llvm-6502/lib/Transforms/Scalar/LoopRerollPass.cpp

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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. git-svn-id: https://llvm.org/svn/llvm-project/llvm/trunk@194939 91177308-0d34-0410-b5e6-96231b3b80d8
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//===-- LoopReroll.cpp - Loop rerolling pass ------------------------------===//
//
// The LLVM Compiler Infrastructure
//
// This file is distributed under the University of Illinois Open Source
// License. See LICENSE.TXT for details.
//
//===----------------------------------------------------------------------===//
//
// This pass implements a simple loop reroller.
//
//===----------------------------------------------------------------------===//
#include "llvm/Transforms/Scalar.h"
#include "llvm/ADT/STLExtras.h"
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. git-svn-id: https://llvm.org/svn/llvm-project/llvm/trunk@194939 91177308-0d34-0410-b5e6-96231b3b80d8
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#include "llvm/ADT/SmallSet.h"
#include "llvm/ADT/Statistic.h"
#include "llvm/Analysis/AliasAnalysis.h"
#include "llvm/Analysis/AliasSetTracker.h"
#include "llvm/Analysis/LoopPass.h"
#include "llvm/Analysis/ScalarEvolution.h"
#include "llvm/Analysis/ScalarEvolutionExpander.h"
#include "llvm/Analysis/ScalarEvolutionExpressions.h"
#include "llvm/Analysis/ValueTracking.h"
#include "llvm/IR/DataLayout.h"
#include "llvm/IR/Dominators.h"
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. git-svn-id: https://llvm.org/svn/llvm-project/llvm/trunk@194939 91177308-0d34-0410-b5e6-96231b3b80d8
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#include "llvm/IR/IntrinsicInst.h"
#include "llvm/Support/CommandLine.h"
#include "llvm/Support/Debug.h"
#include "llvm/Support/raw_ostream.h"
#include "llvm/Target/TargetLibraryInfo.h"
#include "llvm/Transforms/Utils/BasicBlockUtils.h"
#include "llvm/Transforms/Utils/Local.h"
#include "llvm/Transforms/Utils/LoopUtils.h"
using namespace llvm;
#define DEBUG_TYPE "loop-reroll"
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. git-svn-id: https://llvm.org/svn/llvm-project/llvm/trunk@194939 91177308-0d34-0410-b5e6-96231b3b80d8
2013-11-16 23:59:05 +00:00
STATISTIC(NumRerolledLoops, "Number of rerolled loops");
static cl::opt<unsigned>
MaxInc("max-reroll-increment", cl::init(2048), cl::Hidden,
cl::desc("The maximum increment for loop rerolling"));
// This loop re-rolling transformation aims to transform loops like this:
//
// int foo(int a);
// void bar(int *x) {
// for (int i = 0; i < 500; i += 3) {
// foo(i);
// foo(i+1);
// foo(i+2);
// }
// }
//
// into a loop like this:
//
// void bar(int *x) {
// for (int i = 0; i < 500; ++i)
// foo(i);
// }
//
// It does this by looking for loops that, besides the latch code, are composed
// of isomorphic DAGs of instructions, with each DAG rooted at some increment
// to the induction variable, and where each DAG is isomorphic to the DAG
// rooted at the induction variable (excepting the sub-DAGs which root the
// other induction-variable increments). In other words, we're looking for loop
// bodies of the form:
//
// %iv = phi [ (preheader, ...), (body, %iv.next) ]
// f(%iv)
// %iv.1 = add %iv, 1 <-- a root increment
// f(%iv.1)
// %iv.2 = add %iv, 2 <-- a root increment
// f(%iv.2)
// %iv.scale_m_1 = add %iv, scale-1 <-- a root increment
// f(%iv.scale_m_1)
// ...
// %iv.next = add %iv, scale
// %cmp = icmp(%iv, ...)
// br %cmp, header, exit
//
// where each f(i) is a set of instructions that, collectively, are a function
// only of i (and other loop-invariant values).
//
// As a special case, we can also reroll loops like this:
//
// int foo(int);
// void bar(int *x) {
// for (int i = 0; i < 500; ++i) {
// x[3*i] = foo(0);
// x[3*i+1] = foo(0);
// x[3*i+2] = foo(0);
// }
// }
//
// into this:
//
// void bar(int *x) {
// for (int i = 0; i < 1500; ++i)
// x[i] = foo(0);
// }
//
// in which case, we're looking for inputs like this:
//
// %iv = phi [ (preheader, ...), (body, %iv.next) ]
// %scaled.iv = mul %iv, scale
// f(%scaled.iv)
// %scaled.iv.1 = add %scaled.iv, 1
// f(%scaled.iv.1)
// %scaled.iv.2 = add %scaled.iv, 2
// f(%scaled.iv.2)
// %scaled.iv.scale_m_1 = add %scaled.iv, scale-1
// f(%scaled.iv.scale_m_1)
// ...
// %iv.next = add %iv, 1
// %cmp = icmp(%iv, ...)
// br %cmp, header, exit
namespace {
class LoopReroll : public LoopPass {
public:
static char ID; // Pass ID, replacement for typeid
LoopReroll() : LoopPass(ID) {
initializeLoopRerollPass(*PassRegistry::getPassRegistry());
}
bool runOnLoop(Loop *L, LPPassManager &LPM) override;
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. git-svn-id: https://llvm.org/svn/llvm-project/llvm/trunk@194939 91177308-0d34-0410-b5e6-96231b3b80d8
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void getAnalysisUsage(AnalysisUsage &AU) const override {
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. git-svn-id: https://llvm.org/svn/llvm-project/llvm/trunk@194939 91177308-0d34-0410-b5e6-96231b3b80d8
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AU.addRequired<AliasAnalysis>();
AU.addRequired<LoopInfo>();
AU.addPreserved<LoopInfo>();
AU.addRequired<DominatorTreeWrapperPass>();
AU.addPreserved<DominatorTreeWrapperPass>();
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. git-svn-id: https://llvm.org/svn/llvm-project/llvm/trunk@194939 91177308-0d34-0410-b5e6-96231b3b80d8
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AU.addRequired<ScalarEvolution>();
AU.addRequired<TargetLibraryInfo>();
}
protected:
AliasAnalysis *AA;
LoopInfo *LI;
ScalarEvolution *SE;
const DataLayout *DL;
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. git-svn-id: https://llvm.org/svn/llvm-project/llvm/trunk@194939 91177308-0d34-0410-b5e6-96231b3b80d8
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TargetLibraryInfo *TLI;
DominatorTree *DT;
typedef SmallVector<Instruction *, 16> SmallInstructionVector;
typedef SmallSet<Instruction *, 16> SmallInstructionSet;
// A chain of isomorphic instructions, indentified by a single-use PHI,
// representing a reduction. Only the last value may be used outside the
// loop.
struct SimpleLoopReduction {
SimpleLoopReduction(Instruction *P, Loop *L)
: Valid(false), Instructions(1, P) {
assert(isa<PHINode>(P) && "First reduction instruction must be a PHI");
add(L);
}
bool valid() const {
return Valid;
}
Instruction *getPHI() const {
assert(Valid && "Using invalid reduction");
return Instructions.front();
}
Instruction *getReducedValue() const {
assert(Valid && "Using invalid reduction");
return Instructions.back();
}
Instruction *get(size_t i) const {
assert(Valid && "Using invalid reduction");
return Instructions[i+1];
}
Instruction *operator [] (size_t i) const { return get(i); }
// The size, ignoring the initial PHI.
size_t size() const {
assert(Valid && "Using invalid reduction");
return Instructions.size()-1;
}
typedef SmallInstructionVector::iterator iterator;
typedef SmallInstructionVector::const_iterator const_iterator;
iterator begin() {
assert(Valid && "Using invalid reduction");
return std::next(Instructions.begin());
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. git-svn-id: https://llvm.org/svn/llvm-project/llvm/trunk@194939 91177308-0d34-0410-b5e6-96231b3b80d8
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}
const_iterator begin() const {
assert(Valid && "Using invalid reduction");
return std::next(Instructions.begin());
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. git-svn-id: https://llvm.org/svn/llvm-project/llvm/trunk@194939 91177308-0d34-0410-b5e6-96231b3b80d8
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}
iterator end() { return Instructions.end(); }
const_iterator end() const { return Instructions.end(); }
protected:
bool Valid;
SmallInstructionVector Instructions;
void add(Loop *L);
};
// The set of all reductions, and state tracking of possible reductions
// during loop instruction processing.
struct ReductionTracker {
typedef SmallVector<SimpleLoopReduction, 16> SmallReductionVector;
// Add a new possible reduction.
void addSLR(SimpleLoopReduction &SLR) { PossibleReds.push_back(SLR); }
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. git-svn-id: https://llvm.org/svn/llvm-project/llvm/trunk@194939 91177308-0d34-0410-b5e6-96231b3b80d8
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// Setup to track possible reductions corresponding to the provided
// rerolling scale. Only reductions with a number of non-PHI instructions
// that is divisible by the scale are considered. Three instructions sets
// are filled in:
// - A set of all possible instructions in eligible reductions.
// - A set of all PHIs in eligible reductions
// - A set of all reduced values (last instructions) in eligible
// reductions.
