[PATCH] D11442: Create a utility function normalizeEdgeWeights() in BranchProbabilityInfo that is used to normalize a list of weights so that the sum of them does not exceed UINT32_MAX.

[Cong Hou]

congh created this revision.
congh added a reviewer: dexonsmith.
congh added subscribers: llvm-commits, davidxl.

The implementation of the new function is borrowed from MachineBranchProbabilityInfo::getSumForBlock. The type of weight list is a template parameter so that we can pass weights in either uint32_t or uint64_t.

This function can be useful when we need to normalize a list of edge weights.

http://reviews.llvm.org/D11442

Files:
  include/llvm/Analysis/BranchProbabilityInfo.h
  lib/CodeGen/MachineBranchProbabilityInfo.cpp

Index: lib/CodeGen/MachineBranchProbabilityInfo.cpp
===================================================================
--- lib/CodeGen/MachineBranchProbabilityInfo.cpp
+++ lib/CodeGen/MachineBranchProbabilityInfo.cpp
@@ -11,6 +11,7 @@
 //
 //===----------------------------------------------------------------------===//
 
+#include "llvm/Analysis/BranchProbabilityInfo.h"
 #include "llvm/CodeGen/MachineBranchProbabilityInfo.h"
 #include "llvm/CodeGen/MachineBasicBlock.h"
 #include "llvm/IR/Instructions.h"
@@ -30,34 +31,14 @@
 
 uint32_t MachineBranchProbabilityInfo::
 getSumForBlock(const MachineBasicBlock *MBB, uint32_t &Scale) const {
-  // First we compute the sum with 64-bits of precision, ensuring that cannot
-  // overflow by bounding the number of weights considered. Hopefully no one
-  // actually needs 2^32 successors.
   assert(MBB->succ_size() < UINT32_MAX);
-  uint64_t Sum = 0;
-  Scale = 1;
+  SmallVector<uint32_t, 8> Weights;
   for (MachineBasicBlock::const_succ_iterator I = MBB->succ_begin(),
-       E = MBB->succ_end(); I != E; ++I) {
-    uint32_t Weight = getEdgeWeight(MBB, I);
-    Sum += Weight;
-  }
-
-  // If the computed sum fits in 32-bits, we're done.
-  if (Sum <= UINT32_MAX)
-    return Sum;
+       E = MBB->succ_end(); I != E; ++I)
+    Weights.push_back(getEdgeWeight(MBB, I));
 
-  // Otherwise, compute the scale necessary to cause the weights to fit, and
-  // re-sum with that scale applied.
-  assert((Sum / UINT32_MAX) < UINT32_MAX);
-  Scale = (Sum / UINT32_MAX) + 1;
-  Sum = 0;
-  for (MachineBasicBlock::const_succ_iterator I = MBB->succ_begin(),
-       E = MBB->succ_end(); I != E; ++I) {
-    uint32_t Weight = getEdgeWeight(MBB, I);
-    Sum += Weight / Scale;
-  }
-  assert(Sum <= UINT32_MAX);
-  return Sum;
+  Scale = BranchProbabilityInfo::normalizeEdgeWeights(Weights);
+  return std::accumulate(Weights.begin(), Weights.end(), 0u);
 }
 
 uint32_t MachineBranchProbabilityInfo::
Index: include/llvm/Analysis/BranchProbabilityInfo.h
===================================================================
--- include/llvm/Analysis/BranchProbabilityInfo.h
+++ include/llvm/Analysis/BranchProbabilityInfo.h
@@ -112,6 +112,11 @@
     return IsLikely ? (1u << 20) - 1 : 1;
   }
 
+  /// Normalize a list of weights by scaling them down so that the sum of them
+  /// doesn't exceed UINT32_MAX. Return the scale.
+  template <class WeightVec>
+  static uint32_t normalizeEdgeWeights(WeightVec &Weights);
+
   void calculate(Function &F, const LoopInfo& LI);
 
 private:
@@ -151,6 +156,24 @@
   bool calcInvokeHeuristics(BasicBlock *BB);
 };
 
+template <class WeightVec>
+uint32_t BranchProbabilityInfo::normalizeEdgeWeights(WeightVec &Weights) {
+  // First we compute the sum with 64-bits of precision.
+  uint64_t Sum = std::accumulate(Weights.begin(), Weights.end(), uint64_t(0));
+
+  // If the computed sum fits in 32-bits, we're done.
+  if (Sum <= UINT32_MAX)
+    return 1;
+
+  // Otherwise, compute the scale necessary to cause the weights to fit, and
+  // re-sum with that scale applied.
+  assert((Sum / UINT32_MAX) < UINT32_MAX);
+  uint32_t Scale = (Sum / UINT32_MAX) + 1;
+  for (auto &W : Weights)
+    W /= Scale;
+  return Scale;
+}
+
 /// \brief Legacy analysis pass which computes \c BranchProbabilityInfo.
 class BranchProbabilityInfoWrapperPass : public FunctionPass {
   BranchProbabilityInfo BPI;


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