRec->getStepRecurrence(*SE);
|
|
|
|
if (!isLoopInvariant(Step, LoopNest))
|
|
|
|
return false;
|
|
|
|
Loops.set(mapSrcLoop(AddRec->getLoop()));
|
|
|
|
return checkSrcSubscript(Start, LoopNest, Loops);
|
|
|
|
}
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
// Examine the scev and return true iff it's linear.
|
|
|
|
// Collect any loops mentioned in the set of "Loops".
|
|
|
|
bool DependenceAnalysis::checkDstSubscript(const SCEV *Dst,
|
|
|
|
const Loop *LoopNest,
|
|
|
|
SmallBitVector &Loops) {
|
|
|
|
const SCEVAddRecExpr *AddRec = dyn_cast<SCEVAddRecExpr>(Dst);
|
|
|
|
if (!AddRec)
|
|
|
|
return isLoopInvariant(Dst, LoopNest);
|
|
|
|
const SCEV *Start = AddRec->getStart();
|
|
|
|
const SCEV *Step = AddRec->getStepRecurrence(*SE);
|
|
|
|
if (!isLoopInvariant(Step, LoopNest))
|
|
|
|
return false;
|
|
|
|
Loops.set(mapDstLoop(AddRec->getLoop()));
|
|
|
|
return checkDstSubscript(Start, LoopNest, Loops);
|
|
|
|
}
|
|
|
|
|
|
|
|
|
|
|
|
// Examines the subscript pair (the Src and Dst SCEVs)
|
|
|
|
// and classifies it as either ZIV, SIV, RDIV, MIV, or Nonlinear.
|
|
|
|
// Collects the associated loops in a set.
|
|
|
|
DependenceAnalysis::Subscript::ClassificationKind
|
|
|
|
DependenceAnalysis::classifyPair(const SCEV *Src, const Loop *SrcLoopNest,
|
|
|
|
const SCEV *Dst, const Loop *DstLoopNest,
|
|
|
|
SmallBitVector &Loops) {
|
|
|
|
SmallBitVector SrcLoops(MaxLevels + 1);
|
|
|
|
SmallBitVector DstLoops(MaxLevels + 1);
|
|
|
|
if (!checkSrcSubscript(Src, SrcLoopNest, SrcLoops))
|
|
|
|
return Subscript::NonLinear;
|
|
|
|
if (!checkDstSubscript(Dst, DstLoopNest, DstLoops))
|
|
|
|
return Subscript::NonLinear;
|
|
|
|
Loops = SrcLoops;
|
|
|
|
Loops |= DstLoops;
|
|
|
|
unsigned N = Loops.count();
|
|
|
|
if (N == 0)
|
|
|
|
return Subscript::ZIV;
|
|
|
|
if (N == 1)
|
|
|
|
return Subscript::SIV;
|
|
|
|
if (N == 2 && (SrcLoops.count() == 0 ||
|
|
|
|
DstLoops.count() == 0 ||
|
|
|
|
(SrcLoops.count() == 1 && DstLoops.count() == 1)))
|
|
|
|
return Subscript::RDIV;
|
|
|
|
return Subscript::MIV;
|
|
|
|
}
|
|
|
|
|
|
|
|
|
|
|
|
// A wrapper around SCEV::isKnownPredicate.
|
|
|
|
// Looks for cases where we're interested in comparing for equality.
|
|
|
|
// If both X and Y have been identically sign or zero extended,
|
|
|
|
// it strips off the (confusing) extensions before invoking
|
|
|
|
// SCEV::isKnownPredicate. Perhaps, someday, the ScalarEvolution package
|
|
|
|
// will be similarly updated.
|
|
|
|
//
|
|
|
|
// If SCEV::isKnownPredicate can't prove the predicate,
|
|
|
|
// we try simple subtraction, which seems to help in some cases
|
|
|
|
// involving symbolics.
|
|
|
|
bool DependenceAnalysis::isKnownPredicate(ICmpInst::Predicate Pred,
|
|
|
|
const SCEV *X,
|
|
|
|
const SCEV *Y) const {
|
|
|
|
if (Pred == CmpInst::ICMP_EQ ||
|
|
|
|
Pred == CmpInst::ICMP_NE) {
|
|
|
|
if ((isa<SCEVSignExtendExpr>(X) &&
|
|
|
|
isa<SCEVSignExtendExpr>(Y)) ||
|
|
|
|
(isa<SCEVZeroExtendExpr>(X) &&
|
|
|
|
isa<SCEVZeroExtendExpr>(Y))) {
|
|
|
|
const SCEVCastExpr *CX = cast<SCEVCastExpr>(X);
|
|
|
|
const SCEVCastExpr *CY = cast<SCEVCastExpr>(Y);
|
|
|
|
const SCEV *Xop = CX->getOperand();
|
|
|
|
const SCEV *Yop = CY->getOperand();
|
|
|
|
if (Xop->getType() == Yop->getType()) {
|
|
|
|
X = Xop;
|
|
|
|
Y = Yop;
|
|
|
|
}
|
|
|
|
}
|
|
|
|
}
|
|
|
|
if (SE->isKnownPredicate(Pred, X, Y))
|
|
|
|
return true;
|
|
|
|
// If SE->isKnownPredicate can't prove the condition,
|
|
|
|
// we try the brute-force approach of subtracting
|
|
|
|
// and testing the difference.
|
|
|
|
// By testing with SE->isKnownPredicate first, we avoid
|
|
|
|
// the possibility of overflow when the arguments are constants.
|
|
|
|
const SCEV *Delta = SE->getMinusSCEV(X, Y);
|
|
|
|
switch (Pred) {
|
|
|
|
case CmpInst::ICMP_EQ:
|
|
|
|
return Delta->isZero();
|
|
|
|
case CmpInst::ICMP_NE:
|
|
|
|
return SE->isKnownNonZero(Delta);
|
|
|
|
case CmpInst::ICMP_SGE:
|
|
|
|
return SE->isKnownNonNegative(Delta);
|
|
|
|
case CmpInst::ICMP_SLE:
|
|
|
|
return SE->isKnownNonPositive(Delta);
|
|
|
|
case CmpInst::ICMP_SGT:
|
|
|
|
return SE->isKnownPositive(Delta);
|
|
|
|
case CmpInst::ICMP_SLT:
|
|
|
|
return SE->isKnownNegative(Delta);
|
|
|
|
default:
|
|
|
|
llvm_unreachable("unexpected predicate in isKnownPredicate");
|
|
|
|
}
|
|
|
|
}
|
|
|
|
|
|
|
|
|
|
|
|
// All subscripts are all the same type.
|
|
|
|
// Loop bound may be smaller (e.g., a char).
|
|
|
|
// Should zero extend loop bound, since it's always >= 0.
|
|
|
|
// This routine collects upper bound and extends if needed.
|
|
|
|
// Return null if no bound available.
|
|
|
|
const SCEV *DependenceAnalysis::collectUpperBound(const Loop *L,
|
|
|
|
Type *T) const {
|
|
|
|
if (SE->hasLoopInvariantBackedgeTakenCount(L)) {
|
|
|
|
const SCEV *UB = SE->getBackedgeTakenCount(L);
|
|
|
|
return SE->getNoopOrZeroExtend(UB, T);
|
|
|
|
}
|
|
|
|
return NULL;
|
|
|
|
}
|
|
|
|
|
|
|
|
|
|
|
|
// Calls collectUpperBound(), then attempts to cast it to SCEVConstant.
|
|
|
|
// If the cast fails, returns NULL.
|
|
|
|
const SCEVConstant *DependenceAnalysis::collectConstantUpperBound(const Loop *L,
|
|
|
|
Type *T
|
|
|
|
) const {
|
|
|
|
if (const SCEV *UB = collectUpperBound(L, T))
|
|
|
|
return dyn_cast<SCEVConstant>(UB);
|
|
|
|
return NULL;
|
|
|
|
}
|
|
|
|
|
|
|
|
|
|
|
|
// testZIV -
|
|
|
|
// When we have a pair of subscripts of the form [c1] and [c2],
|
|
|
|
// where c1 and c2 are both loop invariant, we attack it using
|
|
|
|
// the ZIV test. Basically, we test by comparing the two values,
|
|
|
|
// but there are actually three possible results:
|
|
|
|
// 1) the values are equal, so there's a dependence
|
|
|
|
// 2) the values are different, so there's no dependence
|
|
|
|
// 3) the values might be equal, so we have to assume a dependence.
|
|
|
|
//
|
|
|
|
// Return true if dependence disproved.
|
|
|
|
bool DependenceAnalysis::testZIV(const SCEV *Src,
|
|
|
|
const SCEV *Dst,
|
|
|
|
FullDependence &Result) const {
|
|
|
|
DEBUG(dbgs() << " src = " << *Src << "\n");
|
|
|
|
DEBUG(dbgs() << " dst = " << *Dst << "\n");
|
|
|
|
++ZIVapplications;
|
|
|
|
if (isKnownPredicate(CmpInst::ICMP_EQ, Src, Dst)) {
|
|
|
|
DEBUG(dbgs() << " provably dependent\n");
|
|
|
|
return false; // provably dependent
|
|
|
|
}
|
|
|
|
if (isKnownPredicate(CmpInst::ICMP_NE, Src, Dst)) {
|
|
|
|
DEBUG(dbgs() << " provably independent\n");
|
|
|
|
++ZIVindependence;
|
|
|
|
return true; // provably independent
|
|
|
|
}
|
|
|
|
DEBUG(dbgs() << " possibly dependent\n");
|
|
|
|
Result.Consistent = false;
|
|
|
|
return false; // possibly dependent
|
|
|
|
}
|
|
|
|
|
|
|
|
|
|
|
|
// strongSIVtest -
|
|
|
|
// From the paper, Practical Dependence Testing, Section 4.2.1
|
|
|
|
//
|
|
|
|
// When we have a pair of subscripts of the form [c1 + a*i] and [c2 + a*i],
|
|
|
|
// where i is an induction variable, c1 and c2 are loop invariant,
|
|
|
|
// and a is a constant, we can solve it exactly using the Strong SIV test.
|
|
|
|
//
|
|
|
|
// Can prove independence. Failing that, can compute distance (and direction).
|
|
|
|
// In the presence of symbolic terms, we can sometimes make progress.
|
|
|
|
//
|
|
|
|
// If there's a dependence,
|
|
|
|
//
|
|
|
|
// c1 + a*i = c2 + a*i'
|
|
|
|
//
|
|
|
|
// The dependence distance is
|
|
|
|
//
|
|
|
|
// d = i' - i = (c1 - c2)/a
|
|
|
|
//
|
|
|
|
// A dependence only exists if d is an integer and abs(d) <= U, where U is the
|
|
|
|
// loop's upper bound. If a dependence exists, the dependence direction is
|
|
|
|
// defined as
|
|
|
|
//
|
|
|
|
// { < if d > 0
|
|
|
|
// direction = { = if d = 0
|
|
|
|
// { > if d < 0
|
|
|
|
//
|
|
|
|
// Return true if dependence disproved.
|
|
|
|
bool DependenceAnalysis::strongSIVtest(const SCEV *Coeff,
|
|
|
|
const SCEV *SrcConst,
|
|
|
|
const SCEV *DstConst,
|
|
|
|
const Loop *CurLoop,
|
|
|
|
unsigned Level,
|
|
|
|
FullDependence &Result,
|
|
|
|
Constraint &NewConstraint) const {
|
|
|
|
DEBUG(dbgs() << "\tStrong SIV test\n");
|
|
|
|
DEBUG(dbgs() << "\t Coeff = " << *Coeff);
|
|
|
|
DEBUG(dbgs() << ", " << *Coeff->getType() << "\n");
|
|
|
|
DEBUG(dbgs() << "\t SrcConst = " << *SrcConst);
|
|
|
|
DEBUG(dbgs() << ", " << *SrcConst->getType() << "\n");
|
|
|
|
DEBUG(dbgs() << "\t DstConst = " << *DstConst);
|
|
|
|
DEBUG(dbgs() << ", " << *DstConst->getType() << "\n");
|
|
|
|
++StrongSIVapplications;
|
|
|
|
assert(0 < Level && Level <= CommonLevels && "level out of range");
|
|
|
|
Level--;
|
|
|
|
|
|
|
|
const SCEV *Delta = SE->getMinusSCEV(SrcConst, DstConst);
|
|
|
|
DEBUG(dbgs() << "\t Delta = " << *Delta);
|
|
|
|
DEBUG(dbgs() << ", " << *Delta->getType() << "\n");
|
|
|
|
|
|
|
|
// check that |Delta| < iteration count
|
|
|
|
if (const SCEV *UpperBound = collectUpperBound(CurLoop, Delta->getType())) {
|
|
|
|
DEBUG(dbgs() << "\t UpperBound = " << *UpperBound);
|
|
|
|
DEBUG(dbgs() << ", " << *UpperBound->getType() << "\n");
|
|
|
|
const SCEV *AbsDelta =
|
|
|
|
SE->isKnownNonNegative(Delta) ? Delta : SE->getNegativeSCEV(Delta);
|
|
|
|
const SCEV *AbsCoeff =
|
|
|
|
SE->isKnownNonNegative(Coeff) ? Coeff : SE->getNegativeSCEV(Coeff);
|
|
|
|
const SCEV *Product = SE->getMulExpr(UpperBound, AbsCoeff);
|
|
|
|
if (isKnownPredicate(CmpInst::ICMP_SGT, AbsDelta, Product)) {
|
|
|
|
// Distance greater than trip count - no dependence
|
|
|
|
++StrongSIVindependence;
|
|
|
|
++StrongSIVsuccesses;
|
|
|
|
return true;
|
|
|
|
}
|
|
|
|
}
|
|
|
|
|
|
|
|
// Can we compute distance?
|
|
|
|
if (isa<SCEVConstant>(Delta) && isa<SCEVConstant>(Coeff)) {
|
|
|
|
APInt ConstDelta = cast<SCEVConstant>(Delta)->getValue()->getValue();
|
|
|
|
APInt ConstCoeff = cast<SCEVConstant>(Coeff)->getValue()->getValue();
|
|
|
|
APInt Distance = ConstDelta; // these need to be initialized
|
|
|
|
APInt Remainder = ConstDelta;
|
|
|
|
APInt::sdivrem(ConstDelta, ConstCoeff, Distance, Remainder);
|
|
|
|
DEBUG(dbgs() << "\t Distance = " << Distance << "\n");
|
|
|
|
DEBUG(dbgs() << "\t Remainder = " << Remainder << "\n");
|
|
|
|
// Make sure Coeff divides Delta exactly
|
|
|
|
if (Remainder != 0) {
|
|
|
|
// Coeff doesn't divide Distance, no dependence
|
|
|
|
++StrongSIVindependence;
|
|
|
|
++StrongSIVsuccesses;
|
|
|
|
return true;
|
|
|
|
}
|
|
|
|
Result.DV[Level].Distance = SE->getConstant(Distance);
|
|
|
|
NewConstraint.setDistance(SE->getConstant(Distance), CurLoop);
|
|
|
|
if (Distance.sgt(0))
|
|
|
|
Result.DV[Level].Direction &= Dependence::DVEntry::LT;
|
|
|
|
else if (Distance.slt(0))
|
|
|
|
Result.DV[Level].Direction &= Dependence::DVEntry::GT;
|
|
|
|
else
|
|
|
|
Result.DV[Level].Direction &= Dependence::DVEntry::EQ;
|
|
|
|
++StrongSIVsuccesses;
|
|
|
|
}
|
|
|
|
else if (Delta->isZero()) {
|
|
|
|
// since 0/X == 0
|
|
|
|
Result.DV[Level].Distance = Delta;
|
|
|
|
NewConstraint.setDistance(Delta, CurLoop);
|
|
|
|
Result.DV[Level].Direction &= Dependence::DVEntry::EQ;
|
|
|
|
++StrongSIVsuccesses;
|
|
|
|
}
|
|
|
|
else {
|
|
|
|
if (Coeff->isOne()) {
|
|
|
|
DEBUG(dbgs() << "\t Distance = " << *Delta << "\n");
|
|
|
|
Result.DV[Level].Distance = Delta; // since X/1 == X
|
|
|
|
NewConstraint.setDistance(Delta, CurLoop);
|
|
|
|
}
|
|
|
|
else {
|
|
|
|
Result.Consistent = false;
|
|
|
|
NewConstraint.setLine(Coeff,
|
|
|
|
SE->getNegativeSCEV(Coeff),
|
|
|
|
SE->getNegativeSCEV(Delta), CurLoop);
|
|
|
|
}
|
|
|
|
|
|
|
|
// maybe we can get a useful direction
|
|
|
|
bool DeltaMaybeZero = !SE->isKnownNonZero(Delta);
|
|
|
|
bool DeltaMaybePositive = !SE->isKnownNonPositive(Delta);
|
|
|
|
bool DeltaMaybeNegative = !SE->isKnownNonNegative(Delta);
|
|
|
|
bool CoeffMaybePositive = !SE->isKnownNonPositive(Coeff);
|
|
|
|
bool CoeffMaybeNegative = !SE->isKnownNonNegative(Coeff);
|
|
|
|
// The double negatives above are confusing.
|
|
|
|
// It helps to read !SE->isKnownNonZero(Delta)
|
|
|
|
// as "Delta might be Zero"
|
|
|
|
unsigned NewDirection = Dependence::DVEntry::NONE;
|
|
|
|
if ((DeltaMaybePositive && CoeffMaybePositive) ||
|
|
|
|
(DeltaMaybeNegative && CoeffMaybeNegative))
|
|
|
|
NewDirection = Dependence::DVEntry::LT;
|
|
|
|
if (DeltaMaybeZero)
|
|
|
|
NewDirection |= Dependence::DVEntry::EQ;
|
|
|
|
if ((DeltaMaybeNegative && CoeffMaybePositive) ||
|
|
|
|
(DeltaMaybePositive && CoeffMaybeNegative))
|
|
|
|
NewDirection |= Dependence::DVEntry::GT;
|
|
|
|
if (NewDirection < Result.DV[Level].Direction)
|
|
|
|
++StrongSIVsuccesses;
|
|
|
|
Result.DV[Level].Direction &= NewDirection;
|
|
|
|
}
|
|
|
|
return false;
|
|
|
|
}
|
|
|
|
|
|
|
|
|
|
|
|
// weakCrossingSIVtest -
|
|
|
|
// From the paper, Practical Dependence Testing, Section 4.2.2
|
|
|
|
//
|
|
|
|
// When we have a pair of subscripts of the form [c1 + a*i] and [c2 - a*i],
|
|
|
|
// where i is an induction variable, c1 and c2 are loop invariant,
|
|
|
|
// and a is a constant, we can solve it exactly using the
|
|
|
|
// Weak-Crossing SIV test.
|
|
|
|
//
|
|
|
|
// Given c1 + a*i = c2 - a*i', we can look for the intersection of
|
|
|
|
// the two lines, where i = i', yielding
|
|
|
|
//
|
|
|
|
// c1 + a*i = c2 - a*i
|
|
|
|
// 2a*i = c2 - c1
|
|
|
|
// i = (c2 - c1)/2a
|
|
|
|
//
|
|
|
|
// If i < 0, there is no dependence.
|
|
|
|
// If i > upperbound, there is no dependence.
|
|
|
|
// If i = 0 (i.e., if c1 = c2), there's a dependence with distance = 0.
|
|
|
|
// If i = upperbound, there's a dependence with distance = 0.
|
|
|
|
// If i is integral, there's a dependence (all directions).
|
|
|
|
// If the non-integer part = 1/2, there's a dependence (<> directions).
|
|
|
|
// Otherwise, there's no dependence.
|
|
|
|
//
|
|
|
|
// Can prove independence. Failing that,
|
|
|
|
// can sometimes refine the directions.
|
|
|
|
// Can determine iteration for splitting.
|
|
|
|
//
|
|
|
|
// Return true if dependence disproved.
|
|
|
|
bool DependenceAnalysis::weakCrossingSIVtest(const SCEV *Coeff,
|
|
|
|
const SCEV *SrcConst,
|
|
|
|
const SCEV *DstConst,
|
|
|
|
const Loop *CurLoop,
|
|
|
|
unsigned Level,
|
|
|
|
FullDependence &Result,
|
|
|
|
Constraint &NewConstraint,
|
|
|
|
const SCEV *&SplitIter) const {
|
|
|
|
DEBUG(dbgs() << "\tWeak-Crossing SIV test\n");
|
|
|
|
DEBUG(dbgs() << "\t Coeff = " << *Coeff << "\n");
|
|
|
|
DEBUG(dbgs() << "\t SrcConst = " << *SrcConst << "\n");
|
|
|
|
DEBUG(dbgs() << "\t DstConst = " << *DstConst << "\n");
|
|
|
|
++WeakCrossingSIVapplications;
|
|
|
|
assert(0 < Level && Level <= CommonLevels && "Level out of range");
|
|
|
|
Level--;
|
|
|
|
Result.Consistent = false;
|
|
|
|
const SCEV *Delta = SE->getMinusSCEV(DstConst, SrcConst);
|
|
|
|
DEBUG(dbgs() << "\t Delta = " << *Delta << "\n");
|
|
|
|
NewConstraint.setLine(Coeff, Coeff, Delta, CurLoop);
|
|
|
|
if (Delta->isZero()) {
|
2012-10-12 02:04:32 +00:00
|
|
|
Result.DV[Level].Direction &= unsigned(~Dependence::DVEntry::LT);
|
|
|
|
Result.DV[Level].Direction &= unsigned(~Dependence::DVEntry::GT);
|
dependence analysis
Patch from Preston Briggs <preston.briggs@gmail.com>.
This is an updated version of the dependence-analysis patch, including an MIV
test based on Banerjee's inequalities.
It's a fairly complete implementation of the paper
Practical Dependence Testing
Gina Goff, Ken Kennedy, and Chau-Wen Tseng
PLDI 1991
It cannot yet propagate constraints between coupled RDIV subscripts (discussed
in Section 5.3.2 of the paper).
It's organized as a FunctionPass with a single entry point that supports testing
for dependence between two instructions in a function. If there's no dependence,
it returns null. If there's a dependence, it returns a pointer to a Dependence
which can be queried about details (what kind of dependence, is it loop
independent, direction and distance vector entries, etc). I haven't included
every imaginable feature, but there's a good selection that should be adequate
for supporting many loop transformations. Of course, it can be extended as
necessary.
Included in the patch file are many test cases, commented with C code showing
the loops and array references.
git-svn-id: https://llvm.org/svn/llvm-project/llvm/trunk@165708 91177308-0d34-0410-b5e6-96231b3b80d8
2012-10-11 07:32:34 +00:00
|
|
|
++WeakCrossingSIVsuccesses;
|
|
|
|
if (!Result.DV[Level].Direction) {
|
|
|
|
++WeakCrossingSIVindependence;
|
|
|
|
return true;
|
|
|
|
}
|
|
|
|
Result.DV[Level].Distance = Delta; // = 0
|
|
|
|
return false;
|
|
|
|
}
|
|
|
|
const SCEVConstant *ConstCoeff = dyn_cast<SCEVConstant>(Coeff);
|
|
|
|
if (!ConstCoeff)
|
|
|
|
return false;
|
|
|
|
|
|
|
|
Result.DV[Level].Splitable = true;
|
|
|
|
if (SE->isKnownNegative(ConstCoeff)) {
|
|
|
|
ConstCoeff = dyn_cast<SCEVConstant>(SE->getNegativeSCEV(ConstCoeff));
|
|
|
|
assert(ConstCoeff &&
|
|
|
|
"dynamic cast of negative of ConstCoeff should yield constant");
|
|
|
|
Delta = SE->getNegativeSCEV(Delta);
|
|
|
|
}
|
|
|
|
assert(SE->isKnownPositive(ConstCoeff) && "ConstCoeff should be positive");
|
|
|
|
|
|
|
|
// compute SplitIter for use by DependenceAnalysis::getSplitIteration()
|
|
|
|
SplitIter =
|
|
|
|
SE->getUDivExpr(SE->getSMaxExpr(SE->getConstant(Delta->getType(), 0),
|
|
|
|
Delta),
|
|
|
|
SE->getMulExpr(SE->getConstant(Delta->getType(), 2),
|
|
|
|
ConstCoeff));
|
|
|
|
DEBUG(dbgs() << "\t Split iter = " << *SplitIter << "\n");
|
|
|
|
|
|
|
|
const SCEVConstant *ConstDelta = dyn_cast<SCEVConstant>(Delta);
|
|
|
|
if (!ConstDelta)
|
|
|
|
return false;
|
|
|
|
|
|
|
|
// We're certain that ConstCoeff > 0; therefore,
|
|
|
|
// if Delta < 0, then no dependence.
|
|
|
|
DEBUG(dbgs() << "\t Delta = " << *Delta << "\n");
|
|
|
|
DEBUG(dbgs() << "\t ConstCoeff = " << *ConstCoeff << "\n");
|
|
|
|
if (SE->isKnownNegative(Delta)) {
|
|
|
|
// No dependence, Delta < 0
|
|
|
|
++WeakCrossingSIVindependence;
|
|
|
|
++WeakCrossingSIVsuccesses;
|
|
|
|
return true;
|
|
|
|
}
|
|
|
|
|
|
|
|
// We're certain that Delta > 0 and ConstCoeff > 0.
|
|
|
|
// Check Delta/(2*ConstCoeff) against upper loop bound
|
|
|
|
if (const SCEV *UpperBound = collectUpperBound(CurLoop, Delta->getType())) {
|
|
|
|
DEBUG(dbgs() << "\t UpperBound = " << *UpperBound << "\n");
|
|
|
|
const SCEV *ConstantTwo = SE->getConstant(UpperBound->getType(), 2);
|
|
|
|
const SCEV *ML = SE->getMulExpr(SE->getMulExpr(ConstCoeff, UpperBound),
|
|
|
|
ConstantTwo);
|
|
|
|
DEBUG(dbgs() << "\t ML = " << *ML << "\n");
|
|
|
|
if (isKnownPredicate(CmpInst::ICMP_SGT, Delta, ML)) {
|
|
|
|
// Delta too big, no dependence
|
|
|
|
++WeakCrossingSIVindependence;
|
|
|
|
++WeakCrossingSIVsuccesses;
|
|
|
|
return true;
|
|
|
|
}
|
|
|
|
if (isKnownPredicate(CmpInst::ICMP_EQ, Delta, ML)) {
|
|
|
|
// i = i' = UB
|
2012-10-12 02:04:32 +00:00
|
|
|
Result.DV[Level].Direction &= unsigned(~Dependence::DVEntry::LT);
|
|
|
|
Result.DV[Level].Direction &= unsigned(~Dependence::DVEntry::GT);
|
dependence analysis
Patch from Preston Briggs <preston.briggs@gmail.com>.
This is an updated version of the dependence-analysis patch, including an MIV
test based on Banerjee's inequalities.
It's a fairly complete implementation of the paper
Practical Dependence Testing
Gina Goff, Ken Kennedy, and Chau-Wen Tseng
PLDI 1991
It cannot yet propagate constraints between coupled RDIV subscripts (discussed
in Section 5.3.2 of the paper).
It's organized as a FunctionPass with a single entry point that supports testing
for dependence between two instructions in a function. If there's no dependence,
it returns null. If there's a dependence, it returns a pointer to a Dependence
which can be queried about details (what kind of dependence, is it loop
independent, direction and distance vector entries, etc). I haven't included
every imaginable feature, but there's a good selection that should be adequate
for supporting many loop transformations. Of course, it can be extended as
necessary.