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. git-svn-id: https://llvm.org/svn/llvm-project/llvm/trunk@194939 91177308-0d34-0410-b5e6-96231b3b80d8
2013-11-16 23:59:05 +00:00
void restrictToScale(uint64_t Scale,
SmallInstructionSet &PossibleRedSet,
SmallInstructionSet &PossibleRedPHISet,
SmallInstructionSet &PossibleRedLastSet) {
PossibleRedIdx.clear();
PossibleRedIter.clear();
Reds.clear();
for (unsigned i = 0, e = PossibleReds.size(); i != e; ++i)
if (PossibleReds[i].size() % Scale == 0) {
PossibleRedLastSet.insert(PossibleReds[i].getReducedValue());
PossibleRedPHISet.insert(PossibleReds[i].getPHI());
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. git-svn-id: https://llvm.org/svn/llvm-project/llvm/trunk@194939 91177308-0d34-0410-b5e6-96231b3b80d8
2013-11-16 23:59:05 +00:00
PossibleRedSet.insert(PossibleReds[i].getPHI());
PossibleRedIdx[PossibleReds[i].getPHI()] = i;
for (Instruction *J : PossibleReds[i]) {
PossibleRedSet.insert(J);
PossibleRedIdx[J] = i;
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. git-svn-id: https://llvm.org/svn/llvm-project/llvm/trunk@194939 91177308-0d34-0410-b5e6-96231b3b80d8
2013-11-16 23:59:05 +00:00
}
}
}
// The functions below are used while processing the loop instructions.
// Are the two instructions both from reductions, and furthermore, from
// the same reduction?
bool isPairInSame(Instruction *J1, Instruction *J2) {
DenseMap<Instruction *, int>::iterator J1I = PossibleRedIdx.find(J1);
if (J1I != PossibleRedIdx.end()) {
DenseMap<Instruction *, int>::iterator J2I = PossibleRedIdx.find(J2);
if (J2I != PossibleRedIdx.end() && J1I->second == J2I->second)
return true;
}
return false;
}
// The two provided instructions, the first from the base iteration, and
// the second from iteration i, form a matched pair. If these are part of
// a reduction, record that fact.
void recordPair(Instruction *J1, Instruction *J2, unsigned i) {
if (PossibleRedIdx.count(J1)) {
assert(PossibleRedIdx.count(J2) &&
"Recording reduction vs. non-reduction instruction?");
PossibleRedIter[J1] = 0;
PossibleRedIter[J2] = i;
int Idx = PossibleRedIdx[J1];
assert(Idx == PossibleRedIdx[J2] &&
"Recording pair from different reductions?");
Reds.insert(Idx);
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. git-svn-id: https://llvm.org/svn/llvm-project/llvm/trunk@194939 91177308-0d34-0410-b5e6-96231b3b80d8
2013-11-16 23:59:05 +00:00
}
}
// The functions below can be called after we've finished processing all
// instructions in the loop, and we know which reductions were selected.
// Is the provided instruction the PHI of a reduction selected for
// rerolling?
bool isSelectedPHI(Instruction *J) {
if (!isa<PHINode>(J))
return false;
for (DenseSet<int>::iterator RI = Reds.begin(), RIE = Reds.end();
RI != RIE; ++RI) {
int i = *RI;
if (cast<Instruction>(J) == PossibleReds[i].getPHI())
return true;
}
return false;
}
bool validateSelected();
void replaceSelected();
protected:
// The vector of all possible reductions (for any scale).
SmallReductionVector PossibleReds;
DenseMap<Instruction *, int> PossibleRedIdx;
DenseMap<Instruction *, int> PossibleRedIter;
DenseSet<int> Reds;
};
void collectPossibleIVs(Loop *L, SmallInstructionVector &PossibleIVs);
void collectPossibleReductions(Loop *L,
ReductionTracker &Reductions);
void collectInLoopUserSet(Loop *L,
const SmallInstructionVector &Roots,
const SmallInstructionSet &Exclude,
const SmallInstructionSet &Final,
DenseSet<Instruction *> &Users);
void collectInLoopUserSet(Loop *L,
Instruction * Root,
const SmallInstructionSet &Exclude,
const SmallInstructionSet &Final,
DenseSet<Instruction *> &Users);
bool findScaleFromMul(Instruction *RealIV, uint64_t &Scale,
Instruction *&IV,
SmallInstructionVector &LoopIncs);
bool collectAllRoots(Loop *L, uint64_t Inc, uint64_t Scale, Instruction *IV,
SmallVector<SmallInstructionVector, 32> &Roots,
SmallInstructionSet &AllRoots,
SmallInstructionVector &LoopIncs);
bool reroll(Instruction *IV, Loop *L, BasicBlock *Header, const SCEV *IterCount,
ReductionTracker &Reductions);
};
}
char LoopReroll::ID = 0;
INITIALIZE_PASS_BEGIN(LoopReroll, "loop-reroll", "Reroll loops", false, false)
INITIALIZE_AG_DEPENDENCY(AliasAnalysis)
INITIALIZE_PASS_DEPENDENCY(LoopInfo)
INITIALIZE_PASS_DEPENDENCY(DominatorTreeWrapperPass)
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. git-svn-id: https://llvm.org/svn/llvm-project/llvm/trunk@194939 91177308-0d34-0410-b5e6-96231b3b80d8
2013-11-16 23:59:05 +00:00
INITIALIZE_PASS_DEPENDENCY(ScalarEvolution)
INITIALIZE_PASS_DEPENDENCY(TargetLibraryInfo)
INITIALIZE_PASS_END(LoopReroll, "loop-reroll", "Reroll loops", false, false)
Pass *llvm::createLoopRerollPass() {
return new LoopReroll;
}
// Returns true if the provided instruction is used outside the given loop.
// This operates like Instruction::isUsedOutsideOfBlock, but considers PHIs in
// non-loop blocks to be outside the loop.
static bool hasUsesOutsideLoop(Instruction *I, Loop *L) {
[C++11] Add range based accessors for the Use-Def chain of a Value. This requires a number of steps. 1) Move value_use_iterator into the Value class as an implementation detail 2) Change it to actually be a *Use* iterator rather than a *User* iterator. 3) Add an adaptor which is a User iterator that always looks through the Use to the User. 4) Wrap these in Value::use_iterator and Value::user_iterator typedefs. 5) Add the range adaptors as Value::uses() and Value::users(). 6) Update *all* of the callers to correctly distinguish between whether they wanted a use_iterator (and to explicitly dig out the User when needed), or a user_iterator which makes the Use itself totally opaque. Because #6 requires churning essentially everything that walked the Use-Def chains, I went ahead and added all of the range adaptors and switched them to range-based loops where appropriate. Also because the renaming requires at least churning every line of code, it didn't make any sense to split these up into multiple commits -- all of which would touch all of the same lies of code. The result is still not quite optimal. The Value::use_iterator is a nice regular iterator, but Value::user_iterator is an iterator over User*s rather than over the User objects themselves. As a consequence, it fits a bit awkwardly into the range-based world and it has the weird extra-dereferencing 'operator->' that so many of our iterators have. I think this could be fixed by providing something which transforms a range of T&s into a range of T*s, but that *can* be separated into another patch, and it isn't yet 100% clear whether this is the right move. However, this change gets us most of the benefit and cleans up a substantial amount of code around Use and User. =] git-svn-id: https://llvm.org/svn/llvm-project/llvm/trunk@203364 91177308-0d34-0410-b5e6-96231b3b80d8
2014-03-09 03:16:01 +00:00
for (User *U : I->users())
if (!L->contains(cast<Instruction>(U)))
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. git-svn-id: https://llvm.org/svn/llvm-project/llvm/trunk@194939 91177308-0d34-0410-b5e6-96231b3b80d8
2013-11-16 23:59:05 +00:00
return true;
return false;
}
// Collect the list of loop induction variables with respect to which it might
// be possible to reroll the loop.
void LoopReroll::collectPossibleIVs(Loop *L,
SmallInstructionVector &PossibleIVs) {
BasicBlock *Header = L->getHeader();
for (BasicBlock::iterator I = Header->begin(),
IE = Header->getFirstInsertionPt(); I != IE; ++I) {
if (!isa<PHINode>(I))
continue;
if (!I->getType()->isIntegerTy())
continue;
if (const SCEVAddRecExpr *PHISCEV =
dyn_cast<SCEVAddRecExpr>(SE->getSCEV(I))) {
if (PHISCEV->getLoop() != L)
continue;
if (!PHISCEV->isAffine())
continue;
if (const SCEVConstant *IncSCEV =
dyn_cast<SCEVConstant>(PHISCEV->getStepRecurrence(*SE))) {
if (!IncSCEV->getValue()->getValue().isStrictlyPositive())
continue;
if (IncSCEV->getValue()->uge(MaxInc))
continue;
DEBUG(dbgs() << "LRR: Possible IV: " << *I << " = " <<
*PHISCEV << "\n");
PossibleIVs.push_back(I);
}
}
}
}
// Add the remainder of the reduction-variable chain to the instruction vector
// (the initial PHINode has already been added). If successful, the object is
// marked as valid.
void LoopReroll::SimpleLoopReduction::add(Loop *L) {
assert(!Valid && "Cannot add to an already-valid chain");
// The reduction variable must be a chain of single-use instructions
// (including the PHI), except for the last value (which is used by the PHI
// and also outside the loop).
