llvm-6502/lib/CodeGen/MachineBlockPlacement.cpp

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Implement a block placement pass based on the branch probability and block frequency analyses. This differs substantially from the existing block-placement pass in LLVM: 1) It operates on the Machine-IR in the CodeGen layer. This exposes much more (and more precise) information and opportunities. Also, the results are more stable due to fewer transforms ocurring after the pass runs. 2) It uses the generalized probability and frequency analyses. These can model static heuristics, code annotation derived heuristics as well as eventual profile loading. By basing the optimization on the analysis interface it can work from any (or a combination) of these inputs. 3) It uses a more aggressive algorithm, both building chains from tho bottom up to maximize benefit, and using an SCC-based walk to layout chains of blocks in a profitable ordering without O(N^2) iterations which the old pass involves. The pass is currently gated behind a flag, and not enabled by default because it still needs to grow some important features. Most notably, it needs to support loop aligning and careful layout of loop structures much as done by hand currently in CodePlacementOpt. Once it supports these, and has sufficient testing and quality tuning, it should replace both of these passes. Thanks to Nick Lewycky and Richard Smith for help authoring & debugging this, and to Jakob, Andy, Eric, Jim, and probably a few others I'm forgetting for reviewing and answering all my questions. Writing a backend pass is *sooo* much better now than it used to be. =D git-svn-id: https://llvm.org/svn/llvm-project/llvm/trunk@142641 91177308-0d34-0410-b5e6-96231b3b80d8
2011-10-21 06:46:38 +00:00
//===-- MachineBlockPlacement.cpp - Basic Block Code Layout optimization --===//
//
// The LLVM Compiler Infrastructure
//
// This file is distributed under the University of Illinois Open Source
// License. See LICENSE.TXT for details.
//
//===----------------------------------------------------------------------===//
//
Completely re-write the algorithm behind MachineBlockPlacement based on discussions with Andy. Fundamentally, the previous algorithm is both counter productive on several fronts and prioritizing things which aren't necessarily the most important: static branch prediction. The new algorithm uses the existing loop CFG structure information to walk through the CFG itself to layout blocks. It coalesces adjacent blocks within the loop where the CFG allows based on the most likely path taken. Finally, it topologically orders the block chains that have been formed. This allows it to choose a (mostly) topologically valid ordering which still priorizes fallthrough within the structural constraints. As a final twist in the algorithm, it does violate the CFG when it discovers a "hot" edge, that is an edge that is more than 4x hotter than the competing edges in the CFG. These are forcibly merged into a fallthrough chain. Future transformations that need te be added are rotation of loop exit conditions to be fallthrough, and better isolation of cold block chains. I'm also planning on adding statistics to model how well the algorithm does at laying out blocks based on the probabilities it receives. The old tests mostly still pass, and I have some new tests to add, but the nested loops are still behaving very strangely. This almost seems like working-as-intended as it rotated the exit branch to be fallthrough, but I'm not convinced this is actually the best layout. It is well supported by the probabilities for loops we currently get, but those are pretty broken for nested loops, so this may change later. git-svn-id: https://llvm.org/svn/llvm-project/llvm/trunk@142743 91177308-0d34-0410-b5e6-96231b3b80d8
2011-10-23 09:18:45 +00:00
// This file implements basic block placement transformations using the CFG
// structure and branch probability estimates.
Implement a block placement pass based on the branch probability and block frequency analyses. This differs substantially from the existing block-placement pass in LLVM: 1) It operates on the Machine-IR in the CodeGen layer. This exposes much more (and more precise) information and opportunities. Also, the results are more stable due to fewer transforms ocurring after the pass runs. 2) It uses the generalized probability and frequency analyses. These can model static heuristics, code annotation derived heuristics as well as eventual profile loading. By basing the optimization on the analysis interface it can work from any (or a combination) of these inputs. 3) It uses a more aggressive algorithm, both building chains from tho bottom up to maximize benefit, and using an SCC-based walk to layout chains of blocks in a profitable ordering without O(N^2) iterations which the old pass involves. The pass is currently gated behind a flag, and not enabled by default because it still needs to grow some important features. Most notably, it needs to support loop aligning and careful layout of loop structures much as done by hand currently in CodePlacementOpt. Once it supports these, and has sufficient testing and quality tuning, it should replace both of these passes. Thanks to Nick Lewycky and Richard Smith for help authoring & debugging this, and to Jakob, Andy, Eric, Jim, and probably a few others I'm forgetting for reviewing and answering all my questions. Writing a backend pass is *sooo* much better now than it used to be. =D git-svn-id: https://llvm.org/svn/llvm-project/llvm/trunk@142641 91177308-0d34-0410-b5e6-96231b3b80d8
2011-10-21 06:46:38 +00:00
//
Completely re-write the algorithm behind MachineBlockPlacement based on discussions with Andy. Fundamentally, the previous algorithm is both counter productive on several fronts and prioritizing things which aren't necessarily the most important: static branch prediction. The new algorithm uses the existing loop CFG structure information to walk through the CFG itself to layout blocks. It coalesces adjacent blocks within the loop where the CFG allows based on the most likely path taken. Finally, it topologically orders the block chains that have been formed. This allows it to choose a (mostly) topologically valid ordering which still priorizes fallthrough within the structural constraints. As a final twist in the algorithm, it does violate the CFG when it discovers a "hot" edge, that is an edge that is more than 4x hotter than the competing edges in the CFG. These are forcibly merged into a fallthrough chain. Future transformations that need te be added are rotation of loop exit conditions to be fallthrough, and better isolation of cold block chains. I'm also planning on adding statistics to model how well the algorithm does at laying out blocks based on the probabilities it receives. The old tests mostly still pass, and I have some new tests to add, but the nested loops are still behaving very strangely. This almost seems like working-as-intended as it rotated the exit branch to be fallthrough, but I'm not convinced this is actually the best layout. It is well supported by the probabilities for loops we currently get, but those are pretty broken for nested loops, so this may change later. git-svn-id: https://llvm.org/svn/llvm-project/llvm/trunk@142743 91177308-0d34-0410-b5e6-96231b3b80d8
2011-10-23 09:18:45 +00:00
// The pass strives to preserve the structure of the CFG (that is, retain
// a topological ordering of basic blocks) in the absense of a *strong* signal
// to the contrary from probabilities. However, within the CFG structure, it
// attempts to choose an ordering which favors placing more likely sequences of
// blocks adjacent to each other.
