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llvm-mirror/lib/Analysis/LegacyDivergenceAnalysis.cpp
Chandler Carruth ae65e281f3 Update the file headers across all of the LLVM projects in the monorepo
to reflect the new license.

We understand that people may be surprised that we're moving the header
entirely to discuss the new license. We checked this carefully with the
Foundation's lawyer and we believe this is the correct approach.

Essentially, all code in the project is now made available by the LLVM
project under our new license, so you will see that the license headers
include that license only. Some of our contributors have contributed
code under our old license, and accordingly, we have retained a copy of
our old license notice in the top-level files in each project and
repository.

llvm-svn: 351636
2019-01-19 08:50:56 +00:00

391 lines
14 KiB
C++

//===- LegacyDivergenceAnalysis.cpp --------- Legacy Divergence Analysis
//Implementation -==//
//
// Part of the LLVM Project, under the Apache License v2.0 with LLVM Exceptions.
// See https://llvm.org/LICENSE.txt for license information.
// SPDX-License-Identifier: Apache-2.0 WITH LLVM-exception
//
//===----------------------------------------------------------------------===//
//
// This file implements 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 "llvm/ADT/PostOrderIterator.h"
#include "llvm/Analysis/CFG.h"
#include "llvm/Analysis/DivergenceAnalysis.h"
#include "llvm/Analysis/LegacyDivergenceAnalysis.h"
#include "llvm/Analysis/Passes.h"
#include "llvm/Analysis/PostDominators.h"
#include "llvm/Analysis/TargetTransformInfo.h"
#include "llvm/IR/Dominators.h"
#include "llvm/IR/InstIterator.h"
#include "llvm/IR/Instructions.h"
#include "llvm/IR/Value.h"
#include "llvm/Support/Debug.h"
#include "llvm/Support/raw_ostream.h"
#include <vector>
using namespace llvm;
#define DEBUG_TYPE "divergence"
// transparently use the GPUDivergenceAnalysis
static cl::opt<bool> UseGPUDA("use-gpu-divergence-analysis", cl::init(false),
cl::Hidden,
cl::desc("turn the LegacyDivergenceAnalysis into "
"a wrapper for GPUDivergenceAnalysis"));
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(Instruction *TI);
// Computes the influence region from Start to End. This region includes all
// basic blocks on any simple 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 : instructions(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(Instruction *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();
// Unreachable blocks may not be in the dominator tree.
if (!DT.isReachableFromEntry(ThisBB))
return;
// If the function has no exit blocks or doesn't reach any exit blocks, the
// post dominator may be null.
DomTreeNode *ThisNode = PDT.getNode(ThisBB);
if (!ThisNode)
return;
BasicBlock *IPostDom = ThisNode->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)->hasConstantOrUndefValue() && 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);
}
}
}
// A helper function for computeInfluenceRegion that adds successors of "ThisBB"
// to the influence region.
static void
addSuccessorsToInfluenceRegion(BasicBlock *ThisBB, BasicBlock *End,
DenseSet<BasicBlock *> &InfluenceRegion,
std::vector<BasicBlock *> &InfluenceStack) {
for (BasicBlock *Succ : successors(ThisBB)) {
if (Succ != End && InfluenceRegion.insert(Succ).second)
InfluenceStack.push_back(Succ);
}
}
void DivergencePropagator::computeInfluenceRegion(
BasicBlock *Start, BasicBlock *End,
DenseSet<BasicBlock *> &InfluenceRegion) {
assert(PDT.properlyDominates(End, Start) &&
"End does not properly dominate Start");
// The influence region starts from the end of "Start" to the beginning of
// "End". Therefore, "Start" should not be in the region unless "Start" is in
// a loop that doesn't contain "End".