Instruction *C = Instructions.front();
do {
[C++11] Add range based accessors for the Use-Def chain of a Value. This requires a number of steps. 1) Move value_use_iterator into the Value class as an implementation detail 2) Change it to actually be a *Use* iterator rather than a *User* iterator. 3) Add an adaptor which is a User iterator that always looks through the Use to the User. 4) Wrap these in Value::use_iterator and Value::user_iterator typedefs. 5) Add the range adaptors as Value::uses() and Value::users(). 6) Update *all* of the callers to correctly distinguish between whether they wanted a use_iterator (and to explicitly dig out the User when needed), or a user_iterator which makes the Use itself totally opaque. Because #6 requires churning essentially everything that walked the Use-Def chains, I went ahead and added all of the range adaptors and switched them to range-based loops where appropriate. Also because the renaming requires at least churning every line of code, it didn't make any sense to split these up into multiple commits -- all of which would touch all of the same lies of code. The result is still not quite optimal. The Value::use_iterator is a nice regular iterator, but Value::user_iterator is an iterator over User*s rather than over the User objects themselves. As a consequence, it fits a bit awkwardly into the range-based world and it has the weird extra-dereferencing 'operator->' that so many of our iterators have. I think this could be fixed by providing something which transforms a range of T&s into a range of T*s, but that *can* be separated into another patch, and it isn't yet 100% clear whether this is the right move. However, this change gets us most of the benefit and cleans up a substantial amount of code around Use and User. =] git-svn-id: https://llvm.org/svn/llvm-project/llvm/trunk@203364 91177308-0d34-0410-b5e6-96231b3b80d8
2014-03-09 03:16:01 +00:00
C = cast<Instruction>(*C->user_begin());
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. git-svn-id: https://llvm.org/svn/llvm-project/llvm/trunk@194939 91177308-0d34-0410-b5e6-96231b3b80d8
2013-11-16 23:59:05 +00:00
if (C->hasOneUse()) {
if (!C->isBinaryOp())
return;
if (!(isa<PHINode>(Instructions.back()) ||
C->isSameOperationAs(Instructions.back())))
return;
Instructions.push_back(C);
}
} while (C->hasOneUse());
if (Instructions.size() < 2 ||
!C->isSameOperationAs(Instructions.back()) ||
[C++11] Add range based accessors for the Use-Def chain of a Value. This requires a number of steps. 1) Move value_use_iterator into the Value class as an implementation detail 2) Change it to actually be a *Use* iterator rather than a *User* iterator. 3) Add an adaptor which is a User iterator that always looks through the Use to the User. 4) Wrap these in Value::use_iterator and Value::user_iterator typedefs. 5) Add the range adaptors as Value::uses() and Value::users(). 6) Update *all* of the callers to correctly distinguish between whether they wanted a use_iterator (and to explicitly dig out the User when needed), or a user_iterator which makes the Use itself totally opaque. Because #6 requires churning essentially everything that walked the Use-Def chains, I went ahead and added all of the range adaptors and switched them to range-based loops where appropriate. Also because the renaming requires at least churning every line of code, it didn't make any sense to split these up into multiple commits -- all of which would touch all of the same lies of code. The result is still not quite optimal. The Value::use_iterator is a nice regular iterator, but Value::user_iterator is an iterator over User*s rather than over the User objects themselves. As a consequence, it fits a bit awkwardly into the range-based world and it has the weird extra-dereferencing 'operator->' that so many of our iterators have. I think this could be fixed by providing something which transforms a range of T&s into a range of T*s, but that *can* be separated into another patch, and it isn't yet 100% clear whether this is the right move. However, this change gets us most of the benefit and cleans up a substantial amount of code around Use and User. =] git-svn-id: https://llvm.org/svn/llvm-project/llvm/trunk@203364 91177308-0d34-0410-b5e6-96231b3b80d8
2014-03-09 03:16:01 +00:00
C->use_empty())
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. git-svn-id: https://llvm.org/svn/llvm-project/llvm/trunk@194939 91177308-0d34-0410-b5e6-96231b3b80d8
2013-11-16 23:59:05 +00:00
return;
// C is now the (potential) last instruction in the reduction chain.
[C++11] Add range based accessors for the Use-Def chain of a Value. This requires a number of steps. 1) Move value_use_iterator into the Value class as an implementation detail 2) Change it to actually be a *Use* iterator rather than a *User* iterator. 3) Add an adaptor which is a User iterator that always looks through the Use to the User. 4) Wrap these in Value::use_iterator and Value::user_iterator typedefs. 5) Add the range adaptors as Value::uses() and Value::users(). 6) Update *all* of the callers to correctly distinguish between whether they wanted a use_iterator (and to explicitly dig out the User when needed), or a user_iterator which makes the Use itself totally opaque. Because #6 requires churning essentially everything that walked the Use-Def chains, I went ahead and added all of the range adaptors and switched them to range-based loops where appropriate. Also because the renaming requires at least churning every line of code, it didn't make any sense to split these up into multiple commits -- all of which would touch all of the same lies of code. The result is still not quite optimal. The Value::use_iterator is a nice regular iterator, but Value::user_iterator is an iterator over User*s rather than over the User objects themselves. As a consequence, it fits a bit awkwardly into the range-based world and it has the weird extra-dereferencing 'operator->' that so many of our iterators have. I think this could be fixed by providing something which transforms a range of T&s into a range of T*s, but that *can* be separated into another patch, and it isn't yet 100% clear whether this is the right move. However, this change gets us most of the benefit and cleans up a substantial amount of code around Use and User. =] git-svn-id: https://llvm.org/svn/llvm-project/llvm/trunk@203364 91177308-0d34-0410-b5e6-96231b3b80d8
2014-03-09 03:16:01 +00:00
for (User *U : C->users())
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. git-svn-id: https://llvm.org/svn/llvm-project/llvm/trunk@194939 91177308-0d34-0410-b5e6-96231b3b80d8
2013-11-16 23:59:05 +00:00
// The only in-loop user can be the initial PHI.
[C++11] Add range based accessors for the Use-Def chain of a Value. This requires a number of steps. 1) Move value_use_iterator into the Value class as an implementation detail 2) Change it to actually be a *Use* iterator rather than a *User* iterator. 3) Add an adaptor which is a User iterator that always looks through the Use to the User. 4) Wrap these in Value::use_iterator and Value::user_iterator typedefs. 5) Add the range adaptors as Value::uses() and Value::users(). 6) Update *all* of the callers to correctly distinguish between whether they wanted a use_iterator (and to explicitly dig out the User when needed), or a user_iterator which makes the Use itself totally opaque. Because #6 requires churning essentially everything that walked the Use-Def chains, I went ahead and added all of the range adaptors and switched them to range-based loops where appropriate. Also because the renaming requires at least churning every line of code, it didn't make any sense to split these up into multiple commits -- all of which would touch all of the same lies of code. The result is still not quite optimal. The Value::use_iterator is a nice regular iterator, but Value::user_iterator is an iterator over User*s rather than over the User objects themselves. As a consequence, it fits a bit awkwardly into the range-based world and it has the weird extra-dereferencing 'operator->' that so many of our iterators have. I think this could be fixed by providing something which transforms a range of T&s into a range of T*s, but that *can* be separated into another patch, and it isn't yet 100% clear whether this is the right move. However, this change gets us most of the benefit and cleans up a substantial amount of code around Use and User. =] git-svn-id: https://llvm.org/svn/llvm-project/llvm/trunk@203364 91177308-0d34-0410-b5e6-96231b3b80d8
2014-03-09 03:16:01 +00:00
if (L->contains(cast<Instruction>(U)))
if (cast<Instruction>(U) != Instructions.front())
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. git-svn-id: https://llvm.org/svn/llvm-project/llvm/trunk@194939 91177308-0d34-0410-b5e6-96231b3b80d8
2013-11-16 23:59:05 +00:00
return;
Instructions.push_back(C);
Valid = true;
}
// Collect the vector of possible reduction variables.
void LoopReroll::collectPossibleReductions(Loop *L,
ReductionTracker &Reductions) {
BasicBlock *Header = L->getHeader();
for (BasicBlock::iterator I = Header->begin(),
IE = Header->getFirstInsertionPt(); I != IE; ++I) {
if (!isa<PHINode>(I))
continue;
if (!I->getType()->isSingleValueType())
continue;
SimpleLoopReduction SLR(I, L);
if (!SLR.valid())
continue;
DEBUG(dbgs() << "LRR: Possible reduction: " << *I << " (with " <<
SLR.size() << " chained instructions)\n");
Reductions.addSLR(SLR);
}
}
// Collect the set of all users of the provided root instruction. This set of
// users contains not only the direct users of the root instruction, but also
// all users of those users, and so on. There are two exceptions:
//
// 1. Instructions in the set of excluded instructions are never added to the
// use set (even if they are users). This is used, for example, to exclude
// including root increments in the use set of the primary IV.
//
// 2. Instructions in the set of final instructions are added to the use set
// if they are users, but their users are not added. This is used, for
// example, to prevent a reduction update from forcing all later reduction
// updates into the use set.
void LoopReroll::collectInLoopUserSet(Loop *L,
Instruction *Root, const SmallInstructionSet &Exclude,
const SmallInstructionSet &Final,
DenseSet<Instruction *> &Users) {
SmallInstructionVector Queue(1, Root);
while (!Queue.empty()) {
Instruction *I = Queue.pop_back_val();
if (!Users.insert(I).second)
continue;
if (!Final.count(I))
[C++11] Add range based accessors for the Use-Def chain of a Value. This requires a number of steps. 1) Move value_use_iterator into the Value class as an implementation detail 2) Change it to actually be a *Use* iterator rather than a *User* iterator. 3) Add an adaptor which is a User iterator that always looks through the Use to the User. 4) Wrap these in Value::use_iterator and Value::user_iterator typedefs. 5) Add the range adaptors as Value::uses() and Value::users(). 6) Update *all* of the callers to correctly distinguish between whether they wanted a use_iterator (and to explicitly dig out the User when needed), or a user_iterator which makes the Use itself totally opaque. Because #6 requires churning essentially everything that walked the Use-Def chains, I went ahead and added all of the range adaptors and switched them to range-based loops where appropriate. Also because the renaming requires at least churning every line of code, it didn't make any sense to split these up into multiple commits -- all of which would touch all of the same lies of code. The result is still not quite optimal. The Value::use_iterator is a nice regular iterator, but Value::user_iterator is an iterator over User*s rather than over the User objects themselves. As a consequence, it fits a bit awkwardly into the range-based world and it has the weird extra-dereferencing 'operator->' that so many of our iterators have. I think this could be fixed by providing something which transforms a range of T&s into a range of T*s, but that *can* be separated into another patch, and it isn't yet 100% clear whether this is the right move. However, this change gets us most of the benefit and cleans up a substantial amount of code around Use and User. =] git-svn-id: https://llvm.org/svn/llvm-project/llvm/trunk@203364 91177308-0d34-0410-b5e6-96231b3b80d8
2014-03-09 03:16:01 +00:00
for (Use &U : I->uses()) {
Instruction *User = cast<Instruction>(U.getUser());
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. git-svn-id: https://llvm.org/svn/llvm-project/llvm/trunk@194939 91177308-0d34-0410-b5e6-96231b3b80d8
2013-11-16 23:59:05 +00:00
if (PHINode *PN = dyn_cast<PHINode>(User)) {
// Ignore "wrap-around" uses to PHIs of this loop's header.