//
// The algorithm works from the inner-most loop within a function outward, and
// at each stage walks through the basic blocks, trying to coalesce them into
// sequential chains where allowed by the CFG (or demanded by heavy
// probabilities). Finally, it walks the blocks in topological order, and the
// first time it reaches a chain of basic blocks, it schedules them in the
// function in-order.
Implement a block placement pass based on the branch probability and block frequency analyses. This differs substantially from the existing block-placement pass in LLVM: 1) It operates on the Machine-IR in the CodeGen layer. This exposes much more (and more precise) information and opportunities. Also, the results are more stable due to fewer transforms ocurring after the pass runs. 2) It uses the generalized probability and frequency analyses. These can model static heuristics, code annotation derived heuristics as well as eventual profile loading. By basing the optimization on the analysis interface it can work from any (or a combination) of these inputs. 3) It uses a more aggressive algorithm, both building chains from tho bottom up to maximize benefit, and using an SCC-based walk to layout chains of blocks in a profitable ordering without O(N^2) iterations which the old pass involves. The pass is currently gated behind a flag, and not enabled by default because it still needs to grow some important features. Most notably, it needs to support loop aligning and careful layout of loop structures much as done by hand currently in CodePlacementOpt. Once it supports these, and has sufficient testing and quality tuning, it should replace both of these passes. Thanks to Nick Lewycky and Richard Smith for help authoring & debugging this, and to Jakob, Andy, Eric, Jim, and probably a few others I'm forgetting for reviewing and answering all my questions. Writing a backend pass is *sooo* much better now than it used to be. =D git-svn-id: https://llvm.org/svn/llvm-project/llvm/trunk@142641 91177308-0d34-0410-b5e6-96231b3b80d8
2011-10-21 06:46:38 +00:00
//
//===----------------------------------------------------------------------===//
#define DEBUG_TYPE "block-placement2"
#include "llvm/CodeGen/MachineBasicBlock.h"
Implement a block placement pass based on the branch probability and block frequency analyses. This differs substantially from the existing block-placement pass in LLVM: 1) It operates on the Machine-IR in the CodeGen layer. This exposes much more (and more precise) information and opportunities. Also, the results are more stable due to fewer transforms ocurring after the pass runs. 2) It uses the generalized probability and frequency analyses. These can model static heuristics, code annotation derived heuristics as well as eventual profile loading. By basing the optimization on the analysis interface it can work from any (or a combination) of these inputs. 3) It uses a more aggressive algorithm, both building chains from tho bottom up to maximize benefit, and using an SCC-based walk to layout chains of blocks in a profitable ordering without O(N^2) iterations which the old pass involves. The pass is currently gated behind a flag, and not enabled by default because it still needs to grow some important features. Most notably, it needs to support loop aligning and careful layout of loop structures much as done by hand currently in CodePlacementOpt. Once it supports these, and has sufficient testing and quality tuning, it should replace both of these passes. Thanks to Nick Lewycky and Richard Smith for help authoring & debugging this, and to Jakob, Andy, Eric, Jim, and probably a few others I'm forgetting for reviewing and answering all my questions. Writing a backend pass is *sooo* much better now than it used to be. =D git-svn-id: https://llvm.org/svn/llvm-project/llvm/trunk@142641 91177308-0d34-0410-b5e6-96231b3b80d8
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#include "llvm/CodeGen/MachineBlockFrequencyInfo.h"
#include "llvm/CodeGen/MachineBranchProbabilityInfo.h"
#include "llvm/CodeGen/MachineFunction.h"
#include "llvm/CodeGen/MachineFunctionPass.h"
#include "llvm/CodeGen/MachineLoopInfo.h"
#include "llvm/CodeGen/MachineModuleInfo.h"
#include "llvm/CodeGen/Passes.h"
Implement a block placement pass based on the branch probability and block frequency analyses. This differs substantially from the existing block-placement pass in LLVM: 1) It operates on the Machine-IR in the CodeGen layer. This exposes much more (and more precise) information and opportunities. Also, the results are more stable due to fewer transforms ocurring after the pass runs. 2) It uses the generalized probability and frequency analyses. These can model static heuristics, code annotation derived heuristics as well as eventual profile loading. By basing the optimization on the analysis interface it can work from any (or a combination) of these inputs. 3) It uses a more aggressive algorithm, both building chains from tho bottom up to maximize benefit, and using an SCC-based walk to layout chains of blocks in a profitable ordering without O(N^2) iterations which the old pass involves. The pass is currently gated behind a flag, and not enabled by default because it still needs to grow some important features. Most notably, it needs to support loop aligning and careful layout of loop structures much as done by hand currently in CodePlacementOpt. Once it supports these, and has sufficient testing and quality tuning, it should replace both of these passes. Thanks to Nick Lewycky and Richard Smith for help authoring & debugging this, and to Jakob, Andy, Eric, Jim, and probably a few others I'm forgetting for reviewing and answering all my questions. Writing a backend pass is *sooo* much better now than it used to be. =D git-svn-id: https://llvm.org/svn/llvm-project/llvm/trunk@142641 91177308-0d34-0410-b5e6-96231b3b80d8
2011-10-21 06:46:38 +00:00
#include "llvm/Support/Allocator.h"
Completely re-write the algorithm behind MachineBlockPlacement based on discussions with Andy. Fundamentally, the previous algorithm is both counter productive on several fronts and prioritizing things which aren't necessarily the most important: static branch prediction. The new algorithm uses the existing loop CFG structure information to walk through the CFG itself to layout blocks. It coalesces adjacent blocks within the loop where the CFG allows based on the most likely path taken. Finally, it topologically orders the block chains that have been formed. This allows it to choose a (mostly) topologically valid ordering which still priorizes fallthrough within the structural constraints. As a final twist in the algorithm, it does violate the CFG when it discovers a "hot" edge, that is an edge that is more than 4x hotter than the competing edges in the CFG. These are forcibly merged into a fallthrough chain. Future transformations that need te be added are rotation of loop exit conditions to be fallthrough, and better isolation of cold block chains. I'm also planning on adding statistics to model how well the algorithm does at laying out blocks based on the probabilities it receives. The old tests mostly still pass, and I have some new tests to add, but the nested loops are still behaving very strangely. This almost seems like working-as-intended as it rotated the exit branch to be fallthrough, but I'm not convinced this is actually the best layout. It is well supported by the probabilities for loops we currently get, but those are pretty broken for nested loops, so this may change later. git-svn-id: https://llvm.org/svn/llvm-project/llvm/trunk@142743 91177308-0d34-0410-b5e6-96231b3b80d8