Included in the patch file are many test cases, commented with C code showing
the loops and array references.
git-svn-id: https://llvm.org/svn/llvm-project/llvm/trunk@165708 91177308-0d34-0410-b5e6-96231b3b80d8
2012-10-11 07:32:34 +00:00
|
|
|
++WeakCrossingSIVsuccesses;
|
|
|
|
if (!Result.DV[Level].Direction) {
|
|
|
|
++WeakCrossingSIVindependence;
|
|
|
|
return true;
|
|
|
|
}
|
|
|
|
Result.DV[Level].Splitable = false;
|
|
|
|
Result.DV[Level].Distance = SE->getConstant(Delta->getType(), 0);
|
|
|
|
return false;
|
|
|
|
}
|
|
|
|
}
|
|
|
|
|
|
|
|
// check that Coeff divides Delta
|
|
|
|
APInt APDelta = ConstDelta->getValue()->getValue();
|
|
|
|
APInt APCoeff = ConstCoeff->getValue()->getValue();
|
|
|
|
APInt Distance = APDelta; // these need to be initialzed
|
|
|
|
APInt Remainder = APDelta;
|
|
|
|
APInt::sdivrem(APDelta, APCoeff, Distance, Remainder);
|
|
|
|
DEBUG(dbgs() << "\t Remainder = " << Remainder << "\n");
|
|
|
|
if (Remainder != 0) {
|
|
|
|
// Coeff doesn't divide Delta, no dependence
|
|
|
|
++WeakCrossingSIVindependence;
|
|
|
|
++WeakCrossingSIVsuccesses;
|
|
|
|
return true;
|
|
|
|
}
|
|
|
|
DEBUG(dbgs() << "\t Distance = " << Distance << "\n");
|
|
|
|
|
|
|
|
// if 2*Coeff doesn't divide Delta, then the equal direction isn't possible
|
|
|
|
APInt Two = APInt(Distance.getBitWidth(), 2, true);
|
|
|
|
Remainder = Distance.srem(Two);
|
|
|
|
DEBUG(dbgs() << "\t Remainder = " << Remainder << "\n");
|
|
|
|
if (Remainder != 0) {
|
|
|
|
// Equal direction isn't possible
|
2012-10-12 02:04:32 +00:00
|
|
|
Result.DV[Level].Direction &= unsigned(~Dependence::DVEntry::EQ);
|
dependence analysis
Patch from Preston Briggs <preston.briggs@gmail.com>.
This is an updated version of the dependence-analysis patch, including an MIV
test based on Banerjee's inequalities.
It's a fairly complete implementation of the paper
Practical Dependence Testing
Gina Goff, Ken Kennedy, and Chau-Wen Tseng
PLDI 1991
It cannot yet propagate constraints between coupled RDIV subscripts (discussed
in Section 5.3.2 of the paper).
It's organized as a FunctionPass with a single entry point that supports testing
for dependence between two instructions in a function. If there's no dependence,
it returns null. If there's a dependence, it returns a pointer to a Dependence
which can be queried about details (what kind of dependence, is it loop
independent, direction and distance vector entries, etc). I haven't included
every imaginable feature, but there's a good selection that should be adequate
for supporting many loop transformations. Of course, it can be extended as
necessary.
Included in the patch file are many test cases, commented with C code showing
the loops and array references.
git-svn-id: https://llvm.org/svn/llvm-project/llvm/trunk@165708 91177308-0d34-0410-b5e6-96231b3b80d8
2012-10-11 07:32:34 +00:00
|
|
|
++WeakCrossingSIVsuccesses;
|
|
|
|
}
|
|
|
|
return false;
|
|
|
|
}
|
|
|
|
|
|
|
|
|
|
|
|
// Kirch's algorithm, from
|
|
|
|
//
|
|
|
|
// Optimizing Supercompilers for Supercomputers
|
|
|
|
// Michael Wolfe
|
|
|
|
// MIT Press, 1989
|
|
|
|
//
|
|
|
|
// Program 2.1, page 29.
|
|
|
|
// Computes the GCD of AM and BM.
|
|
|
|
// Also finds a solution to the equation ax - by = gdc(a, b).
|
|
|
|
// Returns true iff the gcd divides Delta.
|
|
|
|
static
|
|
|
|
bool findGCD(unsigned Bits, APInt AM, APInt BM, APInt Delta,
|
|
|
|
APInt &G, APInt &X, APInt &Y) {
|
|
|
|
APInt A0(Bits, 1, true), A1(Bits, 0, true);
|
|
|
|
APInt B0(Bits, 0, true), B1(Bits, 1, true);
|
|
|
|
APInt G0 = AM.abs();
|
|
|
|
APInt G1 = BM.abs();
|
|
|
|
APInt Q = G0; // these need to be initialized
|
|
|
|
APInt R = G0;
|
|
|
|
APInt::sdivrem(G0, G1, Q, R);
|
|
|
|
while (R != 0) {
|
|
|
|
APInt A2 = A0 - Q*A1; A0 = A1; A1 = A2;
|
|
|
|
APInt B2 = B0 - Q*B1; B0 = B1; B1 = B2;
|
|
|
|
G0 = G1; G1 = R;
|
|
|
|
APInt::sdivrem(G0, G1, Q, R);
|
|
|
|
}
|
|
|
|
G = G1;
|
|
|
|
DEBUG(dbgs() << "\t GCD = " << G << "\n");
|
|
|
|
X = AM.slt(0) ? -A1 : A1;
|
|
|
|
Y = BM.slt(0) ? B1 : -B1;
|
|
|
|
|
|
|
|
// make sure gcd divides Delta
|
|
|
|
R = Delta.srem(G);
|
|
|
|
if (R != 0)
|
|
|
|
return true; // gcd doesn't divide Delta, no dependence
|
|
|
|
Q = Delta.sdiv(G);
|
|
|
|
X *= Q;
|
|
|
|
Y *= Q;
|
|
|
|
return false;
|
|
|
|
}
|
|
|
|
|
|
|
|
|
|
|
|
static
|
|
|
|
APInt floorOfQuotient(APInt A, APInt B) {
|
|
|
|
APInt Q = A; // these need to be initialized
|
|
|
|
APInt R = A;
|
|
|
|
APInt::sdivrem(A, B, Q, R);
|
|
|
|
if (R == 0)
|
|
|
|
return Q;
|
|
|
|
if ((A.sgt(0) && B.sgt(0)) ||
|
|
|
|
(A.slt(0) && B.slt(0)))
|
|
|
|
return Q;
|
|
|
|
else
|
|
|
|
return Q - 1;
|
|
|
|
}
|
|
|
|
|
|
|
|
|
|
|
|
static
|
|
|
|
APInt ceilingOfQuotient(APInt A, APInt B) {
|
|
|
|
APInt Q = A; // these need to be initialized
|
|
|
|
APInt R = A;
|
|
|
|
APInt::sdivrem(A, B, Q, R);
|
|
|
|
if (R == 0)
|
|
|
|
return Q;
|
|
|
|
if ((A.sgt(0) && B.sgt(0)) ||
|
|
|
|
(A.slt(0) && B.slt(0)))
|
|
|
|
return Q + 1;
|
|
|
|
else
|
|
|
|
return Q;
|
|
|
|
}
|
|
|
|
|
|
|
|
|
|
|
|
static
|
|
|
|
APInt maxAPInt(APInt A, APInt B) {
|
|
|
|
return A.sgt(B) ? A : B;
|
|
|
|
}
|
|
|
|
|
|
|
|
|
|
|
|
static
|
|
|
|
APInt minAPInt(APInt A, APInt B) {
|
|
|
|
return A.slt(B) ? A : B;
|
|
|
|
}
|
|
|
|
|
|
|
|
|
|
|
|
// exactSIVtest -
|
|
|
|
// When we have a pair of subscripts of the form [c1 + a1*i] and [c2 + a2*i],
|
|
|
|
// where i is an induction variable, c1 and c2 are loop invariant, and a1
|
|
|
|
// and a2 are constant, we can solve it exactly using an algorithm developed
|
|
|
|
// by Banerjee and Wolfe. See Section 2.5.3 in
|
|
|
|
//
|
|
|
|
// Optimizing Supercompilers for Supercomputers
|
|
|
|
// Michael Wolfe
|
|
|
|
// MIT Press, 1989
|
|
|
|
//
|
|
|
|
// It's slower than the specialized tests (strong SIV, weak-zero SIV, etc),
|
|
|
|
// so use them if possible. They're also a bit better with symbolics and,
|
|
|
|
// in the case of the strong SIV test, can compute Distances.
|
|
|
|
//
|
|
|
|
// Return true if dependence disproved.
|
|
|
|
bool DependenceAnalysis::exactSIVtest(const SCEV *SrcCoeff,
|
|
|
|
const SCEV *DstCoeff,
|
|
|
|
const SCEV *SrcConst,
|
|
|
|
const SCEV *DstConst,
|
|
|
|
const Loop *CurLoop,
|
|
|
|
unsigned Level,
|
|
|
|
FullDependence &Result,
|
|
|
|
Constraint &NewConstraint) const {
|
|
|
|
DEBUG(dbgs() << "\tExact SIV test\n");
|
|
|
|
DEBUG(dbgs() << "\t SrcCoeff = " << *SrcCoeff << " = AM\n");
|
|
|
|
DEBUG(dbgs() << "\t DstCoeff = " << *DstCoeff << " = BM\n");
|
|
|
|
DEBUG(dbgs() << "\t SrcConst = " << *SrcConst << "\n");
|
|
|
|
DEBUG(dbgs() << "\t DstConst = " << *DstConst << "\n");
|
|
|
|
++ExactSIVapplications;
|
|
|
|
assert(0 < Level && Level <= CommonLevels && "Level out of range");
|
|
|
|
Level--;
|
|
|
|
Result.Consistent = false;
|
|
|
|
const SCEV *Delta = SE->getMinusSCEV(DstConst, SrcConst);
|
|
|
|
DEBUG(dbgs() << "\t Delta = " << *Delta << "\n");
|
|
|
|
NewConstraint.setLine(SrcCoeff, SE->getNegativeSCEV(DstCoeff),
|
|
|
|
Delta, CurLoop);
|
|
|
|
const SCEVConstant *ConstDelta = dyn_cast<SCEVConstant>(Delta);
|
|
|
|
const SCEVConstant *ConstSrcCoeff = dyn_cast<SCEVConstant>(SrcCoeff);
|
|
|
|
const SCEVConstant *ConstDstCoeff = dyn_cast<SCEVConstant>(DstCoeff);
|
|
|
|
if (!ConstDelta || !ConstSrcCoeff || !ConstDstCoeff)
|
|
|
|
return false;
|
|
|
|
|
|
|
|
// find gcd
|
|
|
|
APInt G, X, Y;
|
|
|
|
APInt AM = ConstSrcCoeff->getValue()->getValue();
|
|
|
|
APInt BM = ConstDstCoeff->getValue()->getValue();
|
|
|
|
unsigned Bits = AM.getBitWidth();
|
|
|
|
if (findGCD(Bits, AM, BM, ConstDelta->getValue()->getValue(), G, X, Y)) {
|
|
|
|
// gcd doesn't divide Delta, no dependence
|
|
|
|
++ExactSIVindependence;
|
|
|
|
++ExactSIVsuccesses;
|
|
|
|
return true;
|
|
|
|
}
|
|
|
|
|
|
|
|
DEBUG(dbgs() << "\t X = " << X << ", Y = " << Y << "\n");
|
|
|
|
|
|
|
|
// since SCEV construction normalizes, LM = 0
|
|
|
|
APInt UM(Bits, 1, true);
|
|
|
|
bool UMvalid = false;
|
|
|
|
// UM is perhaps unavailable, let's check
|
|
|
|
if (const SCEVConstant *CUB =
|
|
|
|
collectConstantUpperBound(CurLoop, Delta->getType())) {
|
|
|
|
UM = CUB->getValue()->getValue();
|
|
|
|
DEBUG(dbgs() << "\t UM = " << UM << "\n");
|
|
|
|
UMvalid = true;
|
|
|
|
}
|
|
|
|
|
|
|
|
APInt TU(APInt::getSignedMaxValue(Bits));
|
|
|
|
APInt TL(APInt::getSignedMinValue(Bits));
|
|
|
|
|
|
|
|
// test(BM/G, LM-X) and test(-BM/G, X-UM)
|
|
|
|
APInt TMUL = BM.sdiv(G);
|
|
|
|
if (TMUL.sgt(0)) {
|
|
|
|
TL = maxAPInt(TL, ceilingOfQuotient(-X, TMUL));
|
|
|
|
DEBUG(dbgs() << "\t TL = " << TL << "\n");
|
|
|
|
if (UMvalid) {
|
|
|
|
TU = minAPInt(TU, floorOfQuotient(UM - X, TMUL));
|
|
|
|
DEBUG(dbgs() << "\t TU = " << TU << "\n");
|
|
|
|
}
|
|
|
|
}
|
|
|
|
else {
|
|
|
|
TU = minAPInt(TU, floorOfQuotient(-X, TMUL));
|
|
|
|
DEBUG(dbgs() << "\t TU = " << TU << "\n");
|
|
|
|
if (UMvalid) {
|
|
|
|
TL = maxAPInt(TL, ceilingOfQuotient(UM - X, TMUL));
|
|
|
|
DEBUG(dbgs() << "\t TL = " << TL << "\n");
|
|
|
|
}
|
|
|
|
}
|
|
|
|
|
|
|
|
// test(AM/G, LM-Y) and test(-AM/G, Y-UM)
|
|
|
|
TMUL = AM.sdiv(G);
|
|
|
|
if (TMUL.sgt(0)) {
|
|
|
|
TL = maxAPInt(TL, ceilingOfQuotient(-Y, TMUL));
|
|
|
|
DEBUG(dbgs() << "\t TL = " << TL << "\n");
|
|
|
|
if (UMvalid) {
|
|
|
|
TU = minAPInt(TU, floorOfQuotient(UM - Y, TMUL));
|
|
|
|
DEBUG(dbgs() << "\t TU = " << TU << "\n");
|
|
|
|
}
|
|
|
|
}
|
|
|
|
else {
|
|
|
|
TU = minAPInt(TU, floorOfQuotient(-Y, TMUL));
|
|
|
|
DEBUG(dbgs() << "\t TU = " << TU << "\n");
|
|
|
|
if (UMvalid) {
|
|
|
|
TL = maxAPInt(TL, ceilingOfQuotient(UM - Y, TMUL));
|
|
|
|
DEBUG(dbgs() << "\t TL = " << TL << "\n");
|
|
|
|
}
|
|
|
|
}
|
|
|
|
if (TL.sgt(TU)) {
|
|
|
|
++ExactSIVindependence;
|
|
|
|
++ExactSIVsuccesses;
|
|
|
|
return true;
|
|
|
|
}
|
|
|
|
|
|
|
|
// explore directions
|
|
|
|
unsigned NewDirection = Dependence::DVEntry::NONE;
|
|
|
|
|
|
|
|
// less than
|
|
|
|
APInt SaveTU(TU); // save these
|
|
|
|
APInt SaveTL(TL);
|
|
|
|
DEBUG(dbgs() << "\t exploring LT direction\n");
|
|
|
|
TMUL = AM - BM;
|
|
|
|
if (TMUL.sgt(0)) {
|
|
|
|
TL = maxAPInt(TL, ceilingOfQuotient(X - Y + 1, TMUL));
|
|
|
|
DEBUG(dbgs() << "\t\t TL = " << TL << "\n");
|
|
|
|
}
|
|
|
|
else {
|
|
|
|
TU = minAPInt(TU, floorOfQuotient(X - Y + 1, TMUL));
|
|
|
|
DEBUG(dbgs() << "\t\t TU = " << TU << "\n");
|
|
|
|
}
|
|
|
|
if (TL.sle(TU)) {
|
|
|
|
NewDirection |= Dependence::DVEntry::LT;
|
|
|
|
++ExactSIVsuccesses;
|
|
|
|
}
|
|
|
|
|
|
|
|
// equal
|
|
|
|
TU = SaveTU; // restore
|
|
|
|
TL = SaveTL;
|
|
|
|
DEBUG(dbgs() << "\t exploring EQ direction\n");
|
|
|
|
if (TMUL.sgt(0)) {
|
|
|
|
TL = maxAPInt(TL, ceilingOfQuotient(X - Y, TMUL));
|
|
|
|
DEBUG(dbgs() << "\t\t TL = " << TL << "\n");
|
|
|
|
}
|
|
|
|
else {
|
|
|
|
TU = minAPInt(TU, floorOfQuotient(X - Y, TMUL));
|
|
|
|
DEBUG(dbgs() << "\t\t TU = " << TU << "\n");
|
|
|
|
}
|
|
|
|
TMUL = BM - AM;
|
|
|
|
if (TMUL.sgt(0)) {
|
|
|
|
TL = maxAPInt(TL, ceilingOfQuotient(Y - X, TMUL));
|
|
|
|
DEBUG(dbgs() << "\t\t TL = " << TL << "\n");
|
|
|
|
}
|
|
|
|
else {
|
|
|
|
TU = minAPInt(TU, floorOfQuotient(Y - X, TMUL));
|
|
|
|
DEBUG(dbgs() << "\t\t TU = " << TU << "\n");
|
|
|
|
}
|
|
|
|
if (TL.sle(TU)) {
|
|
|
|
NewDirection |= Dependence::DVEntry::EQ;
|
|
|
|
++ExactSIVsuccesses;
|
|
|
|
}
|
|
|
|
|
|
|
|
// greater than
|
|
|
|
TU = SaveTU; // restore
|
|
|
|
TL = SaveTL;
|
|
|
|
DEBUG(dbgs() << "\t exploring GT direction\n");
|
|
|
|
if (TMUL.sgt(0)) {
|
|
|
|
TL = maxAPInt(TL, ceilingOfQuotient(Y - X + 1, TMUL));
|
|
|
|
DEBUG(dbgs() << "\t\t TL = " << TL << "\n");
|
|
|
|
}
|
|
|
|
else {
|
|
|
|
TU = minAPInt(TU, floorOfQuotient(Y - X + 1, TMUL));
|
|
|
|
DEBUG(dbgs() << "\t\t TU = " << TU << "\n");
|
|
|
|
}
|
|
|
|
if (TL.sle(TU)) {
|
|
|
|
NewDirection |= Dependence::DVEntry::GT;
|
|
|
|
++ExactSIVsuccesses;
|
|
|
|
}
|
|
|
|
|
|
|
|
// finished
|
|
|
|
Result.DV[Level].Direction &= NewDirection;
|
|
|
|
if (Result.DV[Level].Direction == Dependence::DVEntry::NONE)
|
|
|
|
++ExactSIVindependence;
|
|
|
|
return Result.DV[Level].Direction == Dependence::DVEntry::NONE;
|
|
|
|
}
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
// Return true if the divisor evenly divides the dividend.
|
|
|
|
static
|
|
|
|
bool isRemainderZero(const SCEVConstant *Dividend,
|
|
|
|
const SCEVConstant *Divisor) {
|
|
|
|
APInt ConstDividend = Dividend->getValue()->getValue();
|
|
|
|
APInt ConstDivisor = Divisor->getValue()->getValue();
|
|
|
|
return ConstDividend.srem(ConstDivisor) == 0;
|
|
|
|
}
|
|
|
|
|
|
|
|
|
|
|
|
// weakZeroSrcSIVtest -
|
|
|
|
// From the paper, Practical Dependence Testing, Section 4.2.2
|
|
|
|
//
|
|
|
|
// When we have a pair of subscripts of the form [c1] and [c2 + a*i],
|
|
|
|
// where i is an induction variable, c1 and c2 are loop invariant,
|
|
|
|
// and a is a constant, we can solve it exactly using the
|
|
|
|
// Weak-Zero SIV test.
|
|
|
|
//
|
|
|
|
// Given
|
|
|
|
//
|
|
|
|
// c1 = c2 + a*i
|
|
|
|
//
|
|
|
|
// we get
|
|
|
|
//
|
|
|
|
// (c1 - c2)/a = i
|
|
|
|
//
|
|
|
|
// If i is not an integer, there's no dependence.
|
|
|
|
// If i < 0 or > UB, there's no dependence.
|
|
|
|
// If i = 0, the direction is <= and peeling the
|
|
|
|
// 1st iteration will break the dependence.
|
|
|
|
// If i = UB, the direction is >= and peeling the
|
|
|
|
// last iteration will break the dependence.
|
|
|
|
// Otherwise, the direction is *.
|
|
|
|
//
|
|
|
|
// Can prove independence. Failing that, we can sometimes refine
|
|
|
|
// the directions. Can sometimes show that first or last
|
|
|
|
// iteration carries all the dependences (so worth peeling).
|
|
|
|
//
|
|
|
|
// (see also weakZeroDstSIVtest)
|
|
|
|
//
|
|
|
|
// Return true if dependence disproved.
|
|
|
|
bool DependenceAnalysis::weakZeroSrcSIVtest(const SCEV *DstCoeff,
|
|
|
|
const SCEV *SrcConst,
|
|
|
|
const SCEV *DstConst,
|
|
|
|
const Loop *CurLoop,
|
|
|
|
unsigned Level,
|
|
|
|
FullDependence &Result,
|
|
|
|
Constraint &NewConstraint) const {
|
|
|
|
// For the WeakSIV test, it's possible the loop isn't common to
|
|
|
|
// the Src and Dst loops. If it isn't, then there's no need to
|
|
|
|
// record a direction.
|
|
|
|
DEBUG(dbgs() << "\tWeak-Zero (src) SIV test\n");
|
|
|
|
DEBUG(dbgs() << "\t DstCoeff = " << *DstCoeff << "\n");
|
|
|
|
DEBUG(dbgs() << "\t SrcConst = " << *SrcConst << "\n");
|
|
|
|
DEBUG(dbgs() << "\t DstConst = " << *DstConst << "\n");
|
|
|
|
++WeakZeroSIVapplications;
|
|
|
|
assert(0 < Level && Level <= MaxLevels && "Level out of range");
|
|
|
|
Level--;
|
|
|
|
Result.Consistent = false;
|
|
|
|
const SCEV *Delta = SE->getMinusSCEV(SrcConst, DstConst);
|
|
|
|
NewConstraint.setLine(SE->getConstant(Delta->getType(), 0),
|
|
|
|
DstCoeff, Delta, CurLoop);
|
|
|
|
DEBUG(dbgs() << "\t Delta = " << *Delta << "\n");
|
|
|
|
if (isKnownPredicate(CmpInst::ICMP_EQ, SrcConst, DstConst)) {
|
|
|
|
if (Level < CommonLevels) {
|
|
|
|
Result.DV[Level].Direction &= Dependence::DVEntry::LE;
|
|
|
|
Result.DV[Level].PeelFirst = true;
|
|
|
|
++WeakZeroSIVsuccesses;
|
|
|
|
}
|
|
|
|
return false; // dependences caused by first iteration
|
|
|
|
}
|
|
|
|
const SCEVConstant *ConstCoeff = dyn_cast<SCEVConstant>(DstCoeff);
|
|
|
|
if (!ConstCoeff)
|
|
|
|
return false;
|
|
|
|
const SCEV *AbsCoeff =
|
|
|
|
SE->isKnownNegative(ConstCoeff) ?
|
|
|
|
SE->getNegativeSCEV(ConstCoeff) : ConstCoeff;
|
|
|
|
const SCEV *NewDelta =
|
|
|
|
SE->isKnownNegative(ConstCoeff) ? SE->getNegativeSCEV(Delta) : Delta;
|
|
|
|
|
|
|
|
// check that Delta/SrcCoeff < iteration count
|
|
|
|
// really check NewDelta < count*AbsCoeff
|
|
|
|
if (const SCEV *UpperBound = collectUpperBound(CurLoop, Delta->getType())) {
|
|
|
|
DEBUG(dbgs() << "\t UpperBound = " << *UpperBound << "\n");
|
|
|
|
const SCEV *Product = SE->getMulExpr(AbsCoeff, UpperBound);
|
|
|
|
if (isKnownPredicate(CmpInst::ICMP_SGT, NewDelta, Product)) {
|
|
|
|
++WeakZeroSIVindependence;
|
|
|
|
++WeakZeroSIVsuccesses;
|
|
|
|
return true;
|
|
|
|
}
|
|
|
|
if (isKnownPredicate(CmpInst::ICMP_EQ, NewDelta, Product)) {
|
|
|
|
// dependences caused by last iteration
|
|
|
|
if (Level < CommonLevels) {
|
|
|
|
Result.DV[Level].Direction &= Dependence::DVEntry::GE;
|
|
|
|
Result.DV[Level].PeelLast = true;
|
|
|
|
++WeakZeroSIVsuccesses;
|
|
|
|
}
|
|
|
|
return false;
|
|
|
|
}
|
|
|
|
}
|
|
|
|
|
|
|
|
// check that Delta/SrcCoeff >= 0
|
|
|
|
// really check that NewDelta >= 0
|
|
|
|
if (SE->isKnownNegative(NewDelta)) {
|
|
|
|
// No dependence, newDelta < 0
|
|
|
|
++WeakZeroSIVindependence;
|
|
|
|
++WeakZeroSIVsuccesses;
|
|
|
|
return true;
|
|
|
|
}
|
|
|
|
|
|
|
|
// if SrcCoeff doesn't divide Delta, then no dependence
|
|
|
|
if (isa<SCEVConstant>(Delta) &&
|
|
|
|
!isRemainderZero(cast<SCEVConstant>(Delta), ConstCoeff)) {
|
|
|
|
++WeakZeroSIVindependence;
|
|
|
|
++WeakZeroSIVsuccesses;
|
|
|
|
return true;
|
|
|
|
}
|
|
|
|
return false;
|
|
|
|
}
|
|
|
|
|
|
|
|
|
|
|
|
// weakZeroDstSIVtest -
|
|
|
|
// From the paper, Practical Dependence Testing, Section 4.2.2
|
|
|
|
//
|
|
|
|
// When we have a pair of subscripts of the form [c1 + a*i] and [c2],
|
|
|
|
// where i is an induction variable, c1 and c2 are loop invariant,
|
|
|
|
// and a is a constant, we can solve it exactly using the
|
|
|
|
// Weak-Zero SIV test.
|
|
|
|
//
|
|
|
|
// Given
|
|
|
|
//
|
|
|
|
// c1 + a*i = c2
|
|
|
|
//
|
|
|
|
// we get
|
|
|
|
//
|
|
|
|
// i = (c2 - c1)/a
|
|
|
|
//
|
|
|
|
// If i is not an integer, there's no dependence.
|
|
|
|
// If i < 0 or > UB, there's no dependence.
|
|
|
|
// If i = 0, the direction is <= and peeling the
|
|
|
|
// 1st iteration will break the dependence.
|
|
|
|
// If i = UB, the direction is >= and peeling the
|
|
|
|
// last iteration will break the dependence.
|
|
|
|
// Otherwise, the direction is *.
|
|
|
|
//
|
|
|
|
// Can prove independence. Failing that, we can sometimes refine
|
|
|
|
// the directions. Can sometimes show that first or last
|
|
|
|
// iteration carries all the dependences (so worth peeling).
|
|
|
|
//
|
|
|
|
// (see also weakZeroSrcSIVtest)
|
|
|
|
//
|
|
|
|
// Return true if dependence disproved.
|
|
|
|
bool DependenceAnalysis::weakZeroDstSIVtest(const SCEV *SrcCoeff,
|
|
|
|
const SCEV *SrcConst,
|
|
|
|
const SCEV *DstConst,
|
|
|
|
const Loop *CurLoop,
|
|
|
|
unsigned Level,
|
|
|
|
FullDependence &Result,
|
|
|
|
Constraint &NewConstraint) const {
|
|
|
|
// For the WeakSIV test, it's possible the loop isn't common to the
|
|
|
|
// Src and Dst loops. If it isn't, then there's no need to record a direction.
