llvm-6502/lib/Analysis/DivergenceAnalysis.cpp
Jingyue Wu 5733100450 Divergence analysis for GPU programs
Summary:
Some optimizations such as jump threading and loop unswitching can negatively
affect performance when applied to divergent branches. The divergence analysis
added in this patch conservatively estimates which branches in a GPU program
can diverge. This information can then help LLVM to run certain optimizations
selectively.

Test Plan: test/Analysis/DivergenceAnalysis/NVPTX/diverge.ll

Reviewers: resistor, hfinkel, eliben, meheff, jholewinski

Subscribers: broune, bjarke.roune, madhur13490, tstellarAMD, dberlin, echristo, jholewinski, llvm-commits

Differential Revision: http://reviews.llvm.org/D8576

git-svn-id: https://llvm.org/svn/llvm-project/llvm/trunk@234567 91177308-0d34-0410-b5e6-96231b3b80d8
2015-04-10 05:03:50 +00:00

338 lines
12 KiB
C++

//===- DivergenceAnalysis.cpp ------ Divergence Analysis ------------------===//
//
// The LLVM Compiler Infrastructure
//
// This file is distributed under the University of Illinois Open Source
// License. See LICENSE.TXT for details.
//
//===----------------------------------------------------------------------===//
//
// This file defines divergence analysis which determines whether a branch in a
// GPU program is divergent. It can help branch optimizations such as jump
// threading and loop unswitching to make better decisions.
//
// GPU programs typically use the SIMD execution model, where multiple threads
// in the same execution group have to execute in lock-step. Therefore, if the
// code contains divergent branches (i.e., threads in a group do not agree on
// which path of the branch to take), the group of threads has to execute all
// the paths from that branch with different subsets of threads enabled until
// they converge at the immediately post-dominating BB of the paths.
//
// Due to this execution model, some optimizations such as jump
// threading and loop unswitching can be unfortunately harmful when performed on
// divergent branches. Therefore, an analysis that computes which branches in a
// GPU program are divergent can help the compiler to selectively run these
// optimizations.
//
// This file defines divergence analysis which computes a conservative but
// non-trivial approximation of all divergent branches in a GPU program. It
// partially implements the approach described in
//
// Divergence Analysis
// Sampaio, Souza, Collange, Pereira
// TOPLAS '13
//
// The divergence analysis identifies the sources of divergence (e.g., special
// variables that hold the thread ID), and recursively marks variables that are
// data or sync dependent on a source of divergence as divergent.
//
// While data dependency is a well-known concept, the notion of sync dependency
// is worth more explanation. Sync dependence characterizes the control flow
// aspect of the propagation of branch divergence. For example,
//
// %cond = icmp slt i32 %tid, 10
// br i1 %cond, label %then, label %else
// then:
// br label %merge
// else:
// br label %merge
// merge:
// %a = phi i32 [ 0, %then ], [ 1, %else ]
//
// Suppose %tid holds the thread ID. Although %a is not data dependent on %tid
// because %tid is not on its use-def chains, %a is sync dependent on %tid
// because the branch "br i1 %cond" depends on %tid and affects which value %a
// is assigned to.
//
// The current implementation has the following limitations:
// 1. intra-procedural. It conservatively considers the arguments of a
// non-kernel-entry function and the return value of a function call as
// divergent.
// 2. memory as black box. It conservatively considers values loaded from
// generic or local address as divergent. This can be improved by leveraging
// pointer analysis.
//===----------------------------------------------------------------------===//
#include <vector>
#include "llvm/IR/Dominators.h"
#include "llvm/ADT/DenseSet.h"
#include "llvm/Analysis/Passes.h"
#include "llvm/Analysis/PostDominators.h"
#include "llvm/Analysis/TargetTransformInfo.h"
#include "llvm/IR/Function.h"