2011-10-23 09:18:45 +00:00
#include "llvm/Support/Debug.h"
Implement a block placement pass based on the branch probability and block frequency analyses. This differs substantially from the existing block-placement pass in LLVM: 1) It operates on the Machine-IR in the CodeGen layer. This exposes much more (and more precise) information and opportunities. Also, the results are more stable due to fewer transforms ocurring after the pass runs. 2) It uses the generalized probability and frequency analyses. These can model static heuristics, code annotation derived heuristics as well as eventual profile loading. By basing the optimization on the analysis interface it can work from any (or a combination) of these inputs. 3) It uses a more aggressive algorithm, both building chains from tho bottom up to maximize benefit, and using an SCC-based walk to layout chains of blocks in a profitable ordering without O(N^2) iterations which the old pass involves. The pass is currently gated behind a flag, and not enabled by default because it still needs to grow some important features. Most notably, it needs to support loop aligning and careful layout of loop structures much as done by hand currently in CodePlacementOpt. Once it supports these, and has sufficient testing and quality tuning, it should replace both of these passes. Thanks to Nick Lewycky and Richard Smith for help authoring & debugging this, and to Jakob, Andy, Eric, Jim, and probably a few others I'm forgetting for reviewing and answering all my questions. Writing a backend pass is *sooo* much better now than it used to be. =D git-svn-id: https://llvm.org/svn/llvm-project/llvm/trunk@142641 91177308-0d34-0410-b5e6-96231b3b80d8
2011-10-21 06:46:38 +00:00
#include "llvm/Support/ErrorHandling.h"
#include "llvm/ADT/DenseMap.h"
Completely re-write the algorithm behind MachineBlockPlacement based on discussions with Andy. Fundamentally, the previous algorithm is both counter productive on several fronts and prioritizing things which aren't necessarily the most important: static branch prediction. The new algorithm uses the existing loop CFG structure information to walk through the CFG itself to layout blocks. It coalesces adjacent blocks within the loop where the CFG allows based on the most likely path taken. Finally, it topologically orders the block chains that have been formed. This allows it to choose a (mostly) topologically valid ordering which still priorizes fallthrough within the structural constraints. As a final twist in the algorithm, it does violate the CFG when it discovers a "hot" edge, that is an edge that is more than 4x hotter than the competing edges in the CFG. These are forcibly merged into a fallthrough chain. Future transformations that need te be added are rotation of loop exit conditions to be fallthrough, and better isolation of cold block chains. I'm also planning on adding statistics to model how well the algorithm does at laying out blocks based on the probabilities it receives. The old tests mostly still pass, and I have some new tests to add, but the nested loops are still behaving very strangely. This almost seems like working-as-intended as it rotated the exit branch to be fallthrough, but I'm not convinced this is actually the best layout. It is well supported by the probabilities for loops we currently get, but those are pretty broken for nested loops, so this may change later. git-svn-id: https://llvm.org/svn/llvm-project/llvm/trunk@142743 91177308-0d34-0410-b5e6-96231b3b80d8
2011-10-23 09:18:45 +00:00
#include "llvm/ADT/PostOrderIterator.h"
Implement a block placement pass based on the branch probability and block frequency analyses. This differs substantially from the existing block-placement pass in LLVM: 1) It operates on the Machine-IR in the CodeGen layer. This exposes much more (and more precise) information and opportunities. Also, the results are more stable due to fewer transforms ocurring after the pass runs. 2) It uses the generalized probability and frequency analyses. These can model static heuristics, code annotation derived heuristics as well as eventual profile loading. By basing the optimization on the analysis interface it can work from any (or a combination) of these inputs. 3) It uses a more aggressive algorithm, both building chains from tho bottom up to maximize benefit, and using an SCC-based walk to layout chains of blocks in a profitable ordering without O(N^2) iterations which the old pass involves. The pass is currently gated behind a flag, and not enabled by default because it still needs to grow some important features. Most notably, it needs to support loop aligning and careful layout of loop structures much as done by hand currently in CodePlacementOpt. Once it supports these, and has sufficient testing and quality tuning, it should replace both of these passes. Thanks to Nick Lewycky and Richard Smith for help authoring & debugging this, and to Jakob, Andy, Eric, Jim, and probably a few others I'm forgetting for reviewing and answering all my questions. Writing a backend pass is *sooo* much better now than it used to be. =D git-svn-id: https://llvm.org/svn/llvm-project/llvm/trunk@142641 91177308-0d34-0410-b5e6-96231b3b80d8
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#include "llvm/ADT/SCCIterator.h"
#include "llvm/ADT/SmallPtrSet.h"
#include "llvm/ADT/SmallVector.h"
#include "llvm/ADT/Statistic.h"
#include "llvm/Target/TargetInstrInfo.h"
#include "llvm/Target/TargetLowering.h"