|
|
|
|
DEBUG(dbgs() << "\tWeak-Zero (dst) SIV test\n");
|
|
|
|
DEBUG(dbgs() << "\t SrcCoeff = " << *SrcCoeff << "\n");
|
|
|
|
DEBUG(dbgs() << "\t SrcConst = " << *SrcConst << "\n");
|
|
|
|
DEBUG(dbgs() << "\t DstConst = " << *DstConst << "\n");
|
|
|
|
++WeakZeroSIVapplications;
|
|
|
|
assert(0 < Level && Level <= SrcLevels && "Level out of range");
|
|
|
|
Level--;
|
|
|
|
Result.Consistent = false;
|
|
|
|
const SCEV *Delta = SE->getMinusSCEV(DstConst, SrcConst);
|
|
|
|
NewConstraint.setLine(SrcCoeff, SE->getConstant(Delta->getType(), 0),
|
|
|
|
Delta, CurLoop);
|
|
|
|
DEBUG(dbgs() << "\t Delta = " << *Delta << "\n");
|
|
|
|
if (isKnownPredicate(CmpInst::ICMP_EQ, DstConst, SrcConst)) {
|
|
|
|
if (Level < CommonLevels) {
|
|
|
|
Result.DV[Level].Direction &= Dependence::DVEntry::LE;
|
|
|
|
Result.DV[Level].PeelFirst = true;
|
|
|
|
++WeakZeroSIVsuccesses;
|
|
|
|
}
|
|
|
|
return false; // dependences caused by first iteration
|
|
|
|
}
|
|
|
|
const SCEVConstant *ConstCoeff = dyn_cast<SCEVConstant>(SrcCoeff);
|
|
|
|
if (!ConstCoeff)
|
|
|
|
return false;
|
|
|
|
const SCEV *AbsCoeff =
|
|
|
|
SE->isKnownNegative(ConstCoeff) ?
|
|
|
|
SE->getNegativeSCEV(ConstCoeff) : ConstCoeff;
|
|
|
|
const SCEV *NewDelta =
|
|
|
|
SE->isKnownNegative(ConstCoeff) ? SE->getNegativeSCEV(Delta) : Delta;
|
|
|
|
|
|
|
|
// check that Delta/SrcCoeff < iteration count
|
|
|
|
// really check NewDelta < count*AbsCoeff
|
|
|
|
if (const SCEV *UpperBound = collectUpperBound(CurLoop, Delta->getType())) {
|
|
|
|
DEBUG(dbgs() << "\t UpperBound = " << *UpperBound << "\n");
|
|
|
|
const SCEV *Product = SE->getMulExpr(AbsCoeff, UpperBound);
|
|
|
|
if (isKnownPredicate(CmpInst::ICMP_SGT, NewDelta, Product)) {
|
|
|
|
++WeakZeroSIVindependence;
|
|
|
|
++WeakZeroSIVsuccesses;
|
|
|
|
return true;
|
|
|
|
}
|
|
|
|
if (isKnownPredicate(CmpInst::ICMP_EQ, NewDelta, Product)) {
|
|
|
|
// dependences caused by last iteration
|
|
|
|
if (Level < CommonLevels) {
|
|
|
|
Result.DV[Level].Direction &= Dependence::DVEntry::GE;
|
|
|
|
Result.DV[Level].PeelLast = true;
|
|
|
|
++WeakZeroSIVsuccesses;
|
|
|
|
}
|
|
|
|
return false;
|
|
|
|
}
|
|
|
|
}
|
|
|
|
|
|
|
|
// check that Delta/SrcCoeff >= 0
|
|
|
|
// really check that NewDelta >= 0
|
|
|
|
if (SE->isKnownNegative(NewDelta)) {
|
|
|
|
// No dependence, newDelta < 0
|
|
|
|
++WeakZeroSIVindependence;
|
|
|
|
++WeakZeroSIVsuccesses;
|
|
|
|
return true;
|
|
|
|
}
|
|
|
|
|
|
|
|
// if SrcCoeff doesn't divide Delta, then no dependence
|
|
|
|
if (isa<SCEVConstant>(Delta) &&
|
|
|
|
!isRemainderZero(cast<SCEVConstant>(Delta), ConstCoeff)) {
|
|
|
|
++WeakZeroSIVindependence;
|
|
|
|
++WeakZeroSIVsuccesses;
|
|
|
|
return true;
|
|
|
|
}
|
|
|
|
return false;
|
|
|
|
}
|
|
|
|
|
|
|
|
|
|
|
|
// exactRDIVtest - Tests the RDIV subscript pair for dependence.
|
|
|
|
// Things of the form [c1 + a*i] and [c2 + b*j],
|
|
|
|
// where i and j are induction variable, c1 and c2 are loop invariant,
|
|
|
|
// and a and b are constants.
|
|
|
|
// Returns true if any possible dependence is disproved.
|
2012-10-31 11:25:32 +00:00
|
|
|
// Marks the result as inconsistent.
|
dependence analysis
Patch from Preston Briggs <preston.briggs@gmail.com>.
This is an updated version of the dependence-analysis patch, including an MIV
test based on Banerjee's inequalities.
It's a fairly complete implementation of the paper
Practical Dependence Testing
Gina Goff, Ken Kennedy, and Chau-Wen Tseng
PLDI 1991
It cannot yet propagate constraints between coupled RDIV subscripts (discussed
in Section 5.3.2 of the paper).
It's organized as a FunctionPass with a single entry point that supports testing
for dependence between two instructions in a function. If there's no dependence,
it returns null. If there's a dependence, it returns a pointer to a Dependence
which can be queried about details (what kind of dependence, is it loop
independent, direction and distance vector entries, etc). I haven't included
every imaginable feature, but there's a good selection that should be adequate
for supporting many loop transformations. Of course, it can be extended as
necessary.
Included in the patch file are many test cases, commented with C code showing
the loops and array references.
git-svn-id: https://llvm.org/svn/llvm-project/llvm/trunk@165708 91177308-0d34-0410-b5e6-96231b3b80d8
2012-10-11 07:32:34 +00:00
|
|
|
// Works in some cases that symbolicRDIVtest doesn't, and vice versa.
|
|
|
|
bool DependenceAnalysis::exactRDIVtest(const SCEV *SrcCoeff,
|
|
|
|
const SCEV *DstCoeff,
|
|
|
|
const SCEV *SrcConst,
|
|
|
|
const SCEV *DstConst,
|
|
|
|
const Loop *SrcLoop,
|
|
|
|
const Loop *DstLoop,
|
|
|
|
FullDependence &Result) const {
|
|
|
|
DEBUG(dbgs() << "\tExact RDIV test\n");
|
|
|
|
DEBUG(dbgs() << "\t SrcCoeff = " << *SrcCoeff << " = AM\n");
|
|
|
|
DEBUG(dbgs() << "\t DstCoeff = " << *DstCoeff << " = BM\n");
|
|
|
|
DEBUG(dbgs() << "\t SrcConst = " << *SrcConst << "\n");
|
|
|
|
DEBUG(dbgs() << "\t DstConst = " << *DstConst << "\n");
|
|
|
|
++ExactRDIVapplications;
|
|
|
|
Result.Consistent = false;
|
|
|
|
const SCEV *Delta = SE->getMinusSCEV(DstConst, SrcConst);
|
|
|
|
DEBUG(dbgs() << "\t Delta = " << *Delta << "\n");
|
|
|
|
const SCEVConstant *ConstDelta = dyn_cast<SCEVConstant>(Delta);
|
|
|
|
const SCEVConstant *ConstSrcCoeff = dyn_cast<SCEVConstant>(SrcCoeff);
|
|
|
|
const SCEVConstant *ConstDstCoeff = dyn_cast<SCEVConstant>(DstCoeff);
|
|
|
|
if (!ConstDelta || !ConstSrcCoeff || !ConstDstCoeff)
|
|
|
|
return false;
|
|
|
|
|
|
|
|
// find gcd
|
|
|
|
APInt G, X, Y;
|
|
|
|
APInt AM = ConstSrcCoeff->getValue()->getValue();
|
|
|
|
APInt BM = ConstDstCoeff->getValue()->getValue();
|
|
|
|
unsigned Bits = AM.getBitWidth();
|
|
|
|
if (findGCD(Bits, AM, BM, ConstDelta->getValue()->getValue(), G, X, Y)) {
|
|
|
|
// gcd doesn't divide Delta, no dependence
|
|
|
|
++ExactRDIVindependence;
|
|
|
|
return true;
|
|
|
|
}
|
|
|
|
|
|
|
|
DEBUG(dbgs() << "\t X = " << X << ", Y = " << Y << "\n");
|
|
|
|
|
|
|
|
// since SCEV construction seems to normalize, LM = 0
|
|
|
|
APInt SrcUM(Bits, 1, true);
|
|
|
|
bool SrcUMvalid = false;
|
|
|
|
// SrcUM is perhaps unavailable, let's check
|
|
|
|
if (const SCEVConstant *UpperBound =
|
|
|
|
collectConstantUpperBound(SrcLoop, Delta->getType())) {
|
|
|
|
SrcUM = UpperBound->getValue()->getValue();
|
|
|
|
DEBUG(dbgs() << "\t SrcUM = " << SrcUM << "\n");
|
|
|
|
SrcUMvalid = true;
|
|
|
|
}
|
|
|
|
|
|
|
|
APInt DstUM(Bits, 1, true);
|
|
|
|
bool DstUMvalid = false;
|
|
|
|
// UM is perhaps unavailable, let's check
|
|
|
|
if (const SCEVConstant *UpperBound =
|
|
|
|
collectConstantUpperBound(DstLoop, Delta->getType())) {
|
|
|
|
DstUM = UpperBound->getValue()->getValue();
|
|
|
|
DEBUG(dbgs() << "\t DstUM = " << DstUM << "\n");
|
|
|
|
DstUMvalid = true;
|
|
|
|
}
|
|
|
|
|
|
|
|
APInt TU(APInt::getSignedMaxValue(Bits));
|
|
|
|
APInt TL(APInt::getSignedMinValue(Bits));
|
|
|
|
|
|
|
|
// test(BM/G, LM-X) and test(-BM/G, X-UM)
|
|
|
|
APInt TMUL = BM.sdiv(G);
|
|
|
|
if (TMUL.sgt(0)) {
|
|
|
|
TL = maxAPInt(TL, ceilingOfQuotient(-X, TMUL));
|
|
|
|
DEBUG(dbgs() << "\t TL = " << TL << "\n");
|
|
|
|
if (SrcUMvalid) {
|
|
|
|
TU = minAPInt(TU, floorOfQuotient(SrcUM - X, TMUL));
|
|
|
|
DEBUG(dbgs() << "\t TU = " << TU << "\n");
|
|
|
|
}
|
|
|
|
}
|
|
|
|
else {
|
|
|
|
TU = minAPInt(TU, floorOfQuotient(-X, TMUL));
|
|
|
|
DEBUG(dbgs() << "\t TU = " << TU << "\n");
|
|
|
|
if (SrcUMvalid) {
|
|
|
|
TL = maxAPInt(TL, ceilingOfQuotient(SrcUM - X, TMUL));
|
|
|
|
DEBUG(dbgs() << "\t TL = " << TL << "\n");
|
|
|
|
}
|
|
|
|
}
|
|
|
|
|
|
|
|
// test(AM/G, LM-Y) and test(-AM/G, Y-UM)
|
|
|
|
TMUL = AM.sdiv(G);
|
|
|
|
if (TMUL.sgt(0)) {
|
|
|
|
TL = maxAPInt(TL, ceilingOfQuotient(-Y, TMUL));
|
|
|
|
DEBUG(dbgs() << "\t TL = " << TL << "\n");
|
|
|
|
if (DstUMvalid) {
|
|
|
|
TU = minAPInt(TU, floorOfQuotient(DstUM - Y, TMUL));
|
|
|
|
DEBUG(dbgs() << "\t TU = " << TU << "\n");
|
|
|
|
}
|
|
|
|
}
|
|
|
|
else {
|
|
|
|
TU = minAPInt(TU, floorOfQuotient(-Y, TMUL));
|
|
|
|
DEBUG(dbgs() << "\t TU = " << TU << "\n");
|
|
|
|
if (DstUMvalid) {
|
|
|
|
TL = maxAPInt(TL, ceilingOfQuotient(DstUM - Y, TMUL));
|
|
|
|
DEBUG(dbgs() << "\t TL = " << TL << "\n");
|
|
|
|
}
|
|
|
|
}
|
|
|
|
if (TL.sgt(TU))
|
|
|
|
++ExactRDIVindependence;
|
|
|
|
return TL.sgt(TU);
|
|
|
|
}
|
|
|
|
|
|
|
|
|
|
|
|
// symbolicRDIVtest -
|
|
|
|
// In Section 4.5 of the Practical Dependence Testing paper,the authors
|
|
|
|
// introduce a special case of Banerjee's Inequalities (also called the
|
|
|
|
// Extreme-Value Test) that can handle some of the SIV and RDIV cases,
|
|
|
|
// particularly cases with symbolics. Since it's only able to disprove
|
|
|
|
// dependence (not compute distances or directions), we'll use it as a
|
|
|
|
// fall back for the other tests.
|
|
|
|
//
|
|
|
|
// When we have a pair of subscripts of the form [c1 + a1*i] and [c2 + a2*j]
|
|
|
|
// where i and j are induction variables and c1 and c2 are loop invariants,
|
|
|
|
// we can use the symbolic tests to disprove some dependences, serving as a
|
|
|
|
// backup for the RDIV test. Note that i and j can be the same variable,
|
|
|
|
// letting this test serve as a backup for the various SIV tests.
|
|
|
|
//
|
|
|
|
// For a dependence to exist, c1 + a1*i must equal c2 + a2*j for some
|
|
|
|
// 0 <= i <= N1 and some 0 <= j <= N2, where N1 and N2 are the (normalized)
|
|
|
|
// loop bounds for the i and j loops, respectively. So, ...
|
|
|
|
//
|
|
|
|
// c1 + a1*i = c2 + a2*j
|
|
|
|
// a1*i - a2*j = c2 - c1
|
|
|
|
//
|
|
|
|
// To test for a dependence, we compute c2 - c1 and make sure it's in the
|
|
|
|
// range of the maximum and minimum possible values of a1*i - a2*j.
|
|
|
|
// Considering the signs of a1 and a2, we have 4 possible cases:
|
|
|
|
//
|
|
|
|
// 1) If a1 >= 0 and a2 >= 0, then
|
|
|
|
// a1*0 - a2*N2 <= c2 - c1 <= a1*N1 - a2*0
|
|
|
|
// -a2*N2 <= c2 - c1 <= a1*N1
|
|
|
|
//
|
|
|
|
// 2) If a1 >= 0 and a2 <= 0, then
|
|
|
|
// a1*0 - a2*0 <= c2 - c1 <= a1*N1 - a2*N2
|
|
|
|
// 0 <= c2 - c1 <= a1*N1 - a2*N2
|
|
|
|
//
|
|
|
|
// 3) If a1 <= 0 and a2 >= 0, then
|
|
|
|
// a1*N1 - a2*N2 <= c2 - c1 <= a1*0 - a2*0
|
|
|
|
// a1*N1 - a2*N2 <= c2 - c1 <= 0
|
|
|
|
//
|
|
|
|
// 4) If a1 <= 0 and a2 <= 0, then
|
|
|
|
// a1*N1 - a2*0 <= c2 - c1 <= a1*0 - a2*N2
|
|
|
|
// a1*N1 <= c2 - c1 <= -a2*N2
|
|
|
|
//
|
|
|
|
// return true if dependence disproved
|
|
|
|
bool DependenceAnalysis::symbolicRDIVtest(const SCEV *A1,
|
|
|
|
const SCEV *A2,
|
|
|
|
const SCEV *C1,
|
|
|
|
const SCEV *C2,
|
|
|
|
const Loop *Loop1,
|
|
|
|
const Loop *Loop2) const {
|
|
|
|
++SymbolicRDIVapplications;
|
|
|
|
DEBUG(dbgs() << "\ttry symbolic RDIV test\n");
|
|
|
|
DEBUG(dbgs() << "\t A1 = " << *A1);
|
|
|
|
DEBUG(dbgs() << ", type = " << *A1->getType() << "\n");
|
|
|
|
DEBUG(dbgs() << "\t A2 = " << *A2 << "\n");
|
|
|
|
DEBUG(dbgs() << "\t C1 = " << *C1 << "\n");
|
|
|
|
DEBUG(dbgs() << "\t C2 = " << *C2 << "\n");
|
|
|
|
const SCEV *N1 = collectUpperBound(Loop1, A1->getType());
|
|
|
|
const SCEV *N2 = collectUpperBound(Loop2, A1->getType());
|
|
|
|
DEBUG(if (N1) dbgs() << "\t N1 = " << *N1 << "\n");
|
|
|
|
DEBUG(if (N2) dbgs() << "\t N2 = " << *N2 << "\n");
|
|
|
|
const SCEV *C2_C1 = SE->getMinusSCEV(C2, C1);
|
|
|
|
const SCEV *C1_C2 = SE->getMinusSCEV(C1, C2);
|
|
|
|
DEBUG(dbgs() << "\t C2 - C1 = " << *C2_C1 << "\n");
|
|
|
|
DEBUG(dbgs() << "\t C1 - C2 = " << *C1_C2 << "\n");
|
|
|
|
if (SE->isKnownNonNegative(A1)) {
|
|
|
|
if (SE->isKnownNonNegative(A2)) {
|
|
|
|
// A1 >= 0 && A2 >= 0
|
|
|
|
if (N1) {
|
|
|
|
// make sure that c2 - c1 <= a1*N1
|
|
|
|
const SCEV *A1N1 = SE->getMulExpr(A1, N1);
|
|
|
|
DEBUG(dbgs() << "\t A1*N1 = " << *A1N1 << "\n");
|
|
|
|
if (isKnownPredicate(CmpInst::ICMP_SGT, C2_C1, A1N1)) {
|
|
|
|
++SymbolicRDIVindependence;
|
|
|
|
return true;
|
|
|
|
}
|
|
|
|
}
|
|
|
|
if (N2) {
|
|
|
|
// make sure that -a2*N2 <= c2 - c1, or a2*N2 >= c1 - c2
|
|
|
|
const SCEV *A2N2 = SE->getMulExpr(A2, N2);
|
|
|
|
DEBUG(dbgs() << "\t A2*N2 = " << *A2N2 << "\n");
|
|
|
|
if (isKnownPredicate(CmpInst::ICMP_SLT, A2N2, C1_C2)) {
|
|
|
|
++SymbolicRDIVindependence;
|
|
|
|
return true;
|
|
|
|
}
|
|
|
|
}
|
|
|
|
}
|
|
|
|
else if (SE->isKnownNonPositive(A2)) {
|
|
|
|
// a1 >= 0 && a2 <= 0
|
|
|
|
if (N1 && N2) {
|
|
|
|
// make sure that c2 - c1 <= a1*N1 - a2*N2
|
|
|
|
const SCEV *A1N1 = SE->getMulExpr(A1, N1);
|
|
|
|
const SCEV *A2N2 = SE->getMulExpr(A2, N2);
|
|
|
|
const SCEV *A1N1_A2N2 = SE->getMinusSCEV(A1N1, A2N2);
|
|
|
|
DEBUG(dbgs() << "\t A1*N1 - A2*N2 = " << *A1N1_A2N2 << "\n");
|
|
|
|
if (isKnownPredicate(CmpInst::ICMP_SGT, C2_C1, A1N1_A2N2)) {
|
|
|
|
++SymbolicRDIVindependence;
|
|
|
|
return true;
|
|
|
|
}
|
|
|
|
}
|
|
|
|
// make sure that 0 <= c2 - c1
|
|
|
|
if (SE->isKnownNegative(C2_C1)) {
|
|
|
|
++SymbolicRDIVindependence;
|
|
|
|
return true;
|
|
|
|
}
|
|
|
|
}
|
|
|
|
}
|
|
|
|
else if (SE->isKnownNonPositive(A1)) {
|
|
|
|
if (SE->isKnownNonNegative(A2)) {
|
|
|
|
// a1 <= 0 && a2 >= 0
|
|
|
|
if (N1 && N2) {
|
|
|
|
// make sure that a1*N1 - a2*N2 <= c2 - c1
|
|
|
|
const SCEV *A1N1 = SE->getMulExpr(A1, N1);
|
|
|
|
const SCEV *A2N2 = SE->getMulExpr(A2, N2);
|
|
|
|
const SCEV *A1N1_A2N2 = SE->getMinusSCEV(A1N1, A2N2);
|
|
|
|
DEBUG(dbgs() << "\t A1*N1 - A2*N2 = " << *A1N1_A2N2 << "\n");
|
|
|
|
if (isKnownPredicate(CmpInst::ICMP_SGT, A1N1_A2N2, C2_C1)) {
|
|
|
|
++SymbolicRDIVindependence;
|
|
|
|
return true;
|
|
|
|
}
|
|
|
|
}
|
|
|
|
// make sure that c2 - c1 <= 0
|
|
|
|
if (SE->isKnownPositive(C2_C1)) {
|
|
|
|
++SymbolicRDIVindependence;
|
|
|
|
return true;
|
|
|
|
}
|
|
|
|
}
|
|
|
|
else if (SE->isKnownNonPositive(A2)) {
|
|
|
|
// a1 <= 0 && a2 <= 0
|
|
|
|
if (N1) {
|
|
|
|
// make sure that a1*N1 <= c2 - c1
|
|
|
|
const SCEV *A1N1 = SE->getMulExpr(A1, N1);
|
|
|
|
DEBUG(dbgs() << "\t A1*N1 = " << *A1N1 << "\n");
|
|
|
|
if (isKnownPredicate(CmpInst::ICMP_SGT, A1N1, C2_C1)) {
|
|
|
|
++SymbolicRDIVindependence;
|
|
|
|
return true;
|
|
|
|
}
|
|
|
|
}
|
|
|
|
if (N2) {
|
|
|
|
// make sure that c2 - c1 <= -a2*N2, or c1 - c2 >= a2*N2
|
|
|
|
const SCEV *A2N2 = SE->getMulExpr(A2, N2);
|
|
|
|
DEBUG(dbgs() << "\t A2*N2 = " << *A2N2 << "\n");
|
|
|
|
if (isKnownPredicate(CmpInst::ICMP_SLT, C1_C2, A2N2)) {
|
|
|
|
++SymbolicRDIVindependence;
|
|
|
|
return true;
|
|
|
|
}
|
|
|
|
}
|
|
|
|
}
|
|
|
|
}
|
|
|
|
return false;
|
|
|
|
}
|
|
|
|
|
|
|
|
|
|
|
|
// testSIV -
|
|
|
|
// When we have a pair of subscripts of the form [c1 + a1*i] and [c2 - a2*i]
|
|
|
|
// where i is an induction variable, c1 and c2 are loop invariant, and a1 and
|
|
|
|
// a2 are constant, we attack it with an SIV test. While they can all be
|
|
|
|
// solved with the Exact SIV test, it's worthwhile to use simpler tests when
|
|
|
|
// they apply; they're cheaper and sometimes more precise.
|
|
|
|
//
|
|
|
|
// Return true if dependence disproved.
|
|
|
|
bool DependenceAnalysis::testSIV(const SCEV *Src,
|
|
|
|
const SCEV *Dst,
|
|
|
|
unsigned &Level,
|
|
|
|
FullDependence &Result,
|
|
|
|
Constraint &NewConstraint,
|
|
|
|
const SCEV *&SplitIter) const {
|
|
|
|
DEBUG(dbgs() << " src = " << *Src << "\n");
|
|
|
|
DEBUG(dbgs() << " dst = " << *Dst << "\n");
|
|
|
|
const SCEVAddRecExpr *SrcAddRec = dyn_cast<SCEVAddRecExpr>(Src);
|
|
|
|
const SCEVAddRecExpr *DstAddRec = dyn_cast<SCEVAddRecExpr>(Dst);
|
|
|
|
if (SrcAddRec && DstAddRec) {
|
|
|
|
const SCEV *SrcConst = SrcAddRec->getStart();
|
|
|
|
const SCEV *DstConst = DstAddRec->getStart();
|
|
|
|
const SCEV *SrcCoeff = SrcAddRec->getStepRecurrence(*SE);
|
|
|
|
const SCEV *DstCoeff = DstAddRec->getStepRecurrence(*SE);
|
|
|
|
const Loop *CurLoop = SrcAddRec->getLoop();
|
|
|
|
assert(CurLoop == DstAddRec->getLoop() &&
|
|
|
|
"both loops in SIV should be same");
|
|
|
|
Level = mapSrcLoop(CurLoop);
|
|
|
|
bool disproven;
|
|
|
|
if (SrcCoeff == DstCoeff)
|
|
|
|
disproven = strongSIVtest(SrcCoeff, SrcConst, DstConst, CurLoop,
|
|
|
|
Level, Result, NewConstraint);
|
|
|
|
else if (SrcCoeff == SE->getNegativeSCEV(DstCoeff))
|
|
|
|
disproven = weakCrossingSIVtest(SrcCoeff, SrcConst, DstConst, CurLoop,
|
|
|
|
Level, Result, NewConstraint, SplitIter);
|
|
|
|
else
|
|
|
|
disproven = exactSIVtest(SrcCoeff, DstCoeff, SrcConst, DstConst, CurLoop,
|
|
|
|
Level, Result, NewConstraint);
|
|
|
|
return disproven ||
|
|
|
|
gcdMIVtest(Src, Dst, Result) ||
|
|
|
|
symbolicRDIVtest(SrcCoeff, DstCoeff, SrcConst, DstConst, CurLoop, CurLoop);
|
|
|
|
}
|
|
|
|
if (SrcAddRec) {
|
|
|
|
const SCEV *SrcConst = SrcAddRec->getStart();
|
|
|
|
const SCEV *SrcCoeff = SrcAddRec->getStepRecurrence(*SE);
|
|
|
|
const SCEV *DstConst = Dst;
|
|
|
|
const Loop *CurLoop = SrcAddRec->getLoop();
|
|
|
|
Level = mapSrcLoop(CurLoop);
|
|
|
|
return weakZeroDstSIVtest(SrcCoeff, SrcConst, DstConst, CurLoop,
|
|
|
|
Level, Result, NewConstraint) ||
|
|
|
|
gcdMIVtest(Src, Dst, Result);
|
|
|
|
}
|
|
|
|
if (DstAddRec) {
|
|
|
|
const SCEV *DstConst = DstAddRec->getStart();
|
|
|
|
const SCEV *DstCoeff = DstAddRec->getStepRecurrence(*SE);
|
|
|
|
const SCEV *SrcConst = Src;
|
|
|
|
const Loop *CurLoop = DstAddRec->getLoop();
|
|
|
|
Level = mapDstLoop(CurLoop);
|
|
|
|
return weakZeroSrcSIVtest(DstCoeff, SrcConst, DstConst,
|
|
|
|
CurLoop, Level, Result, NewConstraint) ||
|
|
|
|
gcdMIVtest(Src, Dst, Result);
|
|
|
|
}
|
|
|
|
llvm_unreachable("SIV test expected at least one AddRec");
|
|
|
|
return false;
|
|
|
|
}
|
|
|
|
|
|
|
|
|
|
|
|
// testRDIV -
|
|
|
|
// When we have a pair of subscripts of the form [c1 + a1*i] and [c2 + a2*j]
|
|
|
|
// where i and j are induction variables, c1 and c2 are loop invariant,
|
|
|
|
// and a1 and a2 are constant, we can solve it exactly with an easy adaptation
|
|
|
|
// of the Exact SIV test, the Restricted Double Index Variable (RDIV) test.