[C++11] Add range based accessors for the Use-Def chain of a Value. This requires a number of steps. 1) Move value_use_iterator into the Value class as an implementation detail 2) Change it to actually be a *Use* iterator rather than a *User* iterator. 3) Add an adaptor which is a User iterator that always looks through the Use to the User. 4) Wrap these in Value::use_iterator and Value::user_iterator typedefs. 5) Add the range adaptors as Value::uses() and Value::users(). 6) Update *all* of the callers to correctly distinguish between whether they wanted a use_iterator (and to explicitly dig out the User when needed), or a user_iterator which makes the Use itself totally opaque. Because #6 requires churning essentially everything that walked the Use-Def chains, I went ahead and added all of the range adaptors and switched them to range-based loops where appropriate. Also because the renaming requires at least churning every line of code, it didn't make any sense to split these up into multiple commits -- all of which would touch all of the same lies of code. The result is still not quite optimal. The Value::use_iterator is a nice regular iterator, but Value::user_iterator is an iterator over User*s rather than over the User objects themselves. As a consequence, it fits a bit awkwardly into the range-based world and it has the weird extra-dereferencing 'operator->' that so many of our iterators have. I think this could be fixed by providing something which transforms a range of T&s into a range of T*s, but that *can* be separated into another patch, and it isn't yet 100% clear whether this is the right move. However, this change gets us most of the benefit and cleans up a substantial amount of code around Use and User. =] git-svn-id: https://llvm.org/svn/llvm-project/llvm/trunk@203364 91177308-0d34-0410-b5e6-96231b3b80d8
2014-03-09 03:16:01 +00:00
if (PN->getIncomingBlock(U) == L->getHeader())
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. git-svn-id: https://llvm.org/svn/llvm-project/llvm/trunk@194939 91177308-0d34-0410-b5e6-96231b3b80d8
2013-11-16 23:59:05 +00:00
continue;
}
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. git-svn-id: https://llvm.org/svn/llvm-project/llvm/trunk@194939 91177308-0d34-0410-b5e6-96231b3b80d8
2013-11-16 23:59:05 +00:00
if (L->contains(User) && !Exclude.count(User)) {
Queue.push_back(User);
}
}
// We also want to collect single-user "feeder" values.
for (User::op_iterator OI = I->op_begin(),
OIE = I->op_end(); OI != OIE; ++OI) {
if (Instruction *Op = dyn_cast<Instruction>(*OI))
if (Op->hasOneUse() && L->contains(Op) && !Exclude.count(Op) &&
!Final.count(Op))
Queue.push_back(Op);
}
}
}
// Collect all of the users of all of the provided root instructions (combined
// into a single set).
void LoopReroll::collectInLoopUserSet(Loop *L,
const SmallInstructionVector &Roots,
const SmallInstructionSet &Exclude,
const SmallInstructionSet &Final,
DenseSet<Instruction *> &Users) {
for (SmallInstructionVector::const_iterator I = Roots.begin(),
IE = Roots.end(); I != IE; ++I)
collectInLoopUserSet(L, *I, Exclude, Final, Users);
}
static bool isSimpleLoadStore(Instruction *I) {
if (LoadInst *LI = dyn_cast<LoadInst>(I))
return LI->isSimple();
if (StoreInst *SI = dyn_cast<StoreInst>(I))
return SI->isSimple();
if (MemIntrinsic *MI = dyn_cast<MemIntrinsic>(I))
return !MI->isVolatile();
return false;
}
// Recognize loops that are setup like this:
//
// %iv = phi [ (preheader, ...), (body, %iv.next) ]
// %scaled.iv = mul %iv, scale
// f(%scaled.iv)
// %scaled.iv.1 = add %scaled.iv, 1
// f(%scaled.iv.1)
// %scaled.iv.2 = add %scaled.iv, 2
// f(%scaled.iv.2)
// %scaled.iv.scale_m_1 = add %scaled.iv, scale-1
// f(%scaled.iv.scale_m_1)
// ...
// %iv.next = add %iv, 1
// %cmp = icmp(%iv, ...)
// br %cmp, header, exit
//
// and, if found, set IV = %scaled.iv, and add %iv.next to LoopIncs.
bool LoopReroll::findScaleFromMul(Instruction *RealIV, uint64_t &Scale,
Instruction *&IV,
SmallInstructionVector &LoopIncs) {
// This is a special case: here we're looking for all uses (except for
// the increment) to be multiplied by a common factor. The increment must
// be by one. This is to capture loops like:
// for (int i = 0; i < 500; ++i) {
// foo(3*i); foo(3*i+1); foo(3*i+2);
// }
if (RealIV->getNumUses() != 2)
return false;
const SCEVAddRecExpr *RealIVSCEV = cast<SCEVAddRecExpr>(SE->getSCEV(RealIV));
[C++11] Add range based accessors for the Use-Def chain of a Value. This requires a number of steps. 1) Move value_use_iterator into the Value class as an implementation detail 2) Change it to actually be a *Use* iterator rather than a *User* iterator. 3) Add an adaptor which is a User iterator that always looks through the Use to the User. 4) Wrap these in Value::use_iterator and Value::user_iterator typedefs. 5) Add the range adaptors as Value::uses() and Value::users(). 6) Update *all* of the callers to correctly distinguish between whether they wanted a use_iterator (and to explicitly dig out the User when needed), or a user_iterator which makes the Use itself totally opaque. Because #6 requires churning essentially everything that walked the Use-Def chains, I went ahead and added all of the range adaptors and switched them to range-based loops where appropriate. Also because the renaming requires at least churning every line of code, it didn't make any sense to split these up into multiple commits -- all of which would touch all of the same lies of code. The result is still not quite optimal. The Value::use_iterator is a nice regular iterator, but Value::user_iterator is an iterator over User*s rather than over the User objects themselves. As a consequence, it fits a bit awkwardly into the range-based world and it has the weird extra-dereferencing 'operator->' that so many of our iterators have. I think this could be fixed by providing something which transforms a range of T&s into a range of T*s, but that *can* be separated into another patch, and it isn't yet 100% clear whether this is the right move. However, this change gets us most of the benefit and cleans up a substantial amount of code around Use and User. =] git-svn-id: https://llvm.org/svn/llvm-project/llvm/trunk@203364 91177308-0d34-0410-b5e6-96231b3b80d8
2014-03-09 03:16:01 +00:00
Instruction *User1 = cast<Instruction>(*RealIV->user_begin()),
*User2 = cast<Instruction>(*std::next(RealIV->user_begin()));
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. git-svn-id: https://llvm.org/svn/llvm-project/llvm/trunk@194939 91177308-0d34-0410-b5e6-96231b3b80d8
2013-11-16 23:59:05 +00:00
if (!SE->isSCEVable(User1->getType()) || !SE->isSCEVable(User2->getType()))
return false;
const SCEVAddRecExpr *User1SCEV =
dyn_cast<SCEVAddRecExpr>(SE->getSCEV(User1)),
*User2SCEV =
dyn_cast<SCEVAddRecExpr>(SE->getSCEV(User2));
if (!User1SCEV || !User1SCEV->isAffine() ||
!User2SCEV || !User2SCEV->isAffine())
return false;
// We assume below that User1 is the scale multiply and User2 is the
// increment. If this can't be true, then swap them.
if (User1SCEV == RealIVSCEV->getPostIncExpr(*SE)) {
std::swap(User1, User2);
std::swap(User1SCEV, User2SCEV);
}
if (User2SCEV != RealIVSCEV->getPostIncExpr(*SE))
return false;
assert(User2SCEV->getStepRecurrence(*SE)->isOne() &&
"Invalid non-unit step for multiplicative scaling");
LoopIncs.push_back(User2);
if (const SCEVConstant *MulScale =
dyn_cast<SCEVConstant>(User1SCEV->getStepRecurrence(*SE))) {
// Make sure that both the start and step have the same multiplier.
if (RealIVSCEV->getStart()->getType() != MulScale->getType())
return false;
if (SE->getMulExpr(RealIVSCEV->getStart(), MulScale) !=
User1SCEV->getStart())
return false;
ConstantInt *MulScaleCI = MulScale->getValue();
if (!MulScaleCI->uge(2) || MulScaleCI->uge(MaxInc))
return false;
Scale = MulScaleCI->getZExtValue();
IV = User1;
} else
return false;
DEBUG(dbgs() << "LRR: Found possible scaling " << *User1 << "\n");
return true;
}
// Collect all root increments with respect to the provided induction variable
// (normally the PHI, but sometimes a multiply). A root increment is an
// instruction, normally an add, with a positive constant less than Scale. In a
// rerollable loop, each of these increments is the root of an instruction
// graph isomorphic to the others. Also, we collect the final induction
// increment (the increment equal to the Scale), and its users in LoopIncs.