Implement a block placement pass based on the branch probability and block frequency analyses. This differs substantially from the existing block-placement pass in LLVM: 1) It operates on the Machine-IR in the CodeGen layer. This exposes much more (and more precise) information and opportunities. Also, the results are more stable due to fewer transforms ocurring after the pass runs. 2) It uses the generalized probability and frequency analyses. These can model static heuristics, code annotation derived heuristics as well as eventual profile loading. By basing the optimization on the analysis interface it can work from any (or a combination) of these inputs. 3) It uses a more aggressive algorithm, both building chains from tho bottom up to maximize benefit, and using an SCC-based walk to layout chains of blocks in a profitable ordering without O(N^2) iterations which the old pass involves. The pass is currently gated behind a flag, and not enabled by default because it still needs to grow some important features. Most notably, it needs to support loop aligning and careful layout of loop structures much as done by hand currently in CodePlacementOpt. Once it supports these, and has sufficient testing and quality tuning, it should replace both of these passes. Thanks to Nick Lewycky and Richard Smith for help authoring & debugging this, and to Jakob, Andy, Eric, Jim, and probably a few others I'm forgetting for reviewing and answering all my questions. Writing a backend pass is *sooo* much better now than it used to be. =D git-svn-id: https://llvm.org/svn/llvm-project/llvm/trunk@142641 91177308-0d34-0410-b5e6-96231b3b80d8
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#include <algorithm>
using namespace llvm;
STATISTIC(NumCondBranches, "Number of conditional branches");
STATISTIC(NumUncondBranches, "Number of uncondittional branches");
STATISTIC(CondBranchTakenFreq,
"Potential frequency of taking conditional branches");
STATISTIC(UncondBranchTakenFreq,
"Potential frequency of taking unconditional branches");
Implement a block placement pass based on the branch probability and block frequency analyses. This differs substantially from the existing block-placement pass in LLVM: 1) It operates on the Machine-IR in the CodeGen layer. This exposes much more (and more precise) information and opportunities. Also, the results are more stable due to fewer transforms ocurring after the pass runs. 2) It uses the generalized probability and frequency analyses. These can model static heuristics, code annotation derived heuristics as well as eventual profile loading. By basing the optimization on the analysis interface it can work from any (or a combination) of these inputs. 3) It uses a more aggressive algorithm, both building chains from tho bottom up to maximize benefit, and using an SCC-based walk to layout chains of blocks in a profitable ordering without O(N^2) iterations which the old pass involves. The pass is currently gated behind a flag, and not enabled by default because it still needs to grow some important features. Most notably, it needs to support loop aligning and careful layout of loop structures much as done by hand currently in CodePlacementOpt. Once it supports these, and has sufficient testing and quality tuning, it should replace both of these passes. Thanks to Nick Lewycky and Richard Smith for help authoring & debugging this, and to Jakob, Andy, Eric, Jim, and probably a few others I'm forgetting for reviewing and answering all my questions. Writing a backend pass is *sooo* much better now than it used to be. =D git-svn-id: https://llvm.org/svn/llvm-project/llvm/trunk@142641 91177308-0d34-0410-b5e6-96231b3b80d8
2011-10-21 06:46:38 +00:00
namespace {
/// \brief A structure for storing a weighted edge.
///
/// This stores an edge and its weight, computed as the product of the
/// frequency that the starting block is entered with the probability of
/// a particular exit block.
struct WeightedEdge {
BlockFrequency EdgeFrequency;
MachineBasicBlock *From, *To;
bool operator<(const WeightedEdge &RHS) const {
return EdgeFrequency < RHS.EdgeFrequency;
}
};
}
namespace {
Completely re-write the algorithm behind MachineBlockPlacement based on discussions with Andy. Fundamentally, the previous algorithm is both counter productive on several fronts and prioritizing things which aren't necessarily the most important: static branch prediction. The new algorithm uses the existing loop CFG structure information to walk through the CFG itself to layout blocks. It coalesces adjacent blocks within the loop where the CFG allows based on the most likely path taken. Finally, it topologically orders the block chains that have been formed. This allows it to choose a (mostly) topologically valid ordering which still priorizes fallthrough within the structural constraints. As a final twist in the algorithm, it does violate the CFG when it discovers a "hot" edge, that is an edge that is more than 4x hotter than the competing edges in the CFG. These are forcibly merged into a fallthrough chain. Future transformations that need te be added are rotation of loop exit conditions to be fallthrough, and better isolation of cold block chains. I'm also planning on adding statistics to model how well the algorithm does at laying out blocks based on the probabilities it receives. The old tests mostly still pass, and I have some new tests to add, but the nested loops are still behaving very strangely. This almost seems like working-as-intended as it rotated the exit branch to be fallthrough, but I'm not convinced this is actually the best layout. It is well supported by the probabilities for loops we currently get, but those are pretty broken for nested loops, so this may change later. git-svn-id: https://llvm.org/svn/llvm-project/llvm/trunk@142743 91177308-0d34-0410-b5e6-96231b3b80d8
2011-10-23 09:18:45 +00:00
class BlockChain;
Implement a block placement pass based on the branch probability and block frequency analyses. This differs substantially from the existing block-placement pass in LLVM: 1) It operates on the Machine-IR in the CodeGen layer. This exposes much more (and more precise) information and opportunities. Also, the results are more stable due to fewer transforms ocurring after the pass runs. 2) It uses the generalized probability and frequency analyses. These can model static heuristics, code annotation derived heuristics as well as eventual profile loading. By basing the optimization on the analysis interface it can work from any (or a combination) of these inputs. 3) It uses a more aggressive algorithm, both building chains from tho bottom up to maximize benefit, and using an SCC-based walk to layout chains of blocks in a profitable ordering without O(N^2) iterations which the old pass involves. The pass is currently gated behind a flag, and not enabled by default because it still needs to grow some important features. Most notably, it needs to support loop aligning and careful layout of loop structures much as done by hand currently in CodePlacementOpt. Once it supports these, and has sufficient testing and quality tuning, it should replace both of these passes. Thanks to Nick Lewycky and Richard Smith for help authoring & debugging this, and to Jakob, Andy, Eric, Jim, and probably a few others I'm forgetting for reviewing and answering all my questions. Writing a backend pass is *sooo* much better now than it used to be. =D git-svn-id: https://llvm.org/svn/llvm-project/llvm/trunk@142641 91177308-0d34-0410-b5e6-96231b3b80d8
2011-10-21 06:46:38 +00:00
/// \brief Type for our function-wide basic block -> block chain mapping.