|
|
|
|
// It doesn't make sense to talk about distance or direction in this case,
|
|
|
|
// so there's no point in making special versions of the Strong SIV test or
|
|
|
|
// the Weak-crossing SIV test.
|
|
|
|
//
|
|
|
|
// With minor algebra, this test can also be used for things like
|
|
|
|
// [c1 + a1*i + a2*j][c2].
|
|
|
|
//
|
|
|
|
// Return true if dependence disproved.
|
|
|
|
bool DependenceAnalysis::testRDIV(const SCEV *Src,
|
|
|
|
const SCEV *Dst,
|
|
|
|
FullDependence &Result) const {
|
|
|
|
// we have 3 possible situations here:
|
|
|
|
// 1) [a*i + b] and [c*j + d]
|
|
|
|
// 2) [a*i + c*j + b] and [d]
|
|
|
|
// 3) [b] and [a*i + c*j + d]
|
|
|
|
// We need to find what we've got and get organized
|
|
|
|
|
|
|
|
const SCEV *SrcConst, *DstConst;
|
|
|
|
const SCEV *SrcCoeff, *DstCoeff;
|
|
|
|
const Loop *SrcLoop, *DstLoop;
|
|
|
|
|
|
|
|
DEBUG(dbgs() << " src = " << *Src << "\n");
|
|
|
|
DEBUG(dbgs() << " dst = " << *Dst << "\n");
|
|
|
|
const SCEVAddRecExpr *SrcAddRec = dyn_cast<SCEVAddRecExpr>(Src);
|
|
|
|
const SCEVAddRecExpr *DstAddRec = dyn_cast<SCEVAddRecExpr>(Dst);
|
|
|
|
if (SrcAddRec && DstAddRec) {
|
|
|
|
SrcConst = SrcAddRec->getStart();
|
|
|
|
SrcCoeff = SrcAddRec->getStepRecurrence(*SE);
|
|
|
|
SrcLoop = SrcAddRec->getLoop();
|
|
|
|
DstConst = DstAddRec->getStart();
|
|
|
|
DstCoeff = DstAddRec->getStepRecurrence(*SE);
|
|
|
|
DstLoop = DstAddRec->getLoop();
|
|
|
|
}
|
|
|
|
else if (SrcAddRec) {
|
|
|
|
if (const SCEVAddRecExpr *tmpAddRec =
|
|
|
|
dyn_cast<SCEVAddRecExpr>(SrcAddRec->getStart())) {
|
|
|
|
SrcConst = tmpAddRec->getStart();
|
|
|
|
SrcCoeff = tmpAddRec->getStepRecurrence(*SE);
|
|
|
|
SrcLoop = tmpAddRec->getLoop();
|
|
|
|
DstConst = Dst;
|
|
|
|
DstCoeff = SE->getNegativeSCEV(SrcAddRec->getStepRecurrence(*SE));
|
|
|
|
DstLoop = SrcAddRec->getLoop();
|
|
|
|
}
|
|
|
|
else
|
|
|
|
llvm_unreachable("RDIV reached by surprising SCEVs");
|
|
|
|
}
|
|
|
|
else if (DstAddRec) {
|
|
|
|
if (const SCEVAddRecExpr *tmpAddRec =
|
|
|
|
dyn_cast<SCEVAddRecExpr>(DstAddRec->getStart())) {
|
|
|
|
DstConst = tmpAddRec->getStart();
|
|
|
|
DstCoeff = tmpAddRec->getStepRecurrence(*SE);
|
|
|
|
DstLoop = tmpAddRec->getLoop();
|
|
|
|
SrcConst = Src;
|
|
|
|
SrcCoeff = SE->getNegativeSCEV(DstAddRec->getStepRecurrence(*SE));
|
|
|
|
SrcLoop = DstAddRec->getLoop();
|
|
|
|
}
|
|
|
|
else
|
|
|
|
llvm_unreachable("RDIV reached by surprising SCEVs");
|
|
|
|
}
|
|
|
|
else
|
|
|
|
llvm_unreachable("RDIV expected at least one AddRec");
|
|
|
|
return exactRDIVtest(SrcCoeff, DstCoeff,
|
|
|
|
SrcConst, DstConst,
|
|
|
|
SrcLoop, DstLoop,
|
|
|
|
Result) ||
|
|
|
|
gcdMIVtest(Src, Dst, Result) ||
|
|
|
|
symbolicRDIVtest(SrcCoeff, DstCoeff,
|
|
|
|
SrcConst, DstConst,
|
|
|
|
SrcLoop, DstLoop);
|
|
|
|
}
|
|
|
|
|
|
|
|
|
|
|
|
// Tests the single-subscript MIV pair (Src and Dst) for dependence.
|
|
|
|
// Return true if dependence disproved.
|
|
|
|
// Can sometimes refine direction vectors.
|
|
|
|
bool DependenceAnalysis::testMIV(const SCEV *Src,
|
|
|
|
const SCEV *Dst,
|
|
|
|
const SmallBitVector &Loops,
|
|
|
|
FullDependence &Result) const {
|
|
|
|
DEBUG(dbgs() << " src = " << *Src << "\n");
|
|
|
|
DEBUG(dbgs() << " dst = " << *Dst << "\n");
|
|
|
|
Result.Consistent = false;
|
|
|
|
return gcdMIVtest(Src, Dst, Result) ||
|
|
|
|
banerjeeMIVtest(Src, Dst, Loops, Result);
|
|
|
|
}
|
|
|
|
|
|
|
|
|
|
|
|
// Given a product, e.g., 10*X*Y, returns the first constant operand,
|
|
|
|
// in this case 10. If there is no constant part, returns NULL.
|
|
|
|
static
|
|
|
|
const SCEVConstant *getConstantPart(const SCEVMulExpr *Product) {
|
|
|
|
for (unsigned Op = 0, Ops = Product->getNumOperands(); Op < Ops; Op++) {
|
|
|
|
if (const SCEVConstant *Constant = dyn_cast<SCEVConstant>(Product->getOperand(Op)))
|
|
|
|
return Constant;
|
|
|
|
}
|
|
|
|
return NULL;
|
|
|
|
}
|
|
|
|
|
|
|
|
|
|
|
|
//===----------------------------------------------------------------------===//
|
|
|
|
// gcdMIVtest -
|
|
|
|
// Tests an MIV subscript pair for dependence.
|
|
|
|
// Returns true if any possible dependence is disproved.
|
2012-10-31 11:25:32 +00:00
|
|
|
// Marks the result as inconsistent.
|
dependence analysis
Patch from Preston Briggs <preston.briggs@gmail.com>.
This is an updated version of the dependence-analysis patch, including an MIV
test based on Banerjee's inequalities.
It's a fairly complete implementation of the paper
Practical Dependence Testing
Gina Goff, Ken Kennedy, and Chau-Wen Tseng
PLDI 1991
It cannot yet propagate constraints between coupled RDIV subscripts (discussed
in Section 5.3.2 of the paper).
It's organized as a FunctionPass with a single entry point that supports testing
for dependence between two instructions in a function. If there's no dependence,
it returns null. If there's a dependence, it returns a pointer to a Dependence
which can be queried about details (what kind of dependence, is it loop
independent, direction and distance vector entries, etc). I haven't included
every imaginable feature, but there's a good selection that should be adequate
for supporting many loop transformations. Of course, it can be extended as
necessary.
Included in the patch file are many test cases, commented with C code showing
the loops and array references.
git-svn-id: https://llvm.org/svn/llvm-project/llvm/trunk@165708 91177308-0d34-0410-b5e6-96231b3b80d8
2012-10-11 07:32:34 +00:00
|
|
|
// Can sometimes disprove the equal direction for 1 or more loops,
|
|
|
|
// as discussed in Michael Wolfe's book,
|
|
|
|
// High Performance Compilers for Parallel Computing, page 235.
|
|
|
|
//
|
|
|
|
// We spend some effort (code!) to handle cases like
|
|
|
|
// [10*i + 5*N*j + 15*M + 6], where i and j are induction variables,
|
|
|
|
// but M and N are just loop-invariant variables.
|
|
|
|
// This should help us handle linearized subscripts;
|
|
|
|
// also makes this test a useful backup to the various SIV tests.
|
|
|
|
//
|
|
|
|
// It occurs to me that the presence of loop-invariant variables
|
|
|
|
// changes the nature of the test from "greatest common divisor"
|
2012-11-29 04:30:52 +00:00
|
|
|
// to "a common divisor".
|
dependence analysis
Patch from Preston Briggs <preston.briggs@gmail.com>.
This is an updated version of the dependence-analysis patch, including an MIV
test based on Banerjee's inequalities.
It's a fairly complete implementation of the paper
Practical Dependence Testing
Gina Goff, Ken Kennedy, and Chau-Wen Tseng
PLDI 1991
It cannot yet propagate constraints between coupled RDIV subscripts (discussed
in Section 5.3.2 of the paper).
It's organized as a FunctionPass with a single entry point that supports testing
for dependence between two instructions in a function. If there's no dependence,
it returns null. If there's a dependence, it returns a pointer to a Dependence
which can be queried about details (what kind of dependence, is it loop
independent, direction and distance vector entries, etc). I haven't included
every imaginable feature, but there's a good selection that should be adequate
for supporting many loop transformations. Of course, it can be extended as
necessary.
Included in the patch file are many test cases, commented with C code showing
the loops and array references.
git-svn-id: https://llvm.org/svn/llvm-project/llvm/trunk@165708 91177308-0d34-0410-b5e6-96231b3b80d8
2012-10-11 07:32:34 +00:00
|
|
|
bool DependenceAnalysis::gcdMIVtest(const SCEV *Src,
|
|
|
|
const SCEV *Dst,
|
|
|
|
FullDependence &Result) const {
|
|
|
|
DEBUG(dbgs() << "starting gcd\n");
|
|
|
|
++GCDapplications;
|
2012-11-21 23:50:04 +00:00
|
|
|
unsigned BitWidth = SE->getTypeSizeInBits(Src->getType());
|
dependence analysis
Patch from Preston Briggs <preston.briggs@gmail.com>.
This is an updated version of the dependence-analysis patch, including an MIV
test based on Banerjee's inequalities.
It's a fairly complete implementation of the paper
Practical Dependence Testing
Gina Goff, Ken Kennedy, and Chau-Wen Tseng
PLDI 1991
It cannot yet propagate constraints between coupled RDIV subscripts (discussed
in Section 5.3.2 of the paper).
It's organized as a FunctionPass with a single entry point that supports testing
for dependence between two instructions in a function. If there's no dependence,
it returns null. If there's a dependence, it returns a pointer to a Dependence
which can be queried about details (what kind of dependence, is it loop
independent, direction and distance vector entries, etc). I haven't included
every imaginable feature, but there's a good selection that should be adequate
for supporting many loop transformations. Of course, it can be extended as
necessary.
Included in the patch file are many test cases, commented with C code showing
the loops and array references.
git-svn-id: https://llvm.org/svn/llvm-project/llvm/trunk@165708 91177308-0d34-0410-b5e6-96231b3b80d8
2012-10-11 07:32:34 +00:00
|
|
|
APInt RunningGCD = APInt::getNullValue(BitWidth);
|
|
|
|
|
|
|
|
// Examine Src coefficients.
|
|
|
|
// Compute running GCD and record source constant.
|
|
|
|
// Because we're looking for the constant at the end of the chain,
|
|
|
|
// we can't quit the loop just because the GCD == 1.
|
|
|
|
const SCEV *Coefficients = Src;
|
|
|
|
while (const SCEVAddRecExpr *AddRec =
|
|
|
|
dyn_cast<SCEVAddRecExpr>(Coefficients)) {
|
|
|
|
const SCEV *Coeff = AddRec->getStepRecurrence(*SE);
|
|
|
|
const SCEVConstant *Constant = dyn_cast<SCEVConstant>(Coeff);
|
|
|
|
if (const SCEVMulExpr *Product = dyn_cast<SCEVMulExpr>(Coeff))
|
|
|
|
// If the coefficient is the product of a constant and other stuff,
|
|
|
|
// we can use the constant in the GCD computation.
|
|
|
|
Constant = getConstantPart(Product);
|
|
|
|
if (!Constant)
|
|
|
|
return false;
|
|
|
|
APInt ConstCoeff = Constant->getValue()->getValue();
|
|
|
|
RunningGCD = APIntOps::GreatestCommonDivisor(RunningGCD, ConstCoeff.abs());
|
|
|
|
Coefficients = AddRec->getStart();
|
|
|
|
}
|
|
|
|
const SCEV *SrcConst = Coefficients;
|
|
|
|
|
|
|
|
// Examine Dst coefficients.
|
|
|
|
// Compute running GCD and record destination constant.
|
|
|
|
// Because we're looking for the constant at the end of the chain,
|
|
|
|
// we can't quit the loop just because the GCD == 1.
|
|
|
|
Coefficients = Dst;
|
|
|
|
while (const SCEVAddRecExpr *AddRec =
|
|
|
|
dyn_cast<SCEVAddRecExpr>(Coefficients)) {
|
|
|
|
const SCEV *Coeff = AddRec->getStepRecurrence(*SE);
|
|
|
|
const SCEVConstant *Constant = dyn_cast<SCEVConstant>(Coeff);
|
|
|
|
if (const SCEVMulExpr *Product = dyn_cast<SCEVMulExpr>(Coeff))
|
|
|
|
// If the coefficient is the product of a constant and other stuff,
|
|
|
|
// we can use the constant in the GCD computation.
|
|
|
|
Constant = getConstantPart(Product);
|
|
|
|
if (!Constant)
|
|
|
|
return false;
|
|
|
|
APInt ConstCoeff = Constant->getValue()->getValue();
|
|
|
|
RunningGCD = APIntOps::GreatestCommonDivisor(RunningGCD, ConstCoeff.abs());
|
|
|
|
Coefficients = AddRec->getStart();
|
|
|
|
}
|
|
|
|
const SCEV *DstConst = Coefficients;
|
|
|
|
|
|
|
|
APInt ExtraGCD = APInt::getNullValue(BitWidth);
|
|
|
|
const SCEV *Delta = SE->getMinusSCEV(DstConst, SrcConst);
|
|
|
|
DEBUG(dbgs() << " Delta = " << *Delta << "\n");
|
|
|
|
const SCEVConstant *Constant = dyn_cast<SCEVConstant>(Delta);
|
|
|
|
if (const SCEVAddExpr *Sum = dyn_cast<SCEVAddExpr>(Delta)) {
|
|
|
|
// If Delta is a sum of products, we may be able to make further progress.
|
|
|
|
for (unsigned Op = 0, Ops = Sum->getNumOperands(); Op < Ops; Op++) {
|
|
|
|
const SCEV *Operand = Sum->getOperand(Op);
|
|
|
|
if (isa<SCEVConstant>(Operand)) {
|
|
|
|
assert(!Constant && "Surprised to find multiple constants");
|
|
|
|
Constant = cast<SCEVConstant>(Operand);
|
|
|
|
}
|
2012-10-31 09:20:38 +00:00
|
|
|
else if (const SCEVMulExpr *Product = dyn_cast<SCEVMulExpr>(Operand)) {
|
dependence analysis
Patch from Preston Briggs <preston.briggs@gmail.com>.
This is an updated version of the dependence-analysis patch, including an MIV
test based on Banerjee's inequalities.
It's a fairly complete implementation of the paper
Practical Dependence Testing
Gina Goff, Ken Kennedy, and Chau-Wen Tseng
PLDI 1991
It cannot yet propagate constraints between coupled RDIV subscripts (discussed
in Section 5.3.2 of the paper).
It's organized as a FunctionPass with a single entry point that supports testing
for dependence between two instructions in a function. If there's no dependence,
it returns null. If there's a dependence, it returns a pointer to a Dependence
which can be queried about details (what kind of dependence, is it loop
independent, direction and distance vector entries, etc). I haven't included
every imaginable feature, but there's a good selection that should be adequate
for supporting many loop transformations. Of course, it can be extended as
necessary.
Included in the patch file are many test cases, commented with C code showing
the loops and array references.
git-svn-id: https://llvm.org/svn/llvm-project/llvm/trunk@165708 91177308-0d34-0410-b5e6-96231b3b80d8
2012-10-11 07:32:34 +00:00
|
|
|
// Search for constant operand to participate in GCD;
|
|
|
|
// If none found; return false.
|
2012-10-31 09:20:38 +00:00
|
|
|
const SCEVConstant *ConstOp = getConstantPart(Product);
|
|
|
|
if (!ConstOp)
|
|
|
|
return false;
|
dependence analysis
Patch from Preston Briggs <preston.briggs@gmail.com>.
This is an updated version of the dependence-analysis patch, including an MIV
test based on Banerjee's inequalities.
It's a fairly complete implementation of the paper
Practical Dependence Testing
Gina Goff, Ken Kennedy, and Chau-Wen Tseng
PLDI 1991
It cannot yet propagate constraints between coupled RDIV subscripts (discussed
in Section 5.3.2 of the paper).
It's organized as a FunctionPass with a single entry point that supports testing
for dependence between two instructions in a function. If there's no dependence,
it returns null. If there's a dependence, it returns a pointer to a Dependence
which can be queried about details (what kind of dependence, is it loop
independent, direction and distance vector entries, etc). I haven't included
every imaginable feature, but there's a good selection that should be adequate
for supporting many loop transformations. Of course, it can be extended as
necessary.
Included in the patch file are many test cases, commented with C code showing
the loops and array references.
git-svn-id: https://llvm.org/svn/llvm-project/llvm/trunk@165708 91177308-0d34-0410-b5e6-96231b3b80d8
2012-10-11 07:32:34 +00:00
|
|
|
APInt ConstOpValue = ConstOp->getValue()->getValue();
|
|
|
|
ExtraGCD = APIntOps::GreatestCommonDivisor(ExtraGCD,
|
|
|
|
ConstOpValue.abs());
|
|
|
|
}
|
|
|
|
else
|
|
|
|
return false;
|
|
|
|
}
|
|
|
|
}
|
|
|
|
if (!Constant)
|
|
|
|
return false;
|
|
|
|
APInt ConstDelta = cast<SCEVConstant>(Constant)->getValue()->getValue();
|
|
|
|
DEBUG(dbgs() << " ConstDelta = " << ConstDelta << "\n");
|
|
|
|
if (ConstDelta == 0)
|
|
|
|
return false;
|
|
|
|
RunningGCD = APIntOps::GreatestCommonDivisor(RunningGCD, ExtraGCD);
|
|
|
|
DEBUG(dbgs() << " RunningGCD = " << RunningGCD << "\n");
|
|
|
|
APInt Remainder = ConstDelta.srem(RunningGCD);
|
|
|
|
if (Remainder != 0) {
|
|
|
|
++GCDindependence;
|
|
|
|
return true;
|
|
|
|
}
|
|
|
|
|
|
|
|
// Try to disprove equal directions.
|
|
|
|
// For example, given a subscript pair [3*i + 2*j] and [i' + 2*j' - 1],
|
|
|
|
// the code above can't disprove the dependence because the GCD = 1.
|
|
|
|
// So we consider what happen if i = i' and what happens if j = j'.
|
|
|
|
// If i = i', we can simplify the subscript to [2*i + 2*j] and [2*j' - 1],
|
|
|
|
// which is infeasible, so we can disallow the = direction for the i level.
|
|
|
|
// Setting j = j' doesn't help matters, so we end up with a direction vector
|
|
|
|
// of [<>, *]
|
|
|
|
//
|
|
|
|
// Given A[5*i + 10*j*M + 9*M*N] and A[15*i + 20*j*M - 21*N*M + 5],
|
|
|
|
// we need to remember that the constant part is 5 and the RunningGCD should
|
|
|
|
// be initialized to ExtraGCD = 30.
|
|
|
|
DEBUG(dbgs() << " ExtraGCD = " << ExtraGCD << '\n');
|
|
|
|
|
|
|
|
bool Improved = false;
|
|
|
|
Coefficients = Src;
|
|
|
|
while (const SCEVAddRecExpr *AddRec =
|
|
|
|
dyn_cast<SCEVAddRecExpr>(Coefficients)) {
|
|
|
|
Coefficients = AddRec->getStart();
|
|
|
|
const Loop *CurLoop = AddRec->getLoop();
|
|
|
|
RunningGCD = ExtraGCD;
|
|
|
|
const SCEV *SrcCoeff = AddRec->getStepRecurrence(*SE);
|
|
|
|
const SCEV *DstCoeff = SE->getMinusSCEV(SrcCoeff, SrcCoeff);
|
|
|
|
const SCEV *Inner = Src;
|
|
|
|
while (RunningGCD != 1 && isa<SCEVAddRecExpr>(Inner)) {
|
|
|
|
AddRec = cast<SCEVAddRecExpr>(Inner);
|
|
|
|
const SCEV *Coeff = AddRec->getStepRecurrence(*SE);
|
|
|
|
if (CurLoop == AddRec->getLoop())
|
|
|
|
; // SrcCoeff == Coeff
|
|
|
|
else {
|
|
|
|
if (const SCEVMulExpr *Product = dyn_cast<SCEVMulExpr>(Coeff))
|
|
|
|
// If the coefficient is the product of a constant and other stuff,
|
|
|
|
// we can use the constant in the GCD computation.
|
|
|
|
Constant = getConstantPart(Product);
|
|
|
|
else
|
|
|
|
Constant = cast<SCEVConstant>(Coeff);
|
|
|
|
APInt ConstCoeff = Constant->getValue()->getValue();
|
|
|
|
RunningGCD = APIntOps::GreatestCommonDivisor(RunningGCD, ConstCoeff.abs());
|
|
|
|
}
|
|
|
|
Inner = AddRec->getStart();
|
|
|
|
}
|
|
|
|
Inner = Dst;
|
|
|
|
while (RunningGCD != 1 && isa<SCEVAddRecExpr>(Inner)) {
|
|
|
|
AddRec = cast<SCEVAddRecExpr>(Inner);
|
|
|
|
const SCEV *Coeff = AddRec->getStepRecurrence(*SE);
|
|
|
|
if (CurLoop == AddRec->getLoop())
|
|
|
|
DstCoeff = Coeff;
|
|
|
|
else {
|
|
|
|
if (const SCEVMulExpr *Product = dyn_cast<SCEVMulExpr>(Coeff))
|
|
|
|
// If the coefficient is the product of a constant and other stuff,
|
|
|
|
// we can use the constant in the GCD computation.
|
|
|
|
Constant = getConstantPart(Product);
|
|
|
|
else
|
|
|
|
Constant = cast<SCEVConstant>(Coeff);
|
|
|
|
APInt ConstCoeff = Constant->getValue()->getValue();
|
|
|
|
RunningGCD = APIntOps::GreatestCommonDivisor(RunningGCD, ConstCoeff.abs());
|
|
|
|
}
|
|
|
|
Inner = AddRec->getStart();
|
|
|
|
}
|
|
|
|
Delta = SE->getMinusSCEV(SrcCoeff, DstCoeff);
|
|
|
|
if (const SCEVMulExpr *Product = dyn_cast<SCEVMulExpr>(Delta))
|
|
|
|
// If the coefficient is the product of a constant and other stuff,
|
|
|
|
// we can use the constant in the GCD computation.
|
|
|
|
Constant = getConstantPart(Product);
|
|
|
|
else if (isa<SCEVConstant>(Delta))
|
|
|
|
Constant = cast<SCEVConstant>(Delta);
|
|
|
|
else {
|
|
|
|
// The difference of the two coefficients might not be a product
|
|
|
|
// or constant, in which case we give up on this direction.
|
|
|
|
continue;
|
|
|
|
}
|
|
|
|
APInt ConstCoeff = Constant->getValue()->getValue();
|
|
|
|
RunningGCD = APIntOps::GreatestCommonDivisor(RunningGCD, ConstCoeff.abs());
|
|
|
|
DEBUG(dbgs() << "\tRunningGCD = " << RunningGCD << "\n");
|
|
|
|
if (RunningGCD != 0) {
|
|
|
|
Remainder = ConstDelta.srem(RunningGCD);
|
|
|
|
DEBUG(dbgs() << "\tRemainder = " << Remainder << "\n");
|
|
|
|
if (Remainder != 0) {
|
|
|
|
unsigned Level = mapSrcLoop(CurLoop);
|
2012-10-12 02:04:32 +00:00
|
|
|
Result.DV[Level - 1].Direction &= unsigned(~Dependence::DVEntry::EQ);
|
dependence analysis
Patch from Preston Briggs <preston.briggs@gmail.com>.
This is an updated version of the dependence-analysis patch, including an MIV
test based on Banerjee's inequalities.
It's a fairly complete implementation of the paper
Practical Dependence Testing
Gina Goff, Ken Kennedy, and Chau-Wen Tseng
PLDI 1991
It cannot yet propagate constraints between coupled RDIV subscripts (discussed
in Section 5.3.2 of the paper).
It's organized as a FunctionPass with a single entry point that supports testing
for dependence between two instructions in a function. If there's no dependence,
it returns null. If there's a dependence, it returns a pointer to a Dependence
which can be queried about details (what kind of dependence, is it loop
independent, direction and distance vector entries, etc). I haven't included
every imaginable feature, but there's a good selection that should be adequate
for supporting many loop transformations. Of course, it can be extended as
necessary.