#include "llvm/IR/InstIterator.h"
#include "llvm/IR/Instructions.h"
#include "llvm/IR/IntrinsicInst.h"
#include "llvm/IR/Value.h"
#include "llvm/Pass.h"
#include "llvm/Support/CommandLine.h"
#include "llvm/Support/Debug.h"
#include "llvm/Support/raw_ostream.h"
#include "llvm/Transforms/Scalar.h"
using namespace llvm;
#define DEBUG_TYPE "divergence"
namespace {
class DivergenceAnalysis : public FunctionPass {
public:
static char ID;
DivergenceAnalysis() : FunctionPass(ID) {
initializeDivergenceAnalysisPass(*PassRegistry::getPassRegistry());
}
void getAnalysisUsage(AnalysisUsage &AU) const override {
AU.addRequired<DominatorTreeWrapperPass>();
AU.addRequired<PostDominatorTree>();
AU.setPreservesAll();
}
bool runOnFunction(Function &F) override;
// Print all divergent branches in the function.
void print(raw_ostream &OS, const Module *) const override;
// Returns true if V is divergent.
bool isDivergent(const Value *V) const { return DivergentValues.count(V); }
// Returns true if V is uniform/non-divergent.
bool isUniform(const Value *V) const { return !isDivergent(V); }
private:
// Stores all divergent values.
DenseSet<const Value *> DivergentValues;
};
} // End of anonymous namespace
// Register this pass.
char DivergenceAnalysis::ID = 0;
INITIALIZE_PASS_BEGIN(DivergenceAnalysis, "divergence", "Divergence Analysis",
false, true)
INITIALIZE_PASS_DEPENDENCY(DominatorTreeWrapperPass)
INITIALIZE_PASS_DEPENDENCY(PostDominatorTree)
INITIALIZE_PASS_END(DivergenceAnalysis, "divergence", "Divergence Analysis",
false, true)
namespace {
class DivergencePropagator {
public:
DivergencePropagator(Function &F, TargetTransformInfo &TTI,
DominatorTree &DT, PostDominatorTree &PDT,
DenseSet<const Value *> &DV)
: F(F), TTI(TTI), DT(DT), PDT(PDT), DV(DV) {}
void populateWithSourcesOfDivergence();
void propagate();
private:
// A helper function that explores data dependents of V.
void exploreDataDependency(Value *V);
// A helper function that explores sync dependents of TI.
void exploreSyncDependency(TerminatorInst *TI);
// Computes the influence region from Start to End. This region includes all
// basic blocks on any path from Start to End.
void computeInfluenceRegion(BasicBlock *Start, BasicBlock *End,
DenseSet<BasicBlock *> &InfluenceRegion);
// Finds all users of I that are outside the influence region, and add these
// users to Worklist.
void findUsersOutsideInfluenceRegion(
Instruction &I, const DenseSet<BasicBlock *> &InfluenceRegion);
Function &F;
TargetTransformInfo &TTI;
DominatorTree &DT;
PostDominatorTree &PDT;
std::vector<Value *> Worklist; // Stack for DFS.
DenseSet<const Value *> &DV; // Stores all divergent values.
};
void DivergencePropagator::populateWithSourcesOfDivergence() {
Worklist.clear();
DV.clear();
for (auto &I : inst_range(F)) {
if (TTI.isSourceOfDivergence(&I)) {
Worklist.push_back(&I);
DV.insert(&I);
}
}
for (auto &Arg : F.args()) {
if (TTI.isSourceOfDivergence(&Arg)) {
Worklist.push_back(&Arg);
DV.insert(&Arg);
}
}
}
void DivergencePropagator::exploreSyncDependency(TerminatorInst *TI) {
// Propagation rule 1: if branch TI is divergent, all PHINodes in TI's
// immediate post dominator are divergent. This rule handles if-then-else
// patterns. For example,
//
// if (tid < 5)
// a1 = 1;
// else
// a2 = 2;
// a = phi(a1, a2); // sync dependent on (tid < 5)
BasicBlock *ThisBB = TI->getParent();
BasicBlock *IPostDom = PDT.getNode(ThisBB)->getIDom()->getBlock();
if (IPostDom == nullptr)
return;
for (auto I = IPostDom->begin(); isa<PHINode>(I); ++I) {
// A PHINode is uniform if it returns the same value no matter which path is
// taken.
if (!cast<PHINode>(I)->hasConstantValue() && DV.insert(I).second)
Worklist.push_back(I);
}
// Propagation rule 2: if a value defined in a loop is used outside, the user
// is sync dependent on the condition of the loop exits that dominate the
// user. For example,
//
// int i = 0;
// do {
// i++;
// if (foo(i)) ... // uniform
// } while (i < tid);
// if (bar(i)) ... // divergent
//
// A program may contain unstructured loops. Therefore, we cannot leverage
// LoopInfo, which only recognizes natural loops.