typedef DenseMap<MachineBasicBlock *, BlockChain *> BlockToChainMapType;
}
namespace {
/// \brief A chain of blocks which will be laid out contiguously.
///
/// This is the datastructure representing a chain of consecutive blocks that
/// are profitable to layout together in order to maximize fallthrough
/// probabilities. We also can use a block chain to represent a sequence of
/// basic blocks which have some external (correctness) requirement for
/// sequential layout.
///
/// Eventually, the block chains will form a directed graph over the function.
/// We provide an SCC-supporting-iterator in order to quicky build and walk the
/// SCCs of block chains within a function.
///
/// The block chains also have support for calculating and caching probability
/// information related to the chain itself versus other chains. This is used
/// for ranking during the final layout of block chains.
Completely re-write the algorithm behind MachineBlockPlacement based on discussions with Andy. Fundamentally, the previous algorithm is both counter productive on several fronts and prioritizing things which aren't necessarily the most important: static branch prediction. The new algorithm uses the existing loop CFG structure information to walk through the CFG itself to layout blocks. It coalesces adjacent blocks within the loop where the CFG allows based on the most likely path taken. Finally, it topologically orders the block chains that have been formed. This allows it to choose a (mostly) topologically valid ordering which still priorizes fallthrough within the structural constraints. As a final twist in the algorithm, it does violate the CFG when it discovers a "hot" edge, that is an edge that is more than 4x hotter than the competing edges in the CFG. These are forcibly merged into a fallthrough chain. Future transformations that need te be added are rotation of loop exit conditions to be fallthrough, and better isolation of cold block chains. I'm also planning on adding statistics to model how well the algorithm does at laying out blocks based on the probabilities it receives. The old tests mostly still pass, and I have some new tests to add, but the nested loops are still behaving very strangely. This almost seems like working-as-intended as it rotated the exit branch to be fallthrough, but I'm not convinced this is actually the best layout. It is well supported by the probabilities for loops we currently get, but those are pretty broken for nested loops, so this may change later. git-svn-id: https://llvm.org/svn/llvm-project/llvm/trunk@142743 91177308-0d34-0410-b5e6-96231b3b80d8
2011-10-23 09:18:45 +00:00
class BlockChain {
/// \brief The sequence of blocks belonging to this chain.
Implement a block placement pass based on the branch probability and block frequency analyses. This differs substantially from the existing block-placement pass in LLVM: 1) It operates on the Machine-IR in the CodeGen layer. This exposes much more (and more precise) information and opportunities. Also, the results are more stable due to fewer transforms ocurring after the pass runs. 2) It uses the generalized probability and frequency analyses. These can model static heuristics, code annotation derived heuristics as well as eventual profile loading. By basing the optimization on the analysis interface it can work from any (or a combination) of these inputs. 3) It uses a more aggressive algorithm, both building chains from tho bottom up to maximize benefit, and using an SCC-based walk to layout chains of blocks in a profitable ordering without O(N^2) iterations which the old pass involves. The pass is currently gated behind a flag, and not enabled by default because it still needs to grow some important features. Most notably, it needs to support loop aligning and careful layout of loop structures much as done by hand currently in CodePlacementOpt. Once it supports these, and has sufficient testing and quality tuning, it should replace both of these passes. Thanks to Nick Lewycky and Richard Smith for help authoring & debugging this, and to Jakob, Andy, Eric, Jim, and probably a few others I'm forgetting for reviewing and answering all my questions. Writing a backend pass is *sooo* much better now than it used to be. =D git-svn-id: https://llvm.org/svn/llvm-project/llvm/trunk@142641 91177308-0d34-0410-b5e6-96231b3b80d8
2011-10-21 06:46:38 +00:00
///
Completely re-write the algorithm behind MachineBlockPlacement based on discussions with Andy. Fundamentally, the previous algorithm is both counter productive on several fronts and prioritizing things which aren't necessarily the most important: static branch prediction. The new algorithm uses the existing loop CFG structure information to walk through the CFG itself to layout blocks. It coalesces adjacent blocks within the loop where the CFG allows based on the most likely path taken. Finally, it topologically orders the block chains that have been formed. This allows it to choose a (mostly) topologically valid ordering which still priorizes fallthrough within the structural constraints. As a final twist in the algorithm, it does violate the CFG when it discovers a "hot" edge, that is an edge that is more than 4x hotter than the competing edges in the CFG. These are forcibly merged into a fallthrough chain. Future transformations that need te be added are rotation of loop exit conditions to be fallthrough, and better isolation of cold block chains. I'm also planning on adding statistics to model how well the algorithm does at laying out blocks based on the probabilities it receives. The old tests mostly still pass, and I have some new tests to add, but the nested loops are still behaving very strangely. This almost seems like working-as-intended as it rotated the exit branch to be fallthrough, but I'm not convinced this is actually the best layout. It is well supported by the probabilities for loops we currently get, but those are pretty broken for nested loops, so this may change later. git-svn-id: https://llvm.org/svn/llvm-project/llvm/trunk@142743 91177308-0d34-0410-b5e6-96231b3b80d8
2011-10-23 09:18:45 +00:00
/// This is the sequence of blocks for a particular chain. These will be laid
/// out in-order within the function.
