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			331 lines
		
	
	
		
			12 KiB
		
	
	
	
		
			C++
		
	
	
	
			
		
		
	
	
			331 lines
		
	
	
		
			12 KiB
		
	
	
	
		
			C++
		
	
	
	
| //===- DivergenceAnalysis.cpp --------- Divergence Analysis Implementation -==//
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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 implements divergence analysis which determines whether a branch
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| // in a 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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| //===----------------------------------------------------------------------===//
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| 
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| #include "llvm/Analysis/DivergenceAnalysis.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/Dominators.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/Support/Debug.h"
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| #include "llvm/Support/raw_ostream.h"
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| #include <vector>
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| using namespace llvm;
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| 
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| namespace {
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| 
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| class DivergencePropagator {
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| public:
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|   DivergencePropagator(Function &F, TargetTransformInfo &TTI, DominatorTree &DT,
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|                        PostDominatorTree &PDT, 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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| 
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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 simple 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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| 
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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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| 
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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 : instructions(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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| 
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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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| 
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|   // Unreachable blocks may not be in the dominator tree.
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|   if (!DT.isReachableFromEntry(ThisBB))
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|     return;
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| 
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|   // If the function has no exit blocks or doesn't reach any exit blocks, the
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|   // post dominator may be null.
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|   DomTreeNode *ThisNode = PDT.getNode(ThisBB);
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|   if (!ThisNode)
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|     return;
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| 
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|   BasicBlock *IPostDom = ThisNode->getIDom()->getBlock();
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|   if (IPostDom == nullptr)
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|     return;
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| 
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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)->hasConstantOrUndefValue() && DV.insert(&*I).second)
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|       Worklist.push_back(&*I);
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|   }
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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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| 
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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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| 
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| // A helper function for computeInfluenceRegion that adds successors of "ThisBB"
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| // to the influence region.
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| static void
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| addSuccessorsToInfluenceRegion(BasicBlock *ThisBB, BasicBlock *End,
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|                                DenseSet<BasicBlock *> &InfluenceRegion,
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|                                std::vector<BasicBlock *> &InfluenceStack) {
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|   for (BasicBlock *Succ : successors(ThisBB)) {
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|     if (Succ != End && 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::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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| 
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|   // The influence region starts from the end of "Start" to the beginning of
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|   // "End". Therefore, "Start" should not be in the region unless "Start" is in
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|   // a loop that doesn't contain "End".
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|   std::vector<BasicBlock *> InfluenceStack;
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|   addSuccessorsToInfluenceRegion(Start, End, InfluenceRegion, InfluenceStack);
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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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|     addSuccessorsToInfluenceRegion(BB, End, InfluenceRegion, InfluenceStack);
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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 (!TTI.isAlwaysUniform(U) && 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::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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| 
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| } /// end namespace anonymous
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| 
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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(PostDominatorTreeWrapperPass)
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| INITIALIZE_PASS_END(DivergenceAnalysis, "divergence", "Divergence Analysis",
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|                     false, true)
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| 
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| FunctionPass *llvm::createDivergenceAnalysisPass() {
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|   return new DivergenceAnalysis();
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| }
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| 
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| void DivergenceAnalysis::getAnalysisUsage(AnalysisUsage &AU) const {
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|   AU.addRequired<DominatorTreeWrapperPass>();
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|   AU.addRequired<PostDominatorTreeWrapperPass>();
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|   AU.setPreservesAll();
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| }
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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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| 
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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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| 
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|   DivergentValues.clear();
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|   auto &PDT = getAnalysis<PostDominatorTreeWrapperPass>().getPostDomTree();
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|   DivergencePropagator DP(F, TTI,
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|                           getAnalysis<DominatorTreeWrapperPass>().getDomTree(),
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|                           PDT, 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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| 
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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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| 
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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 instructions() to ensure a deterministic order.
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|   for (auto &I : instructions(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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