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	The patch is generated using this command: tools/clang/tools/extra/clang-tidy/tool/run-clang-tidy.py -fix \ -checks=-*,llvm-namespace-comment -header-filter='llvm/.*|clang/.*' \ llvm/lib/ Thanks to Eugene Kosov for the original patch! git-svn-id: https://llvm.org/svn/llvm-project/llvm/trunk@240137 91177308-0d34-0410-b5e6-96231b3b80d8
		
			
				
	
	
		
			338 lines
		
	
	
		
			12 KiB
		
	
	
	
		
			C++
		
	
	
	
	
	
			
		
		
	
	
			338 lines
		
	
	
		
			12 KiB
		
	
	
	
		
			C++
		
	
	
	
	
	
//===- DivergenceAnalysis.cpp ------ Divergence Analysis ------------------===//
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//
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//                     The LLVM Compiler Infrastructure
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//
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// This file is distributed under the University of Illinois Open Source
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// License. See LICENSE.TXT for details.
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//
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//===----------------------------------------------------------------------===//
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//
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// This file defines divergence analysis which determines whether a branch in a
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// GPU program is divergent. It can help branch optimizations such as jump
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// threading and loop unswitching to make better decisions.
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//
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// GPU programs typically use the SIMD execution model, where multiple threads
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// in the same execution group have to execute in lock-step. Therefore, if the
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// code contains divergent branches (i.e., threads in a group do not agree on
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// which path of the branch to take), the group of threads has to execute all
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// the paths from that branch with different subsets of threads enabled until
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// they converge at the immediately post-dominating BB of the paths.
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//
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// Due to this execution model, some optimizations such as jump
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// threading and loop unswitching can be unfortunately harmful when performed on
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// divergent branches. Therefore, an analysis that computes which branches in a
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// GPU program are divergent can help the compiler to selectively run these
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// optimizations.
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//
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// This file defines divergence analysis which computes a conservative but
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// non-trivial approximation of all divergent branches in a GPU program. It
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// partially implements the approach described in
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//
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//   Divergence Analysis
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//   Sampaio, Souza, Collange, Pereira
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//   TOPLAS '13
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//
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// The divergence analysis identifies the sources of divergence (e.g., special
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// variables that hold the thread ID), and recursively marks variables that are
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// data or sync dependent on a source of divergence as divergent.
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//
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// While data dependency is a well-known concept, the notion of sync dependency
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// is worth more explanation. Sync dependence characterizes the control flow
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// aspect of the propagation of branch divergence. For example,
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//
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//   %cond = icmp slt i32 %tid, 10
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//   br i1 %cond, label %then, label %else
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// then:
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//   br label %merge
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// else:
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//   br label %merge
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// merge:
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//   %a = phi i32 [ 0, %then ], [ 1, %else ]
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//
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// Suppose %tid holds the thread ID. Although %a is not data dependent on %tid
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// because %tid is not on its use-def chains, %a is sync dependent on %tid
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// because the branch "br i1 %cond" depends on %tid and affects which value %a
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// is assigned to.
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//
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// The current implementation has the following limitations:
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// 1. intra-procedural. It conservatively considers the arguments of a
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//    non-kernel-entry function and the return value of a function call as
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//    divergent.
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// 2. memory as black box. It conservatively considers values loaded from
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//    generic or local address as divergent. This can be improved by leveraging
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//    pointer analysis.
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//===----------------------------------------------------------------------===//
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#include <vector>
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#include "llvm/IR/Dominators.h"
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#include "llvm/ADT/DenseSet.h"
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#include "llvm/Analysis/Passes.h"
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#include "llvm/Analysis/PostDominators.h"
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#include "llvm/Analysis/TargetTransformInfo.h"
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#include "llvm/IR/Function.h"
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#include "llvm/IR/InstIterator.h"
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#include "llvm/IR/Instructions.h"
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#include "llvm/IR/IntrinsicInst.h"
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#include "llvm/IR/Value.h"
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#include "llvm/Pass.h"
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#include "llvm/Support/CommandLine.h"
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#include "llvm/Support/Debug.h"
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#include "llvm/Support/raw_ostream.h"
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#include "llvm/Transforms/Scalar.h"
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using namespace llvm;
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#define DEBUG_TYPE "divergence"
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namespace {
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class DivergenceAnalysis : public FunctionPass {
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public:
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  static char ID;
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  DivergenceAnalysis() : FunctionPass(ID) {
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    initializeDivergenceAnalysisPass(*PassRegistry::getPassRegistry());
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  }
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  void getAnalysisUsage(AnalysisUsage &AU) const override {
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    AU.addRequired<DominatorTreeWrapperPass>();
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    AU.addRequired<PostDominatorTree>();
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    AU.setPreservesAll();
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  }
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  bool runOnFunction(Function &F) override;
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  // Print all divergent branches in the function.