SmallVector<MachineBasicBlock *, 4> Blocks;
Implement a block placement pass based on the branch probability and block frequency analyses. This differs substantially from the existing block-placement pass in LLVM: 1) It operates on the Machine-IR in the CodeGen layer. This exposes much more (and more precise) information and opportunities. Also, the results are more stable due to fewer transforms ocurring after the pass runs. 2) It uses the generalized probability and frequency analyses. These can model static heuristics, code annotation derived heuristics as well as eventual profile loading. By basing the optimization on the analysis interface it can work from any (or a combination) of these inputs. 3) It uses a more aggressive algorithm, both building chains from tho bottom up to maximize benefit, and using an SCC-based walk to layout chains of blocks in a profitable ordering without O(N^2) iterations which the old pass involves. The pass is currently gated behind a flag, and not enabled by default because it still needs to grow some important features. Most notably, it needs to support loop aligning and careful layout of loop structures much as done by hand currently in CodePlacementOpt. Once it supports these, and has sufficient testing and quality tuning, it should replace both of these passes. Thanks to Nick Lewycky and Richard Smith for help authoring & debugging this, and to Jakob, Andy, Eric, Jim, and probably a few others I'm forgetting for reviewing and answering all my questions. Writing a backend pass is *sooo* much better now than it used to be. =D git-svn-id: https://llvm.org/svn/llvm-project/llvm/trunk@142641 91177308-0d34-0410-b5e6-96231b3b80d8
2011-10-21 06:46:38 +00:00
/// \brief A handle to the function-wide basic block to block chain mapping.
///
/// This is retained in each block chain to simplify the computation of child
/// block chains for SCC-formation and iteration. We store the edges to child
/// basic blocks, and map them back to their associated chains using this
/// structure.
BlockToChainMapType &BlockToChain;
Completely re-write the algorithm behind MachineBlockPlacement based on discussions with Andy. Fundamentally, the previous algorithm is both counter productive on several fronts and prioritizing things which aren't necessarily the most important: static branch prediction. The new algorithm uses the existing loop CFG structure information to walk through the CFG itself to layout blocks. It coalesces adjacent blocks within the loop where the CFG allows based on the most likely path taken. Finally, it topologically orders the block chains that have been formed. This allows it to choose a (mostly) topologically valid ordering which still priorizes fallthrough within the structural constraints. As a final twist in the algorithm, it does violate the CFG when it discovers a "hot" edge, that is an edge that is more than 4x hotter than the competing edges in the CFG. These are forcibly merged into a fallthrough chain. Future transformations that need te be added are rotation of loop exit conditions to be fallthrough, and better isolation of cold block chains. I'm also planning on adding statistics to model how well the algorithm does at laying out blocks based on the probabilities it receives. The old tests mostly still pass, and I have some new tests to add, but the nested loops are still behaving very strangely. This almost seems like working-as-intended as it rotated the exit branch to be fallthrough, but I'm not convinced this is actually the best layout. It is well supported by the probabilities for loops we currently get, but those are pretty broken for nested loops, so this may change later. git-svn-id: https://llvm.org/svn/llvm-project/llvm/trunk@142743 91177308-0d34-0410-b5e6-96231b3b80d8
2011-10-23 09:18:45 +00:00
public:
Implement a block placement pass based on the branch probability and block frequency analyses. This differs substantially from the existing block-placement pass in LLVM: 1) It operates on the Machine-IR in the CodeGen layer. This exposes much more (and more precise) information and opportunities. Also, the results are more stable due to fewer transforms ocurring after the pass runs. 2) It uses the generalized probability and frequency analyses. These can model static heuristics, code annotation derived heuristics as well as eventual profile loading. By basing the optimization on the analysis interface it can work from any (or a combination) of these inputs. 3) It uses a more aggressive algorithm, both building chains from tho bottom up to maximize benefit, and using an SCC-based walk to layout chains of blocks in a profitable ordering without O(N^2) iterations which the old pass involves. The pass is currently gated behind a flag, and not enabled by default because it still needs to grow some important features. Most notably, it needs to support loop aligning and careful layout of loop structures much as done by hand currently in CodePlacementOpt. Once it supports these, and has sufficient testing and quality tuning, it should replace both of these passes. Thanks to Nick Lewycky and Richard Smith for help authoring & debugging this, and to Jakob, Andy, Eric, Jim, and probably a few others I'm forgetting for reviewing and answering all my questions. Writing a backend pass is *sooo* much better now than it used to be. =D git-svn-id: https://llvm.org/svn/llvm-project/llvm/trunk@142641 91177308-0d34-0410-b5e6-96231b3b80d8
2011-10-21 06:46:38 +00:00
/// \brief Construct a new BlockChain.
///
/// This builds a new block chain representing a single basic block in the
/// function. It also registers itself as the chain that block participates
/// in with the BlockToChain mapping.
