llvm-project/llvm/lib/Transforms/IPO/PassManagerBuilder.cpp

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//===- PassManagerBuilder.cpp - Build Standard Pass -----------------------===//
//
// The LLVM Compiler Infrastructure
//
// This file is distributed under the University of Illinois Open Source
// License. See LICENSE.TXT for details.
//
//===----------------------------------------------------------------------===//
//
// This file defines the PassManagerBuilder class, which is used to set up a
// "standard" optimization sequence suitable for languages like C and C++.
//
//===----------------------------------------------------------------------===//
#include "llvm/Transforms/IPO/PassManagerBuilder.h"
#include "llvm-c/Transforms/PassManagerBuilder.h"
#include "llvm/ADT/SmallVector.h"
#include "llvm/Analysis/Passes.h"
#include "llvm/IR/DataLayout.h"
#include "llvm/IR/FunctionInfo.h"
#include "llvm/IR/Verifier.h"
#include "llvm/IR/LegacyPassManager.h"
#include "llvm/Support/CommandLine.h"
#include "llvm/Support/ManagedStatic.h"
#include "llvm/Analysis/BasicAliasAnalysis.h"
#include "llvm/Analysis/CFLAliasAnalysis.h"
#include "llvm/Analysis/GlobalsModRef.h"
#include "llvm/Analysis/ScopedNoAliasAA.h"
#include "llvm/Analysis/TargetLibraryInfo.h"
#include "llvm/Analysis/TypeBasedAliasAnalysis.h"
#include "llvm/Target/TargetMachine.h"
#include "llvm/Transforms/IPO.h"
#include "llvm/Transforms/IPO/ForceFunctionAttrs.h"
#include "llvm/Transforms/Scalar.h"
#include "llvm/Transforms/Vectorize.h"
using namespace llvm;
static cl::opt<bool>
RunLoopVectorization("vectorize-loops", cl::Hidden,
2012-10-31 02:37:43 +08:00
cl::desc("Run the Loop vectorization passes"));
static cl::opt<bool>
RunSLPVectorization("vectorize-slp", cl::Hidden,
cl::desc("Run the SLP vectorization passes"));
static cl::opt<bool>
RunBBVectorization("vectorize-slp-aggressive", cl::Hidden,
cl::desc("Run the BB vectorization passes"));
static cl::opt<bool>
UseGVNAfterVectorization("use-gvn-after-vectorization",
cl::init(false), cl::Hidden,
cl::desc("Run GVN instead of Early CSE after vectorization passes"));
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static cl::opt<bool> ExtraVectorizerPasses(
"extra-vectorizer-passes", cl::init(false), cl::Hidden,
cl::desc("Run cleanup optimization passes after vectorization."));
Introduce a new SROA implementation. This is essentially a ground up re-think of the SROA pass in LLVM. It was initially inspired by a few problems with the existing pass: - It is subject to the bane of my existence in optimizations: arbitrary thresholds. - It is overly conservative about which constructs can be split and promoted. - The vector value replacement aspect is separated from the splitting logic, missing many opportunities where splitting and vector value formation can work together. - The splitting is entirely based around the underlying type of the alloca, despite this type often having little to do with the reality of how that memory is used. This is especially prevelant with unions and base classes where we tail-pack derived members. - When splitting fails (often due to the thresholds), the vector value replacement (again because it is separate) can kick in for preposterous cases where we simply should have split the value. This results in forming i1024 and i2048 integer "bit vectors" that tremendously slow down subsequnet IR optimizations (due to large APInts) and impede the backend's lowering. The new design takes an approach that fundamentally is not susceptible to many of these problems. It is the result of a discusison between myself and Duncan Sands over IRC about how to premptively avoid these types of problems and how to do SROA in a more principled way. Since then, it has evolved and grown, but this remains an important aspect: it fixes real world problems with the SROA process today. First, the transform of SROA actually has little to do with replacement. It has more to do with splitting. The goal is to take an aggregate alloca and form a composition of scalar allocas which can replace it and will be most suitable to the eventual replacement by scalar SSA values. The actual replacement is performed by mem2reg (and in the future SSAUpdater). The splitting is divided into four phases. The first phase is an analysis of the uses of the alloca. This phase recursively walks uses, building up a dense datastructure representing the ranges of the alloca's memory actually used and checking for uses which inhibit any aspects of the transform such as the escape of a pointer. Once we have a mapping of the ranges of the alloca used by individual operations, we compute a partitioning of the used ranges. Some uses are inherently splittable (such as memcpy and memset), while scalar uses are not splittable. The goal is to build a partitioning that has the minimum number of splits while placing each unsplittable use in its own partition. Overlapping unsplittable uses belong to the same partition. This is the target split of the aggregate alloca, and it maximizes the number of scalar accesses which become accesses to their own alloca and candidates for promotion. Third, we re-walk the uses of the alloca and assign each specific memory access to all the partitions touched so that we have dense use-lists for each partition. Finally, we build a new, smaller alloca for each partition and rewrite each use of that partition to use the new alloca. During this phase the pass will also work very hard to transform uses of an alloca into a form suitable for promotion, including forming vector operations, speculating loads throguh PHI nodes and selects, etc. After splitting is complete, each newly refined alloca that is a candidate for promotion to a scalar SSA value is run through mem2reg. There are lots of reasonably detailed comments in the source code about the design and algorithms, and I'm going to be trying to improve them in subsequent commits to ensure this is well documented, as the new pass is in many ways more complex than the old one. Some of this is still a WIP, but the current state is reasonbly stable. It has passed bootstrap, the nightly test suite, and Duncan has run it successfully through the ACATS and DragonEgg test suites. That said, it remains behind a default-off flag until the last few pieces are in place, and full testing can be done. Specific areas I'm looking at next: - Improved comments and some code cleanup from reviews. - SSAUpdater and enabling this pass inside the CGSCC pass manager. - Some datastructure tuning and compile-time measurements. - More aggressive FCA splitting and vector formation. Many thanks to Duncan Sands for the thorough final review, as well as Benjamin Kramer for lots of review during the process of writing this pass, and Daniel Berlin for reviewing the data structures and algorithms and general theory of the pass. Also, several other people on IRC, over lunch tables, etc for lots of feedback and advice. llvm-svn: 163883
2012-09-14 17:22:59 +08:00
static cl::opt<bool> UseNewSROA("use-new-sroa",
cl::init(true), cl::Hidden,
Introduce a new SROA implementation. This is essentially a ground up re-think of the SROA pass in LLVM. It was initially inspired by a few problems with the existing pass: - It is subject to the bane of my existence in optimizations: arbitrary thresholds. - It is overly conservative about which constructs can be split and promoted. - The vector value replacement aspect is separated from the splitting logic, missing many opportunities where splitting and vector value formation can work together. - The splitting is entirely based around the underlying type of the alloca, despite this type often having little to do with the reality of how that memory is used. This is especially prevelant with unions and base classes where we tail-pack derived members. - When splitting fails (often due to the thresholds), the vector value replacement (again because it is separate) can kick in for preposterous cases where we simply should have split the value. This results in forming i1024 and i2048 integer "bit vectors" that tremendously slow down subsequnet IR optimizations (due to large APInts) and impede the backend's lowering. The new design takes an approach that fundamentally is not susceptible to many of these problems. It is the result of a discusison between myself and Duncan Sands over IRC about how to premptively avoid these types of problems and how to do SROA in a more principled way. Since then, it has evolved and grown, but this remains an important aspect: it fixes real world problems with the SROA process today. First, the transform of SROA actually has little to do with replacement. It has more to do with splitting. The goal is to take an aggregate alloca and form a composition of scalar allocas which can replace it and will be most suitable to the eventual replacement by scalar SSA values. The actual replacement is performed by mem2reg (and in the future SSAUpdater). The splitting is divided into four phases. The first phase is an analysis of the uses of the alloca. This phase recursively walks uses, building up a dense datastructure representing the ranges of the alloca's memory actually used and checking for uses which inhibit any aspects of the transform such as the escape of a pointer. Once we have a mapping of the ranges of the alloca used by individual operations, we compute a partitioning of the used ranges. Some uses are inherently splittable (such as memcpy and memset), while scalar uses are not splittable. The goal is to build a partitioning that has the minimum number of splits while placing each unsplittable use in its own partition. Overlapping unsplittable uses belong to the same partition. This is the target split of the aggregate alloca, and it maximizes the number of scalar accesses which become