Included in the patch file are many test cases, commented with C code showing
the loops and array references.
git-svn-id: https://llvm.org/svn/llvm-project/llvm/trunk@165708 91177308-0d34-0410-b5e6-96231b3b80d8
2012-10-11 07:32:34 +00:00
|
|
|
Improved = true;
|
|
|
|
}
|
|
|
|
}
|
|
|
|
}
|
|
|
|
if (Improved)
|
|
|
|
++GCDsuccesses;
|
|
|
|
DEBUG(dbgs() << "all done\n");
|
|
|
|
return false;
|
|
|
|
}
|
|
|
|
|
|
|
|
|
|
|
|
//===----------------------------------------------------------------------===//
|
|
|
|
// banerjeeMIVtest -
|
|
|
|
// Use Banerjee's Inequalities to test an MIV subscript pair.
|
|
|
|
// (Wolfe, in the race-car book, calls this the Extreme Value Test.)
|
|
|
|
// Generally follows the discussion in Section 2.5.2 of
|
|
|
|
//
|
|
|
|
// Optimizing Supercompilers for Supercomputers
|
|
|
|
// Michael Wolfe
|
|
|
|
//
|
|
|
|
// The inequalities given on page 25 are simplified in that loops are
|
|
|
|
// normalized so that the lower bound is always 0 and the stride is always 1.
|
|
|
|
// For example, Wolfe gives
|
|
|
|
//
|
|
|
|
// LB^<_k = (A^-_k - B_k)^- (U_k - L_k - N_k) + (A_k - B_k)L_k - B_k N_k
|
|
|
|
//
|
|
|
|
// where A_k is the coefficient of the kth index in the source subscript,
|
|
|
|
// B_k is the coefficient of the kth index in the destination subscript,
|
|
|
|
// U_k is the upper bound of the kth index, L_k is the lower bound of the Kth
|
|
|
|
// index, and N_k is the stride of the kth index. Since all loops are normalized
|
|
|
|
// by the SCEV package, N_k = 1 and L_k = 0, allowing us to simplify the
|
|
|
|
// equation to
|
|
|
|
//
|
|
|
|
// LB^<_k = (A^-_k - B_k)^- (U_k - 0 - 1) + (A_k - B_k)0 - B_k 1
|
|
|
|
// = (A^-_k - B_k)^- (U_k - 1) - B_k
|
|
|
|
//
|
|
|
|
// Similar simplifications are possible for the other equations.
|
|
|
|
//
|
|
|
|
// When we can't determine the number of iterations for a loop,
|
|
|
|
// we use NULL as an indicator for the worst case, infinity.
|
|
|
|
// When computing the upper bound, NULL denotes +inf;
|
|
|
|
// for the lower bound, NULL denotes -inf.
|
|
|
|
//
|
|
|
|
// Return true if dependence disproved.
|
|
|
|
bool DependenceAnalysis::banerjeeMIVtest(const SCEV *Src,
|
|
|
|
const SCEV *Dst,
|
|
|
|
const SmallBitVector &Loops,
|
|
|
|
FullDependence &Result) const {
|
|
|
|
DEBUG(dbgs() << "starting Banerjee\n");
|
|
|
|
++BanerjeeApplications;
|
|
|
|
DEBUG(dbgs() << " Src = " << *Src << '\n');
|
|
|
|
const SCEV *A0;
|
|
|
|
CoefficientInfo *A = collectCoeffInfo(Src, true, A0);
|
|
|
|
DEBUG(dbgs() << " Dst = " << *Dst << '\n');
|
|
|
|
const SCEV *B0;
|
|
|
|
CoefficientInfo *B = collectCoeffInfo(Dst, false, B0);
|
|
|
|
BoundInfo *Bound = new BoundInfo[MaxLevels + 1];
|
|
|
|
const SCEV *Delta = SE->getMinusSCEV(B0, A0);
|
|
|
|
DEBUG(dbgs() << "\tDelta = " << *Delta << '\n');
|
|
|
|
|
|
|
|
// Compute bounds for all the * directions.
|
|
|
|
DEBUG(dbgs() << "\tBounds[*]\n");
|
|
|
|
for (unsigned K = 1; K <= MaxLevels; ++K) {
|
|
|
|
Bound[K].Iterations = A[K].Iterations ? A[K].Iterations : B[K].Iterations;
|
|
|
|
Bound[K].Direction = Dependence::DVEntry::ALL;
|
|
|
|
Bound[K].DirSet = Dependence::DVEntry::NONE;
|
|
|
|
findBoundsALL(A, B, Bound, K);
|
|
|
|
#ifndef NDEBUG
|
|
|
|
DEBUG(dbgs() << "\t " << K << '\t');
|
|
|
|
if (Bound[K].Lower[Dependence::DVEntry::ALL])
|
|
|
|
DEBUG(dbgs() << *Bound[K].Lower[Dependence::DVEntry::ALL] << '\t');
|
|
|
|
else
|
|
|
|
DEBUG(dbgs() << "-inf\t");
|
|
|
|
if (Bound[K].Upper[Dependence::DVEntry::ALL])
|
|
|
|
DEBUG(dbgs() << *Bound[K].Upper[Dependence::DVEntry::ALL] << '\n');
|
|
|
|
else
|
|
|
|
DEBUG(dbgs() << "+inf\n");
|
|
|
|
#endif
|
|
|
|
}
|
|
|
|
|
|
|
|
// Test the *, *, *, ... case.
|
|
|
|
bool Disproved = false;
|
|
|
|
if (testBounds(Dependence::DVEntry::ALL, 0, Bound, Delta)) {
|
|
|
|
// Explore the direction vector hierarchy.
|
|
|
|
unsigned DepthExpanded = 0;
|
|
|
|
unsigned NewDeps = exploreDirections(1, A, B, Bound,
|
|
|
|
Loops, DepthExpanded, Delta);
|
|
|
|
if (NewDeps > 0) {
|
|
|
|
bool Improved = false;
|
|
|
|
for (unsigned K = 1; K <= CommonLevels; ++K) {
|
|
|
|
if (Loops[K]) {
|
|
|
|
unsigned Old = Result.DV[K - 1].Direction;
|
|
|
|
Result.DV[K - 1].Direction = Old & Bound[K].DirSet;
|
|
|
|
Improved |= Old != Result.DV[K - 1].Direction;
|
|
|
|
if (!Result.DV[K - 1].Direction) {
|
|
|
|
Improved = false;
|
|
|
|
Disproved = true;
|
|
|
|
break;
|
|
|
|
}
|
|
|
|
}
|
|
|
|
}
|
|
|
|
if (Improved)
|
|
|
|
++BanerjeeSuccesses;
|
|
|
|
}
|
|
|
|
else {
|
|
|
|
++BanerjeeIndependence;
|
|
|
|
Disproved = true;
|
|
|
|
}
|
|
|
|
}
|
|
|
|
else {
|
|
|
|
++BanerjeeIndependence;
|
|
|
|
Disproved = true;
|
|
|
|
}
|
|
|
|
delete [] Bound;
|
|
|
|
delete [] A;
|
|
|
|
delete [] B;
|
|
|
|
return Disproved;
|
|
|
|
}
|
|
|
|
|
|
|
|
|
|
|
|
// Hierarchically expands the direction vector
|
|
|
|
// search space, combining the directions of discovered dependences
|
|
|
|
// in the DirSet field of Bound. Returns the number of distinct
|
|
|
|
// dependences discovered. If the dependence is disproved,
|
|
|
|
// it will return 0.
|
|
|
|
unsigned DependenceAnalysis::exploreDirections(unsigned Level,
|
|
|
|
CoefficientInfo *A,
|
|
|
|
CoefficientInfo *B,
|
|
|
|
BoundInfo *Bound,
|
|
|
|
const SmallBitVector &Loops,
|
|
|
|
unsigned &DepthExpanded,
|
|
|
|
const SCEV *Delta) const {
|
|
|
|
if (Level > CommonLevels) {
|
|
|
|
// record result
|
|
|
|
DEBUG(dbgs() << "\t[");
|
|
|
|
for (unsigned K = 1; K <= CommonLevels; ++K) {
|
|
|
|
if (Loops[K]) {
|
|
|
|
Bound[K].DirSet |= Bound[K].Direction;
|
|
|
|
#ifndef NDEBUG
|
|
|
|
switch (Bound[K].Direction) {
|
|
|
|
case Dependence::DVEntry::LT:
|
|
|
|
DEBUG(dbgs() << " <");
|
|
|
|
break;
|
|
|
|
case Dependence::DVEntry::EQ:
|
|
|
|
DEBUG(dbgs() << " =");
|
|
|
|
break;
|
|
|
|
case Dependence::DVEntry::GT:
|
|
|
|
DEBUG(dbgs() << " >");
|
|
|
|
break;
|
|
|
|
case Dependence::DVEntry::ALL:
|
|
|
|
DEBUG(dbgs() << " *");
|
|
|
|
break;
|
|
|
|
default:
|
|
|
|
llvm_unreachable("unexpected Bound[K].Direction");
|
|
|
|
}
|
|
|
|
#endif
|
|
|
|
}
|
|
|
|
}
|
|
|
|
DEBUG(dbgs() << " ]\n");
|
|
|
|
return 1;
|
|
|
|
}
|
|
|
|
if (Loops[Level]) {
|
|
|
|
if (Level > DepthExpanded) {
|
|
|
|
DepthExpanded = Level;
|
|
|
|
// compute bounds for <, =, > at current level
|
|
|
|
findBoundsLT(A, B, Bound, Level);
|
|
|
|
findBoundsGT(A, B, Bound, Level);
|
|
|
|
findBoundsEQ(A, B, Bound, Level);
|
|
|
|
#ifndef NDEBUG
|
|
|
|
DEBUG(dbgs() << "\tBound for level = " << Level << '\n');
|
|
|
|
DEBUG(dbgs() << "\t <\t");
|
|
|
|
if (Bound[Level].Lower[Dependence::DVEntry::LT])
|
|
|
|
DEBUG(dbgs() << *Bound[Level].Lower[Dependence::DVEntry::LT] << '\t');
|
|
|
|
else
|
|
|
|
DEBUG(dbgs() << "-inf\t");
|
|
|
|
if (Bound[Level].Upper[Dependence::DVEntry::LT])
|
|
|
|
DEBUG(dbgs() << *Bound[Level].Upper[Dependence::DVEntry::LT] << '\n');
|
|
|
|
else
|
|
|
|
DEBUG(dbgs() << "+inf\n");
|
|
|
|
DEBUG(dbgs() << "\t =\t");
|
|
|
|
if (Bound[Level].Lower[Dependence::DVEntry::EQ])
|
|
|
|
DEBUG(dbgs() << *Bound[Level].Lower[Dependence::DVEntry::EQ] << '\t');
|
|
|
|
else
|
|
|
|
DEBUG(dbgs() << "-inf\t");
|
|
|
|
if (Bound[Level].Upper[Dependence::DVEntry::EQ])
|
|
|
|
DEBUG(dbgs() << *Bound[Level].Upper[Dependence::DVEntry::EQ] << '\n');
|
|
|
|
else
|
|
|
|
DEBUG(dbgs() << "+inf\n");
|
|
|
|
DEBUG(dbgs() << "\t >\t");
|
|
|
|
if (Bound[Level].Lower[Dependence::DVEntry::GT])
|
|
|
|
DEBUG(dbgs() << *Bound[Level].Lower[Dependence::DVEntry::GT] << '\t');
|
|
|
|
else
|
|
|
|
DEBUG(dbgs() << "-inf\t");
|
|
|
|
if (Bound[Level].Upper[Dependence::DVEntry::GT])
|
|
|
|
DEBUG(dbgs() << *Bound[Level].Upper[Dependence::DVEntry::GT] << '\n');
|
|
|
|
else
|
|
|
|
DEBUG(dbgs() << "+inf\n");
|
|
|
|
#endif
|
|
|
|
}
|
|
|
|
|
|
|
|
unsigned NewDeps = 0;
|
|
|
|
|
|
|
|
// test bounds for <, *, *, ...
|
|
|
|
if (testBounds(Dependence::DVEntry::LT, Level, Bound, Delta))
|
|
|
|
NewDeps += exploreDirections(Level + 1, A, B, Bound,
|
|
|
|
Loops, DepthExpanded, Delta);
|
|
|
|
|
|
|
|
// Test bounds for =, *, *, ...
|
|
|
|
if (testBounds(Dependence::DVEntry::EQ, Level, Bound, Delta))
|
|
|
|
NewDeps += exploreDirections(Level + 1, A, B, Bound,
|
|
|
|
Loops, DepthExpanded, Delta);
|
|
|
|
|
|
|
|
// test bounds for >, *, *, ...
|
|
|
|
if (testBounds(Dependence::DVEntry::GT, Level, Bound, Delta))
|
|
|
|
NewDeps += exploreDirections(Level + 1, A, B, Bound,
|
|
|
|
Loops, DepthExpanded, Delta);
|
|
|
|
|
|
|
|
Bound[Level].Direction = Dependence::DVEntry::ALL;
|
|
|
|
return NewDeps;
|
|
|
|
}
|
|
|
|
else
|
|
|
|
return exploreDirections(Level + 1, A, B, Bound, Loops, DepthExpanded, Delta);
|
|
|
|
}
|
|
|
|
|
|
|
|
|
|
|
|
// Returns true iff the current bounds are plausible.
|
|
|
|
bool DependenceAnalysis::testBounds(unsigned char DirKind,
|
|
|
|
unsigned Level,
|
|
|
|
BoundInfo *Bound,
|
|
|
|
const SCEV *Delta) const {
|
|
|
|
Bound[Level].Direction = DirKind;
|
|
|
|
if (const SCEV *LowerBound = getLowerBound(Bound))
|
|
|
|
if (isKnownPredicate(CmpInst::ICMP_SGT, LowerBound, Delta))
|
|
|
|
return false;
|
|
|
|
if (const SCEV *UpperBound = getUpperBound(Bound))
|
|
|
|
if (isKnownPredicate(CmpInst::ICMP_SGT, Delta, UpperBound))
|
|
|
|
return false;
|
|
|
|
return true;
|
|
|
|
}
|
|
|
|
|
|
|
|
|
|
|
|
// Computes the upper and lower bounds for level K
|
|
|
|
// using the * direction. Records them in Bound.
|
|
|
|
// Wolfe gives the equations
|
|
|
|
//
|
|
|
|
// LB^*_k = (A^-_k - B^+_k)(U_k - L_k) + (A_k - B_k)L_k
|
|
|
|
// UB^*_k = (A^+_k - B^-_k)(U_k - L_k) + (A_k - B_k)L_k
|
|
|
|
//
|
|
|
|
// Since we normalize loops, we can simplify these equations to
|
|
|
|
//
|
|
|
|
// LB^*_k = (A^-_k - B^+_k)U_k
|
|
|
|
// UB^*_k = (A^+_k - B^-_k)U_k
|
|
|
|
//
|
|
|
|
// We must be careful to handle the case where the upper bound is unknown.
|
|
|
|
// Note that the lower bound is always <= 0
|
|
|
|
// and the upper bound is always >= 0.
|
|
|
|
void DependenceAnalysis::findBoundsALL(CoefficientInfo *A,
|
|
|
|
CoefficientInfo *B,
|
|
|
|
BoundInfo *Bound,
|
|
|
|
unsigned K) const {
|
|
|
|
Bound[K].Lower[Dependence::DVEntry::ALL] = NULL; // Default value = -infinity.
|
|
|
|
Bound[K].Upper[Dependence::DVEntry::ALL] = NULL; // Default value = +infinity.
|
|
|
|
if (Bound[K].Iterations) {
|
|
|
|
Bound[K].Lower[Dependence::DVEntry::ALL] =
|
|
|
|
SE->getMulExpr(SE->getMinusSCEV(A[K].NegPart, B[K].PosPart),
|
|
|
|
Bound[K].Iterations);
|
|
|
|
Bound[K].Upper[Dependence::DVEntry::ALL] =
|
|
|
|
SE->getMulExpr(SE->getMinusSCEV(A[K].PosPart, B[K].NegPart),
|
|
|
|
Bound[K].Iterations);
|
|
|
|
}
|
|
|
|
else {
|
|
|
|
// If the difference is 0, we won't need to know the number of iterations.
|
|
|
|
if (isKnownPredicate(CmpInst::ICMP_EQ, A[K].NegPart, B[K].PosPart))
|
|
|
|
Bound[K].Lower[Dependence::DVEntry::ALL] =
|
|
|
|
SE->getConstant(A[K].Coeff->getType(), 0);
|
|
|
|
if (isKnownPredicate(CmpInst::ICMP_EQ, A[K].PosPart, B[K].NegPart))
|
|
|
|
Bound[K].Upper[Dependence::DVEntry::ALL] =
|
|
|
|
SE->getConstant(A[K].Coeff->getType(), 0);
|
|
|
|
}
|
|
|
|
}
|
|
|
|
|
|
|
|
|
|
|
|
// Computes the upper and lower bounds for level K
|
|
|
|
// using the = direction. Records them in Bound.
|
|
|
|
// Wolfe gives the equations
|
|
|
|
//
|
|
|
|
// LB^=_k = (A_k - B_k)^- (U_k - L_k) + (A_k - B_k)L_k
|
|
|
|
// UB^=_k = (A_k - B_k)^+ (U_k - L_k) + (A_k - B_k)L_k
|
|
|
|
//
|
|
|
|
// Since we normalize loops, we can simplify these equations to
|
|
|
|
//
|
|
|
|
// LB^=_k = (A_k - B_k)^- U_k
|
|
|
|
// UB^=_k = (A_k - B_k)^+ U_k
|
|
|
|
//
|
|
|
|
// We must be careful to handle the case where the upper bound is unknown.
|
|
|
|
// Note that the lower bound is always <= 0
|
|
|
|
// and the upper bound is always >= 0.
|
|
|
|
void DependenceAnalysis::findBoundsEQ(CoefficientInfo *A,
|
|
|
|
CoefficientInfo *B,
|
|
|
|
BoundInfo *Bound,
|
|
|
|
unsigned K) const {
|
|
|
|
Bound[K].Lower[Dependence::DVEntry::EQ] = NULL; // Default value = -infinity.
|
|
|
|
Bound[K].Upper[Dependence::DVEntry::EQ] = NULL; // Default value = +infinity.
|
|
|
|
if (Bound[K].Iterations) {
|
|
|
|
const SCEV *Delta = SE->getMinusSCEV(A[K].Coeff, B[K].Coeff);
|
|
|
|
const SCEV *NegativePart = getNegativePart(Delta);
|
|
|
|
Bound[K].Lower[Dependence::DVEntry::EQ] =
|
|
|
|
SE->getMulExpr(NegativePart, Bound[K].Iterations);
|
|
|
|
const SCEV *PositivePart = getPositivePart(Delta);
|
|
|
|
Bound[K].Upper[Dependence::DVEntry::EQ] =
|
|
|
|
SE->getMulExpr(PositivePart, Bound[K].Iterations);
|
|
|
|
}
|
|
|
|
else {
|
|
|
|
// If the positive/negative part of the difference is 0,
|
|
|
|
// we won't need to know the number of iterations.
|
|
|
|
const SCEV *Delta = SE->getMinusSCEV(A[K].Coeff, B[K].Coeff);
|
|
|
|
const SCEV *NegativePart = getNegativePart(Delta);
|
|
|
|
if (NegativePart->isZero())
|
|
|
|
Bound[K].Lower[Dependence::DVEntry::EQ] = NegativePart; // Zero
|
|
|
|
const SCEV *PositivePart = getPositivePart(Delta);
|
|
|
|
if (PositivePart->isZero())
|
|
|
|
Bound[K].Upper[Dependence::DVEntry::EQ] = PositivePart; // Zero
|
|
|
|
}
|
|
|
|
}
|
|
|
|
|
|
|
|
|
|
|
|
// Computes the upper and lower bounds for level K
|
|
|
|
// using the < direction. Records them in Bound.
|
|
|
|
// Wolfe gives the equations
|
|
|
|
//
|
|
|
|
// LB^<_k = (A^-_k - B_k)^- (U_k - L_k - N_k) + (A_k - B_k)L_k - B_k N_k
|
|
|
|
// UB^<_k = (A^+_k - B_k)^+ (U_k - L_k - N_k) + (A_k - B_k)L_k - B_k N_k
|
|
|
|
//
|
|
|
|
// Since we normalize loops, we can simplify these equations to
|
|
|
|
//
|
|
|
|
// LB^<_k = (A^-_k - B_k)^- (U_k - 1) - B_k
|
|
|
|
// UB^<_k = (A^+_k - B_k)^+ (U_k - 1) - B_k
|
|
|
|
//
|
|
|
|
// We must be careful to handle the case where the upper bound is unknown.
|
|
|
|
void DependenceAnalysis::findBoundsLT(CoefficientInfo *A,
|
|
|
|
CoefficientInfo *B,
|
|
|
|
BoundInfo *Bound,
|
|
|
|
unsigned K) const {
|
|
|
|
Bound[K].Lower[Dependence::DVEntry::LT] = NULL; // Default value = -infinity.
|
|
|
|
Bound[K].Upper[Dependence::DVEntry::LT] = NULL; // Default value = +infinity.
|
|
|
|
if (Bound[K].Iterations) {
|
|
|
|
const SCEV *Iter_1 =
|
|
|
|
SE->getMinusSCEV(Bound[K].Iterations,
|
|
|
|
SE->getConstant(Bound[K].Iterations->getType(), 1));
|
|
|
|
const SCEV *NegPart =
|
|
|
|
getNegativePart(SE->getMinusSCEV(A[K].NegPart, B[K].Coeff));
|
|
|
|
Bound[K].Lower[Dependence::DVEntry::LT] =
|
|
|
|
SE->getMinusSCEV(SE->getMulExpr(NegPart, Iter_1), B[K].Coeff);
|
|
|
|
const SCEV *PosPart =
|
|
|
|
getPositivePart(SE->getMinusSCEV(A[K].PosPart, B[K].Coeff));
|
|
|
|
Bound[K].Upper[Dependence::DVEntry::LT] =
|
|
|
|
SE->getMinusSCEV(SE->getMulExpr(PosPart, Iter_1), B[K].Coeff);
|
|
|
|
}
|
|
|
|
else {
|
|
|
|
// If the positive/negative part of the difference is 0,
|
|
|
|
// we won't need to know the number of iterations.
|
|
|
|
const SCEV *NegPart =
|
|
|
|
getNegativePart(SE->getMinusSCEV(A[K].NegPart, B[K].Coeff));
|
|
|
|
if (NegPart->isZero())
|
|
|
|
Bound[K].Lower[Dependence::DVEntry::LT] = SE->getNegativeSCEV(B[K].Coeff);
|
|
|
|
const SCEV *PosPart =
|
|
|
|
getPositivePart(SE->getMinusSCEV(A[K].PosPart, B[K].Coeff));
|
|
|
|
if (PosPart->isZero())
|
|
|
|
Bound[K].Upper[Dependence::DVEntry::LT] = SE->getNegativeSCEV(B[K].Coeff);
|
|
|
|
}
|
|
|
|
}
|
|
|
|
|
|
|
|
|
|
|
|
// Computes the upper and lower bounds for level K
|
|
|
|
// using the > direction. Records them in Bound.
|
|
|
|
// Wolfe gives the equations
|
|
|
|
//
|
|
|
|
// LB^>_k = (A_k - B^+_k)^- (U_k - L_k - N_k) + (A_k - B_k)L_k + A_k N_k
|
|
|
|
// UB^>_k = (A_k - B^-_k)^+ (U_k - L_k - N_k) + (A_k - B_k)L_k + A_k N_k
|
|
|
|
//
|
|
|
|
// Since we normalize loops, we can simplify these equations to
|
|
|
|
//
|
|
|
|
// LB^>_k = (A_k - B^+_k)^- (U_k - 1) + A_k
|
|
|
|
// UB^>_k = (A_k - B^-_k)^+ (U_k - 1) + A_k
|
|
|
|
//
|
|
|
|
// We must be careful to handle the case where the upper bound is unknown.
|
|
|
|
void DependenceAnalysis::findBoundsGT(CoefficientInfo *A,
|
|
|
|
CoefficientInfo *B,
|
|
|
|
BoundInfo *Bound,
|
|
|
|
unsigned K) const {
|
|
|
|
Bound[K].Lower[Dependence::DVEntry::GT] = NULL; // Default value = -infinity.
|
|
|
|
Bound[K].Upper[Dependence::DVEntry::GT] = NULL; // Default value = +infinity.
|
|
|
|
if (Bound[K].Iterations) {
|
|
|
|
const SCEV *Iter_1 =
|
|
|
|
SE->getMinusSCEV(Bound[K].Iterations,
|
|
|
|
SE->getConstant(Bound[K].Iterations->getType(), 1));
|
|
|
|
const SCEV *NegPart =
|
|
|
|
getNegativePart(SE->getMinusSCEV(A[K].Coeff, B[K].PosPart));
|
|
|
|
Bound[K].Lower[Dependence::DVEntry::GT] =
|
|
|
|
SE->getAddExpr(SE->getMulExpr(NegPart, Iter_1), A[K].Coeff);
|
|
|
|
const SCEV *PosPart =
|
|
|
|
getPositivePart(SE->getMinusSCEV(A[K].Coeff, B[K].NegPart));
|
|
|
|
Bound[K].Upper[Dependence::DVEntry::GT] =
|
|
|
|
SE->getAddExpr(SE->getMulExpr(PosPart, Iter_1), A[K].Coeff);
|
|
|
|
}
|
|
|
|
else {
|
|
|
|
// If the positive/negative part of the difference is 0,
|
|
|
|
// we won't need to know the number of iterations.
|
|
|
|
const SCEV *NegPart = getNegativePart(SE->getMinusSCEV(A[K].Coeff, B[K].PosPart));
|
|
|
|
if (NegPart->isZero())
|
|
|
|
Bound[K].Lower[Dependence::DVEntry::GT] = A[K].Coeff;
|
|
|
|
const SCEV *PosPart = getPositivePart(SE->getMinusSCEV(A[K].Coeff, B[K].NegPart));
|
|
|
|
if (PosPart->isZero())
|
|
|
|
Bound[K].Upper[Dependence::DVEntry::GT] = A[K].Coeff;
|
|
|
|
}
|
|
|
|
}
|
|
|
|
|
|
|
|
|
|
|
|
// X^+ = max(X, 0)
|
|
|
|
const SCEV *DependenceAnalysis::getPositivePart(const SCEV *X) const {
|
|
|
|
return SE->getSMaxExpr(X, SE->getConstant(X->getType(), 0));
|
|
|
|
}
|
|
|
|
|
|
|
|
|
|
|
|
// X^- = min(X, 0)
|
|
|
|
const SCEV *DependenceAnalysis::getNegativePart(const SCEV *X) const {
|
|
|
|
return SE->getSMinExpr(X, SE->getConstant(X->getType(), 0));
|
|
|
|
}
|
|
|
|
|
|
|
|
|
|
|
|
// Walks through the subscript,
|
|
|
|
// collecting each coefficient, the associated loop bounds,
|
|
|
|
// and recording its positive and negative parts for later use.
|
|
|
|
DependenceAnalysis::CoefficientInfo *
|
|
|
|
DependenceAnalysis::collectCoeffInfo(const SCEV *Subscript,
|
|
|
|
bool SrcFlag,
|
|
|
|
const SCEV *&Constant) const {
|
|
|
|
const SCEV *Zero = SE->getConstant(Subscript->getType(), 0);
|
|
|
|
CoefficientInfo *CI = new CoefficientInfo[MaxLevels + 1];
|
|
|
|
for (unsigned K = 1; K <= MaxLevels; ++K) {
|
|
|
|
CI[K].Coeff = Zero;
|
|
|
|
CI[K].PosPart = Zero;
|
|
|
|
CI[K].NegPart = Zero;
|
|
|
|
CI[K].Iterations = NULL;
|
|
|
|
}
|
|
|
|
while (const SCEVAddRecExpr *AddRec = dyn_cast<SCEVAddRecExpr>(Subscript)) {
|
|
|
|
const Loop *L = AddRec->getLoop();
|
|
|
|
unsigned K = SrcFlag ? mapSrcLoop(L) : mapDstLoop(L);
|
|
|
|
CI[K].Coeff = AddRec->getStepRecurrence(*SE);
|
|
|
|
CI[K].PosPart = getPositivePart(CI[K].Coeff);
|
|
|
|
CI[K].NegPart = getNegativePart(CI[K].Coeff);
|
|
|
|
CI[K].Iterations = collectUpperBound(L, Subscript->getType());
|
|
|
|
Subscript = AddRec->getStart();
|
|
|
|
}
|
|
|
|
Constant = Subscript;
|
|
|
|
#ifndef NDEBUG
|
|
|
|
DEBUG(dbgs() << "\tCoefficient Info\n");
|
|
|
|
for (unsigned K = 1; K <= MaxLevels; ++K) {
|
|
|
|
DEBUG(dbgs() << "\t " << K << "\t" << *CI[K].Coeff);
|
|
|
|
DEBUG(dbgs() << "\tPos Part = ");
|
|
|
|
DEBUG(dbgs() << *CI[K].PosPart);
|
|
|
|
DEBUG(dbgs() << "\tNeg Part = ");
|
|
|
|
DEBUG(dbgs() << *CI[K].NegPart);
|
|
|
|
DEBUG(dbgs() << "\tUpper Bound = ");
|
|
|
|
if (CI[K].Iterations)
|
|
|
|
DEBUG(dbgs() << *CI[K].Iterations);
|
|
|
|
else
|
|
|
|
DEBUG(dbgs() << "+inf");
|
|
|
|
DEBUG(dbgs() << '\n');
|
|
|
|
}
|
|
|
|
DEBUG(dbgs() << "\t Constant = " << *Subscript << '\n');
|
|
|
|
#endif
|
|
|
|
return CI;
|
|
|
|
}
|
|
|
|
|
|
|
|
|
|
|
|
// Looks through all the bounds info and
|
|
|
|
// computes the lower bound given the current direction settings
|
|
|
|
// at each level. If the lower bound for any level is -inf,
|
|
|
|
// the result is -inf.