BlockChain(BlockToChainMapType &BlockToChain, MachineBasicBlock *BB)
Completely re-write the algorithm behind MachineBlockPlacement based on discussions with Andy. Fundamentally, the previous algorithm is both counter productive on several fronts and prioritizing things which aren't necessarily the most important: static branch prediction. The new algorithm uses the existing loop CFG structure information to walk through the CFG itself to layout blocks. It coalesces adjacent blocks within the loop where the CFG allows based on the most likely path taken. Finally, it topologically orders the block chains that have been formed. This allows it to choose a (mostly) topologically valid ordering which still priorizes fallthrough within the structural constraints. As a final twist in the algorithm, it does violate the CFG when it discovers a "hot" edge, that is an edge that is more than 4x hotter than the competing edges in the CFG. These are forcibly merged into a fallthrough chain. Future transformations that need te be added are rotation of loop exit conditions to be fallthrough, and better isolation of cold block chains. I'm also planning on adding statistics to model how well the algorithm does at laying out blocks based on the probabilities it receives. The old tests mostly still pass, and I have some new tests to add, but the nested loops are still behaving very strangely. This almost seems like working-as-intended as it rotated the exit branch to be fallthrough, but I'm not convinced this is actually the best layout. It is well supported by the probabilities for loops we currently get, but those are pretty broken for nested loops, so this may change later. git-svn-id: https://llvm.org/svn/llvm-project/llvm/trunk@142743 91177308-0d34-0410-b5e6-96231b3b80d8
2011-10-23 09:18:45 +00:00
: Blocks(1, BB), BlockToChain(BlockToChain) {
Implement a block placement pass based on the branch probability and block frequency analyses. This differs substantially from the existing block-placement pass in LLVM: 1) It operates on the Machine-IR in the CodeGen layer. This exposes much more (and more precise) information and opportunities. Also, the results are more stable due to fewer transforms ocurring after the pass runs. 2) It uses the generalized probability and frequency analyses. These can model static heuristics, code annotation derived heuristics as well as eventual profile loading. By basing the optimization on the analysis interface it can work from any (or a combination) of these inputs. 3) It uses a more aggressive algorithm, both building chains from tho bottom up to maximize benefit, and using an SCC-based walk to layout chains of blocks in a profitable ordering without O(N^2) iterations which the old pass involves. The pass is currently gated behind a flag, and not enabled by default because it still needs to grow some important features. Most notably, it needs to support loop aligning and careful layout of loop structures much as done by hand currently in CodePlacementOpt. Once it supports these, and has sufficient testing and quality tuning, it should replace both of these passes. Thanks to Nick Lewycky and Richard Smith for help authoring & debugging this, and to Jakob, Andy, Eric, Jim, and probably a few others I'm forgetting for reviewing and answering all my questions. Writing a backend pass is *sooo* much better now than it used to be. =D git-svn-id: https://llvm.org/svn/llvm-project/llvm/trunk@142641 91177308-0d34-0410-b5e6-96231b3b80d8
2011-10-21 06:46:38 +00:00
assert(BB && "Cannot create a chain with a null basic block");
BlockToChain[BB] = this;
}
Completely re-write the algorithm behind MachineBlockPlacement based on discussions with Andy. Fundamentally, the previous algorithm is both counter productive on several fronts and prioritizing things which aren't necessarily the most important: static branch prediction. The new algorithm uses the existing loop CFG structure information to walk through the CFG itself to layout blocks. It coalesces adjacent blocks within the loop where the CFG allows based on the most likely path taken. Finally, it topologically orders the block chains that have been formed. This allows it to choose a (mostly) topologically valid ordering which still priorizes fallthrough within the structural constraints. As a final twist in the algorithm, it does violate the CFG when it discovers a "hot" edge, that is an edge that is more than 4x hotter than the competing edges in the CFG. These are forcibly merged into a fallthrough chain. Future transformations that need te be added are rotation of loop exit conditions to be fallthrough, and better isolation of cold block chains. I'm also planning on adding statistics to model how well the algorithm does at laying out blocks based on the probabilities it receives. The old tests mostly still pass, and I have some new tests to add, but the nested loops are still behaving very strangely. This almost seems like working-as-intended as it rotated the exit branch to be fallthrough, but I'm not convinced this is actually the best layout. It is well supported by the probabilities for loops we currently get, but those are pretty broken for nested loops, so this may change later. git-svn-id: https://llvm.org/svn/llvm-project/llvm/trunk@142743 91177308-0d34-0410-b5e6-96231b3b80d8
2011-10-23 09:18:45 +00:00
/// \brief Iterator over blocks within the chain.
typedef SmallVectorImpl<MachineBasicBlock *>::const_iterator iterator;
/// \brief Beginning of blocks within the chain.
iterator begin() const { return Blocks.begin(); }
/// \brief End of blocks within the chain.
iterator end() const { return Blocks.end(); }
/// \brief Merge a block chain into this one.
Implement a block placement pass based on the branch probability and block frequency analyses. This differs substantially from the existing block-placement pass in LLVM: 1) It operates on the Machine-IR in the CodeGen layer. This exposes much more (and more precise) information and opportunities. Also, the results are more stable due to fewer transforms ocurring after the pass runs. 2) It uses the generalized probability and frequency analyses. These can model static heuristics, code annotation derived heuristics as well as eventual profile loading. By basing the optimization on the analysis interface it can work from any (or a combination) of these inputs. 3) It uses a more aggressive algorithm, both building chains from tho bottom up to maximize benefit, and using an SCC-based walk to layout chains of blocks in a profitable ordering without O(N^2) iterations which the old pass involves. The pass is currently gated behind a flag, and not enabled by default because it still needs to grow some important features. Most notably, it needs to support loop aligning and careful layout of loop structures much as done by hand currently in CodePlacementOpt. Once it supports these, and has sufficient testing and quality tuning, it should replace both of these passes. Thanks to Nick Lewycky and Richard Smith for help authoring & debugging this, and to Jakob, Andy, Eric, Jim, and probably a few others I'm forgetting for reviewing and answering all my questions. Writing a backend pass is *sooo* much better now than it used to be. =D git-svn-id: https://llvm.org/svn/llvm-project/llvm/trunk@142641 91177308-0d34-0410-b5e6-96231b3b80d8
2011-10-21 06:46:38 +00:00
///
/// This routine merges a block chain into this one. It takes care of forming
/// a contiguous sequence of basic blocks, updating the edge list, and
/// updating the block -> chain mapping. It does not free or tear down the
/// old chain, but the old chain's block list is no longer valid.
