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llvm-mirror/lib/Analysis/DivergenceAnalysis.cpp
Nicolai Haehnle bbd04b8c49 [DivergenceAnalysis] Treat PHI with incoming undef as constant
Summary:
If a PHI has an incoming undef, we can pretend that it is equal to one
non-undef, non-self incoming value.

This is particularly relevant in combination with the StructurizeCFG
pass, which introduces PHI nodes with undefs. Previously, this lead to
branch conditions that were uniform before StructurizeCFG to become
non-uniform afterwards, which confused the SIAnnotateControlFlow
pass.

This fixes a crash when Mesa radeonsi compiles a shader from
dEQP-GLES3.functional.shaders.switch.switch_in_for_loop_dynamic_vertex

Reviewers: arsenm, tstellarAMD, jingyue

Subscribers: llvm-commits

Differential Revision: http://reviews.llvm.org/D19013

llvm-svn: 266347
2016-04-14 17:42:47 +00:00

321 lines
12 KiB
C++

//===- DivergenceAnalysis.cpp --------- Divergence Analysis Implementation -==//
//
// The LLVM Compiler Infrastructure
//
// This file is distributed under the University of Illinois Open Source
// License. See LICENSE.TXT for details.
//
//===----------------------------------------------------------------------===//
//
// 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/Analysis/DivergenceAnalysis.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/IntrinsicInst.h"
#include "llvm/IR/Value.h"
#include "llvm/Support/CommandLine.h"
#include "llvm/Support/Debug.h"
#include "llvm/Support/raw_ostream.h"
#include <vector>
using namespace llvm;
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 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(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)->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 (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
// Register this pass.
char DivergenceAnalysis::ID = 0;
INITIALIZE_PASS_BEGIN(DivergenceAnalysis, "divergence", "Divergence Analysis",
false, true)
INITIALIZE_PASS_DEPENDENCY(DominatorTreeWrapperPass)
INITIALIZE_PASS_DEPENDENCY(PostDominatorTreeWrapperPass)
INITIALIZE_PASS_END(DivergenceAnalysis, "divergence", "Divergence Analysis",
false, true)
FunctionPass *llvm::createDivergenceAnalysisPass() {
return new DivergenceAnalysis();
}
void DivergenceAnalysis::getAnalysisUsage(AnalysisUsage &AU) const {
AU.addRequired<DominatorTreeWrapperPass>();
AU.addRequired<PostDominatorTreeWrapperPass>();
AU.setPreservesAll();
}
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();
auto &PDT = getAnalysis<PostDominatorTreeWrapperPass>().getPostDomTree();
DivergencePropagator DP(F, TTI,
getAnalysis<DominatorTreeWrapperPass>().getDomTree(),
PDT, 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 instructions() to ensure a deterministic order.
for (auto &I : instructions(F)) {
if (DivergentValues.count(&I))
OS << "DIVERGENT:" << I << "\n";
}
}