|
|
|
|
const SCEV *DependenceAnalysis::getLowerBound(BoundInfo *Bound) const {
|
|
|
|
const SCEV *Sum = Bound[1].Lower[Bound[1].Direction];
|
|
|
|
for (unsigned K = 2; Sum && K <= MaxLevels; ++K) {
|
|
|
|
if (Bound[K].Lower[Bound[K].Direction])
|
|
|
|
Sum = SE->getAddExpr(Sum, Bound[K].Lower[Bound[K].Direction]);
|
|
|
|
else
|
|
|
|
Sum = NULL;
|
|
|
|
}
|
|
|
|
return Sum;
|
|
|
|
}
|
|
|
|
|
|
|
|
|
|
|
|
// Looks through all the bounds info and
|
|
|
|
// computes the upper bound given the current direction settings
|
|
|
|
// at each level. If the upper bound at any level is +inf,
|
|
|
|
// the result is +inf.
|
|
|
|
const SCEV *DependenceAnalysis::getUpperBound(BoundInfo *Bound) const {
|
|
|
|
const SCEV *Sum = Bound[1].Upper[Bound[1].Direction];
|
|
|
|
for (unsigned K = 2; Sum && K <= MaxLevels; ++K) {
|
|
|
|
if (Bound[K].Upper[Bound[K].Direction])
|
|
|
|
Sum = SE->getAddExpr(Sum, Bound[K].Upper[Bound[K].Direction]);
|
|
|
|
else
|
|
|
|
Sum = NULL;
|
|
|
|
}
|
|
|
|
return Sum;
|
|
|
|
}
|
|
|
|
|
|
|
|
|
|
|
|
//===----------------------------------------------------------------------===//
|
|
|
|
// Constraint manipulation for Delta test.
|
|
|
|
|
|
|
|
// Given a linear SCEV,
|
|
|
|
// return the coefficient (the step)
|
|
|
|
// corresponding to the specified loop.
|
|
|
|
// If there isn't one, return 0.
|
|
|
|
// For example, given a*i + b*j + c*k, zeroing the coefficient
|
|
|
|
// corresponding to the j loop would yield b.
|
|
|
|
const SCEV *DependenceAnalysis::findCoefficient(const SCEV *Expr,
|
|
|
|
const Loop *TargetLoop) const {
|
|
|
|
const SCEVAddRecExpr *AddRec = dyn_cast<SCEVAddRecExpr>(Expr);
|
|
|
|
if (!AddRec)
|
|
|
|
return SE->getConstant(Expr->getType(), 0);
|
|
|
|
if (AddRec->getLoop() == TargetLoop)
|
|
|
|
return AddRec->getStepRecurrence(*SE);
|
|
|
|
return findCoefficient(AddRec->getStart(), TargetLoop);
|
|
|
|
}
|
|
|
|
|
|
|
|
|
|
|
|
// Given a linear SCEV,
|
|
|
|
// return the SCEV given by zeroing out the coefficient
|
|
|
|
// corresponding to the specified loop.
|
|
|
|
// For example, given a*i + b*j + c*k, zeroing the coefficient
|
|
|
|
// corresponding to the j loop would yield a*i + c*k.
|
|
|
|
const SCEV *DependenceAnalysis::zeroCoefficient(const SCEV *Expr,
|
|
|
|
const Loop *TargetLoop) const {
|
|
|
|
const SCEVAddRecExpr *AddRec = dyn_cast<SCEVAddRecExpr>(Expr);
|
|
|
|
if (!AddRec)
|
|
|
|
return Expr; // ignore
|
|
|
|
if (AddRec->getLoop() == TargetLoop)
|
|
|
|
return AddRec->getStart();
|
|
|
|
return SE->getAddRecExpr(zeroCoefficient(AddRec->getStart(), TargetLoop),
|
|
|
|
AddRec->getStepRecurrence(*SE),
|
|
|
|
AddRec->getLoop(),
|
|
|
|
AddRec->getNoWrapFlags());
|
|
|
|
}
|
|
|
|
|
|
|
|
|
|
|
|
// Given a linear SCEV Expr,
|
|
|
|
// return the SCEV given by adding some Value to the
|
|
|
|
// coefficient corresponding to the specified TargetLoop.
|
|
|
|
// For example, given a*i + b*j + c*k, adding 1 to the coefficient
|
|
|
|
// corresponding to the j loop would yield a*i + (b+1)*j + c*k.
|
|
|
|
const SCEV *DependenceAnalysis::addToCoefficient(const SCEV *Expr,
|
|
|
|
const Loop *TargetLoop,
|
|
|
|
const SCEV *Value) const {
|
|
|
|
const SCEVAddRecExpr *AddRec = dyn_cast<SCEVAddRecExpr>(Expr);
|
|
|
|
if (!AddRec) // create a new addRec
|
|
|
|
return SE->getAddRecExpr(Expr,
|
|
|
|
Value,
|
|
|
|
TargetLoop,
|
|
|
|
SCEV::FlagAnyWrap); // Worst case, with no info.
|
|
|
|
if (AddRec->getLoop() == TargetLoop) {
|
|
|
|
const SCEV *Sum = SE->getAddExpr(AddRec->getStepRecurrence(*SE), Value);
|
|
|
|
if (Sum->isZero())
|
|
|
|
return AddRec->getStart();
|
|
|
|
return SE->getAddRecExpr(AddRec->getStart(),
|
|
|
|
Sum,
|
|
|
|
AddRec->getLoop(),
|
|
|
|
AddRec->getNoWrapFlags());
|
|
|
|
}
|
2013-06-28 18:44:48 +00:00
|
|
|
if (SE->isLoopInvariant(AddRec, TargetLoop))
|
|
|
|
return SE->getAddRecExpr(AddRec,
|
|
|
|
Value,
|
|
|
|
TargetLoop,
|
|
|
|
SCEV::FlagAnyWrap);
|
dependence analysis
Patch from Preston Briggs <preston.briggs@gmail.com>.
This is an updated version of the dependence-analysis patch, including an MIV
test based on Banerjee's inequalities.
It's a fairly complete implementation of the paper
Practical Dependence Testing
Gina Goff, Ken Kennedy, and Chau-Wen Tseng
PLDI 1991
It cannot yet propagate constraints between coupled RDIV subscripts (discussed
in Section 5.3.2 of the paper).
It's organized as a FunctionPass with a single entry point that supports testing
for dependence between two instructions in a function. If there's no dependence,
it returns null. If there's a dependence, it returns a pointer to a Dependence
which can be queried about details (what kind of dependence, is it loop
independent, direction and distance vector entries, etc). I haven't included
every imaginable feature, but there's a good selection that should be adequate
for supporting many loop transformations. Of course, it can be extended as
necessary.
Included in the patch file are many test cases, commented with C code showing
the loops and array references.
git-svn-id: https://llvm.org/svn/llvm-project/llvm/trunk@165708 91177308-0d34-0410-b5e6-96231b3b80d8
2012-10-11 07:32:34 +00:00
|
|
|
return SE->getAddRecExpr(addToCoefficient(AddRec->getStart(),
|
|
|
|
TargetLoop, Value),
|
|
|
|
AddRec->getStepRecurrence(*SE),
|
|
|
|
AddRec->getLoop(),
|
|
|
|
AddRec->getNoWrapFlags());
|
|
|
|
}
|
|
|
|
|
|
|
|
|
|
|
|
// Review the constraints, looking for opportunities
|
|
|
|
// to simplify a subscript pair (Src and Dst).
|
|
|
|
// Return true if some simplification occurs.
|
|
|
|
// If the simplification isn't exact (that is, if it is conservative
|
|
|
|
// in terms of dependence), set consistent to false.
|
|
|
|
// Corresponds to Figure 5 from the paper
|
|
|
|
//
|
|
|
|
// Practical Dependence Testing
|
|
|
|
// Goff, Kennedy, Tseng
|
|
|
|
// PLDI 1991
|
|
|
|
bool DependenceAnalysis::propagate(const SCEV *&Src,
|
|
|
|
const SCEV *&Dst,
|
|
|
|
SmallBitVector &Loops,
|
2013-07-14 04:42:23 +00:00
|
|
|
SmallVectorImpl<Constraint> &Constraints,
|
dependence analysis
Patch from Preston Briggs <preston.briggs@gmail.com>.
This is an updated version of the dependence-analysis patch, including an MIV
test based on Banerjee's inequalities.
It's a fairly complete implementation of the paper
Practical Dependence Testing
Gina Goff, Ken Kennedy, and Chau-Wen Tseng
PLDI 1991
It cannot yet propagate constraints between coupled RDIV subscripts (discussed
in Section 5.3.2 of the paper).
It's organized as a FunctionPass with a single entry point that supports testing
for dependence between two instructions in a function. If there's no dependence,
it returns null. If there's a dependence, it returns a pointer to a Dependence
which can be queried about details (what kind of dependence, is it loop
independent, direction and distance vector entries, etc). I haven't included
every imaginable feature, but there's a good selection that should be adequate
for supporting many loop transformations. Of course, it can be extended as
necessary.
Included in the patch file are many test cases, commented with C code showing
the loops and array references.
git-svn-id: https://llvm.org/svn/llvm-project/llvm/trunk@165708 91177308-0d34-0410-b5e6-96231b3b80d8
2012-10-11 07:32:34 +00:00
|
|
|
bool &Consistent) {
|
|
|
|
bool Result = false;
|
|
|
|
for (int LI = Loops.find_first(); LI >= 0; LI = Loops.find_next(LI)) {
|
|
|
|
DEBUG(dbgs() << "\t Constraint[" << LI << "] is");
|
|
|
|
DEBUG(Constraints[LI].dump(dbgs()));
|
|
|
|
if (Constraints[LI].isDistance())
|
|
|
|
Result |= propagateDistance(Src, Dst, Constraints[LI], Consistent);
|
|
|
|
else if (Constraints[LI].isLine())
|
|
|
|
Result |= propagateLine(Src, Dst, Constraints[LI], Consistent);
|
|
|
|
else if (Constraints[LI].isPoint())
|
|
|
|
Result |= propagatePoint(Src, Dst, Constraints[LI]);
|
|
|
|
}
|
|
|
|
return Result;
|
|
|
|
}
|
|
|
|
|
|
|
|
|
|
|
|
// Attempt to propagate a distance
|
|
|
|
// constraint into a subscript pair (Src and Dst).
|
|
|
|
// Return true if some simplification occurs.
|
|
|
|
// If the simplification isn't exact (that is, if it is conservative
|
|
|
|
// in terms of dependence), set consistent to false.
|
|
|
|
bool DependenceAnalysis::propagateDistance(const SCEV *&Src,
|
|
|
|
const SCEV *&Dst,
|
|
|
|
Constraint &CurConstraint,
|
|
|
|
bool &Consistent) {
|
|
|
|
const Loop *CurLoop = CurConstraint.getAssociatedLoop();
|
|
|
|
DEBUG(dbgs() << "\t\tSrc is " << *Src << "\n");
|
|
|
|
const SCEV *A_K = findCoefficient(Src, CurLoop);
|
|
|
|
if (A_K->isZero())
|
|
|
|
return false;
|
|
|
|
const SCEV *DA_K = SE->getMulExpr(A_K, CurConstraint.getD());
|
|
|
|
Src = SE->getMinusSCEV(Src, DA_K);
|
|
|
|
Src = zeroCoefficient(Src, CurLoop);
|
|
|
|
DEBUG(dbgs() << "\t\tnew Src is " << *Src << "\n");
|
|
|
|
DEBUG(dbgs() << "\t\tDst is " << *Dst << "\n");
|
|
|
|
Dst = addToCoefficient(Dst, CurLoop, SE->getNegativeSCEV(A_K));
|
|
|
|
DEBUG(dbgs() << "\t\tnew Dst is " << *Dst << "\n");
|
|
|
|
if (!findCoefficient(Dst, CurLoop)->isZero())
|
|
|
|
Consistent = false;
|
|
|
|
return true;
|
|
|
|
}
|
|
|
|
|
|
|
|
|
|
|
|
// Attempt to propagate a line
|
|
|
|
// constraint into a subscript pair (Src and Dst).
|
|
|
|
// Return true if some simplification occurs.
|
|
|
|
// If the simplification isn't exact (that is, if it is conservative
|
|
|
|
// in terms of dependence), set consistent to false.
|
|
|
|
bool DependenceAnalysis::propagateLine(const SCEV *&Src,
|
|
|
|
const SCEV *&Dst,
|
|
|
|
Constraint &CurConstraint,
|
|
|
|
bool &Consistent) {
|
|
|
|
const Loop *CurLoop = CurConstraint.getAssociatedLoop();
|
|
|
|
const SCEV *A = CurConstraint.getA();
|
|
|
|
const SCEV *B = CurConstraint.getB();
|
|
|
|
const SCEV *C = CurConstraint.getC();
|
|
|
|
DEBUG(dbgs() << "\t\tA = " << *A << ", B = " << *B << ", C = " << *C << "\n");
|
|
|
|
DEBUG(dbgs() << "\t\tSrc = " << *Src << "\n");
|
|
|
|
DEBUG(dbgs() << "\t\tDst = " << *Dst << "\n");
|
|
|
|
if (A->isZero()) {
|
|
|
|
const SCEVConstant *Bconst = dyn_cast<SCEVConstant>(B);
|
|
|
|
const SCEVConstant *Cconst = dyn_cast<SCEVConstant>(C);
|
|
|
|
if (!Bconst || !Cconst) return false;
|
|
|
|
APInt Beta = Bconst->getValue()->getValue();
|
|
|
|
APInt Charlie = Cconst->getValue()->getValue();
|
|
|
|
APInt CdivB = Charlie.sdiv(Beta);
|
|
|
|
assert(Charlie.srem(Beta) == 0 && "C should be evenly divisible by B");
|
|
|
|
const SCEV *AP_K = findCoefficient(Dst, CurLoop);
|
|
|
|
// Src = SE->getAddExpr(Src, SE->getMulExpr(AP_K, SE->getConstant(CdivB)));
|
|
|
|
Src = SE->getMinusSCEV(Src, SE->getMulExpr(AP_K, SE->getConstant(CdivB)));
|
|
|
|
Dst = zeroCoefficient(Dst, CurLoop);
|
|
|
|
if (!findCoefficient(Src, CurLoop)->isZero())
|
|
|
|
Consistent = false;
|
|
|
|
}
|
|
|
|
else if (B->isZero()) {
|
|
|
|
const SCEVConstant *Aconst = dyn_cast<SCEVConstant>(A);
|
|
|
|
const SCEVConstant *Cconst = dyn_cast<SCEVConstant>(C);
|
|
|
|
if (!Aconst || !Cconst) return false;
|
|
|
|
APInt Alpha = Aconst->getValue()->getValue();
|
|
|
|
APInt Charlie = Cconst->getValue()->getValue();
|
|
|
|
APInt CdivA = Charlie.sdiv(Alpha);
|
|
|
|
assert(Charlie.srem(Alpha) == 0 && "C should be evenly divisible by A");
|
|
|
|
const SCEV *A_K = findCoefficient(Src, CurLoop);
|
|
|
|
Src = SE->getAddExpr(Src, SE->getMulExpr(A_K, SE->getConstant(CdivA)));
|
|
|
|
Src = zeroCoefficient(Src, CurLoop);
|
|
|
|
if (!findCoefficient(Dst, CurLoop)->isZero())
|
|
|
|
Consistent = false;
|
|
|
|
}
|
|
|
|
else if (isKnownPredicate(CmpInst::ICMP_EQ, A, B)) {
|
|
|
|
const SCEVConstant *Aconst = dyn_cast<SCEVConstant>(A);
|
|
|
|
const SCEVConstant *Cconst = dyn_cast<SCEVConstant>(C);
|
|
|
|
if (!Aconst || !Cconst) return false;
|
|
|
|
APInt Alpha = Aconst->getValue()->getValue();
|
|
|
|
APInt Charlie = Cconst->getValue()->getValue();
|
|
|
|
APInt CdivA = Charlie.sdiv(Alpha);
|
|
|
|
assert(Charlie.srem(Alpha) == 0 && "C should be evenly divisible by A");
|
|
|
|
const SCEV *A_K = findCoefficient(Src, CurLoop);
|
|
|
|
Src = SE->getAddExpr(Src, SE->getMulExpr(A_K, SE->getConstant(CdivA)));
|
|
|
|
Src = zeroCoefficient(Src, CurLoop);
|
|
|
|
Dst = addToCoefficient(Dst, CurLoop, A_K);
|
|
|
|
if (!findCoefficient(Dst, CurLoop)->isZero())
|
|
|
|
Consistent = false;
|
|
|
|
}
|
|
|
|
else {
|
|
|
|
// paper is incorrect here, or perhaps just misleading
|
|
|
|
const SCEV *A_K = findCoefficient(Src, CurLoop);
|
|
|
|
Src = SE->getMulExpr(Src, A);
|
|
|
|
Dst = SE->getMulExpr(Dst, A);
|
|
|
|
Src = SE->getAddExpr(Src, SE->getMulExpr(A_K, C));
|
|
|
|
Src = zeroCoefficient(Src, CurLoop);
|
|
|
|
Dst = addToCoefficient(Dst, CurLoop, SE->getMulExpr(A_K, B));
|
|
|
|
if (!findCoefficient(Dst, CurLoop)->isZero())
|
|
|
|
Consistent = false;
|
|
|
|
}
|
|
|
|
DEBUG(dbgs() << "\t\tnew Src = " << *Src << "\n");
|
|
|
|
DEBUG(dbgs() << "\t\tnew Dst = " << *Dst << "\n");
|
|
|
|
return true;
|
|
|
|
}
|
|
|
|
|
|
|
|
|
|
|
|
// Attempt to propagate a point
|
|
|
|
// constraint into a subscript pair (Src and Dst).
|
|
|
|
// Return true if some simplification occurs.
|
|
|
|
bool DependenceAnalysis::propagatePoint(const SCEV *&Src,
|
|
|
|
const SCEV *&Dst,
|
|
|
|
Constraint &CurConstraint) {
|
|
|
|
const Loop *CurLoop = CurConstraint.getAssociatedLoop();
|
|
|
|
const SCEV *A_K = findCoefficient(Src, CurLoop);
|
|
|
|
const SCEV *AP_K = findCoefficient(Dst, CurLoop);
|
|
|
|
const SCEV *XA_K = SE->getMulExpr(A_K, CurConstraint.getX());
|
|
|
|
const SCEV *YAP_K = SE->getMulExpr(AP_K, CurConstraint.getY());
|
|
|
|
DEBUG(dbgs() << "\t\tSrc is " << *Src << "\n");
|
|
|
|
Src = SE->getAddExpr(Src, SE->getMinusSCEV(XA_K, YAP_K));
|
|
|
|
Src = zeroCoefficient(Src, CurLoop);
|
|
|
|
DEBUG(dbgs() << "\t\tnew Src is " << *Src << "\n");
|
|
|
|
DEBUG(dbgs() << "\t\tDst is " << *Dst << "\n");
|
|
|
|
Dst = zeroCoefficient(Dst, CurLoop);
|
|
|
|
DEBUG(dbgs() << "\t\tnew Dst is " << *Dst << "\n");
|
|
|
|
return true;
|
|
|
|
}
|
|
|
|
|
|
|
|
|
|
|
|
// Update direction vector entry based on the current constraint.
|
|
|
|
void DependenceAnalysis::updateDirection(Dependence::DVEntry &Level,
|
|
|
|
const Constraint &CurConstraint
|
|
|
|
) const {
|
|
|
|
DEBUG(dbgs() << "\tUpdate direction, constraint =");
|
|
|
|
DEBUG(CurConstraint.dump(dbgs()));
|
|
|
|
if (CurConstraint.isAny())
|
|
|
|
; // use defaults
|
|
|
|
else if (CurConstraint.isDistance()) {
|
|
|
|
// this one is consistent, the others aren't
|
|
|
|
Level.Scalar = false;
|
|
|
|
Level.Distance = CurConstraint.getD();
|
|
|
|
unsigned NewDirection = Dependence::DVEntry::NONE;
|
|
|
|
if (!SE->isKnownNonZero(Level.Distance)) // if may be zero
|
|
|
|
NewDirection = Dependence::DVEntry::EQ;
|
|
|
|
if (!SE->isKnownNonPositive(Level.Distance)) // if may be positive
|
|
|
|
NewDirection |= Dependence::DVEntry::LT;
|
|
|
|
if (!SE->isKnownNonNegative(Level.Distance)) // if may be negative
|
|
|
|
NewDirection |= Dependence::DVEntry::GT;
|
|
|
|
Level.Direction &= NewDirection;
|
|
|
|
}
|
|
|
|
else if (CurConstraint.isLine()) {
|
|
|
|
Level.Scalar = false;
|
|
|
|
Level.Distance = NULL;
|
|
|
|
// direction should be accurate
|
|
|
|
}
|
|
|
|
else if (CurConstraint.isPoint()) {
|
|
|
|
Level.Scalar = false;
|
|
|
|
Level.Distance = NULL;
|
|
|
|
unsigned NewDirection = Dependence::DVEntry::NONE;
|
|
|
|
if (!isKnownPredicate(CmpInst::ICMP_NE,
|
|
|
|
CurConstraint.getY(),
|
|
|
|
CurConstraint.getX()))
|
|
|
|
// if X may be = Y
|
|
|
|
NewDirection |= Dependence::DVEntry::EQ;
|
|
|
|
if (!isKnownPredicate(CmpInst::ICMP_SLE,
|
|
|
|
CurConstraint.getY(),
|
|
|
|
CurConstraint.getX()))
|
|
|
|
// if Y may be > X
|
|
|
|
NewDirection |= Dependence::DVEntry::LT;
|
|
|
|
if (!isKnownPredicate(CmpInst::ICMP_SGE,
|
|
|
|
CurConstraint.getY(),
|
|
|
|
CurConstraint.getX()))
|
|
|
|
// if Y may be < X
|
|
|
|
NewDirection |= Dependence::DVEntry::GT;
|
|
|
|
Level.Direction &= NewDirection;
|
|
|
|
}
|
|
|
|
else
|
|
|
|
llvm_unreachable("constraint has unexpected kind");
|
|
|
|
}
|
|
|
|
|
|
|
|
|
|
|
|
//===----------------------------------------------------------------------===//
|
|
|
|
|
|
|
|
#ifndef NDEBUG
|
|
|
|
// For debugging purposes, dump a small bit vector to dbgs().
|
|
|
|
static void dumpSmallBitVector(SmallBitVector &BV) {
|
|
|
|
dbgs() << "{";
|
|
|
|
for (int VI = BV.find_first(); VI >= 0; VI = BV.find_next(VI)) {
|
|
|
|
dbgs() << VI;
|
|
|
|
if (BV.find_next(VI) >= 0)
|
|
|
|
dbgs() << ' ';
|
|
|
|
}
|
|
|
|
dbgs() << "}\n";
|
|
|
|
}
|
|
|
|
#endif
|
|
|
|
|
|
|
|
|
|
|
|
// depends -
|
|
|
|
// Returns NULL if there is no dependence.
|
|
|
|
// Otherwise, return a Dependence with as many details as possible.
|
|
|
|
// Corresponds to Section 3.1 in the paper
|
|
|
|
//
|
|
|
|
// Practical Dependence Testing
|
|
|
|
// Goff, Kennedy, Tseng
|
|
|
|
// PLDI 1991
|
|
|
|
//
|
2012-11-21 23:50:04 +00:00
|
|
|
// Care is required to keep the routine below, getSplitIteration(),
|
|
|
|
// up to date with respect to this routine.
|
2012-11-20 22:28:04 +00:00
|
|
|
Dependence *DependenceAnalysis::depends(Instruction *Src,
|
|
|
|
Instruction *Dst,
|
dependence analysis
Patch from Preston Briggs <preston.briggs@gmail.com>.
This is an updated version of the dependence-analysis patch, including an MIV
test based on Banerjee's inequalities.
It's a fairly complete implementation of the paper
Practical Dependence Testing
Gina Goff, Ken Kennedy, and Chau-Wen Tseng
PLDI 1991
It cannot yet propagate constraints between coupled RDIV subscripts (discussed
in Section 5.3.2 of the paper).
It's organized as a FunctionPass with a single entry point that supports testing
for dependence between two instructions in a function. If there's no dependence,
it returns null. If there's a dependence, it returns a pointer to a Dependence
which can be queried about details (what kind of dependence, is it loop
independent, direction and distance vector entries, etc). I haven't included
every imaginable feature, but there's a good selection that should be adequate
for supporting many loop transformations. Of course, it can be extended as
necessary.
Included in the patch file are many test cases, commented with C code showing
the loops and array references.
git-svn-id: https://llvm.org/svn/llvm-project/llvm/trunk@165708 91177308-0d34-0410-b5e6-96231b3b80d8
2012-10-11 07:32:34 +00:00
|
|
|
bool PossiblyLoopIndependent) {
|
2012-11-27 06:41:46 +00:00
|
|
|
if (Src == Dst)
|
|
|
|
PossiblyLoopIndependent = false;
|
|
|
|
|
dependence analysis
Patch from Preston Briggs <preston.briggs@gmail.com>.
This is an updated version of the dependence-analysis patch, including an MIV
test based on Banerjee's inequalities.
It's a fairly complete implementation of the paper
Practical Dependence Testing
Gina Goff, Ken Kennedy, and Chau-Wen Tseng
PLDI 1991
It cannot yet propagate constraints between coupled RDIV subscripts (discussed
in Section 5.3.2 of the paper).
It's organized as a FunctionPass with a single entry point that supports testing
for dependence between two instructions in a function. If there's no dependence,
it returns null. If there's a dependence, it returns a pointer to a Dependence
which can be queried about details (what kind of dependence, is it loop
independent, direction and distance vector entries, etc). I haven't included
every imaginable feature, but there's a good selection that should be adequate
for supporting many loop transformations. Of course, it can be extended as
necessary.
Included in the patch file are many test cases, commented with C code showing
the loops and array references.
git-svn-id: https://llvm.org/svn/llvm-project/llvm/trunk@165708 91177308-0d34-0410-b5e6-96231b3b80d8
2012-10-11 07:32:34 +00:00
|
|
|
if ((!Src->mayReadFromMemory() && !Src->mayWriteToMemory()) ||
|
|
|
|
(!Dst->mayReadFromMemory() && !Dst->mayWriteToMemory()))
|
|
|
|
// if both instructions don't reference memory, there's no dependence
|
|
|
|
return NULL;
|
|
|
|
|
2012-11-21 23:50:04 +00:00
|
|
|
if (!isLoadOrStore(Src) || !isLoadOrStore(Dst)) {
|
dependence analysis
Patch from Preston Briggs <preston.briggs@gmail.com>.
This is an updated version of the dependence-analysis patch, including an MIV
test based on Banerjee's inequalities.
It's a fairly complete implementation of the paper
Practical Dependence Testing
Gina Goff, Ken Kennedy, and Chau-Wen Tseng
PLDI 1991
It cannot yet propagate constraints between coupled RDIV subscripts (discussed
in Section 5.3.2 of the paper).
It's organized as a FunctionPass with a single entry point that supports testing
for dependence between two instructions in a function. If there's no dependence,
it returns null. If there's a dependence, it returns a pointer to a Dependence
which can be queried about details (what kind of dependence, is it loop
independent, direction and distance vector entries, etc). I haven't included
every imaginable feature, but there's a good selection that should be adequate
for supporting many loop transformations. Of course, it can be extended as
necessary.
Included in the patch file are many test cases, commented with C code showing
the loops and array references.
git-svn-id: https://llvm.org/svn/llvm-project/llvm/trunk@165708 91177308-0d34-0410-b5e6-96231b3b80d8
2012-10-11 07:32:34 +00:00
|
|
|
// can only analyze simple loads and stores, i.e., no calls, invokes, etc.
|
2012-11-21 23:50:04 +00:00
|
|
|
DEBUG(dbgs() << "can only handle simple loads and stores\n");
|
dependence analysis
Patch from Preston Briggs <preston.briggs@gmail.com>.