//
// The algorithm used here handles both natural and unstructured loops. Given
// a branch TI, we first compute its influence region, the union of all simple
// paths from TI to its immediate post dominator (IPostDom). Then, we search
// for all the values defined in the influence region but used outside. All
// these users are sync dependent on TI.
DenseSet<BasicBlock *> InfluenceRegion;
computeInfluenceRegion(ThisBB, IPostDom, InfluenceRegion);
// An insight that can speed up the search process is that all the in-region
// values that are used outside must dominate TI. Therefore, instead of
// searching every basic blocks in the influence region, we search all the
// dominators of TI until it is outside the influence region.
BasicBlock *InfluencedBB = ThisBB;
while (InfluenceRegion.count(InfluencedBB)) {
for (auto &I : *InfluencedBB)
findUsersOutsideInfluenceRegion(I, InfluenceRegion);
DomTreeNode *IDomNode = DT.getNode(InfluencedBB)->getIDom();
if (IDomNode == nullptr)
break;
InfluencedBB = IDomNode->getBlock();
}
}
void DivergencePropagator::findUsersOutsideInfluenceRegion(
Instruction &I, const DenseSet<BasicBlock *> &InfluenceRegion) {
for (User *U : I.users()) {
Instruction *UserInst = cast<Instruction>(U);
if (!InfluenceRegion.count(UserInst->getParent())) {
if (DV.insert(UserInst).second)
Worklist.push_back(UserInst);
}
}
}
void DivergencePropagator::computeInfluenceRegion(
BasicBlock *Start, BasicBlock *End,
DenseSet<BasicBlock *> &InfluenceRegion) {
assert(PDT.properlyDominates(End, Start) &&
"End does not properly dominate Start");
std::vector<BasicBlock *> InfluenceStack;
InfluenceStack.push_back(Start);
InfluenceRegion.insert(Start);
while (!InfluenceStack.empty()) {
BasicBlock *BB = InfluenceStack.back();
InfluenceStack.pop_back();
for (BasicBlock *Succ : successors(BB)) {
if (End != Succ && InfluenceRegion.insert(Succ).second)
InfluenceStack.push_back(Succ);
}
}
}
void DivergencePropagator::exploreDataDependency(Value *V) {
// Follow def-use chains of V.
for (User *U : V->users()) {
Instruction *UserInst = cast<Instruction>(U);
if (DV.insert(UserInst).second)
Worklist.push_back(UserInst);
}
}
void DivergencePropagator::propagate() {
// Traverse the dependency graph using DFS.
while (!Worklist.empty()) {
Value *V = Worklist.back();
Worklist.pop_back();
if (TerminatorInst *TI = dyn_cast<TerminatorInst>(V)) {
// Terminators with less than two successors won't introduce sync
// dependency. Ignore them.
if (TI->getNumSuccessors() > 1)
exploreSyncDependency(TI);
}
exploreDataDependency(V);
}
}
} /// end namespace anonymous
FunctionPass *llvm::createDivergenceAnalysisPass() {
return new DivergenceAnalysis();
}
bool DivergenceAnalysis::runOnFunction(Function &F) {
auto *TTIWP = getAnalysisIfAvailable<TargetTransformInfoWrapperPass>();
if (TTIWP == nullptr)
return false;
TargetTransformInfo &TTI = TTIWP->getTTI(F);
// Fast path: if the target does not have branch divergence, we do not mark
// any branch as divergent.
if (!TTI.hasBranchDivergence())
return false;
DivergentValues.clear();
DivergencePropagator DP(F, TTI,
getAnalysis<DominatorTreeWrapperPass>().getDomTree(),
getAnalysis<PostDominatorTree>(), DivergentValues);
DP.populateWithSourcesOfDivergence();
DP.propagate();
return false;
}
void DivergenceAnalysis::print(raw_ostream &OS, const Module *) const {
if (DivergentValues.empty())
return;
const Value *FirstDivergentValue = *DivergentValues.begin();
const Function *F;
if (const Argument *Arg = dyn_cast<Argument>(FirstDivergentValue)) {
F = Arg->getParent();
} else if (const Instruction *I =
dyn_cast<Instruction>(FirstDivergentValue)) {
F = I->getParent()->getParent();
} else {
llvm_unreachable("Only arguments and instructions can be divergent");
}
// Dumps all divergent values in F, arguments and then instructions.
for (auto &Arg : F->args()) {
if (DivergentValues.count(&Arg))
OS << "DIVERGENT: " << Arg << "\n";
}
// Iterate instructions using inst_range to ensure a deterministic order.
for (auto &I : inst_range(F)) {
if (DivergentValues.count(&I))
OS << "DIVERGENT:" << I << "\n";
}
}