accesses to their own alloca and candidates for promotion. Third, we re-walk the uses of the alloca and assign each specific memory access to all the partitions touched so that we have dense use-lists for each partition. Finally, we build a new, smaller alloca for each partition and rewrite each use of that partition to use the new alloca. During this phase the pass will also work very hard to transform uses of an alloca into a form suitable for promotion, including forming vector operations, speculating loads throguh PHI nodes and selects, etc. After splitting is complete, each newly refined alloca that is a candidate for promotion to a scalar SSA value is run through mem2reg. There are lots of reasonably detailed comments in the source code about the design and algorithms, and I'm going to be trying to improve them in subsequent commits to ensure this is well documented, as the new pass is in many ways more complex than the old one. Some of this is still a WIP, but the current state is reasonbly stable. It has passed bootstrap, the nightly test suite, and Duncan has run it successfully through the ACATS and DragonEgg test suites. That said, it remains behind a default-off flag until the last few pieces are in place, and full testing can be done. Specific areas I'm looking at next: - Improved comments and some code cleanup from reviews. - SSAUpdater and enabling this pass inside the CGSCC pass manager. - Some datastructure tuning and compile-time measurements. - More aggressive FCA splitting and vector formation. Many thanks to Duncan Sands for the thorough final review, as well as Benjamin Kramer for lots of review during the process of writing this pass, and Daniel Berlin for reviewing the data structures and algorithms and general theory of the pass. Also, several other people on IRC, over lunch tables, etc for lots of feedback and advice. llvm-svn: 163883
2012-09-14 17:22:59 +08:00
cl::desc("Enable the new, experimental SROA pass"));
Add a loop rerolling pass This adds a loop rerolling pass: the opposite of (partial) loop unrolling. The transformation aims to take loops like this: for (int i = 0; i < 3200; i += 5) { a[i] += alpha * b[i]; a[i + 1] += alpha * b[i + 1]; a[i + 2] += alpha * b[i + 2]; a[i + 3] += alpha * b[i + 3]; a[i + 4] += alpha * b[i + 4]; } and turn them into this: for (int i = 0; i < 3200; ++i) { a[i] += alpha * b[i]; } and loops like this: for (int i = 0; i < 500; ++i) { x[3*i] = foo(0); x[3*i+1] = foo(0); x[3*i+2] = foo(0); } and turn them into this: for (int i = 0; i < 1500; ++i) { x[i] = foo(0); } There are two motivations for this transformation: 1. Code-size reduction (especially relevant, obviously, when compiling for code size). 2. Providing greater choice to the loop vectorizer (and generic unroller) to choose the unrolling factor (and a better ability to vectorize). The loop vectorizer can take vector lengths and register pressure into account when choosing an unrolling factor, for example, and a pre-unrolled loop limits that choice. This is especially problematic if the manual unrolling was optimized for a machine different from the current target. The current implementation is limited to single basic-block loops only. The rerolling recognition should work regardless of how the loop iterations are intermixed within the loop body (subject to dependency and side-effect constraints), but the significant restriction is that the order of the instructions in each iteration must be identical. This seems sufficient to capture all current use cases. This pass is not currently enabled by default at any optimization level. llvm-svn: 194939
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static cl::opt<bool>
RunLoopRerolling("reroll-loops", cl::Hidden,
cl::desc("Run the loop rerolling pass"));
static cl::opt<bool>
RunFloat2Int("float-to-int", cl::Hidden, cl::init(true),
cl::desc("Run the float2int (float demotion) pass"));
static cl::opt<bool> RunLoadCombine("combine-loads", cl::init(false),
cl::Hidden,
cl::desc("Run the load combining pass"));
static cl::opt<bool>
RunSLPAfterLoopVectorization("run-slp-after-loop-vectorization",
cl::init(true), cl::Hidden,
cl::desc("Run the SLP vectorizer (and BB vectorizer) after the Loop "
"vectorizer instead of before"));
static cl::opt<bool> UseCFLAA("use-cfl-aa",
cl::init(false), cl::Hidden,
cl::desc("Enable the new, experimental CFL alias analysis"));
static cl::opt<bool>
EnableMLSM("mlsm", cl::init(true), cl::Hidden,
cl::desc("Enable motion of merged load and store"));
static cl::opt<bool> EnableLoopInterchange(
"enable-loopinterchange", cl::init(false), cl::Hidden,
cl::desc("Enable the new, experimental LoopInterchange Pass"));
static cl::opt<bool> EnableLoopDistribute(
"enable-loop-distribute", cl::init(false), cl::Hidden,
cl::desc("Enable the new, experimental LoopDistribution Pass"));
static cl::opt<bool> EnableNonLTOGlobalsModRef(
"enable-non-lto-gmr", cl::init(true), cl::Hidden,
cl::desc(
"Enable the GlobalsModRef AliasAnalysis outside of the LTO pipeline."));
LLE 6/6: Add LoopLoadElimination pass Summary: The goal of this pass is to perform store-to-load forwarding across the backedge of a loop. E.g.: for (i) A[i + 1] = A[i] + B[i] => T = A[0] for (i) T = T + B[i] A[i + 1] = T The pass relies on loop dependence analysis via LoopAccessAnalisys to find opportunities of loop-carried dependences with a distance of one between a store and a load. Since it's using LoopAccessAnalysis, it was easy to also add support for versioning away may-aliasing intervening stores that would otherwise prevent this transformation. This optimization is also performed by Load-PRE in GVN without the option of multi-versioning. As was discussed with Daniel Berlin in http://reviews.llvm.org/D9548, this is inferior to a more loop-aware solution applied here. Hopefully, we will be able to remove some complexity from GVN/MemorySSA as a consequence. In the long run, we may want to extend this pass (or create a new one if there is little overlap) to also eliminate loop-indepedent redundant loads and store that *require* versioning due to may-aliasing intervening stores/loads. I have some motivating cases for store elimination. My plan right now is to wait for MemorySSA to come online first rather than using memdep for this. The main motiviation for this pass is the 456.hmmer loop in SPECint2006 where after distributing the original loop and vectorizing the top part, we are left with the critical path exposed in the bottom loop. Being able to promote the memory dependence into a register depedence (even though the HW does perform store-to-load fowarding as well) results in a major gain (~20%). This gain also transfers over to x86: it's around 8-10%. Right now the pass is off by default and can be enabled with -enable-loop-load-elim. On the LNT testsuite, there are two performance changes (negative number -> improvement): 1. -28% in Polybench/linear-algebra/solvers/dynprog: the length of the critical paths is reduced 2. +2% in Polybench/stencils/adi: Unfortunately, I couldn't reproduce this outside of LNT The pass is scheduled after the loop vectorizer (which is after loop distribution). The rational is to try to reuse LAA state, rather than recomputing it. The order between LV and LLE is not critical because normally LV does not touch scalar st->ld forwarding cases where vectorizing would inhibit the CPU's st->ld forwarding to kick in. LoopLoadElimination requires LAA to provide the full set of dependences (including forward dependences). LAA is known to omit loop-independent dependences in certain situations. The big comment before removeDependencesFromMultipleStores explains why this should not occur for the cases that we're interested in. Reviewers: dberlin, hfinkel Subscribers: junbuml, dberlin, mssimpso, rengolin, sanjoy, llvm-commits Differential Revision: http://reviews.llvm.org/D13259 llvm-svn: 252017
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static cl::opt<bool> EnableLoopLoadElim(
"enable-loop-load-elim", cl::init(false), cl::Hidden,
cl::desc("Enable the new, experimental LoopLoadElimination Pass"));
PassManagerBuilder::PassManagerBuilder() {
OptLevel = 2;
SizeLevel = 0;
LibraryInfo = nullptr;
Inliner = nullptr;
FunctionIndex = nullptr;
DisableUnitAtATime = false;
DisableUnrollLoops = false;
BBVectorize = RunBBVectorization;
SLPVectorize = RunSLPVectorization;
LoopVectorize = RunLoopVectorization;
RerollLoops = RunLoopRerolling;
LoadCombine = RunLoadCombine;
DisableGVNLoadPRE = false;
VerifyInput = false;
VerifyOutput = false;
MergeFunctions = false;
PrepareForLTO = false;
}
PassManagerBuilder::~PassManagerBuilder() {
delete LibraryInfo;
delete Inliner;
}
/// Set of global extensions, automatically added as part of the standard set.
static ManagedStatic<SmallVector<std::pair<PassManagerBuilder::ExtensionPointTy,
PassManagerBuilder::ExtensionFn>, 8> > GlobalExtensions;
void PassManagerBuilder::addGlobalExtension(
PassManagerBuilder::ExtensionPointTy Ty,
PassManagerBuilder::ExtensionFn Fn) {
GlobalExtensions->push_back(std::make_pair(Ty, Fn));
}
void PassManagerBuilder::addExtension(ExtensionPointTy Ty, ExtensionFn Fn) {
Extensions.push_back(std::make_pair(Ty, Fn));
}
void PassManagerBuilder::addExtensionsToPM(ExtensionPointTy ETy,
legacy::PassManagerBase &PM) const {
for (unsigned i = 0, e = GlobalExtensions->size(); i != e; ++i)
if ((*GlobalExtensions)[i].first == ETy)
(*GlobalExtensions)[i].second(*this, PM);
for (unsigned i = 0, e = Extensions.size(); i != e; ++i)
if (Extensions[i].first == ETy)
Extensions[i].second(*this, PM);
}
void PassManagerBuilder::addInitialAliasAnalysisPasses(
legacy::PassManagerBase &PM) const {
// Add TypeBasedAliasAnalysis before BasicAliasAnalysis so that
// BasicAliasAnalysis wins if they disagree. This is intended to help
// support "obvious" type-punning idioms.