This is an updated version of the dependence-analysis patch, including an MIV
test based on Banerjee's inequalities.
It's a fairly complete implementation of the paper
Practical Dependence Testing
Gina Goff, Ken Kennedy, and Chau-Wen Tseng
PLDI 1991
It cannot yet propagate constraints between coupled RDIV subscripts (discussed
in Section 5.3.2 of the paper).
It's organized as a FunctionPass with a single entry point that supports testing
for dependence between two instructions in a function. If there's no dependence,
it returns null. If there's a dependence, it returns a pointer to a Dependence
which can be queried about details (what kind of dependence, is it loop
independent, direction and distance vector entries, etc). I haven't included
every imaginable feature, but there's a good selection that should be adequate
for supporting many loop transformations. Of course, it can be extended as
necessary.
Included in the patch file are many test cases, commented with C code showing
the loops and array references.
git-svn-id: https://llvm.org/svn/llvm-project/llvm/trunk@165708 91177308-0d34-0410-b5e6-96231b3b80d8
2012-10-11 07:32:34 +00:00
|
|
|
return new Dependence(Src, Dst);
|
2012-11-21 23:50:04 +00:00
|
|
|
}
|
dependence analysis
Patch from Preston Briggs <preston.briggs@gmail.com>.
This is an updated version of the dependence-analysis patch, including an MIV
test based on Banerjee's inequalities.
It's a fairly complete implementation of the paper
Practical Dependence Testing
Gina Goff, Ken Kennedy, and Chau-Wen Tseng
PLDI 1991
It cannot yet propagate constraints between coupled RDIV subscripts (discussed
in Section 5.3.2 of the paper).
It's organized as a FunctionPass with a single entry point that supports testing
for dependence between two instructions in a function. If there's no dependence,
it returns null. If there's a dependence, it returns a pointer to a Dependence
which can be queried about details (what kind of dependence, is it loop
independent, direction and distance vector entries, etc). I haven't included
every imaginable feature, but there's a good selection that should be adequate
for supporting many loop transformations. Of course, it can be extended as
necessary.
Included in the patch file are many test cases, commented with C code showing
the loops and array references.
git-svn-id: https://llvm.org/svn/llvm-project/llvm/trunk@165708 91177308-0d34-0410-b5e6-96231b3b80d8
2012-10-11 07:32:34 +00:00
|
|
|
|
2012-11-20 22:28:04 +00:00
|
|
|
Value *SrcPtr = getPointerOperand(Src);
|
|
|
|
Value *DstPtr = getPointerOperand(Dst);
|
dependence analysis
Patch from Preston Briggs <preston.briggs@gmail.com>.
This is an updated version of the dependence-analysis patch, including an MIV
test based on Banerjee's inequalities.
It's a fairly complete implementation of the paper
Practical Dependence Testing
Gina Goff, Ken Kennedy, and Chau-Wen Tseng
PLDI 1991
It cannot yet propagate constraints between coupled RDIV subscripts (discussed
in Section 5.3.2 of the paper).
It's organized as a FunctionPass with a single entry point that supports testing
for dependence between two instructions in a function. If there's no dependence,
it returns null. If there's a dependence, it returns a pointer to a Dependence
which can be queried about details (what kind of dependence, is it loop
independent, direction and distance vector entries, etc). I haven't included
every imaginable feature, but there's a good selection that should be adequate
for supporting many loop transformations. Of course, it can be extended as
necessary.
Included in the patch file are many test cases, commented with C code showing
the loops and array references.
git-svn-id: https://llvm.org/svn/llvm-project/llvm/trunk@165708 91177308-0d34-0410-b5e6-96231b3b80d8
2012-10-11 07:32:34 +00:00
|
|
|
|
|
|
|
switch (underlyingObjectsAlias(AA, DstPtr, SrcPtr)) {
|
|
|
|
case AliasAnalysis::MayAlias:
|
|
|
|
case AliasAnalysis::PartialAlias:
|
|
|
|
// cannot analyse objects if we don't understand their aliasing.
|
2012-11-21 23:50:04 +00:00
|
|
|
DEBUG(dbgs() << "can't analyze may or partial alias\n");
|
dependence analysis
Patch from Preston Briggs <preston.briggs@gmail.com>.
This is an updated version of the dependence-analysis patch, including an MIV
test based on Banerjee's inequalities.
It's a fairly complete implementation of the paper
Practical Dependence Testing
Gina Goff, Ken Kennedy, and Chau-Wen Tseng
PLDI 1991
It cannot yet propagate constraints between coupled RDIV subscripts (discussed
in Section 5.3.2 of the paper).
It's organized as a FunctionPass with a single entry point that supports testing
for dependence between two instructions in a function. If there's no dependence,
it returns null. If there's a dependence, it returns a pointer to a Dependence
which can be queried about details (what kind of dependence, is it loop
independent, direction and distance vector entries, etc). I haven't included
every imaginable feature, but there's a good selection that should be adequate
for supporting many loop transformations. Of course, it can be extended as
necessary.
Included in the patch file are many test cases, commented with C code showing
the loops and array references.
git-svn-id: https://llvm.org/svn/llvm-project/llvm/trunk@165708 91177308-0d34-0410-b5e6-96231b3b80d8
2012-10-11 07:32:34 +00:00
|
|
|
return new Dependence(Src, Dst);
|
|
|
|
case AliasAnalysis::NoAlias:
|
|
|
|
// If the objects noalias, they are distinct, accesses are independent.
|
2012-11-21 23:50:04 +00:00
|
|
|
DEBUG(dbgs() << "no alias\n");
|
dependence analysis
Patch from Preston Briggs <preston.briggs@gmail.com>.
This is an updated version of the dependence-analysis patch, including an MIV
test based on Banerjee's inequalities.
It's a fairly complete implementation of the paper
Practical Dependence Testing
Gina Goff, Ken Kennedy, and Chau-Wen Tseng
PLDI 1991
It cannot yet propagate constraints between coupled RDIV subscripts (discussed
in Section 5.3.2 of the paper).
It's organized as a FunctionPass with a single entry point that supports testing
for dependence between two instructions in a function. If there's no dependence,
it returns null. If there's a dependence, it returns a pointer to a Dependence
which can be queried about details (what kind of dependence, is it loop
independent, direction and distance vector entries, etc). I haven't included
every imaginable feature, but there's a good selection that should be adequate
for supporting many loop transformations. Of course, it can be extended as
necessary.
Included in the patch file are many test cases, commented with C code showing
the loops and array references.
git-svn-id: https://llvm.org/svn/llvm-project/llvm/trunk@165708 91177308-0d34-0410-b5e6-96231b3b80d8
2012-10-11 07:32:34 +00:00
|
|
|
return NULL;
|
|
|
|
case AliasAnalysis::MustAlias:
|
|
|
|
break; // The underlying objects alias; test accesses for dependence.
|
|
|
|
}
|
|
|
|
|
|
|
|
// establish loop nesting levels
|
|
|
|
establishNestingLevels(Src, Dst);
|
|
|
|
DEBUG(dbgs() << " common nesting levels = " << CommonLevels << "\n");
|
|
|
|
DEBUG(dbgs() << " maximum nesting levels = " << MaxLevels << "\n");
|
|
|
|
|
|
|
|
FullDependence Result(Src, Dst, PossiblyLoopIndependent, CommonLevels);
|
|
|
|
++TotalArrayPairs;
|
|
|
|
|
2012-11-21 23:50:04 +00:00
|
|
|
// See if there are GEPs we can use.
|
|
|
|
bool UsefulGEP = false;
|
|
|
|
GEPOperator *SrcGEP = dyn_cast<GEPOperator>(SrcPtr);
|
|
|
|
GEPOperator *DstGEP = dyn_cast<GEPOperator>(DstPtr);
|
|
|
|
if (SrcGEP && DstGEP &&
|
|
|
|
SrcGEP->getPointerOperandType() == DstGEP->getPointerOperandType()) {
|
|
|
|
const SCEV *SrcPtrSCEV = SE->getSCEV(SrcGEP->getPointerOperand());
|
|
|
|
const SCEV *DstPtrSCEV = SE->getSCEV(DstGEP->getPointerOperand());
|
|
|
|
DEBUG(dbgs() << " SrcPtrSCEV = " << *SrcPtrSCEV << "\n");
|
|
|
|
DEBUG(dbgs() << " DstPtrSCEV = " << *DstPtrSCEV << "\n");
|
|
|
|
|
|
|
|
UsefulGEP =
|
|
|
|
isLoopInvariant(SrcPtrSCEV, LI->getLoopFor(Src->getParent())) &&
|
|
|
|
isLoopInvariant(DstPtrSCEV, LI->getLoopFor(Dst->getParent()));
|
|
|
|
}
|
|
|
|
unsigned Pairs = UsefulGEP ? SrcGEP->idx_end() - SrcGEP->idx_begin() : 1;
|
dependence analysis
Patch from Preston Briggs <preston.briggs@gmail.com>.
This is an updated version of the dependence-analysis patch, including an MIV
test based on Banerjee's inequalities.
It's a fairly complete implementation of the paper
Practical Dependence Testing
Gina Goff, Ken Kennedy, and Chau-Wen Tseng
PLDI 1991
It cannot yet propagate constraints between coupled RDIV subscripts (discussed
in Section 5.3.2 of the paper).
It's organized as a FunctionPass with a single entry point that supports testing
for dependence between two instructions in a function. If there's no dependence,
it returns null. If there's a dependence, it returns a pointer to a Dependence
which can be queried about details (what kind of dependence, is it loop
independent, direction and distance vector entries, etc). I haven't included
every imaginable feature, but there's a good selection that should be adequate
for supporting many loop transformations. Of course, it can be extended as
necessary.
Included in the patch file are many test cases, commented with C code showing
the loops and array references.
git-svn-id: https://llvm.org/svn/llvm-project/llvm/trunk@165708 91177308-0d34-0410-b5e6-96231b3b80d8
2012-10-11 07:32:34 +00:00
|
|
|
SmallVector<Subscript, 4> Pair(Pairs);
|
2012-11-21 23:50:04 +00:00
|
|
|
if (UsefulGEP) {
|
|
|
|
DEBUG(dbgs() << " using GEPs\n");
|
|
|
|
unsigned P = 0;
|
|
|
|
for (GEPOperator::const_op_iterator SrcIdx = SrcGEP->idx_begin(),
|
|
|
|
SrcEnd = SrcGEP->idx_end(),
|
|
|
|
DstIdx = DstGEP->idx_begin();
|
|
|
|
SrcIdx != SrcEnd;
|
|
|
|
++SrcIdx, ++DstIdx, ++P) {
|
|
|
|
Pair[P].Src = SE->getSCEV(*SrcIdx);
|
|
|
|
Pair[P].Dst = SE->getSCEV(*DstIdx);
|
|
|
|
}
|
|
|
|
}
|
|
|
|
else {
|
|
|
|
DEBUG(dbgs() << " ignoring GEPs\n");
|
|
|
|
const SCEV *SrcSCEV = SE->getSCEV(SrcPtr);
|
|
|
|
const SCEV *DstSCEV = SE->getSCEV(DstPtr);
|
|
|
|
DEBUG(dbgs() << " SrcSCEV = " << *SrcSCEV << "\n");
|
|
|
|
DEBUG(dbgs() << " DstSCEV = " << *DstSCEV << "\n");
|
|
|
|
Pair[0].Src = SrcSCEV;
|
|
|
|
Pair[0].Dst = DstSCEV;
|
|
|
|
}
|
|
|
|
|
|
|
|
for (unsigned P = 0; P < Pairs; ++P) {
|
|
|
|
Pair[P].Loops.resize(MaxLevels + 1);
|
|
|
|
Pair[P].GroupLoops.resize(MaxLevels + 1);
|
|
|
|
Pair[P].Group.resize(Pairs);
|
|
|
|
removeMatchingExtensions(&Pair[P]);
|
|
|
|
Pair[P].Classification =
|
|
|
|
classifyPair(Pair[P].Src, LI->getLoopFor(Src->getParent()),
|
|
|
|
Pair[P].Dst, LI->getLoopFor(Dst->getParent()),
|
|
|
|
Pair[P].Loops);
|
|
|
|
Pair[P].GroupLoops = Pair[P].Loops;
|
|
|
|
Pair[P].Group.set(P);
|
|
|
|
DEBUG(dbgs() << " subscript " << P << "\n");
|
|
|
|
DEBUG(dbgs() << "\tsrc = " << *Pair[P].Src << "\n");
|
|
|
|
DEBUG(dbgs() << "\tdst = " << *Pair[P].Dst << "\n");
|
|
|
|
DEBUG(dbgs() << "\tclass = " << Pair[P].Classification << "\n");
|
dependence analysis
Patch from Preston Briggs <preston.briggs@gmail.com>.
This is an updated version of the dependence-analysis patch, including an MIV
test based on Banerjee's inequalities.
It's a fairly complete implementation of the paper
Practical Dependence Testing
Gina Goff, Ken Kennedy, and Chau-Wen Tseng
PLDI 1991
It cannot yet propagate constraints between coupled RDIV subscripts (discussed
in Section 5.3.2 of the paper).
It's organized as a FunctionPass with a single entry point that supports testing
for dependence between two instructions in a function. If there's no dependence,
it returns null. If there's a dependence, it returns a pointer to a Dependence
which can be queried about details (what kind of dependence, is it loop
independent, direction and distance vector entries, etc). I haven't included
every imaginable feature, but there's a good selection that should be adequate
for supporting many loop transformations. Of course, it can be extended as
necessary.
Included in the patch file are many test cases, commented with C code showing
the loops and array references.
git-svn-id: https://llvm.org/svn/llvm-project/llvm/trunk@165708 91177308-0d34-0410-b5e6-96231b3b80d8
2012-10-11 07:32:34 +00:00
|
|
|
DEBUG(dbgs() << "\tloops = ");
|
2012-11-21 23:50:04 +00:00
|
|
|
DEBUG(dumpSmallBitVector(Pair[P].Loops));
|
dependence analysis
Patch from Preston Briggs <preston.briggs@gmail.com>.
This is an updated version of the dependence-analysis patch, including an MIV
test based on Banerjee's inequalities.
It's a fairly complete implementation of the paper
Practical Dependence Testing
Gina Goff, Ken Kennedy, and Chau-Wen Tseng
PLDI 1991
It cannot yet propagate constraints between coupled RDIV subscripts (discussed
in Section 5.3.2 of the paper).
It's organized as a FunctionPass with a single entry point that supports testing
for dependence between two instructions in a function. If there's no dependence,
it returns null. If there's a dependence, it returns a pointer to a Dependence
which can be queried about details (what kind of dependence, is it loop
independent, direction and distance vector entries, etc). I haven't included
every imaginable feature, but there's a good selection that should be adequate
for supporting many loop transformations. Of course, it can be extended as
necessary.
Included in the patch file are many test cases, commented with C code showing
the loops and array references.
git-svn-id: https://llvm.org/svn/llvm-project/llvm/trunk@165708 91177308-0d34-0410-b5e6-96231b3b80d8
2012-10-11 07:32:34 +00:00
|
|
|
}
|
|
|
|
|
|
|
|
SmallBitVector Separable(Pairs);
|
|
|
|
SmallBitVector Coupled(Pairs);
|
|
|
|
|
|
|
|
// Partition subscripts into separable and minimally-coupled groups
|
|
|
|
// Algorithm in paper is algorithmically better;
|
|
|
|
// this may be faster in practice. Check someday.
|
|
|
|
//
|
|
|
|
// Here's an example of how it works. Consider this code:
|
|
|
|
//
|
|
|
|
// for (i = ...) {
|
|
|
|
// for (j = ...) {
|
|
|
|
// for (k = ...) {
|
|
|
|
// for (l = ...) {
|
|
|
|
// for (m = ...) {
|
|
|
|
// A[i][j][k][m] = ...;
|
|
|
|
// ... = A[0][j][l][i + j];
|
|
|
|
// }
|
|
|
|
// }
|
|
|
|
// }
|
|
|
|
// }
|
|
|
|
// }
|
|
|
|
//
|
|
|
|
// There are 4 subscripts here:
|
|
|
|
// 0 [i] and [0]
|
|
|
|
// 1 [j] and [j]
|
|
|
|
// 2 [k] and [l]
|
|
|
|
// 3 [m] and [i + j]
|
|
|
|
//
|
|
|
|
// We've already classified each subscript pair as ZIV, SIV, etc.,
|
|
|
|
// and collected all the loops mentioned by pair P in Pair[P].Loops.
|
|
|
|
// In addition, we've initialized Pair[P].GroupLoops to Pair[P].Loops
|
|
|
|
// and set Pair[P].Group = {P}.
|
|
|
|
//
|
|
|
|
// Src Dst Classification Loops GroupLoops Group
|
|
|
|
// 0 [i] [0] SIV {1} {1} {0}
|
|
|
|
// 1 [j] [j] SIV {2} {2} {1}
|
|
|
|
// 2 [k] [l] RDIV {3,4} {3,4} {2}
|
|
|
|
// 3 [m] [i + j] MIV {1,2,5} {1,2,5} {3}
|
|
|
|
//
|
|
|
|
// For each subscript SI 0 .. 3, we consider each remaining subscript, SJ.
|
|
|
|
// So, 0 is compared against 1, 2, and 3; 1 is compared against 2 and 3, etc.
|
|
|
|
//
|
|
|
|
// We begin by comparing 0 and 1. The intersection of the GroupLoops is empty.
|
|
|
|
// Next, 0 and 2. Again, the intersection of their GroupLoops is empty.
|
|
|
|
// Next 0 and 3. The intersection of their GroupLoop = {1}, not empty,
|
|
|
|
// so Pair[3].Group = {0,3} and Done = false (that is, 0 will not be added
|
|
|
|
// to either Separable or Coupled).
|
|
|
|
//
|
|
|
|
// Next, we consider 1 and 2. The intersection of the GroupLoops is empty.
|
|
|
|
// Next, 1 and 3. The intersectionof their GroupLoops = {2}, not empty,
|
|
|
|
// so Pair[3].Group = {0, 1, 3} and Done = false.
|
|
|
|
//
|
|
|
|
// Next, we compare 2 against 3. The intersection of the GroupLoops is empty.
|
|
|
|
// Since Done remains true, we add 2 to the set of Separable pairs.
|
|
|
|
//
|
|
|
|
// Finally, we consider 3. There's nothing to compare it with,
|
|
|
|
// so Done remains true and we add it to the Coupled set.
|
|
|
|
// Pair[3].Group = {0, 1, 3} and GroupLoops = {1, 2, 5}.
|
|
|
|
//
|
|
|
|
// In the end, we've got 1 separable subscript and 1 coupled group.
|
|
|
|
for (unsigned SI = 0; SI < Pairs; ++SI) {
|
|
|
|
if (Pair[SI].Classification == Subscript::NonLinear) {
|
|
|
|
// ignore these, but collect loops for later
|
|
|
|
++NonlinearSubscriptPairs;
|
|
|
|
collectCommonLoops(Pair[SI].Src,
|
|
|
|
LI->getLoopFor(Src->getParent()),
|
|
|
|
Pair[SI].Loops);
|
|
|
|
collectCommonLoops(Pair[SI].Dst,
|
|
|
|
LI->getLoopFor(Dst->getParent()),
|
|
|
|
Pair[SI].Loops);
|
|
|
|
Result.Consistent = false;
|
|
|
|
}
|
|
|
|
else if (Pair[SI].Classification == Subscript::ZIV) {
|
|
|
|
// always separable
|
|
|
|
Separable.set(SI);
|
|
|
|
}
|
|
|
|
else {
|
|
|
|
// SIV, RDIV, or MIV, so check for coupled group
|
|
|
|
bool Done = true;
|
|
|
|
for (unsigned SJ = SI + 1; SJ < Pairs; ++SJ) {
|
|
|
|
SmallBitVector Intersection = Pair[SI].GroupLoops;
|
|
|
|
Intersection &= Pair[SJ].GroupLoops;
|
|
|
|
if (Intersection.any()) {
|
|
|
|
// accumulate set of all the loops in group
|
|
|
|
Pair[SJ].GroupLoops |= Pair[SI].GroupLoops;
|
|
|
|
// accumulate set of all subscripts in group
|
|
|
|
Pair[SJ].Group |= Pair[SI].Group;
|
|
|
|
Done = false;
|
|
|
|
}
|
|
|
|
}
|
|
|
|
if (Done) {
|
|
|
|
if (Pair[SI].Group.count() == 1) {
|
|
|
|
Separable.set(SI);
|
|
|
|
++SeparableSubscriptPairs;
|
|
|
|
}
|
|
|
|
else {
|
|
|
|
Coupled.set(SI);
|
|
|
|
++CoupledSubscriptPairs;
|
|
|
|
}
|
|
|
|
}
|
|
|
|
}
|
|
|
|
}
|
|
|
|
|
|
|
|
DEBUG(dbgs() << " Separable = ");
|
|
|
|
DEBUG(dumpSmallBitVector(Separable));
|
|
|
|
DEBUG(dbgs() << " Coupled = ");
|
|
|
|
DEBUG(dumpSmallBitVector(Coupled));
|
|
|
|
|
|
|
|
Constraint NewConstraint;
|
|
|
|
NewConstraint.setAny(SE);
|
|
|
|
|
|
|
|
// test separable subscripts
|
|
|
|
for (int SI = Separable.find_first(); SI >= 0; SI = Separable.find_next(SI)) {
|
|
|
|
DEBUG(dbgs() << "testing subscript " << SI);
|
|
|
|
switch (Pair[SI].Classification) {
|
|
|
|
case Subscript::ZIV:
|
|
|
|
DEBUG(dbgs() << ", ZIV\n");
|
|
|
|
if (testZIV(Pair[SI].Src, Pair[SI].Dst, Result))
|
|
|
|
return NULL;
|
|
|
|
break;
|
|
|
|
case Subscript::SIV: {
|
|
|
|
DEBUG(dbgs() << ", SIV\n");
|
|
|
|
unsigned Level;
|
|
|
|
const SCEV *SplitIter = NULL;
|
|
|
|
if (testSIV(Pair[SI].Src, Pair[SI].Dst, Level,
|
|
|
|
Result, NewConstraint, SplitIter))
|
|
|
|
return NULL;
|
|
|
|
break;
|
|
|
|
}
|
|
|
|
case Subscript::RDIV:
|
|
|
|
DEBUG(dbgs() << ", RDIV\n");
|
|
|
|
if (testRDIV(Pair[SI].Src, Pair[SI].Dst, Result))
|
|
|
|
return NULL;
|
|
|
|
break;
|
|
|
|
case Subscript::MIV:
|
|
|
|
DEBUG(dbgs() << ", MIV\n");
|
|
|
|
if (testMIV(Pair[SI].Src, Pair[SI].Dst, Pair[SI].Loops, Result))
|
|
|
|
return NULL;
|
|
|
|
break;
|
|
|
|
default:
|
|
|
|
llvm_unreachable("subscript has unexpected classification");
|
|
|
|
}
|
|
|
|
}
|
|
|
|
|
|
|
|
if (Coupled.count()) {
|
|
|
|
// test coupled subscript groups
|
|
|
|
DEBUG(dbgs() << "starting on coupled subscripts\n");
|
|
|
|
DEBUG(dbgs() << "MaxLevels + 1 = " << MaxLevels + 1 << "\n");
|
|
|
|
SmallVector<Constraint, 4> Constraints(MaxLevels + 1);
|
|
|
|
for (unsigned II = 0; II <= MaxLevels; ++II)
|
|
|
|
Constraints[II].setAny(SE);
|
|
|
|
for (int SI = Coupled.find_first(); SI >= 0; SI = Coupled.find_next(SI)) {
|
|
|
|
DEBUG(dbgs() << "testing subscript group " << SI << " { ");
|
|
|
|
SmallBitVector Group(Pair[SI].Group);
|
|
|
|
SmallBitVector Sivs(Pairs);
|
|
|
|
SmallBitVector Mivs(Pairs);
|
|
|
|
SmallBitVector ConstrainedLevels(MaxLevels + 1);
|
|
|
|
for (int SJ = Group.find_first(); SJ >= 0; SJ = Group.find_next(SJ)) {
|
|
|
|
DEBUG(dbgs() << SJ << " ");
|
|
|
|
if (Pair[SJ].Classification == Subscript::SIV)
|
|
|
|
Sivs.set(SJ);
|
|
|
|
else
|
|
|
|
Mivs.set(SJ);
|
|
|
|
}
|
|
|
|
DEBUG(dbgs() << "}\n");
|
|
|
|
while (Sivs.any()) {
|
|
|
|
bool Changed = false;
|
|
|
|
for (int SJ = Sivs.find_first(); SJ >= 0; SJ = Sivs.find_next(SJ)) {
|
|
|
|
DEBUG(dbgs() << "testing subscript " << SJ << ", SIV\n");
|
|
|
|
// SJ is an SIV subscript that's part of the current coupled group
|
|
|
|
unsigned Level;
|
|
|
|
const SCEV *SplitIter = NULL;
|
|
|
|
DEBUG(dbgs() << "SIV\n");
|
|
|
|
if (testSIV(Pair[SJ].Src, Pair[SJ].Dst, Level,
|
|
|
|
Result, NewConstraint, SplitIter))
|
|
|
|
return NULL;
|
|
|
|
ConstrainedLevels.set(Level);
|
|
|
|
if (intersectConstraints(&Constraints[Level], &NewConstraint)) {
|
|
|
|
if (Constraints[Level].isEmpty()) {
|
|
|
|
++DeltaIndependence;
|
|
|
|
return NULL;
|
|
|
|
}
|
|
|
|
Changed = true;
|
|
|
|
}
|
|
|
|
Sivs.reset(SJ);
|
|
|
|
}
|
|
|
|
if (Changed) {
|
|
|
|
// propagate, possibly creating new SIVs and ZIVs
|
|
|
|
DEBUG(dbgs() << " propagating\n");
|
|
|
|
DEBUG(dbgs() << "\tMivs = ");
|
|
|
|
DEBUG(dumpSmallBitVector(Mivs));
|
|
|
|
for (int SJ = Mivs.find_first(); SJ >= 0; SJ = Mivs.find_next(SJ)) {
|
|
|
|
// SJ is an MIV subscript that's part of the current coupled group
|
|
|
|
DEBUG(dbgs() << "\tSJ = " << SJ << "\n");
|
|
|
|
if (propagate(Pair[SJ].Src, Pair[SJ].Dst, Pair[SJ].Loops,
|
|
|
|
Constraints, Result.Consistent)) {
|
|
|
|
DEBUG(dbgs() << "\t Changed\n");
|
|
|
|
++DeltaPropagations;
|
|
|
|
Pair[SJ].Classification =
|
|
|
|
classifyPair(Pair[SJ].Src, LI->getLoopFor(Src->getParent()),
|
|
|
|
Pair[SJ].Dst, LI->getLoopFor(Dst->getParent()),
|
|
|
|
Pair[SJ].Loops);
|
|
|
|
switch (Pair[SJ].Classification) {
|
|
|
|
case Subscript::ZIV:
|
|
|
|
DEBUG(dbgs() << "ZIV\n");
|
|
|
|
if (testZIV(Pair[SJ].Src, Pair[SJ].Dst, Result))
|
|
|
|
return NULL;
|
|
|
|
Mivs.reset(SJ);
|
|
|
|
break;
|
|
|
|
case Subscript::SIV:
|
|
|
|
Sivs.set(SJ);
|
|
|
|
Mivs.reset(SJ);
|
|
|
|
break;
|
|
|
|
case Subscript::RDIV:
|
|
|
|
case Subscript::MIV:
|
|
|
|
break;
|
|
|
|
default:
|
|
|
|
llvm_unreachable("bad subscript classification");
|
|
|
|
}
|
|
|
|
}
|
|
|
|
}
|
|
|
|
}
|
|
|
|
}
|
|
|
|
|
|
|
|
// test & propagate remaining RDIVs
|
|
|
|
for (int SJ = Mivs.find_first(); SJ >= 0; SJ = Mivs.find_next(SJ)) {
|
|
|
|
if (Pair[SJ].Classification == Subscript::RDIV) {
|
|
|
|
DEBUG(dbgs() << "RDIV test\n");
|
|
|
|
if (testRDIV(Pair[SJ].Src, Pair[SJ].Dst, Result))
|
|
|
|
return NULL;
|
|
|
|
// I don't yet understand how to propagate RDIV results
|
|
|
|
Mivs.reset(SJ);
|
|
|
|
}
|
|
|
|
}
|
|
|
|
|
|
|
|
// test remaining MIVs
|
|
|
|
// This code is temporary.
|
|
|
|
// Better to somehow test all remaining subscripts simultaneously.
|
|
|
|
for (int SJ = Mivs.find_first(); SJ >= 0; SJ = Mivs.find_next(SJ)) {
|
|
|
|
if (Pair[SJ].Classification == Subscript::MIV) {
|
|
|
|
DEBUG(dbgs() << "MIV test\n");
|
|
|
|
if (testMIV(Pair[SJ].Src, Pair[SJ].Dst, Pair[SJ].Loops, Result))
|
|
|
|
return NULL;
|
|
|
|
}
|
|
|
|
else
|
|
|
|
llvm_unreachable("expected only MIV subscripts at this point");
|
|
|
|
}
|
|
|
|
|
|
|
|
// update Result.DV from constraint vector
|
|
|
|
DEBUG(dbgs() << " updating\n");
|
|
|
|
for (int SJ = ConstrainedLevels.find_first();
|
|
|
|
SJ >= 0; SJ = ConstrainedLevels.find_next(SJ)) {
|
|
|
|
updateDirection(Result.DV[SJ - 1], Constraints[SJ]);
|
|
|
|
if (Result.DV[SJ - 1].Direction == Dependence::DVEntry::NONE)
|
|
|
|
return NULL;
|
|
|
|
}
|
|
|
|
}
|
|
|
|
}
|
|
|
|
|
2012-11-29 04:30:52 +00:00
|
|
|
// Make sure the Scalar flags are set correctly.
|
dependence analysis
Patch from Preston Briggs <preston.briggs@gmail.com>.