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  void print(raw_ostream &OS, const Module *) const override;
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  // Returns true if V is divergent.
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  bool isDivergent(const Value *V) const { return DivergentValues.count(V); }
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  // Returns true if V is uniform/non-divergent.
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  bool isUniform(const Value *V) const { return !isDivergent(V); }
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private:
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  // Stores all divergent values.
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  DenseSet<const Value *> DivergentValues;
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};
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} // End of anonymous namespace
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// Register this pass.
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char DivergenceAnalysis::ID = 0;
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INITIALIZE_PASS_BEGIN(DivergenceAnalysis, "divergence", "Divergence Analysis",
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                      false, true)
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INITIALIZE_PASS_DEPENDENCY(DominatorTreeWrapperPass)
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INITIALIZE_PASS_DEPENDENCY(PostDominatorTree)
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INITIALIZE_PASS_END(DivergenceAnalysis, "divergence", "Divergence Analysis",
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                    false, true)
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namespace {
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class DivergencePropagator {
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public:
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  DivergencePropagator(Function &F, TargetTransformInfo &TTI,
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                       DominatorTree &DT, PostDominatorTree &PDT,
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                       DenseSet<const Value *> &DV)
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      : F(F), TTI(TTI), DT(DT), PDT(PDT), DV(DV) {}
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  void populateWithSourcesOfDivergence();
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  void propagate();
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private:
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  // A helper function that explores data dependents of V.
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  void exploreDataDependency(Value *V);
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  // A helper function that explores sync dependents of TI.
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  void exploreSyncDependency(TerminatorInst *TI);
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  // Computes the influence region from Start to End. This region includes all
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  // basic blocks on any path from Start to End.
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  void computeInfluenceRegion(BasicBlock *Start, BasicBlock *End,
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                              DenseSet<BasicBlock *> &InfluenceRegion);
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  // Finds all users of I that are outside the influence region, and add these
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  // users to Worklist.
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  void findUsersOutsideInfluenceRegion(
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      Instruction &I, const DenseSet<BasicBlock *> &InfluenceRegion);
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  Function &F;
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  TargetTransformInfo &TTI;
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  DominatorTree &DT;
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  PostDominatorTree &PDT;
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  std::vector<Value *> Worklist; // Stack for DFS.
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  DenseSet<const Value *> &DV; // Stores all divergent values.
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};
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void DivergencePropagator::populateWithSourcesOfDivergence() {
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  Worklist.clear();
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  DV.clear();
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  for (auto &I : inst_range(F)) {
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    if (TTI.isSourceOfDivergence(&I)) {
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      Worklist.push_back(&I);
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      DV.insert(&I);
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    }
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  }
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  for (auto &Arg : F.args()) {
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    if (TTI.isSourceOfDivergence(&Arg)) {
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      Worklist.push_back(&Arg);
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      DV.insert(&Arg);
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    }
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  }
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}
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void DivergencePropagator::exploreSyncDependency(TerminatorInst *TI) {
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  // Propagation rule 1: if branch TI is divergent, all PHINodes in TI's
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  // immediate post dominator are divergent. This rule handles if-then-else
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  // patterns. For example,
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  //
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  // if (tid < 5)
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  //   a1 = 1;
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  // else
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  //   a2 = 2;
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  // a = phi(a1, a2); // sync dependent on (tid < 5)
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  BasicBlock *ThisBB = TI->getParent();
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  BasicBlock *IPostDom = PDT.getNode(ThisBB)->getIDom()->getBlock();
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  if (IPostDom == nullptr)
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    return;
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  for (auto I = IPostDom->begin(); isa<PHINode>(I); ++I) {
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    // A PHINode is uniform if it returns the same value no matter which path is
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    // taken.
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    if (!cast<PHINode>(I)->hasConstantValue() && DV.insert(I).second)
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      Worklist.push_back(I);
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  }
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  // Propagation rule 2: if a value defined in a loop is used outside, the user
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  // is sync dependent on the condition of the loop exits that dominate the
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  // user. For example,
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  //
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  // int i = 0;
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  // do {
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  //   i++;
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  //   if (foo(i)) ... // uniform
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  // } while (i < tid);
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  // if (bar(i)) ...   // divergent
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  //
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  // A program may contain unstructured loops. Therefore, we cannot leverage
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  // LoopInfo, which only recognizes natural loops.
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  //
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  // The algorithm used here handles both natural and unstructured loops.  Given
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  // a branch TI, we first compute its influence region, the union of all simple
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  // paths from TI to its immediate post dominator (IPostDom). Then, we search
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  // for all the values defined in the influence region but used outside. All
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  // these users are sync dependent on TI.
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  DenseSet<BasicBlock *> InfluenceRegion;
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  computeInfluenceRegion(ThisBB, IPostDom, InfluenceRegion);
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  // An insight that can speed up the search process is that all the in-region