bool LoopReroll::collectAllRoots(Loop *L, uint64_t Inc, uint64_t Scale,
Instruction *IV,
SmallVector<SmallInstructionVector, 32> &Roots,
SmallInstructionSet &AllRoots,
SmallInstructionVector &LoopIncs) {
[C++11] Add range based accessors for the Use-Def chain of a Value. This requires a number of steps. 1) Move value_use_iterator into the Value class as an implementation detail 2) Change it to actually be a *Use* iterator rather than a *User* iterator. 3) Add an adaptor which is a User iterator that always looks through the Use to the User. 4) Wrap these in Value::use_iterator and Value::user_iterator typedefs. 5) Add the range adaptors as Value::uses() and Value::users(). 6) Update *all* of the callers to correctly distinguish between whether they wanted a use_iterator (and to explicitly dig out the User when needed), or a user_iterator which makes the Use itself totally opaque. Because #6 requires churning essentially everything that walked the Use-Def chains, I went ahead and added all of the range adaptors and switched them to range-based loops where appropriate. Also because the renaming requires at least churning every line of code, it didn't make any sense to split these up into multiple commits -- all of which would touch all of the same lies of code. The result is still not quite optimal. The Value::use_iterator is a nice regular iterator, but Value::user_iterator is an iterator over User*s rather than over the User objects themselves. As a consequence, it fits a bit awkwardly into the range-based world and it has the weird extra-dereferencing 'operator->' that so many of our iterators have. I think this could be fixed by providing something which transforms a range of T&s into a range of T*s, but that *can* be separated into another patch, and it isn't yet 100% clear whether this is the right move. However, this change gets us most of the benefit and cleans up a substantial amount of code around Use and User. =] git-svn-id: https://llvm.org/svn/llvm-project/llvm/trunk@203364 91177308-0d34-0410-b5e6-96231b3b80d8
2014-03-09 03:16:01 +00:00
for (User *U : IV->users()) {
Instruction *UI = cast<Instruction>(U);
if (!SE->isSCEVable(UI->getType()))
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. git-svn-id: https://llvm.org/svn/llvm-project/llvm/trunk@194939 91177308-0d34-0410-b5e6-96231b3b80d8
2013-11-16 23:59:05 +00:00
continue;
[C++11] Add range based accessors for the Use-Def chain of a Value. This requires a number of steps. 1) Move value_use_iterator into the Value class as an implementation detail 2) Change it to actually be a *Use* iterator rather than a *User* iterator. 3) Add an adaptor which is a User iterator that always looks through the Use to the User. 4) Wrap these in Value::use_iterator and Value::user_iterator typedefs. 5) Add the range adaptors as Value::uses() and Value::users(). 6) Update *all* of the callers to correctly distinguish between whether they wanted a use_iterator (and to explicitly dig out the User when needed), or a user_iterator which makes the Use itself totally opaque. Because #6 requires churning essentially everything that walked the Use-Def chains, I went ahead and added all of the range adaptors and switched them to range-based loops where appropriate. Also because the renaming requires at least churning every line of code, it didn't make any sense to split these up into multiple commits -- all of which would touch all of the same lies of code. The result is still not quite optimal. The Value::use_iterator is a nice regular iterator, but Value::user_iterator is an iterator over User*s rather than over the User objects themselves. As a consequence, it fits a bit awkwardly into the range-based world and it has the weird extra-dereferencing 'operator->' that so many of our iterators have. I think this could be fixed by providing something which transforms a range of T&s into a range of T*s, but that *can* be separated into another patch, and it isn't yet 100% clear whether this is the right move. However, this change gets us most of the benefit and cleans up a substantial amount of code around Use and User. =] git-svn-id: https://llvm.org/svn/llvm-project/llvm/trunk@203364 91177308-0d34-0410-b5e6-96231b3b80d8
2014-03-09 03:16:01 +00:00
if (UI->getType() != IV->getType())
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. git-svn-id: https://llvm.org/svn/llvm-project/llvm/trunk@194939 91177308-0d34-0410-b5e6-96231b3b80d8
2013-11-16 23:59:05 +00:00
continue;
[C++11] Add range based accessors for the Use-Def chain of a Value. This requires a number of steps. 1) Move value_use_iterator into the Value class as an implementation detail 2) Change it to actually be a *Use* iterator rather than a *User* iterator. 3) Add an adaptor which is a User iterator that always looks through the Use to the User. 4) Wrap these in Value::use_iterator and Value::user_iterator typedefs. 5) Add the range adaptors as Value::uses() and Value::users(). 6) Update *all* of the callers to correctly distinguish between whether they wanted a use_iterator (and to explicitly dig out the User when needed), or a user_iterator which makes the Use itself totally opaque. Because #6 requires churning essentially everything that walked the Use-Def chains, I went ahead and added all of the range adaptors and switched them to range-based loops where appropriate. Also because the renaming requires at least churning every line of code, it didn't make any sense to split these up into multiple commits -- all of which would touch all of the same lies of code. The result is still not quite optimal. The Value::use_iterator is a nice regular iterator, but Value::user_iterator is an iterator over User*s rather than over the User objects themselves. As a consequence, it fits a bit awkwardly into the range-based world and it has the weird extra-dereferencing 'operator->' that so many of our iterators have. I think this could be fixed by providing something which transforms a range of T&s into a range of T*s, but that *can* be separated into another patch, and it isn't yet 100% clear whether this is the right move. However, this change gets us most of the benefit and cleans up a substantial amount of code around Use and User. =] git-svn-id: https://llvm.org/svn/llvm-project/llvm/trunk@203364 91177308-0d34-0410-b5e6-96231b3b80d8
2014-03-09 03:16:01 +00:00
if (!L->contains(UI))
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. git-svn-id: https://llvm.org/svn/llvm-project/llvm/trunk@194939 91177308-0d34-0410-b5e6-96231b3b80d8
2013-11-16 23:59:05 +00:00
continue;
[C++11] Add range based accessors for the Use-Def chain of a Value. This requires a number of steps. 1) Move value_use_iterator into the Value class as an implementation detail 2) Change it to actually be a *Use* iterator rather than a *User* iterator. 3) Add an adaptor which is a User iterator that always looks through the Use to the User. 4) Wrap these in Value::use_iterator and Value::user_iterator typedefs. 5) Add the range adaptors as Value::uses() and Value::users(). 6) Update *all* of the callers to correctly distinguish between whether they wanted a use_iterator (and to explicitly dig out the User when needed), or a user_iterator which makes the Use itself totally opaque. Because #6 requires churning essentially everything that walked the Use-Def chains, I went ahead and added all of the range adaptors and switched them to range-based loops where appropriate. Also because the renaming requires at least churning every line of code, it didn't make any sense to split these up into multiple commits -- all of which would touch all of the same lies of code. The result is still not quite optimal. The Value::use_iterator is a nice regular iterator, but Value::user_iterator is an iterator over User*s rather than over the User objects themselves. As a consequence, it fits a bit awkwardly into the range-based world and it has the weird extra-dereferencing 'operator->' that so many of our iterators have. I think this could be fixed by providing something which transforms a range of T&s into a range of T*s, but that *can* be separated into another patch, and it isn't yet 100% clear whether this is the right move. However, this change gets us most of the benefit and cleans up a substantial amount of code around Use and User. =] git-svn-id: https://llvm.org/svn/llvm-project/llvm/trunk@203364 91177308-0d34-0410-b5e6-96231b3b80d8
2014-03-09 03:16:01 +00:00
if (hasUsesOutsideLoop(UI, L))
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. git-svn-id: https://llvm.org/svn/llvm-project/llvm/trunk@194939 91177308-0d34-0410-b5e6-96231b3b80d8
2013-11-16 23:59:05 +00:00
continue;
if (const SCEVConstant *Diff = dyn_cast<SCEVConstant>(SE->getMinusSCEV(
[C++11] Add range based accessors for the Use-Def chain of a Value. This requires a number of steps. 1) Move value_use_iterator into the Value class as an implementation detail 2) Change it to actually be a *Use* iterator rather than a *User* iterator. 3) Add an adaptor which is a User iterator that always looks through the Use to the User. 4) Wrap these in Value::use_iterator and Value::user_iterator typedefs. 5) Add the range adaptors as Value::uses() and Value::users(). 6) Update *all* of the callers to correctly distinguish between whether they wanted a use_iterator (and to explicitly dig out the User when needed), or a user_iterator which makes the Use itself totally opaque. Because #6 requires churning essentially everything that walked the Use-Def chains, I went ahead and added all of the range adaptors and switched them to range-based loops where appropriate. Also because the renaming requires at least churning every line of code, it didn't make any sense to split these up into multiple commits -- all of which would touch all of the same lies of code. The result is still not quite optimal. The Value::use_iterator is a nice regular iterator, but Value::user_iterator is an iterator over User*s rather than over the User objects themselves. As a consequence, it fits a bit awkwardly into the range-based world and it has the weird extra-dereferencing 'operator->' that so many of our iterators have. I think this could be fixed by providing something which transforms a range of T&s into a range of T*s, but that *can* be separated into another patch, and it isn't yet 100% clear whether this is the right move. However, this change gets us most of the benefit and cleans up a substantial amount of code around Use and User. =] git-svn-id: https://llvm.org/svn/llvm-project/llvm/trunk@203364 91177308-0d34-0410-b5e6-96231b3b80d8