if (UseCFLAA)
[PM/AA] Rebuild LLVM's alias analysis infrastructure in a way compatible with the new pass manager, and no longer relying on analysis groups. This builds essentially a ground-up new AA infrastructure stack for LLVM. The core ideas are the same that are used throughout the new pass manager: type erased polymorphism and direct composition. The design is as follows: - FunctionAAResults is a type-erasing alias analysis results aggregation interface to walk a single query across a range of results from different alias analyses. Currently this is function-specific as we always assume that aliasing queries are *within* a function. - AAResultBase is a CRTP utility providing stub implementations of various parts of the alias analysis result concept, notably in several cases in terms of other more general parts of the interface. This can be used to implement only a narrow part of the interface rather than the entire interface. This isn't really ideal, this logic should be hoisted into FunctionAAResults as currently it will cause a significant amount of redundant work, but it faithfully models the behavior of the prior infrastructure. - All the alias analysis passes are ported to be wrapper passes for the legacy PM and new-style analysis passes for the new PM with a shared result object. In some cases (most notably CFL), this is an extremely naive approach that we should revisit when we can specialize for the new pass manager. - BasicAA has been restructured to reflect that it is much more fundamentally a function analysis because it uses dominator trees and loop info that need to be constructed for each function. All of the references to getting alias analysis results have been updated to use the new aggregation interface. All the preservation and other pass management code has been updated accordingly. The way the FunctionAAResultsWrapperPass works is to detect the available alias analyses when run, and add them to the results object. This means that we should be able to continue to respect when various passes are added to the pipeline, for example adding CFL or adding TBAA passes should just cause their results to be available and to get folded into this. The exception to this rule is BasicAA which really needs to be a function pass due to using dominator trees and loop info. As a consequence, the FunctionAAResultsWrapperPass directly depends on BasicAA and always includes it in the aggregation. This has significant implications for preserving analyses. Generally, most passes shouldn't bother preserving FunctionAAResultsWrapperPass because rebuilding the results just updates the set of known AA passes. The exception to this rule are LoopPass instances which need to preserve all the function analyses that the loop pass manager will end up needing. This means preserving both BasicAAWrapperPass and the aggregating FunctionAAResultsWrapperPass. Now, when preserving an alias analysis, you do so by directly preserving that analysis. This is only necessary for non-immutable-pass-provided alias analyses though, and there are only three of interest: BasicAA, GlobalsAA (formerly GlobalsModRef), and SCEVAA. Usually BasicAA is preserved when needed because it (like DominatorTree and LoopInfo) is marked as a CFG-only pass. I've expanded GlobalsAA into the preserved set everywhere we previously were preserving all of AliasAnalysis, and I've added SCEVAA in the intersection of that with where we preserve SCEV itself. One significant challenge to all of this is that the CGSCC passes were actually using the alias analysis implementations by taking advantage of a pretty amazing set of loop holes in the old pass manager's analysis management code which allowed analysis groups to slide through in many cases. Moving away from analysis groups makes this problem much more obvious. To fix it, I've leveraged the flexibility the design of the new PM components provides to just directly construct the relevant alias analyses for the relevant functions in the IPO passes that need them. This is a bit hacky, but should go away with the new pass manager, and is already in many ways cleaner than the prior state. Another significant challenge is that various facilities of the old alias analysis infrastructure just don't fit any more. The most significant of these is the alias analysis 'counter' pass. That pass relied on the ability to snoop on AA queries at different points in the analysis group chain. Instead, I'm planning to build printing functionality directly into the aggregation layer. I've not included that in this patch merely to keep it smaller. Note that all of this needs a nearly complete rewrite of the AA documentation. I'm planning to do that, but I'd like to make sure the new design settles, and to flesh out a bit more of what it looks like in the new pass manager first. Differential Revision: http://reviews.llvm.org/D12080 llvm-svn: 247167
2015-09-10 01:55:00 +08:00
PM.add(createCFLAAWrapperPass());
PM.add(createTypeBasedAAWrapperPass());
PM.add(createScopedNoAliasAAWrapperPass());
}
void PassManagerBuilder::populateFunctionPassManager(
legacy::FunctionPassManager &FPM) {
addExtensionsToPM(EP_EarlyAsPossible, FPM);
// Add LibraryInfo if we have some.
if (LibraryInfo)
FPM.add(new TargetLibraryInfoWrapperPass(*LibraryInfo));
if (OptLevel == 0) return;
addInitialAliasAnalysisPasses(FPM);
FPM.add(createCFGSimplificationPass());
Introduce a new SROA implementation. This is essentially a ground up re-think of the SROA pass in LLVM. It was initially inspired by a few problems with the existing pass: - It is subject to the bane of my existence in optimizations: arbitrary thresholds. - It is overly conservative about which constructs can be split and promoted. - The vector value replacement aspect is separated from the splitting logic, missing many opportunities where splitting and vector value formation can work together. - The splitting is entirely based around the underlying type of the alloca, despite this type often having little to do with the reality of how that memory is used. This is especially prevelant with unions and base classes where we tail-pack derived members. - When splitting fails (often due to the thresholds), the vector value replacement (again because it is separate) can kick in for preposterous cases where we simply should have split the value. This results in forming i1024 and i2048 integer "bit vectors" that tremendously slow down subsequnet IR optimizations (due to large APInts) and impede the backend's lowering. The new design takes an approach that fundamentally is not susceptible to many of these problems. It is the result of a discusison between myself and Duncan Sands over IRC about how to premptively avoid these types of problems and how to do SROA in a more principled way. Since then, it has evolved and grown, but this remains an important aspect: it fixes real world problems with the SROA process today. First, the transform of SROA actually has little to do with replacement. It has more to do with splitting. The goal is to take an aggregate alloca and form a composition of scalar allocas which can replace it and will be most suitable to the eventual replacement by scalar SSA values. The actual replacement is performed by mem2reg (and in the future SSAUpdater). The splitting is divided into four phases. The first phase is an analysis of the uses of the alloca. This phase recursively walks uses, building up a dense datastructure representing the ranges of the alloca's memory actually used and checking for uses which inhibit any aspects of the transform such as the escape of a pointer. Once we have a mapping of the ranges of the alloca used by individual operations, we compute a partitioning of the used ranges. Some uses are inherently splittable (such as memcpy and memset), while scalar uses are not splittable. The goal is to build a partitioning that has the minimum number of splits while placing each unsplittable use in its own partition. Overlapping unsplittable uses belong to the same partition. This is the target split of the aggregate alloca, and it maximizes the number of scalar accesses which become accesses to their own alloca and candidates for promotion. Third, we re-walk the uses of the alloca and assign each specific memory access to all the partitions touched so that we have dense use-lists for each partition. Finally, we build a new, smaller alloca for each partition and rewrite each use of that partition to use the new alloca. During this phase the pass will also work very hard to transform uses of an alloca into a form suitable for promotion, including forming vector operations, speculating loads throguh PHI nodes and selects, etc. After splitting is complete, each newly refined alloca that is a candidate for promotion to a scalar SSA value is run through mem2reg. There are lots of reasonably detailed comments in the source code about the design and algorithms, and I'm going to be trying to improve them in subsequent commits to ensure this is well documented, as the new pass is in many ways more complex than the old one. Some of this is still a WIP, but the current state is reasonbly stable. It has passed bootstrap, the nightly test suite, and Duncan has run it successfully through the ACATS and DragonEgg test suites. That said, it remains behind a default-off flag until the last few pieces are in place, and full testing can be done. Specific areas I'm looking at next: - Improved comments and some code cleanup from reviews. - SSAUpdater and enabling this pass inside the CGSCC pass manager. - Some datastructure tuning and compile-time measurements. - More aggressive FCA splitting and vector formation. Many thanks to Duncan Sands for the thorough final review, as well as Benjamin Kramer for lots of review during the process of writing this pass, and Daniel Berlin for reviewing the data structures and algorithms and general theory of the pass. Also, several other people on IRC, over lunch tables, etc for lots of feedback and advice. llvm-svn: 163883
2012-09-14 17:22:59 +08:00
if (UseNewSROA)
FPM.add(createSROAPass());
else
FPM.add(createScalarReplAggregatesPass());
FPM.add(createEarlyCSEPass());
FPM.add(createLowerExpectIntrinsicPass());
}
void PassManagerBuilder::populateModulePassManager(
legacy::PassManagerBase &MPM) {
// Allow forcing function attributes as a debugging and tuning aid.
MPM.add(createForceFunctionAttrsLegacyPass());
// If all optimizations are disabled, just run the always-inline pass and,
// if enabled, the function merging pass.
if (OptLevel == 0) {
if (Inliner) {
MPM.add(Inliner);
Inliner = nullptr;
}
// FIXME: The BarrierNoopPass is a HACK! The inliner pass above implicitly
// creates a CGSCC pass manager, but we don't want to add extensions into
// that pass manager. To prevent this we insert a no-op module pass to reset
// the pass manager to get the same behavior as EP_OptimizerLast in non-O0
// builds. The function merging pass is
if (MergeFunctions)
MPM.add(createMergeFunctionsPass());
else if (!GlobalExtensions->empty() || !Extensions.empty())
MPM.add(createBarrierNoopPass());
addExtensionsToPM(EP_EnabledOnOptLevel0, MPM);
return;
}
// Add LibraryInfo if we have some.
if (LibraryInfo)
MPM.add(new TargetLibraryInfoWrapperPass(*LibraryInfo));
addInitialAliasAnalysisPasses(MPM);
if (!DisableUnitAtATime) {
addExtensionsToPM(EP_ModuleOptimizerEarly, MPM);
MPM.add(createIPSCCPPass()); // IP SCCP
MPM.add(createGlobalOptimizerPass()); // Optimize out global vars
// Promote any localized global vars
MPM.add(createPromoteMemoryToRegisterPass());
MPM.add(createDeadArgEliminationPass()); // Dead argument elimination
MPM.add(createInstructionCombiningPass());// Clean up after IPCP & DAE
addExtensionsToPM(EP_Peephole, MPM);
MPM.add(createCFGSimplificationPass()); // Clean up after IPCP & DAE
}
if (EnableNonLTOGlobalsModRef)
// We add a module alias analysis pass here. In part due to bugs in the
// analysis infrastructure this "works" in that the analysis stays alive
// for the entire SCC pass run below.
