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Vectorize long blocks in groups.

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Long basic blocks with many candidate pairs (such as in the SHA implementation in Perl 5.14; thanks to Roman Divacky for the example) used to take an unacceptably-long time to compile. Instead, break long blocks into groups so that no group has too many candidate pairs.

git-svn-id: https://llvm.org/svn/llvm-project/llvm/trunk@149595 91177308-0d34-0410-b5e6-96231b3b80d8
This commit is contained in:
Hal Finkel 2012-02-02 06:14:56 +00:00
parent 02e08d5b4d
commit 5d4e18bc39
1 changed files with 87 additions and 38 deletions
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125
lib/Transforms/Vectorize/BBVectorize.cpp
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@ -66,6 +66,10 @@ static cl::opt<unsigned>
MaxIter("bb-vectorize-max-iter", cl::init(0), cl::Hidden,
cl::desc("The maximum number of pairing iterations"));
static cl::opt<unsigned>
MaxInsts("bb-vectorize-max-instr-per-group", cl::init(500), cl::Hidden,
cl::desc("The maximum number of pairable instructions per group"));
static cl::opt<unsigned>
MaxCandPairsForCycleCheck("bb-vectorize-max-cycle-check-pairs", cl::init(200),
cl::Hidden, cl::desc("The maximum number of candidate pairs with which to use"
@ -152,7 +156,8 @@ namespace {
bool vectorizePairs(BasicBlock &BB);
void getCandidatePairs(BasicBlock &BB,
bool getCandidatePairs(BasicBlock &BB,
BasicBlock::iterator &Start,
std::multimap<Value *, Value *> &CandidatePairs,
std::vector<Value *> &PairableInsts);
@ -429,49 +434,62 @@ namespace {
// This function implements one vectorization iteration on the provided
// basic block. It returns true if the block is changed.
bool BBVectorize::vectorizePairs(BasicBlock &BB) {
std::vector<Value *> PairableInsts;
std::multimap<Value *, Value *> CandidatePairs;
getCandidatePairs(BB, CandidatePairs, PairableInsts);
if (PairableInsts.empty()) return false;
bool ShouldContinue;
BasicBlock::iterator Start = BB.getFirstInsertionPt();
// Now we have a map of all of the pairable instructions and we need to
// select the best possible pairing. A good pairing is one such that the
// users of the pair are also paired. This defines a (directed) forest
// over the pairs such that two pairs are connected iff the second pair
// uses the first.
std::vector<Value *> AllPairableInsts;
DenseMap<Value *, Value *> AllChosenPairs;
// Note that it only matters that both members of the second pair use some
// element of the first pair (to allow for splatting).
std::multimap<ValuePair, ValuePair> ConnectedPairs;
computeConnectedPairs(CandidatePairs, PairableInsts, ConnectedPairs);
if (ConnectedPairs.empty()) return false;
// Build the pairable-instruction dependency map
DenseSet<ValuePair> PairableInstUsers;
buildDepMap(BB, CandidatePairs, PairableInsts, PairableInstUsers);
// There is now a graph of the connected pairs. For each variable, pick the
// pairing with the largest tree meeting the depth requirement on at least
// one branch. Then select all pairings that are part of that tree and
// remove them from the list of available pairings and pairable variables.
DenseMap<Value *, Value *> ChosenPairs;
choosePairs(CandidatePairs, PairableInsts, ConnectedPairs,
PairableInstUsers, ChosenPairs);
if (ChosenPairs.empty()) return false;
NumFusedOps += ChosenPairs.size();
do {
std::vector<Value *> PairableInsts;
std::multimap<Value *, Value *> CandidatePairs;
ShouldContinue = getCandidatePairs(BB, Start, CandidatePairs,
PairableInsts);
if (PairableInsts.empty()) continue;
// Now we have a map of all of the pairable instructions and we need to
// select the best possible pairing. A good pairing is one such that the
// users of the pair are also paired. This defines a (directed) forest
// over the pairs such that two pairs are connected iff the second pair
// uses the first.