2014-03-09 03:16:01 +00:00
SE->getSCEV(UI), SE->getSCEV(IV)))) {
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. git-svn-id: https://llvm.org/svn/llvm-project/llvm/trunk@194939 91177308-0d34-0410-b5e6-96231b3b80d8
2013-11-16 23:59:05 +00:00
uint64_t Idx = Diff->getValue()->getValue().getZExtValue();
if (Idx > 0 && Idx < Scale) {
[C++11] Add range based accessors for the Use-Def chain of a Value. This requires a number of steps. 1) Move value_use_iterator into the Value class as an implementation detail 2) Change it to actually be a *Use* iterator rather than a *User* iterator. 3) Add an adaptor which is a User iterator that always looks through the Use to the User. 4) Wrap these in Value::use_iterator and Value::user_iterator typedefs. 5) Add the range adaptors as Value::uses() and Value::users(). 6) Update *all* of the callers to correctly distinguish between whether they wanted a use_iterator (and to explicitly dig out the User when needed), or a user_iterator which makes the Use itself totally opaque. Because #6 requires churning essentially everything that walked the Use-Def chains, I went ahead and added all of the range adaptors and switched them to range-based loops where appropriate. Also because the renaming requires at least churning every line of code, it didn't make any sense to split these up into multiple commits -- all of which would touch all of the same lies of code. The result is still not quite optimal. The Value::use_iterator is a nice regular iterator, but Value::user_iterator is an iterator over User*s rather than over the User objects themselves. As a consequence, it fits a bit awkwardly into the range-based world and it has the weird extra-dereferencing 'operator->' that so many of our iterators have. I think this could be fixed by providing something which transforms a range of T&s into a range of T*s, but that *can* be separated into another patch, and it isn't yet 100% clear whether this is the right move. However, this change gets us most of the benefit and cleans up a substantial amount of code around Use and User. =] git-svn-id: https://llvm.org/svn/llvm-project/llvm/trunk@203364 91177308-0d34-0410-b5e6-96231b3b80d8
2014-03-09 03:16:01 +00:00
Roots[Idx-1].push_back(UI);
AllRoots.insert(UI);
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. git-svn-id: https://llvm.org/svn/llvm-project/llvm/trunk@194939 91177308-0d34-0410-b5e6-96231b3b80d8
2013-11-16 23:59:05 +00:00
} else if (Idx == Scale && Inc > 1) {
[C++11] Add range based accessors for the Use-Def chain of a Value. This requires a number of steps. 1) Move value_use_iterator into the Value class as an implementation detail 2) Change it to actually be a *Use* iterator rather than a *User* iterator. 3) Add an adaptor which is a User iterator that always looks through the Use to the User. 4) Wrap these in Value::use_iterator and Value::user_iterator typedefs. 5) Add the range adaptors as Value::uses() and Value::users(). 6) Update *all* of the callers to correctly distinguish between whether they wanted a use_iterator (and to explicitly dig out the User when needed), or a user_iterator which makes the Use itself totally opaque. Because #6 requires churning essentially everything that walked the Use-Def chains, I went ahead and added all of the range adaptors and switched them to range-based loops where appropriate. Also because the renaming requires at least churning every line of code, it didn't make any sense to split these up into multiple commits -- all of which would touch all of the same lies of code. The result is still not quite optimal. The Value::use_iterator is a nice regular iterator, but Value::user_iterator is an iterator over User*s rather than over the User objects themselves. As a consequence, it fits a bit awkwardly into the range-based world and it has the weird extra-dereferencing 'operator->' that so many of our iterators have. I think this could be fixed by providing something which transforms a range of T&s into a range of T*s, but that *can* be separated into another patch, and it isn't yet 100% clear whether this is the right move. However, this change gets us most of the benefit and cleans up a substantial amount of code around Use and User. =] git-svn-id: https://llvm.org/svn/llvm-project/llvm/trunk@203364 91177308-0d34-0410-b5e6-96231b3b80d8
2014-03-09 03:16:01 +00:00
LoopIncs.push_back(UI);
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. git-svn-id: https://llvm.org/svn/llvm-project/llvm/trunk@194939 91177308-0d34-0410-b5e6-96231b3b80d8
2013-11-16 23:59:05 +00:00
}
}
}
if (Roots[0].empty())
return false;
bool AllSame = true;
for (unsigned i = 1; i < Scale-1; ++i)
if (Roots[i].size() != Roots[0].size()) {
AllSame = false;
break;
}
if (!AllSame)
return false;
return true;
}
// Validate the selected reductions. All iterations must have an isomorphic
// part of the reduction chain and, for non-associative reductions, the chain
// entries must appear in order.
bool LoopReroll::ReductionTracker::validateSelected() {
// For a non-associative reduction, the chain entries must appear in order.
for (DenseSet<int>::iterator RI = Reds.begin(), RIE = Reds.end();
RI != RIE; ++RI) {
int i = *RI;
int PrevIter = 0, BaseCount = 0, Count = 0;
for (Instruction *J : PossibleReds[i]) {
// Note that all instructions in the chain must have been found because
// all instructions in the function must have been assigned to some
// iteration.
int Iter = PossibleRedIter[J];
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. git-svn-id: https://llvm.org/svn/llvm-project/llvm/trunk@194939 91177308-0d34-0410-b5e6-96231b3b80d8
2013-11-16 23:59:05 +00:00
if (Iter != PrevIter && Iter != PrevIter + 1 &&
!PossibleReds[i].getReducedValue()->isAssociative()) {
DEBUG(dbgs() << "LRR: Out-of-order non-associative reduction: " <<
J << "\n");
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. git-svn-id: https://llvm.org/svn/llvm-project/llvm/trunk@194939 91177308-0d34-0410-b5e6-96231b3b80d8
2013-11-16 23:59:05 +00:00
return false;
}
if (Iter != PrevIter) {
if (Count != BaseCount) {
DEBUG(dbgs() << "LRR: Iteration " << PrevIter <<
" reduction use count " << Count <<
" is not equal to the base use count " <<
BaseCount << "\n");
return false;
}
Count = 0;
}
++Count;
if (Iter == 0)
++BaseCount;
PrevIter = Iter;
}
}
return true;
}
// For all selected reductions, remove all parts except those in the first
// iteration (and the PHI). Replace outside uses of the reduced value with uses
// of the first-iteration reduced value (in other words, reroll the selected
// reductions).
void LoopReroll::ReductionTracker::replaceSelected() {
// Fixup reductions to refer to the last instruction associated with the
// first iteration (not the last).
for (DenseSet<int>::iterator RI = Reds.begin(), RIE = Reds.end();
RI != RIE; ++RI) {
int i = *RI;
int j = 0;
for (int e = PossibleReds[i].size(); j != e; ++j)
if (PossibleRedIter[PossibleReds[i][j]] != 0) {
--j;
break;
}
// Replace users with the new end-of-chain value.
SmallInstructionVector Users;
[C++11] Add range based accessors for the Use-Def chain of a Value. This requires a number of steps. 1) Move value_use_iterator into the Value class as an implementation detail 2) Change it to actually be a *Use* iterator rather than a *User* iterator. 3) Add an adaptor which is a User iterator that always looks through the Use to the User. 4) Wrap these in Value::use_iterator and Value::user_iterator typedefs. 5) Add the range adaptors as Value::uses() and Value::users(). 6) Update *all* of the callers to correctly distinguish between whether they wanted a use_iterator (and to explicitly dig out the User when needed), or a user_iterator which makes the Use itself totally opaque. Because #6 requires churning essentially everything that walked the Use-Def chains, I went ahead and added all of the range adaptors and switched them to range-based loops where appropriate. Also because the renaming requires at least churning every line of code, it didn't make any sense to split these up into multiple commits -- all of which would touch all of the same lies of code. The result is still not quite optimal. The Value::use_iterator is a nice regular iterator, but Value::user_iterator is an iterator over User*s rather than over the User objects themselves. As a consequence, it fits a bit awkwardly into the range-based world and it has the weird extra-dereferencing 'operator->' that so many of our iterators have. I think this could be fixed by providing something which transforms a range of T&s into a range of T*s, but that *can* be separated into another patch, and it isn't yet 100% clear whether this is the right move. However, this change gets us most of the benefit and cleans up a substantial amount of code around Use and User. =] git-svn-id: https://llvm.org/svn/llvm-project/llvm/trunk@203364 91177308-0d34-0410-b5e6-96231b3b80d8
2014-03-09 03:16:01 +00:00
for (User *U : PossibleReds[i].getReducedValue()->users())
Users.push_back(cast<Instruction>(U));
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. git-svn-id: https://llvm.org/svn/llvm-project/llvm/trunk@194939 91177308-0d34-0410-b5e6-96231b3b80d8
2013-11-16 23:59:05 +00:00
for (SmallInstructionVector::iterator J = Users.begin(),
JE = Users.end(); J != JE; ++J)
(*J)->replaceUsesOfWith(PossibleReds[i].getReducedValue(),
PossibleReds[i][j]);
}
}
// Reroll the provided loop with respect to the provided induction variable.
// Generally, we're looking for a loop like this:
//
// %iv = phi [ (preheader, ...), (body, %iv.next) ]
// f(%iv)
// %iv.1 = add %iv, 1 <-- a root increment
// f(%iv.1)
// %iv.2 = add %iv, 2 <-- a root increment
// f(%iv.2)
// %iv.scale_m_1 = add %iv, scale-1 <-- a root increment
// f(%iv.scale_m_1)
// ...
// %iv.next = add %iv, scale
// %cmp = icmp(%iv, ...)