[PM/AA] Rebuild LLVM's alias analysis infrastructure in a way compatible with the new pass manager, and no longer relying on analysis groups. This builds essentially a ground-up new AA infrastructure stack for LLVM. The core ideas are the same that are used throughout the new pass manager: type erased polymorphism and direct composition. The design is as follows: - FunctionAAResults is a type-erasing alias analysis results aggregation interface to walk a single query across a range of results from different alias analyses. Currently this is function-specific as we always assume that aliasing queries are *within* a function. - AAResultBase is a CRTP utility providing stub implementations of various parts of the alias analysis result concept, notably in several cases in terms of other more general parts of the interface. This can be used to implement only a narrow part of the interface rather than the entire interface. This isn't really ideal, this logic should be hoisted into FunctionAAResults as currently it will cause a significant amount of redundant work, but it faithfully models the behavior of the prior infrastructure. - All the alias analysis passes are ported to be wrapper passes for the legacy PM and new-style analysis passes for the new PM with a shared result object. In some cases (most notably CFL), this is an extremely naive approach that we should revisit when we can specialize for the new pass manager. - BasicAA has been restructured to reflect that it is much more fundamentally a function analysis because it uses dominator trees and loop info that need to be constructed for each function. All of the references to getting alias analysis results have been updated to use the new aggregation interface. All the preservation and other pass management code has been updated accordingly. The way the FunctionAAResultsWrapperPass works is to detect the available alias analyses when run, and add them to the results object. This means that we should be able to continue to respect when various passes are added to the pipeline, for example adding CFL or adding TBAA passes should just cause their results to be available and to get folded into this. The exception to this rule is BasicAA which really needs to be a function pass due to using dominator trees and loop info. As a consequence, the FunctionAAResultsWrapperPass directly depends on BasicAA and always includes it in the aggregation. This has significant implications for preserving analyses. Generally, most passes shouldn't bother preserving FunctionAAResultsWrapperPass because rebuilding the results just updates the set of known AA passes. The exception to this rule are LoopPass instances which need to preserve all the function analyses that the loop pass manager will end up needing. This means preserving both BasicAAWrapperPass and the aggregating FunctionAAResultsWrapperPass. Now, when preserving an alias analysis, you do so by directly preserving that analysis. This is only necessary for non-immutable-pass-provided alias analyses though, and there are only three of interest: BasicAA, GlobalsAA (formerly GlobalsModRef), and SCEVAA. Usually BasicAA is preserved when needed because it (like DominatorTree and LoopInfo) is marked as a CFG-only pass. I've expanded GlobalsAA into the preserved set everywhere we previously were preserving all of AliasAnalysis, and I've added SCEVAA in the intersection of that with where we preserve SCEV itself. One significant challenge to all of this is that the CGSCC passes were actually using the alias analysis implementations by taking advantage of a pretty amazing set of loop holes in the old pass manager's analysis management code which allowed analysis groups to slide through in many cases. Moving away from analysis groups makes this problem much more obvious. To fix it, I've leveraged the flexibility the design of the new PM components provides to just directly construct the relevant alias analyses for the relevant functions in the IPO passes that need them. This is a bit hacky, but should go away with the new pass manager, and is already in many ways cleaner than the prior state. Another significant challenge is that various facilities of the old alias analysis infrastructure just don't fit any more. The most significant of these is the alias analysis 'counter' pass. That pass relied on the ability to snoop on AA queries at different points in the analysis group chain. Instead, I'm planning to build printing functionality directly into the aggregation layer. I've not included that in this patch merely to keep it smaller. Note that all of this needs a nearly complete rewrite of the AA documentation. I'm planning to do that, but I'd like to make sure the new design settles, and to flesh out a bit more of what it looks like in the new pass manager first. Differential Revision: http://reviews.llvm.org/D12080 llvm-svn: 247167
2015-09-10 01:55:00 +08:00
MPM.add(createGlobalsAAWrapperPass());
// Start of CallGraph SCC passes.
if (!DisableUnitAtATime)
MPM.add(createPruneEHPass()); // Remove dead EH info
if (Inliner) {
MPM.add(Inliner);
Inliner = nullptr;
}
if (!DisableUnitAtATime)
MPM.add(createFunctionAttrsPass()); // Set readonly/readnone attrs
if (OptLevel > 2)
MPM.add(createArgumentPromotionPass()); // Scalarize uninlined fn args
// Start of function pass.
// Break up aggregate allocas, using SSAUpdater.
Port the SSAUpdater-based promotion logic from the old SROA pass to the new one, and add support for running the new pass in that mode and in that slot of the pass manager. With this the new pass can completely replace the old one within the pipeline. The strategy for enabling or disabling the SSAUpdater logic is to do it by making the requirement of the domtree analysis optional. By default, it is required and we get the standard mem2reg approach. This is usually the desired strategy when run in stand-alone situations. Within the CGSCC pass manager, we disable requiring of the domtree analysis and consequentially trigger fallback to the SSAUpdater promotion. In theory this would allow the pass to re-use a domtree if one happened to be available even when run in a mode that doesn't require it. In practice, it lets us have a single pass rather than two which was simpler for me to wrap my head around. There is a hidden flag to force the use of the SSAUpdater code path for the purpose of testing. The primary testing strategy is just to run the existing tests through that path. One notable difference is that it has custom code to handle lifetime markers, and one of the tests has been enhanced to exercise that code. This has survived a bootstrap and the test suite without serious correctness issues, however my run of the test suite produced *very* alarming performance numbers. I don't entirely understand or trust them though, so more investigation is on-going. To aid my understanding of the performance impact of the new SROA now that it runs throughout the optimization pipeline, I'm enabling it by default in this commit, and will disable it again once the LNT bots have picked up one iteration with it. I want to get those bots (which are much more stable) to evaluate the impact of the change before I jump to any conclusions. NOTE: Several Clang tests will fail because they run -O3 and check the result's order of output. They'll go back to passing once I disable it again. llvm-svn: 163965
2012-09-15 19:43:14 +08:00
if (UseNewSROA)
Require Dominator Tree For SROA, improve compile-time TL-DR: SROA is followed by EarlyCSE which requires the DominatorTree. There is no reason not to require it up-front for SROA. Some history is necessary to understand why we ended-up here. r123437 switched the second (Legacy)SROA in the optimizer pipeline to use SSAUpdater in order to avoid recomputing the costly DominanceFrontier. The purpose was to speed-up the compile-time. Later r123609 removed the need for the DominanceFrontier in (Legacy)SROA. Right after, some cleanup was made in r123724 to remove any reference to the DominanceFrontier. SROA existed in two flavors: SROA_SSAUp and SROA_DT (the latter replacing SROA_DF). The second argument of `createScalarReplAggregatesPass` was renamed from `UseDomFrontier` to `UseDomTree`. I believe this is were a mistake was made. The pipeline was not updated and the call site was still: PM->add(createScalarReplAggregatesPass(-1, false)); At that time, SROA was immediately followed in the pipeline by EarlyCSE which required alread the DominatorTree. Not requiring the DominatorTree in SROA didn't save anything, but unfortunately it was lost at this point. When the new SROA Pass was introduced in r163965, I believe the goal was to have an exact replacement of the existing SROA, this bug slipped through. You can see currently: $ echo "" | clang -x c++ -O3 -c - -mllvm -debug-pass=Structure ... ... FunctionPass Manager SROA Dominator Tree Construction Early CSE After this patch: $ echo "" | clang -x c++ -O3 -c - -mllvm -debug-pass=Structure ... ... FunctionPass Manager Dominator Tree Construction SROA Early CSE This improves the compile time from 88s to 23s for PR17855. https://llvm.org/bugs/show_bug.cgi?id=17855 And from 113s to 12s for PR16756 https://llvm.org/bugs/show_bug.cgi?id=16756 Reviewers: chandlerc Differential Revision: http://reviews.llvm.org/D12267 From: Mehdi Amini <mehdi.amini@apple.com> llvm-svn: 245820
2015-08-24 06:15:49 +08:00
MPM.add(createSROAPass());
Port the SSAUpdater-based promotion logic from the old SROA pass to the new one, and add support for running the new pass in that mode and in that slot of the pass manager. With this the new pass can completely replace the old one within the pipeline. The strategy for enabling or disabling the SSAUpdater logic is to do it by making the requirement of the domtree analysis optional. By default, it is required and we get the standard mem2reg approach. This is usually the desired strategy when run in stand-alone situations. Within the CGSCC pass manager, we disable requiring of the domtree analysis and consequentially trigger fallback to the SSAUpdater promotion. In theory this would allow the pass to re-use a domtree if one happened to be available even when run in a mode that doesn't require it. In practice, it lets us have a single pass rather than two which was simpler for me to wrap my head around. There is a hidden flag to force the use of the SSAUpdater code path for the purpose of testing. The primary testing strategy is just to run the existing tests through that path. One notable difference is that it has custom code to handle lifetime markers, and one of the tests has been enhanced to exercise that code. This has survived a bootstrap and the test suite without serious correctness issues, however my run of the test suite produced *very* alarming performance numbers. I don't entirely understand or trust them though, so more investigation is on-going. To aid my understanding of the performance impact of the new SROA now that it runs throughout the optimization pipeline, I'm enabling it by default in this commit, and will disable it again once the LNT bots have picked up one iteration with it. I want to get those bots (which are much more stable) to evaluate the impact of the change before I jump to any conclusions. NOTE: Several Clang tests will fail because they run -O3 and check the result's order of output. They'll go back to passing once I disable it again. llvm-svn: 163965
2012-09-15 19:43:14 +08:00
else
MPM.add(createScalarReplAggregatesPass(-1, false));
MPM.add(createEarlyCSEPass()); // Catch trivial redundancies
MPM.add(createJumpThreadingPass()); // Thread jumps.