// Note that it only matters that both members of the second pair use some
// element of the first pair (to allow for splatting).
std::multimap<ValuePair, ValuePair> ConnectedPairs;
computeConnectedPairs(CandidatePairs, PairableInsts, ConnectedPairs);
if (ConnectedPairs.empty()) continue;
// Build the pairable-instruction dependency map
DenseSet<ValuePair> PairableInstUsers;
buildDepMap(BB, CandidatePairs, PairableInsts, PairableInstUsers);
// There is now a graph of the connected pairs. For each variable, pick the
// pairing with the largest tree meeting the depth requirement on at least
// one branch. Then select all pairings that are part of that tree and
// remove them from the list of available pairings and pairable variables.
DenseMap<Value *, Value *> ChosenPairs;
choosePairs(CandidatePairs, PairableInsts, ConnectedPairs,
PairableInstUsers, ChosenPairs);
if (ChosenPairs.empty()) continue;
AllPairableInsts.insert(AllPairableInsts.end(), PairableInsts.begin(),
PairableInsts.end());
AllChosenPairs.insert(ChosenPairs.begin(), ChosenPairs.end());
} while (ShouldContinue);
if (AllChosenPairs.empty()) return false;
NumFusedOps += AllChosenPairs.size();
// A set of pairs has now been selected. It is now necessary to replace the
// paired instructions with vector instructions. For this procedure each
// operand much be replaced with a vector operand. This vector is formed
// by using build_vector on the old operands. The replaced values are then
// replaced with a vector_extract on the result. Subsequent optimization
// passes should coalesce the build/extract combinations.
fuseChosenPairs(BB, PairableInsts, ChosenPairs);
fuseChosenPairs(BB, AllPairableInsts, AllChosenPairs);
return true;
}
@ -687,19 +705,28 @@ namespace {
// This function iterates over all instruction pairs in the provided
// basic block and collects all candidate pairs for vectorization.
void BBVectorize::getCandidatePairs(BasicBlock &BB,
bool BBVectorize::getCandidatePairs(BasicBlock &BB,
BasicBlock::iterator &Start,
std::multimap<Value *, Value *> &CandidatePairs,
std::vector<Value *> &PairableInsts) {
BasicBlock::iterator E = BB.end();
for (BasicBlock::iterator I = BB.getFirstInsertionPt(); I != E; ++I) {
if (Start == E) return false;
bool ShouldContinue = false, IAfterStart = false;
for (BasicBlock::iterator I = Start++; I != E; ++I) {
if (I == Start) IAfterStart = true;
bool IsSimpleLoadStore;
if (!isInstVectorizable(I, IsSimpleLoadStore)) continue;
// Look for an instruction with which to pair instruction *I...
DenseSet<Value *> Users;
AliasSetTracker WriteSet(*AA);
BasicBlock::iterator J = I; ++J;
bool JAfterStart = IAfterStart;
BasicBlock::iterator J = llvm::next(I);
for (unsigned ss = 0; J != E && ss <= SearchLimit; ++J, ++ss) {
if (J == Start) JAfterStart = true;
// Determine if J uses I, if so, exit the loop.
bool UsesI = trackUsesOfI(Users, WriteSet, I, J, !FastDep);
if (FastDep) {
@ -724,14 +751,36 @@ namespace {
PairableInsts[PairableInsts.size()-1] != I) {
PairableInsts.push_back(I);
}
CandidatePairs.insert(ValuePair(I, J));
// The next call to this function must start after the last instruction
// selected during this invocation.
if (JAfterStart) {
Start = llvm::next(J);
IAfterStart = JAfterStart = false;
}
DEBUG(if (DebugCandidateSelection) dbgs() << "BBV: candidate pair "
<< *I << " <-> " << *J << "\n");
// If we have already found too many pairs, break here and this function
// will be called again starting after the last instruction selected
// during this invocation.
if (PairableInsts.size() >= MaxInsts) {
ShouldContinue = true;
break;
}
}
if (ShouldContinue)
break;
}
DEBUG(dbgs() << "BBV: found " << PairableInsts.size()
<< " instructions with candidate pairs\n");
return ShouldContinue;
}
// Finds candidate pairs connected to the pair P = <PI, PJ>. This means that