// br %cmp, header, exit
//
// Notably, we do not require that f(%iv), f(%iv.1), etc. be isolated groups of
// instructions. In other words, the instructions in f(%iv), f(%iv.1), etc. can
// be intermixed with eachother. The restriction imposed by this algorithm is
// that the relative order of the isomorphic instructions in f(%iv), f(%iv.1),
// etc. be the same.
//
// First, we collect the use set of %iv, excluding the other increment roots.
// This gives us f(%iv). Then we iterate over the loop instructions (scale-1)
// times, having collected the use set of f(%iv.(i+1)), during which we:
// - Ensure that the next unmatched instruction in f(%iv) is isomorphic to
// the next unmatched instruction in f(%iv.(i+1)).
// - Ensure that both matched instructions don't have any external users
// (with the exception of last-in-chain reduction instructions).
// - Track the (aliasing) write set, and other side effects, of all
// instructions that belong to future iterations that come before the matched
// instructions. If the matched instructions read from that write set, then
// f(%iv) or f(%iv.(i+1)) has some dependency on instructions in
// f(%iv.(j+1)) for some j > i, and we cannot reroll the loop. Similarly,
// if any of these future instructions had side effects (could not be
// speculatively executed), and so do the matched instructions, when we
// cannot reorder those side-effect-producing instructions, and rerolling
// fails.
//
// Finally, we make sure that all loop instructions are either loop increment
// roots, belong to simple latch code, parts of validated reductions, part of
// f(%iv) or part of some f(%iv.i). If all of that is true (and all reductions
// have been validated), then we reroll the loop.
bool LoopReroll::reroll(Instruction *IV, Loop *L, BasicBlock *Header,
const SCEV *IterCount,
ReductionTracker &Reductions) {
const SCEVAddRecExpr *RealIVSCEV = cast<SCEVAddRecExpr>(SE->getSCEV(IV));
uint64_t Inc = cast<SCEVConstant>(RealIVSCEV->getOperand(1))->
getValue()->getZExtValue();
// The collection of loop increment instructions.
SmallInstructionVector LoopIncs;
uint64_t Scale = Inc;
// The effective induction variable, IV, is normally also the real induction
// variable. When we're dealing with a loop like:
// for (int i = 0; i < 500; ++i)
// x[3*i] = ...;
// x[3*i+1] = ...;
// x[3*i+2] = ...;
// then the real IV is still i, but the effective IV is (3*i).
Instruction *RealIV = IV;
if (Inc == 1 && !findScaleFromMul(RealIV, Scale, IV, LoopIncs))
return false;
assert(Scale <= MaxInc && "Scale is too large");
assert(Scale > 1 && "Scale must be at least 2");
// The set of increment instructions for each increment value.
SmallVector<SmallInstructionVector, 32> Roots(Scale-1);
SmallInstructionSet AllRoots;
if (!collectAllRoots(L, Inc, Scale, IV, Roots, AllRoots, LoopIncs))
return false;
DEBUG(dbgs() << "LRR: Found all root induction increments for: " <<
*RealIV << "\n");
// An array of just the possible reductions for this scale factor. When we
// collect the set of all users of some root instructions, these reduction
// instructions are treated as 'final' (their uses are not considered).
// This is important because we don't want the root use set to search down
// the reduction chain.
SmallInstructionSet PossibleRedSet;
SmallInstructionSet PossibleRedLastSet, PossibleRedPHISet;
Reductions.restrictToScale(Scale, PossibleRedSet, PossibleRedPHISet,
PossibleRedLastSet);
// We now need to check for equivalence of the use graph of each root with
// that of the primary induction variable (excluding the roots). Our goal
// here is not to solve the full graph isomorphism problem, but rather to
// catch common cases without a lot of work. As a result, we will assume
// that the relative order of the instructions in each unrolled iteration
// is the same (although we will not make an assumption about how the
// different iterations are intermixed). Note that while the order must be
// the same, the instructions may not be in the same basic block.
SmallInstructionSet Exclude(AllRoots);
Exclude.insert(LoopIncs.begin(), LoopIncs.end());
DenseSet<Instruction *> BaseUseSet;
collectInLoopUserSet(L, IV, Exclude, PossibleRedSet, BaseUseSet);
DenseSet<Instruction *> AllRootUses;
std::vector<DenseSet<Instruction *> > RootUseSets(Scale-1);
bool MatchFailed = false;
for (unsigned i = 0; i < Scale-1 && !MatchFailed; ++i) {
DenseSet<Instruction *> &RootUseSet = RootUseSets[i];
collectInLoopUserSet(L, Roots[i], SmallInstructionSet(),
PossibleRedSet, RootUseSet);
DEBUG(dbgs() << "LRR: base use set size: " << BaseUseSet.size() <<
" vs. iteration increment " << (i+1) <<
" use set size: " << RootUseSet.size() << "\n");
if (BaseUseSet.size() != RootUseSet.size()) {
MatchFailed = true;
break;
}
// In addition to regular aliasing information, we need to look for
// instructions from later (future) iterations that have side effects
// preventing us from reordering them past other instructions with side
// effects.
bool FutureSideEffects = false;
AliasSetTracker AST(*AA);
// The map between instructions in f(%iv.(i+1)) and f(%iv).
DenseMap<Value *, Value *> BaseMap;
assert(L->getNumBlocks() == 1 && "Cannot handle multi-block loops");
for (BasicBlock::iterator J1 = Header->begin(), J2 = Header->begin(),
JE = Header->end(); J1 != JE && !MatchFailed; ++J1) {
if (cast<Instruction>(J1) == RealIV)
continue;
if (cast<Instruction>(J1) == IV)
continue;
if (!BaseUseSet.count(J1))
continue;
if (PossibleRedPHISet.count(J1)) // Skip reduction PHIs.
continue;
while (J2 != JE && (!RootUseSet.count(J2) ||
std::find(Roots[i].begin(), Roots[i].end(), J2) !=
Roots[i].end())) {
// As we iterate through the instructions, instructions that don't
// belong to previous iterations (or the base case), must belong to
// future iterations. We want to track the alias set of writes from
// previous iterations.
if (!isa<PHINode>(J2) && !BaseUseSet.count(J2) &&
!AllRootUses.count(J2)) {
if (J2->mayWriteToMemory())
AST.add(J2);
// Note: This is specifically guarded by a check on isa<PHINode>,
// which while a valid (somewhat arbitrary) micro-optimization, is
// needed because otherwise isSafeToSpeculativelyExecute returns
// false on PHI nodes.
if (!isSimpleLoadStore(J2) && !isSafeToSpeculativelyExecute(J2, DL))
FutureSideEffects = true;
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. git-svn-id: https://llvm.org/svn/llvm-project/llvm/trunk@194939 91177308-0d34-0410-b5e6-96231b3b80d8
2013-11-16 23:59:05 +00:00
}
++J2;
}
if (!J1->isSameOperationAs(J2)) {
DEBUG(dbgs() << "LRR: iteration root match failed at " << *J1 <<
" vs. " << *J2 << "\n");
MatchFailed = true;
break;
}
// Make sure that this instruction, which is in the use set of this
// root instruction, does not also belong to the base set or the set of
// some previous root instruction.
if (BaseUseSet.count(J2) || AllRootUses.count(J2)) {
DEBUG(dbgs() << "LRR: iteration root match failed at " << *J1 <<
" vs. " << *J2 << " (prev. case overlap)\n");
MatchFailed = true;
break;
}
// Make sure that we don't alias with any instruction in the alias set
// tracker. If we do, then we depend on a future iteration, and we
// can't reroll.
if (J2->mayReadFromMemory()) {
for (AliasSetTracker::iterator K = AST.begin(), KE = AST.end();
K != KE && !MatchFailed; ++K) {
if (K->aliasesUnknownInst(J2, *AA)) {
DEBUG(dbgs() << "LRR: iteration root match failed at " << *J1 <<
" vs. " << *J2 << " (depends on future store)\n");
MatchFailed = true;
break;
}
}
}
// If we've past an instruction from a future iteration that may have
// side effects, and this instruction might also, then we can't reorder
// them, and this matching fails. As an exception, we allow the alias
// set tracker to handle regular (simple) load/store dependencies.
if (FutureSideEffects &&
((!isSimpleLoadStore(J1) &&
!isSafeToSpeculativelyExecute(J1, DL)) ||
(!isSimpleLoadStore(J2) &&
!isSafeToSpeculativelyExecute(J2, DL)))) {
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. git-svn-id: https://llvm.org/svn/llvm-project/llvm/trunk@194939 91177308-0d34-0410-b5e6-96231b3b80d8
2013-11-16 23:59:05 +00:00
DEBUG(dbgs() << "LRR: iteration root match failed at " << *J1 <<
" vs. " << *J2 <<
" (side effects prevent reordering)\n");
MatchFailed = true;
break;
}
// For instructions that are part of a reduction, if the operation is
// associative, then don't bother matching the operands (because we
// already know that the instructions are isomorphic, and the order
// within the iteration does not matter). For non-associative reductions,
// we do need to match the operands, because we need to reject
// out-of-order instructions within an iteration!
// For example (assume floating-point addition), we need to reject this:
// x += a[i]; x += b[i];
// x += a[i+1]; x += b[i+1];
// x += b[i+2]; x += a[i+2];
bool InReduction = Reductions.isPairInSame(J1, J2);
if (!(InReduction && J1->isAssociative())) {
bool Swapped = false, SomeOpMatched = false;
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. git-svn-id: https://llvm.org/svn/llvm-project/llvm/trunk@194939 91177308-0d34-0410-b5e6-96231b3b80d8
2013-11-16 23:59:05 +00:00
for (unsigned j = 0; j < J1->getNumOperands() && !MatchFailed; ++j) {
Value *Op2 = J2->getOperand(j);
// If this is part of a reduction (and the operation is not
// associatve), then we match all operands, but not those that are
// part of the reduction.