MPM.add(createCorrelatedValuePropagationPass()); // Propagate conditionals
MPM.add(createCFGSimplificationPass()); // Merge & remove BBs
MPM.add(createInstructionCombiningPass()); // Combine silly seq's
addExtensionsToPM(EP_Peephole, MPM);
MPM.add(createTailCallEliminationPass()); // Eliminate tail calls
MPM.add(createCFGSimplificationPass()); // Merge & remove BBs
MPM.add(createReassociatePass()); // Reassociate expressions
// Rotate Loop - disable header duplication at -Oz
MPM.add(createLoopRotatePass(SizeLevel == 2 ? 0 : -1));
MPM.add(createLICMPass()); // Hoist loop invariants
MPM.add(createLoopUnswitchPass(SizeLevel || OptLevel < 3));
MPM.add(createCFGSimplificationPass());
MPM.add(createInstructionCombiningPass());
MPM.add(createIndVarSimplifyPass()); // Canonicalize indvars
MPM.add(createLoopIdiomPass()); // Recognize idioms like memset.
MPM.add(createLoopDeletionPass()); // Delete dead loops
if (EnableLoopInterchange) {
MPM.add(createLoopInterchangePass()); // Interchange loops
MPM.add(createCFGSimplificationPass());
}
if (!DisableUnrollLoops)
MPM.add(createSimpleLoopUnrollPass()); // Unroll small loops
addExtensionsToPM(EP_LoopOptimizerEnd, MPM);
if (OptLevel > 1) {
if (EnableMLSM)
MPM.add(createMergedLoadStoreMotionPass()); // Merge ld/st in diamonds
MPM.add(createGVNPass(DisableGVNLoadPRE)); // Remove redundancies
}
MPM.add(createMemCpyOptPass()); // Remove memcpy / form memset
MPM.add(createSCCPPass()); // Constant prop with SCCP
[BDCE] Add a bit-tracking DCE pass BDCE is a bit-tracking dead code elimination pass. It is based on ADCE (the "aggressive DCE" pass), with the added capability to track dead bits of integer valued instructions and remove those instructions when all of the bits are dead. Currently, it does not actually do this all-bits-dead removal, but rather replaces the instruction's uses with a constant zero, and lets instcombine (and the later run of ADCE) do the rest. Because we essentially get a run of ADCE "for free" while tracking the dead bits, we also do what ADCE does and removes actually-dead instructions as well (this includes instructions newly trivially dead because all bits were dead, but not all such instructions can be removed). The motivation for this is a case like: int __attribute__((const)) foo(int i); int bar(int x) { x |= (4 & foo(5)); x |= (8 & foo(3)); x |= (16 & foo(2)); x |= (32 & foo(1)); x |= (64 & foo(0)); x |= (128& foo(4)); return x >> 4; } As it turns out, if you order the bit-field insertions so that all of the dead ones come last, then instcombine will remove them. However, if you pick some other order (such as the one above), the fact that some of the calls to foo() are useless is not locally obvious, and we don't remove them (without this pass). I did a quick compile-time overhead check using sqlite from the test suite (Release+Asserts). BDCE took ~0.4% of the compilation time (making it about twice as expensive as ADCE). I've not looked at why yet, but we eliminate instructions due to having all-dead bits in: External/SPEC/CFP2006/447.dealII/447.dealII External/SPEC/CINT2006/400.perlbench/400.perlbench External/SPEC/CINT2006/403.gcc/403.gcc MultiSource/Applications/ClamAV/clamscan MultiSource/Benchmarks/7zip/7zip-benchmark llvm-svn: 229462
2015-02-17 09:36:59 +08:00
// Delete dead bit computations (instcombine runs after to fold away the dead
// computations, and then ADCE will run later to exploit any new DCE
// opportunities that creates).
MPM.add(createBitTrackingDCEPass()); // Delete dead bit computations
// Run instcombine after redundancy elimination to exploit opportunities
// opened up by them.
MPM.add(createInstructionCombiningPass());
addExtensionsToPM(EP_Peephole, MPM);
MPM.add(createJumpThreadingPass()); // Thread jumps
MPM.add(createCorrelatedValuePropagationPass());
MPM.add(createDeadStoreEliminationPass()); // Delete dead stores
MPM.add(createLICMPass());
addExtensionsToPM(EP_ScalarOptimizerLate, MPM);
if (RerollLoops)
Add a loop rerolling pass This adds a loop rerolling pass: the opposite of (partial) loop unrolling. The transformation aims to take loops like this: for (int i = 0; i < 3200; i += 5) { a[i] += alpha * b[i]; a[i + 1] += alpha * b[i + 1]; a[i + 2] += alpha * b[i + 2]; a[i + 3] += alpha * b[i + 3]; a[i + 4] += alpha * b[i + 4]; } and turn them into this: for (int i = 0; i < 3200; ++i) { a[i] += alpha * b[i]; } and loops like this: for (int i = 0; i < 500; ++i) { x[3*i] = foo(0); x[3*i+1] = foo(0); x[3*i+2] = foo(0); } and turn them into this: for (int i = 0; i < 1500; ++i) { x[i] = foo(0); } There are two motivations for this transformation: 1. Code-size reduction (especially relevant, obviously, when compiling for code size). 2. Providing greater choice to the loop vectorizer (and generic unroller) to choose the unrolling factor (and a better ability to vectorize). The loop vectorizer can take vector lengths and register pressure into account when choosing an unrolling factor, for example, and a pre-unrolled loop limits that choice. This is especially problematic if the manual unrolling was optimized for a machine different from the current target. The current implementation is limited to single basic-block loops only. The rerolling recognition should work regardless of how the loop iterations are intermixed within the loop body (subject to dependency and side-effect constraints), but the significant restriction is that the order of the instructions in each iteration must be identical. This seems sufficient to capture all current use cases. This pass is not currently enabled by default at any optimization level. llvm-svn: 194939
2013-11-17 07:59:05 +08:00
MPM.add(createLoopRerollPass());
if (!RunSLPAfterLoopVectorization) {
if (SLPVectorize)
MPM.add(createSLPVectorizerPass()); // Vectorize parallel scalar chains.
if (BBVectorize) {
MPM.add(createBBVectorizePass());
MPM.add(createInstructionCombiningPass());
addExtensionsToPM(EP_Peephole, MPM);
if (OptLevel > 1 && UseGVNAfterVectorization)
MPM.add(createGVNPass(DisableGVNLoadPRE)); // Remove redundancies
else
MPM.add(createEarlyCSEPass()); // Catch trivial redundancies
// BBVectorize may have significantly shortened a loop body; unroll again.
if (!DisableUnrollLoops)
MPM.add(createLoopUnrollPass());
}
}
if (LoadCombine)
MPM.add(createLoadCombinePass());
MPM.add(createAggressiveDCEPass()); // Delete dead instructions
MPM.add(createCFGSimplificationPass()); // Merge & remove BBs
MPM.add(createInstructionCombiningPass()); // Clean up after everything.
addExtensionsToPM(EP_Peephole, MPM);
// FIXME: This is a HACK! The inliner pass above implicitly creates a CGSCC
// pass manager that we are specifically trying to avoid. To prevent this
// we must insert a no-op module pass to reset the pass manager.
MPM.add(createBarrierNoopPass());
2014-10-14 08:31:29 +08:00
if (!DisableUnitAtATime && OptLevel > 1 && !PrepareForLTO) {
// Remove avail extern fns and globals definitions if we aren't
// compiling an object file for later LTO. For LTO we want to preserve
// these so they are eligible for inlining at link-time. Note if they
// are unreferenced they will be removed by GlobalDCE later, so
// this only impacts referenced available externally globals.
// Eventually they will be suppressed during codegen, but eliminating
// here enables more opportunity for GlobalDCE as it may make
// globals referenced by available external functions dead
// and saves running remaining passes on the eliminated functions.
MPM.add(createEliminateAvailableExternallyPass());
}
if (EnableNonLTOGlobalsModRef)
// We add a fresh GlobalsModRef run at this point. This is particularly
// useful as the above will have inlined, DCE'ed, and function-attr
// propagated everything. We should at this point have a reasonably minimal
// and richly annotated call graph. By computing aliasing and mod/ref
// information for all local globals here, the late loop passes and notably
// the vectorizer will be able to use them to help recognize vectorizable
// memory operations.