This is an updated version of the dependence-analysis patch, including an MIV
test based on Banerjee's inequalities.
It's a fairly complete implementation of the paper
Practical Dependence Testing
Gina Goff, Ken Kennedy, and Chau-Wen Tseng
PLDI 1991
It cannot yet propagate constraints between coupled RDIV subscripts (discussed
in Section 5.3.2 of the paper).
It's organized as a FunctionPass with a single entry point that supports testing
for dependence between two instructions in a function. If there's no dependence,
it returns null. If there's a dependence, it returns a pointer to a Dependence
which can be queried about details (what kind of dependence, is it loop
independent, direction and distance vector entries, etc). I haven't included
every imaginable feature, but there's a good selection that should be adequate
for supporting many loop transformations. Of course, it can be extended as
necessary.
Included in the patch file are many test cases, commented with C code showing
the loops and array references.
git-svn-id: https://llvm.org/svn/llvm-project/llvm/trunk@165708 91177308-0d34-0410-b5e6-96231b3b80d8
2012-10-11 07:32:34 +00:00
|
|
|
SmallBitVector CompleteLoops(MaxLevels + 1);
|
|
|
|
for (unsigned SI = 0; SI < Pairs; ++SI)
|
|
|
|
CompleteLoops |= Pair[SI].Loops;
|
|
|
|
for (unsigned II = 1; II <= CommonLevels; ++II)
|
|
|
|
if (CompleteLoops[II])
|
|
|
|
Result.DV[II - 1].Scalar = false;
|
|
|
|
|
|
|
|
if (PossiblyLoopIndependent) {
|
2012-11-27 19:12:26 +00:00
|
|
|
// Make sure the LoopIndependent flag is set correctly.
|
|
|
|
// All directions must include equal, otherwise no
|
|
|
|
// loop-independent dependence is possible.
|
dependence analysis
Patch from Preston Briggs <preston.briggs@gmail.com>.
This is an updated version of the dependence-analysis patch, including an MIV
test based on Banerjee's inequalities.
It's a fairly complete implementation of the paper
Practical Dependence Testing
Gina Goff, Ken Kennedy, and Chau-Wen Tseng
PLDI 1991
It cannot yet propagate constraints between coupled RDIV subscripts (discussed
in Section 5.3.2 of the paper).
It's organized as a FunctionPass with a single entry point that supports testing
for dependence between two instructions in a function. If there's no dependence,
it returns null. If there's a dependence, it returns a pointer to a Dependence
which can be queried about details (what kind of dependence, is it loop
independent, direction and distance vector entries, etc). I haven't included
every imaginable feature, but there's a good selection that should be adequate
for supporting many loop transformations. Of course, it can be extended as
necessary.
Included in the patch file are many test cases, commented with C code showing
the loops and array references.
git-svn-id: https://llvm.org/svn/llvm-project/llvm/trunk@165708 91177308-0d34-0410-b5e6-96231b3b80d8
2012-10-11 07:32:34 +00:00
|
|
|
for (unsigned II = 1; II <= CommonLevels; ++II) {
|
|
|
|
if (!(Result.getDirection(II) & Dependence::DVEntry::EQ)) {
|
|
|
|
Result.LoopIndependent = false;
|
|
|
|
break;
|
|
|
|
}
|
|
|
|
}
|
|
|
|
}
|
2012-11-27 19:12:26 +00:00
|
|
|
else {
|
|
|
|
// On the other hand, if all directions are equal and there's no
|
|
|
|
// loop-independent dependence possible, then no dependence exists.
|
|
|
|
bool AllEqual = true;
|
|
|
|
for (unsigned II = 1; II <= CommonLevels; ++II) {
|
|
|
|
if (Result.getDirection(II) != Dependence::DVEntry::EQ) {
|
2012-11-29 04:30:52 +00:00
|
|
|
AllEqual = false;
|
|
|
|
break;
|
2012-11-27 19:12:26 +00:00
|
|
|
}
|
|
|
|
}
|
|
|
|
if (AllEqual)
|
|
|
|
return NULL;
|
|
|
|
}
|
dependence analysis
Patch from Preston Briggs <preston.briggs@gmail.com>.
This is an updated version of the dependence-analysis patch, including an MIV
test based on Banerjee's inequalities.
It's a fairly complete implementation of the paper
Practical Dependence Testing
Gina Goff, Ken Kennedy, and Chau-Wen Tseng
PLDI 1991
It cannot yet propagate constraints between coupled RDIV subscripts (discussed
in Section 5.3.2 of the paper).
It's organized as a FunctionPass with a single entry point that supports testing
for dependence between two instructions in a function. If there's no dependence,
it returns null. If there's a dependence, it returns a pointer to a Dependence
which can be queried about details (what kind of dependence, is it loop
independent, direction and distance vector entries, etc). I haven't included
every imaginable feature, but there's a good selection that should be adequate
for supporting many loop transformations. Of course, it can be extended as
necessary.
Included in the patch file are many test cases, commented with C code showing
the loops and array references.
git-svn-id: https://llvm.org/svn/llvm-project/llvm/trunk@165708 91177308-0d34-0410-b5e6-96231b3b80d8
2012-10-11 07:32:34 +00:00
|
|
|
|
|
|
|
FullDependence *Final = new FullDependence(Result);
|
|
|
|
Result.DV = NULL;
|
|
|
|
return Final;
|
|
|
|
}
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
//===----------------------------------------------------------------------===//
|
|
|
|
// getSplitIteration -
|
|
|
|
// Rather than spend rarely-used space recording the splitting iteration
|
|
|
|
// during the Weak-Crossing SIV test, we re-compute it on demand.
|
|
|
|
// The re-computation is basically a repeat of the entire dependence test,
|
|
|
|
// though simplified since we know that the dependence exists.
|
|
|
|
// It's tedious, since we must go through all propagations, etc.
|
|
|
|
//
|
2012-11-21 23:50:04 +00:00
|
|
|
// Care is required to keep this code up to date with respect to the routine
|
|
|
|
// above, depends().
|
dependence analysis
Patch from Preston Briggs <preston.briggs@gmail.com>.
This is an updated version of the dependence-analysis patch, including an MIV
test based on Banerjee's inequalities.
It's a fairly complete implementation of the paper
Practical Dependence Testing
Gina Goff, Ken Kennedy, and Chau-Wen Tseng
PLDI 1991
It cannot yet propagate constraints between coupled RDIV subscripts (discussed
in Section 5.3.2 of the paper).
It's organized as a FunctionPass with a single entry point that supports testing
for dependence between two instructions in a function. If there's no dependence,
it returns null. If there's a dependence, it returns a pointer to a Dependence
which can be queried about details (what kind of dependence, is it loop
independent, direction and distance vector entries, etc). I haven't included
every imaginable feature, but there's a good selection that should be adequate
for supporting many loop transformations. Of course, it can be extended as
necessary.
Included in the patch file are many test cases, commented with C code showing
the loops and array references.
git-svn-id: https://llvm.org/svn/llvm-project/llvm/trunk@165708 91177308-0d34-0410-b5e6-96231b3b80d8
2012-10-11 07:32:34 +00:00
|
|
|
//
|
|
|
|
// Generally, the dependence analyzer will be used to build
|
|
|
|
// a dependence graph for a function (basically a map from instructions
|
|
|
|
// to dependences). Looking for cycles in the graph shows us loops
|
|
|
|
// that cannot be trivially vectorized/parallelized.
|
|
|
|
//
|
|
|
|
// We can try to improve the situation by examining all the dependences
|
|
|
|
// that make up the cycle, looking for ones we can break.
|
|
|
|
// Sometimes, peeling the first or last iteration of a loop will break
|
|
|
|
// dependences, and we've got flags for those possibilities.
|
|
|
|
// Sometimes, splitting a loop at some other iteration will do the trick,
|
|
|
|
// and we've got a flag for that case. Rather than waste the space to
|
|
|
|
// record the exact iteration (since we rarely know), we provide
|
|
|
|
// a method that calculates the iteration. It's a drag that it must work
|
|
|
|
// from scratch, but wonderful in that it's possible.
|
|
|
|
//
|
|
|
|
// Here's an example:
|
|
|
|
//
|
|
|
|
// for (i = 0; i < 10; i++)
|
|
|
|
// A[i] = ...
|
|
|
|
// ... = A[11 - i]
|
|
|
|
//
|
|
|
|
// There's a loop-carried flow dependence from the store to the load,
|
|
|
|
// found by the weak-crossing SIV test. The dependence will have a flag,
|
|
|
|
// indicating that the dependence can be broken by splitting the loop.
|
|
|
|
// Calling getSplitIteration will return 5.
|
|
|
|
// Splitting the loop breaks the dependence, like so:
|
|
|
|
//
|
|
|
|
// for (i = 0; i <= 5; i++)
|
|
|
|
// A[i] = ...
|
|
|
|
// ... = A[11 - i]
|
|
|
|
// for (i = 6; i < 10; i++)
|
|
|
|
// A[i] = ...
|
|
|
|
// ... = A[11 - i]
|
|
|
|
//
|
|
|
|
// breaks the dependence and allows us to vectorize/parallelize
|
|
|
|
// both loops.
|
|
|
|
const SCEV *DependenceAnalysis::getSplitIteration(const Dependence *Dep,
|
|
|
|
unsigned SplitLevel) {
|
|
|
|
assert(Dep && "expected a pointer to a Dependence");
|
|
|
|
assert(Dep->isSplitable(SplitLevel) &&
|
|
|
|
"Dep should be splitable at SplitLevel");
|
2012-11-20 22:28:04 +00:00
|
|
|
Instruction *Src = Dep->getSrc();
|
|
|
|
Instruction *Dst = Dep->getDst();
|
dependence analysis
Patch from Preston Briggs <preston.briggs@gmail.com>.
This is an updated version of the dependence-analysis patch, including an MIV
test based on Banerjee's inequalities.
It's a fairly complete implementation of the paper
Practical Dependence Testing
Gina Goff, Ken Kennedy, and Chau-Wen Tseng
PLDI 1991
It cannot yet propagate constraints between coupled RDIV subscripts (discussed
in Section 5.3.2 of the paper).
It's organized as a FunctionPass with a single entry point that supports testing
for dependence between two instructions in a function. If there's no dependence,
it returns null. If there's a dependence, it returns a pointer to a Dependence
which can be queried about details (what kind of dependence, is it loop
independent, direction and distance vector entries, etc). I haven't included
every imaginable feature, but there's a good selection that should be adequate
for supporting many loop transformations. Of course, it can be extended as
necessary.
Included in the patch file are many test cases, commented with C code showing
the loops and array references.
git-svn-id: https://llvm.org/svn/llvm-project/llvm/trunk@165708 91177308-0d34-0410-b5e6-96231b3b80d8
2012-10-11 07:32:34 +00:00
|
|
|
assert(Src->mayReadFromMemory() || Src->mayWriteToMemory());
|
|
|
|
assert(Dst->mayReadFromMemory() || Dst->mayWriteToMemory());
|
|
|
|
assert(isLoadOrStore(Src));
|
|
|
|
assert(isLoadOrStore(Dst));
|
2012-11-21 23:50:04 +00:00
|
|
|
Value *SrcPtr = getPointerOperand(Src);
|
|
|
|
Value *DstPtr = getPointerOperand(Dst);
|
dependence analysis
Patch from Preston Briggs <preston.briggs@gmail.com>.
This is an updated version of the dependence-analysis patch, including an MIV
test based on Banerjee's inequalities.
It's a fairly complete implementation of the paper
Practical Dependence Testing
Gina Goff, Ken Kennedy, and Chau-Wen Tseng
PLDI 1991
It cannot yet propagate constraints between coupled RDIV subscripts (discussed
in Section 5.3.2 of the paper).
It's organized as a FunctionPass with a single entry point that supports testing
for dependence between two instructions in a function. If there's no dependence,
it returns null. If there's a dependence, it returns a pointer to a Dependence
which can be queried about details (what kind of dependence, is it loop
independent, direction and distance vector entries, etc). I haven't included
every imaginable feature, but there's a good selection that should be adequate
for supporting many loop transformations. Of course, it can be extended as
necessary.
Included in the patch file are many test cases, commented with C code showing
the loops and array references.
git-svn-id: https://llvm.org/svn/llvm-project/llvm/trunk@165708 91177308-0d34-0410-b5e6-96231b3b80d8
2012-10-11 07:32:34 +00:00
|
|
|
assert(underlyingObjectsAlias(AA, DstPtr, SrcPtr) ==
|
|
|
|
AliasAnalysis::MustAlias);
|
|
|
|
|
|
|
|
// establish loop nesting levels
|
|
|
|
establishNestingLevels(Src, Dst);
|
|
|
|
|
|
|
|
FullDependence Result(Src, Dst, false, CommonLevels);
|
|
|
|
|
2012-11-21 23:50:04 +00:00
|
|
|
// See if there are GEPs we can use.
|
|
|
|
bool UsefulGEP = false;
|
|
|
|
GEPOperator *SrcGEP = dyn_cast<GEPOperator>(SrcPtr);
|
|
|
|
GEPOperator *DstGEP = dyn_cast<GEPOperator>(DstPtr);
|
|
|
|
if (SrcGEP && DstGEP &&
|
|
|
|
SrcGEP->getPointerOperandType() == DstGEP->getPointerOperandType()) {
|
|
|
|
const SCEV *SrcPtrSCEV = SE->getSCEV(SrcGEP->getPointerOperand());
|
|
|
|
const SCEV *DstPtrSCEV = SE->getSCEV(DstGEP->getPointerOperand());
|
|
|
|
UsefulGEP =
|
|
|
|
isLoopInvariant(SrcPtrSCEV, LI->getLoopFor(Src->getParent())) &&
|
|
|
|
isLoopInvariant(DstPtrSCEV, LI->getLoopFor(Dst->getParent()));
|
|
|
|
}
|
|
|
|
unsigned Pairs = UsefulGEP ? SrcGEP->idx_end() - SrcGEP->idx_begin() : 1;
|
dependence analysis
Patch from Preston Briggs <preston.briggs@gmail.com>.
This is an updated version of the dependence-analysis patch, including an MIV
test based on Banerjee's inequalities.
It's a fairly complete implementation of the paper
Practical Dependence Testing
Gina Goff, Ken Kennedy, and Chau-Wen Tseng
PLDI 1991
It cannot yet propagate constraints between coupled RDIV subscripts (discussed
in Section 5.3.2 of the paper).
It's organized as a FunctionPass with a single entry point that supports testing
for dependence between two instructions in a function. If there's no dependence,
it returns null. If there's a dependence, it returns a pointer to a Dependence
which can be queried about details (what kind of dependence, is it loop
independent, direction and distance vector entries, etc). I haven't included
every imaginable feature, but there's a good selection that should be adequate
for supporting many loop transformations. Of course, it can be extended as
necessary.
Included in the patch file are many test cases, commented with C code showing
the loops and array references.
git-svn-id: https://llvm.org/svn/llvm-project/llvm/trunk@165708 91177308-0d34-0410-b5e6-96231b3b80d8
2012-10-11 07:32:34 +00:00
|
|
|
SmallVector<Subscript, 4> Pair(Pairs);
|
2012-11-21 23:50:04 +00:00
|
|
|
if (UsefulGEP) {
|
|
|
|
unsigned P = 0;
|
|
|
|
for (GEPOperator::const_op_iterator SrcIdx = SrcGEP->idx_begin(),
|
|
|
|
SrcEnd = SrcGEP->idx_end(),
|
|
|
|
DstIdx = DstGEP->idx_begin();
|
|
|
|
SrcIdx != SrcEnd;
|
|
|
|
++SrcIdx, ++DstIdx, ++P) {
|
|
|
|
Pair[P].Src = SE->getSCEV(*SrcIdx);
|
|
|
|
Pair[P].Dst = SE->getSCEV(*DstIdx);
|
|
|
|
}
|
|
|
|
}
|
|
|
|
else {
|
|
|
|
const SCEV *SrcSCEV = SE->getSCEV(SrcPtr);
|
|
|
|
const SCEV *DstSCEV = SE->getSCEV(DstPtr);
|
|
|
|
Pair[0].Src = SrcSCEV;
|
|
|
|
Pair[0].Dst = DstSCEV;
|
|
|
|
}
|
|
|
|
|
|
|
|
for (unsigned P = 0; P < Pairs; ++P) {
|
|
|
|
Pair[P].Loops.resize(MaxLevels + 1);
|
|
|
|
Pair[P].GroupLoops.resize(MaxLevels + 1);
|
|
|
|
Pair[P].Group.resize(Pairs);
|
|
|
|
removeMatchingExtensions(&Pair[P]);
|
|
|
|
Pair[P].Classification =
|
|
|
|
classifyPair(Pair[P].Src, LI->getLoopFor(Src->getParent()),
|
|
|
|
Pair[P].Dst, LI->getLoopFor(Dst->getParent()),
|
|
|
|
Pair[P].Loops);
|
|
|
|
Pair[P].GroupLoops = Pair[P].Loops;
|
|
|
|
Pair[P].Group.set(P);
|
dependence analysis
Patch from Preston Briggs <preston.briggs@gmail.com>.
This is an updated version of the dependence-analysis patch, including an MIV
test based on Banerjee's inequalities.
It's a fairly complete implementation of the paper
Practical Dependence Testing
Gina Goff, Ken Kennedy, and Chau-Wen Tseng
PLDI 1991
It cannot yet propagate constraints between coupled RDIV subscripts (discussed
in Section 5.3.2 of the paper).
It's organized as a FunctionPass with a single entry point that supports testing
for dependence between two instructions in a function. If there's no dependence,
it returns null. If there's a dependence, it returns a pointer to a Dependence
which can be queried about details (what kind of dependence, is it loop
independent, direction and distance vector entries, etc). I haven't included
every imaginable feature, but there's a good selection that should be adequate
for supporting many loop transformations. Of course, it can be extended as
necessary.
Included in the patch file are many test cases, commented with C code showing
the loops and array references.
git-svn-id: https://llvm.org/svn/llvm-project/llvm/trunk@165708 91177308-0d34-0410-b5e6-96231b3b80d8
2012-10-11 07:32:34 +00:00
|
|
|
}
|
|
|
|
|
|
|
|
SmallBitVector Separable(Pairs);
|
|
|
|
SmallBitVector Coupled(Pairs);
|
|
|
|
|
|
|
|
// partition subscripts into separable and minimally-coupled groups
|
|
|
|
for (unsigned SI = 0; SI < Pairs; ++SI) {
|
|
|
|
if (Pair[SI].Classification == Subscript::NonLinear) {
|
|
|
|
// ignore these, but collect loops for later
|
|
|
|
collectCommonLoops(Pair[SI].Src,
|
|
|
|
LI->getLoopFor(Src->getParent()),
|
|
|
|
Pair[SI].Loops);
|
|
|
|
collectCommonLoops(Pair[SI].Dst,
|
|
|
|
LI->getLoopFor(Dst->getParent()),
|
|
|
|
Pair[SI].Loops);
|
|
|
|
Result.Consistent = false;
|
|
|
|
}
|
|
|
|
else if (Pair[SI].Classification == Subscript::ZIV)
|
|
|
|
Separable.set(SI);
|
|
|
|
else {
|
|
|
|
// SIV, RDIV, or MIV, so check for coupled group
|
|
|
|
bool Done = true;
|
|
|
|
for (unsigned SJ = SI + 1; SJ < Pairs; ++SJ) {
|
|
|
|
SmallBitVector Intersection = Pair[SI].GroupLoops;
|
|
|
|
Intersection &= Pair[SJ].GroupLoops;
|
|
|
|
if (Intersection.any()) {
|
|
|
|
// accumulate set of all the loops in group
|
|
|
|
Pair[SJ].GroupLoops |= Pair[SI].GroupLoops;
|
|
|
|
// accumulate set of all subscripts in group
|
|
|
|
Pair[SJ].Group |= Pair[SI].Group;
|
|
|
|
Done = false;
|
|
|
|
}
|
|
|
|
}
|
|
|
|
if (Done) {
|
|
|
|
if (Pair[SI].Group.count() == 1)
|
|
|
|
Separable.set(SI);
|
|
|
|
else
|
|
|
|
Coupled.set(SI);
|
|
|
|
}
|
|
|
|
}
|
|
|
|
}
|
|
|
|
|
|
|
|
Constraint NewConstraint;
|
|
|
|
NewConstraint.setAny(SE);
|
|
|
|
|
|
|
|
// test separable subscripts
|
|
|
|
for (int SI = Separable.find_first(); SI >= 0; SI = Separable.find_next(SI)) {
|
|
|
|
switch (Pair[SI].Classification) {
|
|
|
|
case Subscript::SIV: {
|
|
|
|
unsigned Level;
|
|
|
|
const SCEV *SplitIter = NULL;
|
|
|
|
(void) testSIV(Pair[SI].Src, Pair[SI].Dst, Level,
|
|
|
|
Result, NewConstraint, SplitIter);
|
|
|
|
if (Level == SplitLevel) {
|
|
|
|
assert(SplitIter != NULL);
|
|
|
|
return SplitIter;
|
|
|
|
}
|
|
|
|
break;
|
|
|
|
}
|
|
|
|
case Subscript::ZIV:
|
|
|
|
case Subscript::RDIV:
|
|
|
|
case Subscript::MIV:
|
|
|
|
break;
|
|
|
|
default:
|
|
|
|
llvm_unreachable("subscript has unexpected classification");
|
|
|
|
}
|
|
|
|
}
|
|
|
|
|
|
|
|
if (Coupled.count()) {
|
|
|
|
// test coupled subscript groups
|
|
|
|
SmallVector<Constraint, 4> Constraints(MaxLevels + 1);
|
|
|
|
for (unsigned II = 0; II <= MaxLevels; ++II)
|
|
|
|
Constraints[II].setAny(SE);
|
|
|
|
for (int SI = Coupled.find_first(); SI >= 0; SI = Coupled.find_next(SI)) {
|
|
|
|
SmallBitVector Group(Pair[SI].Group);
|
|
|
|
SmallBitVector Sivs(Pairs);
|
|
|
|
SmallBitVector Mivs(Pairs);
|
|
|
|
SmallBitVector ConstrainedLevels(MaxLevels + 1);
|
|
|
|
for (int SJ = Group.find_first(); SJ >= 0; SJ = Group.find_next(SJ)) {
|
|
|
|
if (Pair[SJ].Classification == Subscript::SIV)
|
|
|
|
Sivs.set(SJ);
|
|
|
|
else
|
|
|
|
Mivs.set(SJ);
|
|
|
|
}
|
|
|
|
while (Sivs.any()) {
|
|
|
|
bool Changed = false;
|
|
|
|
for (int SJ = Sivs.find_first(); SJ >= 0; SJ = Sivs.find_next(SJ)) {
|
|
|
|
// SJ is an SIV subscript that's part of the current coupled group
|
|
|
|
unsigned Level;
|
|
|
|
const SCEV *SplitIter = NULL;
|
|
|
|
(void) testSIV(Pair[SJ].Src, Pair[SJ].Dst, Level,
|
|
|
|
Result, NewConstraint, SplitIter);
|
|
|
|
if (Level == SplitLevel && SplitIter)
|
|
|
|
return SplitIter;
|
|
|
|
ConstrainedLevels.set(Level);
|
|
|
|
if (intersectConstraints(&Constraints[Level], &NewConstraint))
|
|
|
|
Changed = true;
|
|
|
|
Sivs.reset(SJ);
|
|
|
|
}
|
|
|
|
if (Changed) {
|
|
|
|
// propagate, possibly creating new SIVs and ZIVs
|
|
|
|
for (int SJ = Mivs.find_first(); SJ >= 0; SJ = Mivs.find_next(SJ)) {
|
|
|
|
// SJ is an MIV subscript that's part of the current coupled group
|
|
|
|
if (propagate(Pair[SJ].Src, Pair[SJ].Dst,
|
|
|
|
Pair[SJ].Loops, Constraints, Result.Consistent)) {
|
|
|
|
Pair[SJ].Classification =
|
|
|
|
classifyPair(Pair[SJ].Src, LI->getLoopFor(Src->getParent()),
|
|
|
|
Pair[SJ].Dst, LI->getLoopFor(Dst->getParent()),
|
|
|
|
Pair[SJ].Loops);
|
|
|
|
switch (Pair[SJ].Classification) {
|
|
|
|
case Subscript::ZIV:
|
|
|
|
Mivs.reset(SJ);
|
|
|
|
break;
|
|
|
|
case Subscript::SIV:
|
|
|
|
Sivs.set(SJ);
|
|
|
|
Mivs.reset(SJ);
|
|
|
|
break;
|
|
|
|
case Subscript::RDIV:
|
|
|
|
case Subscript::MIV:
|
|
|
|
break;
|
|
|
|
default:
|
|
|
|
llvm_unreachable("bad subscript classification");
|
|
|
|
}
|
|
|
|
}
|
|
|
|
}
|
|
|
|
}
|
|
|
|
}
|
|
|
|
}
|
|
|
|
}
|
|
|
|
llvm_unreachable("somehow reached end of routine");
|
|
|
|
return NULL;
|
|
|
|
}
|