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. git-svn-id: https://llvm.org/svn/llvm-project/llvm/trunk@194939 91177308-0d34-0410-b5e6-96231b3b80d8
2013-11-16 23:59:05 +00:00
if (InReduction)
if (Instruction *Op2I = dyn_cast<Instruction>(Op2))
if (Reductions.isPairInSame(J2, Op2I))
continue;
DenseMap<Value *, Value *>::iterator BMI = BaseMap.find(Op2);
if (BMI != BaseMap.end())
Op2 = BMI->second;
else if (std::find(Roots[i].begin(), Roots[i].end(),
(Instruction*) Op2) != Roots[i].end())
Op2 = IV;
if (J1->getOperand(Swapped ? unsigned(!j) : j) != Op2) {
// If we've not already decided to swap the matched operands, and
// we've not already matched our first operand (note that we could
// have skipped matching the first operand because it is part of a
// reduction above), and the instruction is commutative, then try
// the swapped match.
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. git-svn-id: https://llvm.org/svn/llvm-project/llvm/trunk@194939 91177308-0d34-0410-b5e6-96231b3b80d8
2013-11-16 23:59:05 +00:00
if (!Swapped && J1->isCommutative() && !SomeOpMatched &&
J1->getOperand(!j) == Op2) {
Swapped = true;
} else {
DEBUG(dbgs() << "LRR: iteration root match failed at " << *J1 <<
" vs. " << *J2 << " (operand " << j << ")\n");
MatchFailed = true;
break;
}
}
SomeOpMatched = true;
}
}
if ((!PossibleRedLastSet.count(J1) && hasUsesOutsideLoop(J1, L)) ||
(!PossibleRedLastSet.count(J2) && hasUsesOutsideLoop(J2, L))) {
DEBUG(dbgs() << "LRR: iteration root match failed at " << *J1 <<
" vs. " << *J2 << " (uses outside loop)\n");
MatchFailed = true;
break;
}
if (!MatchFailed)
BaseMap.insert(std::pair<Value *, Value *>(J2, J1));
AllRootUses.insert(J2);
Reductions.recordPair(J1, J2, i+1);
++J2;
}
}
if (MatchFailed)
return false;
DEBUG(dbgs() << "LRR: Matched all iteration increments for " <<
*RealIV << "\n");
DenseSet<Instruction *> LoopIncUseSet;
collectInLoopUserSet(L, LoopIncs, SmallInstructionSet(),
SmallInstructionSet(), LoopIncUseSet);
DEBUG(dbgs() << "LRR: Loop increment set size: " <<
LoopIncUseSet.size() << "\n");
// Make sure that all instructions in the loop have been included in some
// use set.
for (BasicBlock::iterator J = Header->begin(), JE = Header->end();
J != JE; ++J) {
if (isa<DbgInfoIntrinsic>(J))
continue;
if (cast<Instruction>(J) == RealIV)
continue;
if (cast<Instruction>(J) == IV)
continue;
if (BaseUseSet.count(J) || AllRootUses.count(J) ||
(LoopIncUseSet.count(J) && (J->isTerminator() ||
isSafeToSpeculativelyExecute(J, DL))))
continue;
if (AllRoots.count(J))
continue;
if (Reductions.isSelectedPHI(J))
continue;
DEBUG(dbgs() << "LRR: aborting reroll based on " << *RealIV <<
" unprocessed instruction found: " << *J << "\n");
MatchFailed = true;
break;
}
if (MatchFailed)
return false;
DEBUG(dbgs() << "LRR: all instructions processed from " <<
*RealIV << "\n");
if (!Reductions.validateSelected())
return false;
// At this point, we've validated the rerolling, and we're committed to
// making changes!
Reductions.replaceSelected();
// Remove instructions associated with non-base iterations.
for (BasicBlock::reverse_iterator J = Header->rbegin();
J != Header->rend();) {
if (AllRootUses.count(&*J)) {
Instruction *D = &*J;
DEBUG(dbgs() << "LRR: removing: " << *D << "\n");
D->eraseFromParent();
continue;
}
++J;
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. git-svn-id: https://llvm.org/svn/llvm-project/llvm/trunk@194939 91177308-0d34-0410-b5e6-96231b3b80d8
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}
// Insert the new induction variable.
const SCEV *Start = RealIVSCEV->getStart();
if (Inc == 1)
Start = SE->getMulExpr(Start,
SE->getConstant(Start->getType(), Scale));
const SCEVAddRecExpr *H =
cast<SCEVAddRecExpr>(SE->getAddRecExpr(Start,
SE->getConstant(RealIVSCEV->getType(), 1),
L, SCEV::FlagAnyWrap));
{ // Limit the lifetime of SCEVExpander.
SCEVExpander Expander(*SE, "reroll");
Value *NewIV = Expander.expandCodeFor(H, IV->getType(), Header->begin());
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. git-svn-id: https://llvm.org/svn/llvm-project/llvm/trunk@194939 91177308-0d34-0410-b5e6-96231b3b80d8
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for (DenseSet<Instruction *>::iterator J = BaseUseSet.begin(),
JE = BaseUseSet.end(); J != JE; ++J)
(*J)->replaceUsesOfWith(IV, NewIV);
if (BranchInst *BI = dyn_cast<BranchInst>(Header->getTerminator())) {
if (LoopIncUseSet.count(BI)) {
const SCEV *ICSCEV = RealIVSCEV->evaluateAtIteration(IterCount, *SE);
if (Inc == 1)
ICSCEV =
SE->getMulExpr(ICSCEV, SE->getConstant(ICSCEV->getType(), Scale));
// Iteration count SCEV minus 1
const SCEV *ICMinus1SCEV =
SE->getMinusSCEV(ICSCEV, SE->getConstant(ICSCEV->getType(), 1));
Value *ICMinus1; // Iteration count minus 1
if (isa<SCEVConstant>(ICMinus1SCEV)) {
ICMinus1 = Expander.expandCodeFor(ICMinus1SCEV, NewIV->getType(), BI);
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. git-svn-id: https://llvm.org/svn/llvm-project/llvm/trunk@194939 91177308-0d34-0410-b5e6-96231b3b80d8
2013-11-16 23:59:05 +00:00
} else {
BasicBlock *Preheader = L->getLoopPreheader();
if (!Preheader)
Preheader = InsertPreheaderForLoop(L, this);
ICMinus1 = Expander.expandCodeFor(ICMinus1SCEV, NewIV->getType(),
Preheader->getTerminator());
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. git-svn-id: https://llvm.org/svn/llvm-project/llvm/trunk@194939 91177308-0d34-0410-b5e6-96231b3b80d8
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}
Value *Cond =
new ICmpInst(BI, CmpInst::ICMP_EQ, NewIV, ICMinus1, "exitcond");
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. git-svn-id: https://llvm.org/svn/llvm-project/llvm/trunk@194939 91177308-0d34-0410-b5e6-96231b3b80d8
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BI->setCondition(Cond);
if (BI->getSuccessor(1) != Header)
BI->swapSuccessors();
}
}
}
SimplifyInstructionsInBlock(Header, DL, TLI);
DeleteDeadPHIs(Header, TLI);
++NumRerolledLoops;
return true;
}
bool LoopReroll::runOnLoop(Loop *L, LPPassManager &LPM) {
if (skipOptnoneFunction(L))
return false;
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. git-svn-id: https://llvm.org/svn/llvm-project/llvm/trunk@194939 91177308-0d34-0410-b5e6-96231b3b80d8
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AA = &getAnalysis<AliasAnalysis>();
LI = &getAnalysis<LoopInfo>();
SE = &getAnalysis<ScalarEvolution>();
TLI = &getAnalysis<TargetLibraryInfo>();
DataLayoutPass *DLP = getAnalysisIfAvailable<DataLayoutPass>();
DL = DLP ? &DLP->getDataLayout() : nullptr;
DT = &getAnalysis<DominatorTreeWrapperPass>().getDomTree();
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. git-svn-id: https://llvm.org/svn/llvm-project/llvm/trunk@194939 91177308-0d34-0410-b5e6-96231b3b80d8
2013-11-16 23:59:05 +00:00
BasicBlock *Header = L->getHeader();
DEBUG(dbgs() << "LRR: F[" << Header->getParent()->getName() <<
"] Loop %" << Header->getName() << " (" <<
L->getNumBlocks() << " block(s))\n");
bool Changed = false;
// For now, we'll handle only single BB loops.
if (L->getNumBlocks() > 1)
return Changed;
if (!SE->hasLoopInvariantBackedgeTakenCount(L))
return Changed;
const SCEV *LIBETC = SE->getBackedgeTakenCount(L);
const SCEV *IterCount =
SE->getAddExpr(LIBETC, SE->getConstant(LIBETC->getType(), 1));
DEBUG(dbgs() << "LRR: iteration count = " << *IterCount << "\n");
// First, we need to find the induction variable with respect to which we can
// reroll (there may be several possible options).
SmallInstructionVector PossibleIVs;
collectPossibleIVs(L, PossibleIVs);
if (PossibleIVs.empty()) {
DEBUG(dbgs() << "LRR: No possible IVs found\n");
return Changed;
}
ReductionTracker Reductions;
collectPossibleReductions(L, Reductions);
// For each possible IV, collect the associated possible set of 'root' nodes
// (i+1, i+2, etc.).
for (SmallInstructionVector::iterator I = PossibleIVs.begin(),
IE = PossibleIVs.end(); I != IE; ++I)
if (reroll(*I, L, Header, IterCount, Reductions)) {
Changed = true;
break;
}
return Changed;
}