//
// Note that this relies on a bug in the pass manager which preserves
// a module analysis into a function pass pipeline (and throughout it) so
// long as the first function pass doesn't invalidate the module analysis.
// Thus both Float2Int and LoopRotate have to preserve AliasAnalysis for
// this to work. Fortunately, it is trivial to preserve AliasAnalysis
// (doing nothing preserves it as it is required to be conservatively
// correct in the face of IR changes).
[PM/AA] Rebuild LLVM's alias analysis infrastructure in a way compatible with the new pass manager, and no longer relying on analysis groups. This builds essentially a ground-up new AA infrastructure stack for LLVM. The core ideas are the same that are used throughout the new pass manager: type erased polymorphism and direct composition. The design is as follows: - FunctionAAResults is a type-erasing alias analysis results aggregation interface to walk a single query across a range of results from different alias analyses. Currently this is function-specific as we always assume that aliasing queries are *within* a function. - AAResultBase is a CRTP utility providing stub implementations of various parts of the alias analysis result concept, notably in several cases in terms of other more general parts of the interface. This can be used to implement only a narrow part of the interface rather than the entire interface. This isn't really ideal, this logic should be hoisted into FunctionAAResults as currently it will cause a significant amount of redundant work, but it faithfully models the behavior of the prior infrastructure. - All the alias analysis passes are ported to be wrapper passes for the legacy PM and new-style analysis passes for the new PM with a shared result object. In some cases (most notably CFL), this is an extremely naive approach that we should revisit when we can specialize for the new pass manager. - BasicAA has been restructured to reflect that it is much more fundamentally a function analysis because it uses dominator trees and loop info that need to be constructed for each function. All of the references to getting alias analysis results have been updated to use the new aggregation interface. All the preservation and other pass management code has been updated accordingly. The way the FunctionAAResultsWrapperPass works is to detect the available alias analyses when run, and add them to the results object. This means that we should be able to continue to respect when various passes are added to the pipeline, for example adding CFL or adding TBAA passes should just cause their results to be available and to get folded into this. The exception to this rule is BasicAA which really needs to be a function pass due to using dominator trees and loop info. As a consequence, the FunctionAAResultsWrapperPass directly depends on BasicAA and always includes it in the aggregation. This has significant implications for preserving analyses. Generally, most passes shouldn't bother preserving FunctionAAResultsWrapperPass because rebuilding the results just updates the set of known AA passes. The exception to this rule are LoopPass instances which need to preserve all the function analyses that the loop pass manager will end up needing. This means preserving both BasicAAWrapperPass and the aggregating FunctionAAResultsWrapperPass. Now, when preserving an alias analysis, you do so by directly preserving that analysis. This is only necessary for non-immutable-pass-provided alias analyses though, and there are only three of interest: BasicAA, GlobalsAA (formerly GlobalsModRef), and SCEVAA. Usually BasicAA is preserved when needed because it (like DominatorTree and LoopInfo) is marked as a CFG-only pass. I've expanded GlobalsAA into the preserved set everywhere we previously were preserving all of AliasAnalysis, and I've added SCEVAA in the intersection of that with where we preserve SCEV itself. One significant challenge to all of this is that the CGSCC passes were actually using the alias analysis implementations by taking advantage of a pretty amazing set of loop holes in the old pass manager's analysis management code which allowed analysis groups to slide through in many cases. Moving away from analysis groups makes this problem much more obvious. To fix it, I've leveraged the flexibility the design of the new PM components provides to just directly construct the relevant alias analyses for the relevant functions in the IPO passes that need them. This is a bit hacky, but should go away with the new pass manager, and is already in many ways cleaner than the prior state. Another significant challenge is that various facilities of the old alias analysis infrastructure just don't fit any more. The most significant of these is the alias analysis 'counter' pass. That pass relied on the ability to snoop on AA queries at different points in the analysis group chain. Instead, I'm planning to build printing functionality directly into the aggregation layer. I've not included that in this patch merely to keep it smaller. Note that all of this needs a nearly complete rewrite of the AA documentation. I'm planning to do that, but I'd like to make sure the new design settles, and to flesh out a bit more of what it looks like in the new pass manager first. Differential Revision: http://reviews.llvm.org/D12080 llvm-svn: 247167
2015-09-10 01:55:00 +08:00
MPM.add(createGlobalsAAWrapperPass());
if (RunFloat2Int)
MPM.add(createFloat2IntPass());
addExtensionsToPM(EP_VectorizerStart, MPM);
2014-10-14 08:31:29 +08:00
// Re-rotate loops in all our loop nests. These may have fallout out of
// rotated form due to GVN or other transformations, and the vectorizer relies
// on the rotated form. Disable header duplication at -Oz.
MPM.add(createLoopRotatePass(SizeLevel == 2 ? 0 : -1));
2014-10-14 08:31:29 +08:00
// Distribute loops to allow partial vectorization. I.e. isolate dependences
// into separate loop that would otherwise inhibit vectorization.
if (EnableLoopDistribute)
MPM.add(createLoopDistributePass());
MPM.add(createLoopVectorizePass(DisableUnrollLoops, LoopVectorize));
LLE 6/6: Add LoopLoadElimination pass Summary: The goal of this pass is to perform store-to-load forwarding across the backedge of a loop. E.g.: for (i) A[i + 1] = A[i] + B[i] => T = A[0] for (i) T = T + B[i] A[i + 1] = T The pass relies on loop dependence analysis via LoopAccessAnalisys to find opportunities of loop-carried dependences with a distance of one between a store and a load. Since it's using LoopAccessAnalysis, it was easy to also add support for versioning away may-aliasing intervening stores that would otherwise prevent this transformation. This optimization is also performed by Load-PRE in GVN without the option of multi-versioning. As was discussed with Daniel Berlin in http://reviews.llvm.org/D9548, this is inferior to a more loop-aware solution applied here. Hopefully, we will be able to remove some complexity from GVN/MemorySSA as a consequence. In the long run, we may want to extend this pass (or create a new one if there is little overlap) to also eliminate loop-indepedent redundant loads and store that *require* versioning due to may-aliasing intervening stores/loads. I have some motivating cases for store elimination. My plan right now is to wait for MemorySSA to come online first rather than using memdep for this. The main motiviation for this pass is the 456.hmmer loop in SPECint2006 where after distributing the original loop and vectorizing the top part, we are left with the critical path exposed in the bottom loop. Being able to promote the memory dependence into a register depedence (even though the HW does perform store-to-load fowarding as well) results in a major gain (~20%). This gain also transfers over to x86: it's around 8-10%. Right now the pass is off by default and can be enabled with -enable-loop-load-elim. On the LNT testsuite, there are two performance changes (negative number -> improvement): 1. -28% in Polybench/linear-algebra/solvers/dynprog: the length of the critical paths is reduced 2. +2% in Polybench/stencils/adi: Unfortunately, I couldn't reproduce this outside of LNT The pass is scheduled after the loop vectorizer (which is after loop distribution). The rational is to try to reuse LAA state, rather than recomputing it. The order between LV and LLE is not critical because normally LV does not touch scalar st->ld forwarding cases where vectorizing would inhibit the CPU's st->ld forwarding to kick in. LoopLoadElimination requires LAA to provide the full set of dependences (including forward dependences). LAA is known to omit loop-independent dependences in certain situations. The big comment before removeDependencesFromMultipleStores explains why this should not occur for the cases that we're interested in. Reviewers: dberlin, hfinkel Subscribers: junbuml, dberlin, mssimpso, rengolin, sanjoy, llvm-commits Differential Revision: http://reviews.llvm.org/D13259 llvm-svn: 252017
2015-11-04 07:50:08 +08:00
// Eliminate loads by forwarding stores from the previous iteration to loads
// of the current iteration.
if (EnableLoopLoadElim)
MPM.add(createLoopLoadEliminationPass());
// FIXME: Because of #pragma vectorize enable, the passes below are always
// inserted in the pipeline, even when the vectorizer doesn't run (ex. when
// on -O1 and no #pragma is found). Would be good to have these two passes
// as function calls, so that we can only pass them when the vectorizer
// changed the code.
MPM.add(createInstructionCombiningPass());
2014-10-14 08:31:29 +08:00
if (OptLevel > 1 && ExtraVectorizerPasses) {
// At higher optimization levels, try to clean up any runtime overlap and
// alignment checks inserted by the vectorizer. We want to track correllated
// runtime checks for two inner loops in the same outer loop, fold any
// common computations, hoist loop-invariant aspects out of any outer loop,
// and unswitch the runtime checks if possible. Once hoisted, we may have
// dead (or speculatable) control flows or more combining opportunities.
MPM.add(createEarlyCSEPass());
MPM.add(createCorrelatedValuePropagationPass());
MPM.add(createInstructionCombiningPass());
MPM.add(createLICMPass());
MPM.add(createLoopUnswitchPass(SizeLevel || OptLevel < 3));
MPM.add(createCFGSimplificationPass());
MPM.add(createInstructionCombiningPass());
}
if (RunSLPAfterLoopVectorization) {
2014-10-14 08:31:29 +08:00
if (SLPVectorize) {
MPM.add(createSLPVectorizerPass()); // Vectorize parallel scalar chains.