std::vector<BasicBlock *> InfluenceStack;
addSuccessorsToInfluenceRegion(Start, End, InfluenceRegion, InfluenceStack);
while (!InfluenceStack.empty()) {
BasicBlock *BB = InfluenceStack.back();
InfluenceStack.pop_back();
addSuccessorsToInfluenceRegion(BB, End, InfluenceRegion, InfluenceStack);
}
}
void DivergencePropagator::exploreDataDependency(Value *V) {
// Follow def-use chains of V.
for (User *U : V->users()) {
Instruction *UserInst = cast<Instruction>(U);
if (!TTI.isAlwaysUniform(U) && 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 (Instruction *I = dyn_cast<Instruction>(V)) {
// Terminators with less than two successors won't introduce sync
// dependency. Ignore them.
if (I->isTerminator() && I->getNumSuccessors() > 1)
exploreSyncDependency(I);
}
exploreDataDependency(V);
}
}
} // namespace
// Register this pass.
char LegacyDivergenceAnalysis::ID = 0;
INITIALIZE_PASS_BEGIN(LegacyDivergenceAnalysis, "divergence",
"Legacy Divergence Analysis", false, true)
INITIALIZE_PASS_DEPENDENCY(DominatorTreeWrapperPass)
INITIALIZE_PASS_DEPENDENCY(PostDominatorTreeWrapperPass)
INITIALIZE_PASS_DEPENDENCY(LoopInfoWrapperPass)
INITIALIZE_PASS_END(LegacyDivergenceAnalysis, "divergence",
"Legacy Divergence Analysis", false, true)
FunctionPass *llvm::createLegacyDivergenceAnalysisPass() {
return new LegacyDivergenceAnalysis();
}
void LegacyDivergenceAnalysis::getAnalysisUsage(AnalysisUsage &AU) const {
AU.addRequired<DominatorTreeWrapperPass>();
AU.addRequired<PostDominatorTreeWrapperPass>();
if (UseGPUDA)
AU.addRequired<LoopInfoWrapperPass>();
AU.setPreservesAll();
}
bool LegacyDivergenceAnalysis::shouldUseGPUDivergenceAnalysis(
const Function &F) const {
if (!UseGPUDA)
return false;
// GPUDivergenceAnalysis requires a reducible CFG.
auto &LI = getAnalysis<LoopInfoWrapperPass>().getLoopInfo();
using RPOTraversal = ReversePostOrderTraversal<const Function *>;
RPOTraversal FuncRPOT(&F);
return !containsIrreducibleCFG<const BasicBlock *, const RPOTraversal,
const LoopInfo>(FuncRPOT, LI);
}
bool LegacyDivergenceAnalysis::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();
gpuDA = nullptr;
auto &DT = getAnalysis<DominatorTreeWrapperPass>().getDomTree();
auto &PDT = getAnalysis<PostDominatorTreeWrapperPass>().getPostDomTree();
if (shouldUseGPUDivergenceAnalysis(F)) {
// run the new GPU divergence analysis
auto &LI = getAnalysis<LoopInfoWrapperPass>().getLoopInfo();
gpuDA = llvm::make_unique<GPUDivergenceAnalysis>(F, DT, PDT, LI, TTI);
} else {
// run LLVM's existing DivergenceAnalysis
DivergencePropagator DP(F, TTI, DT, PDT, DivergentValues);
DP.populateWithSourcesOfDivergence();
DP.propagate();
}
LLVM_DEBUG(dbgs() << "\nAfter divergence analysis on " << F.getName()
<< ":\n";
print(dbgs(), F.getParent()));
return false;
}
bool LegacyDivergenceAnalysis::isDivergent(const Value *V) const {
if (gpuDA) {
return gpuDA->isDivergent(*V);
}
return DivergentValues.count(V);
}
void LegacyDivergenceAnalysis::print(raw_ostream &OS, const Module *) const {
if ((!gpuDA || !gpuDA->hasDivergence()) && DivergentValues.empty())
return;
const Function *F = nullptr;
if (!DivergentValues.empty()) {
const Value *FirstDivergentValue = *DivergentValues.begin();
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");
}
} else if (gpuDA) {
F = &gpuDA->getFunction();
}
if (!F)
return;
// Dumps all divergent values in F, arguments and then instructions.
for (auto &Arg : F->args()) {
OS << (isDivergent(&Arg) ? "DIVERGENT: " : " ");
OS << Arg << "\n";
}
// Iterate instructions using instructions() to ensure a deterministic order.
for (auto BI = F->begin(), BE = F->end(); BI != BE; ++BI) {
auto &BB = *BI;
OS << "\n " << BB.getName() << ":\n";
for (auto &I : BB.instructionsWithoutDebug()) {
OS << (isDivergent(&I) ? "DIVERGENT: " : " ");
OS << I << "\n";
}
}
OS << "\n";
}