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  // values that are used outside must dominate TI. Therefore, instead of
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  // searching every basic blocks in the influence region, we search all the
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  // dominators of TI until it is outside the influence region.
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  BasicBlock *InfluencedBB = ThisBB;
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  while (InfluenceRegion.count(InfluencedBB)) {
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    for (auto &I : *InfluencedBB)
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      findUsersOutsideInfluenceRegion(I, InfluenceRegion);
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    DomTreeNode *IDomNode = DT.getNode(InfluencedBB)->getIDom();
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    if (IDomNode == nullptr)
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      break;
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    InfluencedBB = IDomNode->getBlock();
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  }
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}
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void DivergencePropagator::findUsersOutsideInfluenceRegion(
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    Instruction &I, const DenseSet<BasicBlock *> &InfluenceRegion) {
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  for (User *U : I.users()) {
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    Instruction *UserInst = cast<Instruction>(U);
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    if (!InfluenceRegion.count(UserInst->getParent())) {
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      if (DV.insert(UserInst).second)
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        Worklist.push_back(UserInst);
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    }
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  }
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}
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void DivergencePropagator::computeInfluenceRegion(
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    BasicBlock *Start, BasicBlock *End,
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    DenseSet<BasicBlock *> &InfluenceRegion) {
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  assert(PDT.properlyDominates(End, Start) &&
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         "End does not properly dominate Start");
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  std::vector<BasicBlock *> InfluenceStack;
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  InfluenceStack.push_back(Start);
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  InfluenceRegion.insert(Start);
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  while (!InfluenceStack.empty()) {
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    BasicBlock *BB = InfluenceStack.back();
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    InfluenceStack.pop_back();
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    for (BasicBlock *Succ : successors(BB)) {
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      if (End != Succ && InfluenceRegion.insert(Succ).second)
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        InfluenceStack.push_back(Succ);
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    }
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  }
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}
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void DivergencePropagator::exploreDataDependency(Value *V) {
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  // Follow def-use chains of V.
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  for (User *U : V->users()) {
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    Instruction *UserInst = cast<Instruction>(U);
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    if (DV.insert(UserInst).second)
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      Worklist.push_back(UserInst);
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  }
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}
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void DivergencePropagator::propagate() {
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  // Traverse the dependency graph using DFS.
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  while (!Worklist.empty()) {
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    Value *V = Worklist.back();
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    Worklist.pop_back();
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    if (TerminatorInst *TI = dyn_cast<TerminatorInst>(V)) {
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      // Terminators with less than two successors won't introduce sync
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      // dependency. Ignore them.
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      if (TI->getNumSuccessors() > 1)
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        exploreSyncDependency(TI);
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    }
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    exploreDataDependency(V);
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  }
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}
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} // namespace
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FunctionPass *llvm::createDivergenceAnalysisPass() {
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  return new DivergenceAnalysis();
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}
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bool DivergenceAnalysis::runOnFunction(Function &F) {
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  auto *TTIWP = getAnalysisIfAvailable<TargetTransformInfoWrapperPass>();
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  if (TTIWP == nullptr)
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    return false;
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  TargetTransformInfo &TTI = TTIWP->getTTI(F);
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  // Fast path: if the target does not have branch divergence, we do not mark
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  // any branch as divergent.
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  if (!TTI.hasBranchDivergence())
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    return false;
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  DivergentValues.clear();
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  DivergencePropagator DP(F, TTI,
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                          getAnalysis<DominatorTreeWrapperPass>().getDomTree(),
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                          getAnalysis<PostDominatorTree>(), DivergentValues);
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  DP.populateWithSourcesOfDivergence();
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  DP.propagate();
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  return false;
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}
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void DivergenceAnalysis::print(raw_ostream &OS, const Module *) const {
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  if (DivergentValues.empty())
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    return;
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  const Value *FirstDivergentValue = *DivergentValues.begin();
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  const Function *F;
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  if (const Argument *Arg = dyn_cast<Argument>(FirstDivergentValue)) {
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    F = Arg->getParent();
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  } else if (const Instruction *I =
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                 dyn_cast<Instruction>(FirstDivergentValue)) {
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    F = I->getParent()->getParent();
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  } else {
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    llvm_unreachable("Only arguments and instructions can be divergent");
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  }
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  // Dumps all divergent values in F, arguments and then instructions.
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  for (auto &Arg : F->args()) {
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    if (DivergentValues.count(&Arg))
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      OS << "DIVERGENT:  " << Arg << "\n";
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  }
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  // Iterate instructions using inst_range to ensure a deterministic order.
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  for (auto &I : inst_range(F)) {
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    if (DivergentValues.count(&I))
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      OS << "DIVERGENT:" << I << "\n";
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  }
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}
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