2014-10-14 08:31:29 +08:00
if (OptLevel > 1 && ExtraVectorizerPasses) {
MPM.add(createEarlyCSEPass());
}
}
if (BBVectorize) {
MPM.add(createBBVectorizePass());
MPM.add(createInstructionCombiningPass());
addExtensionsToPM(EP_Peephole, MPM);
if (OptLevel > 1 && UseGVNAfterVectorization)
MPM.add(createGVNPass(DisableGVNLoadPRE)); // Remove redundancies
else
MPM.add(createEarlyCSEPass()); // Catch trivial redundancies
// BBVectorize may have significantly shortened a loop body; unroll again.
if (!DisableUnrollLoops)
MPM.add(createLoopUnrollPass());
}
}
addExtensionsToPM(EP_Peephole, MPM);
MPM.add(createCFGSimplificationPass());
2014-10-14 08:31:29 +08:00
MPM.add(createInstructionCombiningPass());
if (!DisableUnrollLoops) {
MPM.add(createLoopUnrollPass()); // Unroll small loops
// LoopUnroll may generate some redundency to cleanup.
MPM.add(createInstructionCombiningPass());
// Runtime unrolling will introduce runtime check in loop prologue. If the
// unrolled loop is a inner loop, then the prologue will be inside the
// outer loop. LICM pass can help to promote the runtime check out if the
// checked value is loop invariant.
MPM.add(createLICMPass());
}
// After vectorization and unrolling, assume intrinsics may tell us more
// about pointer alignments.
MPM.add(createAlignmentFromAssumptionsPass());
if (!DisableUnitAtATime) {
// FIXME: We shouldn't bother with this anymore.
MPM.add(createStripDeadPrototypesPass()); // Get rid of dead prototypes
// GlobalOpt already deletes dead functions and globals, at -O2 try a
// late pass of GlobalDCE. It is capable of deleting dead cycles.
if (OptLevel > 1) {
MPM.add(createGlobalDCEPass()); // Remove dead fns and globals.
MPM.add(createConstantMergePass()); // Merge dup global constants
}
}
if (MergeFunctions)
MPM.add(createMergeFunctionsPass());
addExtensionsToPM(EP_OptimizerLast, MPM);
}
void PassManagerBuilder::addLTOOptimizationPasses(legacy::PassManagerBase &PM) {
// Provide AliasAnalysis services for optimizations.
addInitialAliasAnalysisPasses(PM);
if (FunctionIndex)
PM.add(createFunctionImportPass(FunctionIndex));
// Allow forcing function attributes as a debugging and tuning aid.
PM.add(createForceFunctionAttrsLegacyPass());
// Propagate constants at call sites into the functions they call. This
// opens opportunities for globalopt (and inlining) by substituting function
// pointers passed as arguments to direct uses of functions.
PM.add(createIPSCCPPass());
// Now that we internalized some globals, see if we can hack on them!
PM.add(createFunctionAttrsPass()); // Add norecurse if possible.
PM.add(createGlobalOptimizerPass());
// Promote any localized global vars.
PM.add(createPromoteMemoryToRegisterPass());
// Linking modules together can lead to duplicated global constants, only
// keep one copy of each constant.
PM.add(createConstantMergePass());
// Remove unused arguments from functions.
PM.add(createDeadArgEliminationPass());
// Reduce the code after globalopt and ipsccp. Both can open up significant
// simplification opportunities, and both can propagate functions through
// function pointers. When this happens, we often have to resolve varargs
// calls, etc, so let instcombine do this.
PM.add(createInstructionCombiningPass());
addExtensionsToPM(EP_Peephole, PM);
// Inline small functions
bool RunInliner = Inliner;
if (RunInliner) {
PM.add(Inliner);
Inliner = nullptr;
}
PM.add(createPruneEHPass()); // Remove dead EH info.
// Optimize globals again if we ran the inliner.
if (RunInliner)
PM.add(createGlobalOptimizerPass());
PM.add(createGlobalDCEPass()); // Remove dead functions.
// If we didn't decide to inline a function, check to see if we can
// transform it to pass arguments by value instead of by reference.
PM.add(createArgumentPromotionPass());
// The IPO passes may leave cruft around. Clean up after them.
PM.add(createInstructionCombiningPass());
addExtensionsToPM(EP_Peephole, PM);
PM.add(createJumpThreadingPass());
// Break up allocas
Introduce a new SROA implementation. This is essentially a ground up re-think of the SROA pass in LLVM. It was initially inspired by a few problems with the existing pass: - It is subject to the bane of my existence in optimizations: arbitrary thresholds. - It is overly conservative about which constructs can be split and promoted. - The vector value replacement aspect is separated from the splitting logic, missing many opportunities where splitting and vector value formation can work together. - The splitting is entirely based around the underlying type of the alloca, despite this type often having little to do with the reality of how that memory is used. This is especially prevelant with unions and base classes where we tail-pack derived members. - When splitting fails (often due to the thresholds), the vector value replacement (again because it is separate) can kick in for preposterous cases where we simply should have split the value. This results in forming i1024 and i2048 integer "bit vectors" that tremendously slow down subsequnet IR optimizations (due to large APInts) and impede the backend's lowering. The new design takes an approach that fundamentally is not susceptible to many of these problems. It is the result of a discusison between myself and Duncan Sands over IRC about how to premptively avoid these types of problems and how to do SROA in a more principled way. Since then, it has evolved and grown, but this remains an important aspect: it fixes real world problems with the SROA process today. First, the transform of SROA actually has little to do with replacement. It has more to do with splitting. The goal is to take an aggregate alloca and form a composition of scalar allocas which can replace it and will be most suitable to the eventual replacement by scalar SSA values. The actual replacement is performed by mem2reg (and in the future SSAUpdater). The splitting is divided into four phases. The first phase is an analysis of the uses of the alloca. This phase recursively walks uses, building up a dense datastructure representing the ranges of the alloca's memory actually used and checking for uses which inhibit any aspects of the transform such as the escape of a pointer. Once we have a mapping of the ranges of the alloca used by individual operations, we compute a partitioning of the used ranges. Some uses are inherently splittable (such as memcpy and memset), while scalar uses are not splittable. The goal is to build a partitioning that has the minimum number of splits while placing each unsplittable use in its own partition. Overlapping unsplittable uses belong to the same partition. This is the target split of the aggregate alloca, and it maximizes the number of scalar accesses which become accesses to their own alloca and candidates for promotion. Third, we re-walk the uses of the alloca and assign each specific memory access to all the partitions touched so that we have dense use-lists for each partition. Finally, we build a new, smaller alloca for each partition and rewrite each use of that partition to use the new alloca. During this phase the pass will also work very hard to transform uses of an alloca into a form suitable for promotion, including forming vector operations, speculating loads throguh PHI nodes and selects, etc. After splitting is complete, each newly refined alloca that is a candidate for promotion to a scalar SSA value is run through mem2reg. There are lots of reasonably detailed comments in the source code about the design and algorithms, and I'm going to be trying to improve them in subsequent commits to ensure this is well documented, as the new pass is in many ways more complex than the old one. Some of this is still a WIP, but the current state is reasonbly stable. It has passed bootstrap, the nightly test suite, and Duncan has run it successfully through the ACATS and DragonEgg test suites. That said, it remains behind a default-off flag until the last few pieces are in place, and full testing can be done. Specific areas I'm looking at next: - Improved comments and some code cleanup from reviews. - SSAUpdater and enabling this pass inside the CGSCC pass manager. - Some datastructure tuning and compile-time measurements. - More aggressive FCA splitting and vector formation. Many thanks to Duncan Sands for the thorough final review, as well as Benjamin Kramer for lots of review during the process of writing this pass, and Daniel Berlin for reviewing the data structures and algorithms and general theory of the pass. Also, several other people on IRC, over lunch tables, etc for lots of feedback and advice. llvm-svn: 163883
2012-09-14 17:22:59 +08:00
if (UseNewSROA)
PM.add(createSROAPass());
else
PM.add(createScalarReplAggregatesPass());
// Run a few AA driven optimizations here and now, to cleanup the code.
PM.add(createFunctionAttrsPass()); // Add nocapture.
[PM/AA] Rebuild LLVM's alias analysis infrastructure in a way compatible with the new pass manager, and no longer relying on analysis groups. This builds essentially a ground-up new AA infrastructure stack for LLVM. The core ideas are the same that are used throughout the new pass manager: type erased polymorphism and direct composition. The design is as follows: - FunctionAAResults is a type-erasing alias analysis results aggregation interface to walk a single query across a range of results from different alias analyses. Currently this is function-specific as we always assume that aliasing queries are *within* a function. - AAResultBase is a CRTP utility providing stub implementations of various parts of the alias analysis result concept, notably in several cases in terms of other more general parts of the interface. This can be used to implement only a narrow part of the interface rather than the entire interface. This isn't really ideal, this logic should be hoisted into FunctionAAResults as currently it will cause a significant amount of redundant work, but it faithfully models the behavior of the prior infrastructure. - All the alias analysis passes are ported to be wrapper passes for the legacy PM and new-style analysis passes for the new PM with a shared result object. In some cases (most notably CFL), this is an extremely naive approach that we should revisit when we can specialize for the new pass manager. - BasicAA has been restructured to reflect that it is much more fundamentally a function analysis because it uses dominator trees and loop info that need to be constructed for each function. All of the references to getting alias analysis results have been updated to use the new aggregation interface. All the preservation and other pass management code has been updated accordingly. The way the FunctionAAResultsWrapperPass works is to detect the available alias analyses when run, and add them to the results object. This means that we should be able to continue to respect when various passes are added to the pipeline, for example adding CFL or adding TBAA passes should just cause their results to be available and to get folded into this. The exception to this rule is BasicAA which really needs to be a function pass due to using dominator trees and loop info. As a consequence, the FunctionAAResultsWrapperPass directly depends on BasicAA and always includes it in the aggregation. This has significant implications for preserving analyses. Generally, most passes shouldn't bother preserving FunctionAAResultsWrapperPass because rebuilding the results just updates the set of known AA passes. The exception to this rule are LoopPass instances which need to preserve all the function analyses that the loop pass manager will end up needing. This means preserving both BasicAAWrapperPass and the aggregating FunctionAAResultsWrapperPass. Now, when preserving an alias analysis, you do so by directly preserving that analysis. This is only necessary for non-immutable-pass-provided alias analyses though, and there are only three of interest: BasicAA, GlobalsAA (formerly GlobalsModRef), and SCEVAA. Usually BasicAA is preserved when needed because it (like DominatorTree and LoopInfo) is marked as a CFG-only pass. I've expanded GlobalsAA into the preserved set everywhere we previously were preserving all of AliasAnalysis, and I've added SCEVAA in the intersection of that with where we preserve SCEV itself. One significant challenge to all of this is that the CGSCC passes were actually using the alias analysis implementations by taking advantage of a pretty amazing set of loop holes in the old pass manager's analysis management code which allowed analysis groups to slide through in many cases. Moving away from analysis groups makes this problem much more obvious. To fix it, I've leveraged the flexibility the design of the new PM components provides to just directly construct the relevant alias analyses for the relevant functions in the IPO passes that need them. This is a bit hacky, but should go away with the new pass manager, and is already in many ways cleaner than the prior state. Another significant challenge is that various facilities of the old alias analysis infrastructure just don't fit any more. The most significant of these is the alias analysis 'counter' pass. That pass relied on the ability to snoop on AA queries at different points in the analysis group chain. Instead, I'm planning to build printing functionality directly into the aggregation layer. I've not included that in this patch merely to keep it smaller. Note that all of this needs a nearly complete rewrite of the AA documentation. I'm planning to do that, but I'd like to make sure the new design settles, and to flesh out a bit more of what it looks like in the new pass manager first. Differential Revision: http://reviews.llvm.org/D12080 llvm-svn: 247167
2015-09-10 01:55:00 +08:00
PM.add(createGlobalsAAWrapperPass()); // IP alias analysis.
PM.add(createLICMPass()); // Hoist loop invariants.
if (EnableMLSM)
PM.add(createMergedLoadStoreMotionPass()); // Merge ld/st in diamonds.
PM.add(createGVNPass(DisableGVNLoadPRE)); // Remove redundancies.
PM.add(createMemCpyOptPass()); // Remove dead memcpys.
// Nuke dead stores.
PM.add(createDeadStoreEliminationPass());
// More loops are countable; try to optimize them.
PM.add(createIndVarSimplifyPass());
PM.add(createLoopDeletionPass());
if (EnableLoopInterchange)
PM.add(createLoopInterchangePass());
PM.add(createLoopVectorizePass(true, LoopVectorize));
// Now that we've optimized loops (in particular loop induction variables),
// we may have exposed more scalar opportunities. Run parts of the scalar
// optimizer again at this point.
PM.add(createInstructionCombiningPass()); // Initial cleanup
PM.add(createCFGSimplificationPass()); // if-convert
PM.add(createSCCPPass()); // Propagate exposed constants
PM.add(createInstructionCombiningPass()); // Clean up again
PM.add(createBitTrackingDCEPass());
// More scalar chains could be vectorized due to more alias information
if (RunSLPAfterLoopVectorization)
if (SLPVectorize)
PM.add(createSLPVectorizerPass()); // Vectorize parallel scalar chains.
// After vectorization, assume intrinsics may tell us more about pointer
// alignments.
PM.add(createAlignmentFromAssumptionsPass());
if (LoadCombine)
PM.add(createLoadCombinePass());
// Cleanup and simplify the code after the scalar optimizations.
PM.add(createInstructionCombiningPass());
addExtensionsToPM(EP_Peephole, PM);
PM.add(createJumpThreadingPass());
}
void PassManagerBuilder::addLateLTOOptimizationPasses(
legacy::PassManagerBase &PM) {
// Delete basic blocks, which optimization passes may have killed.
PM.add(createCFGSimplificationPass());
// Drop bodies of available externally objects to improve GlobalDCE.
PM.add(createEliminateAvailableExternallyPass());
// Now that we have optimized the program, discard unreachable functions.
PM.add(createGlobalDCEPass());
// FIXME: this is profitable (for compiler time) to do at -O0 too, but
// currently it damages debug info.
if (MergeFunctions)
PM.add(createMergeFunctionsPass());
}
void PassManagerBuilder::populateLTOPassManager(legacy::PassManagerBase &PM) {
if (LibraryInfo)
PM.add(new TargetLibraryInfoWrapperPass(*LibraryInfo));
if (VerifyInput)
PM.add(createVerifierPass());
if (OptLevel > 1)
addLTOOptimizationPasses(PM);
// Create a function that performs CFI checks for cross-DSO calls with targets
// in the current module.
PM.add(createCrossDSOCFIPass());
// Lower bit sets to globals. This pass supports Clang's control flow
// integrity mechanisms (-fsanitize=cfi*) and needs to run at link time if CFI
// is enabled. The pass does nothing if CFI is disabled.
PM.add(createLowerBitSetsPass());
if (OptLevel != 0)
addLateLTOOptimizationPasses(PM);
if (VerifyOutput)
PM.add(createVerifierPass());
}
inline PassManagerBuilder *unwrap(LLVMPassManagerBuilderRef P) {
return reinterpret_cast<PassManagerBuilder*>(P);
}
inline LLVMPassManagerBuilderRef wrap(PassManagerBuilder *P) {
return reinterpret_cast<LLVMPassManagerBuilderRef>(P);
}
LLVMPassManagerBuilderRef LLVMPassManagerBuilderCreate() {
PassManagerBuilder *PMB = new PassManagerBuilder();
return wrap(PMB);
}
void LLVMPassManagerBuilderDispose(LLVMPassManagerBuilderRef PMB) {
PassManagerBuilder *Builder = unwrap(PMB);
delete Builder;
}
void
LLVMPassManagerBuilderSetOptLevel(LLVMPassManagerBuilderRef PMB,
unsigned OptLevel) {
PassManagerBuilder *Builder = unwrap(PMB);
Builder->OptLevel = OptLevel;
}
void
LLVMPassManagerBuilderSetSizeLevel(LLVMPassManagerBuilderRef PMB,
unsigned SizeLevel) {
PassManagerBuilder *Builder = unwrap(PMB);
Builder->SizeLevel = SizeLevel;
}
void
LLVMPassManagerBuilderSetDisableUnitAtATime(LLVMPassManagerBuilderRef PMB,
LLVMBool Value) {
PassManagerBuilder *Builder = unwrap(PMB);
Builder->DisableUnitAtATime = Value;
}
void
LLVMPassManagerBuilderSetDisableUnrollLoops(LLVMPassManagerBuilderRef PMB,
LLVMBool Value) {
PassManagerBuilder *Builder = unwrap(PMB);
Builder->DisableUnrollLoops = Value;
}
void
LLVMPassManagerBuilderSetDisableSimplifyLibCalls(LLVMPassManagerBuilderRef PMB,
LLVMBool Value) {
// NOTE: The simplify-libcalls pass has been removed.
}
void
LLVMPassManagerBuilderUseInlinerWithThreshold(LLVMPassManagerBuilderRef PMB,
unsigned Threshold) {
PassManagerBuilder *Builder = unwrap(PMB);
Builder->Inliner = createFunctionInliningPass(Threshold);
}
void
LLVMPassManagerBuilderPopulateFunctionPassManager(LLVMPassManagerBuilderRef PMB,
LLVMPassManagerRef PM) {
PassManagerBuilder *Builder = unwrap(PMB);
legacy::FunctionPassManager *FPM = unwrap<legacy::FunctionPassManager>(PM);
Builder->populateFunctionPassManager(*FPM);
}
void
LLVMPassManagerBuilderPopulateModulePassManager(LLVMPassManagerBuilderRef PMB,
LLVMPassManagerRef PM) {
PassManagerBuilder *Builder = unwrap(PMB);
legacy::PassManagerBase *MPM = unwrap(PM);
Builder->populateModulePassManager(*MPM);
}
void LLVMPassManagerBuilderPopulateLTOPassManager(LLVMPassManagerBuilderRef PMB,
LLVMPassManagerRef PM,
LLVMBool Internalize,
LLVMBool RunInliner) {
PassManagerBuilder *Builder = unwrap(PMB);
legacy::PassManagerBase *LPM = unwrap(PM);
// A small backwards compatibility hack. populateLTOPassManager used to take
// an RunInliner option.
if (RunInliner && !Builder->Inliner)
Builder->Inliner = createFunctionInliningPass();
Builder->populateLTOPassManager(*LPM);
}