Completely re-write the algorithm behind MachineBlockPlacement based on discussions with Andy. Fundamentally, the previous algorithm is both counter productive on several fronts and prioritizing things which aren't necessarily the most important: static branch prediction. The new algorithm uses the existing loop CFG structure information to walk through the CFG itself to layout blocks. It coalesces adjacent blocks within the loop where the CFG allows based on the most likely path taken. Finally, it topologically orders the block chains that have been formed. This allows it to choose a (mostly) topologically valid ordering which still priorizes fallthrough within the structural constraints. As a final twist in the algorithm, it does violate the CFG when it discovers a "hot" edge, that is an edge that is more than 4x hotter than the competing edges in the CFG. These are forcibly merged into a fallthrough chain. Future transformations that need te be added are rotation of loop exit conditions to be fallthrough, and better isolation of cold block chains. I'm also planning on adding statistics to model how well the algorithm does at laying out blocks based on the probabilities it receives. The old tests mostly still pass, and I have some new tests to add, but the nested loops are still behaving very strangely. This almost seems like working-as-intended as it rotated the exit branch to be fallthrough, but I'm not convinced this is actually the best layout. It is well supported by the probabilities for loops we currently get, but those are pretty broken for nested loops, so this may change later. git-svn-id: https://llvm.org/svn/llvm-project/llvm/trunk@142743 91177308-0d34-0410-b5e6-96231b3b80d8
2011-10-23 09:18:45 +00:00
void merge(MachineBasicBlock *BB, BlockChain *Chain) {
assert(BB);
assert(!Blocks.empty());
assert(Blocks.back()->isSuccessor(BB));
// Fast path in case we don't have a chain already.
if (!Chain) {
assert(!BlockToChain[BB]);
Blocks.push_back(BB);
BlockToChain[BB] = this;
return;
Implement a block placement pass based on the branch probability and block frequency analyses. This differs substantially from the existing block-placement pass in LLVM: 1) It operates on the Machine-IR in the CodeGen layer. This exposes much more (and more precise) information and opportunities. Also, the results are more stable due to fewer transforms ocurring after the pass runs. 2) It uses the generalized probability and frequency analyses. These can model static heuristics, code annotation derived heuristics as well as eventual profile loading. By basing the optimization on the analysis interface it can work from any (or a combination) of these inputs. 3) It uses a more aggressive algorithm, both building chains from tho bottom up to maximize benefit, and using an SCC-based walk to layout chains of blocks in a profitable ordering without O(N^2) iterations which the old pass involves. The pass is currently gated behind a flag, and not enabled by default because it still needs to grow some important features. Most notably, it needs to support loop aligning and careful layout of loop structures much as done by hand currently in CodePlacementOpt. Once it supports these, and has sufficient testing and quality tuning, it should replace both of these passes. Thanks to Nick Lewycky and Richard Smith for help authoring & debugging this, and to Jakob, Andy, Eric, Jim, and probably a few others I'm forgetting for reviewing and answering all my questions. Writing a backend pass is *sooo* much better now than it used to be. =D git-svn-id: https://llvm.org/svn/llvm-project/llvm/trunk@142641 91177308-0d34-0410-b5e6-96231b3b80d8
2011-10-21 06:46:38 +00:00
}
Completely re-write the algorithm behind MachineBlockPlacement based on discussions with Andy. Fundamentally, the previous algorithm is both counter productive on several fronts and prioritizing things which aren't necessarily the most important: static branch prediction. The new algorithm uses the existing loop CFG structure information to walk through the CFG itself to layout blocks. It coalesces adjacent blocks within the loop where the CFG allows based on the most likely path taken. Finally, it topologically orders the block chains that have been formed. This allows it to choose a (mostly) topologically valid ordering which still priorizes fallthrough within the structural constraints. As a final twist in the algorithm, it does violate the CFG when it discovers a "hot" edge, that is an edge that is more than 4x hotter than the competing edges in the CFG. These are forcibly merged into a fallthrough chain. Future transformations that need te be added are rotation of loop exit conditions to be fallthrough, and better isolation of cold block chains. I'm also planning on adding statistics to model how well the algorithm does at laying out blocks based on the probabilities it receives. The old tests mostly still pass, and I have some new tests to add, but the nested loops are still behaving very strangely. This almost seems like working-as-intended as it rotated the exit branch to be fallthrough, but I'm not convinced this is actually the best layout. It is well supported by the probabilities for loops we currently get, but those are pretty broken for nested loops, so this may change later. git-svn-id: https://llvm.org/svn/llvm-project/llvm/trunk@142743 91177308-0d34-0410-b5e6-96231b3b80d8
2011-10-23 09:18:45 +00:00
assert(BB == *Chain->begin());
assert(Chain->begin() != Chain->end());
Implement a block placement pass based on the branch probability and block frequency analyses. This differs substantially from the existing block-placement pass in LLVM: 1) It operates on the Machine-IR in the CodeGen layer. This exposes much more (and more precise) information and opportunities. Also, the results are more stable due to fewer transforms ocurring after the pass runs. 2) It uses the generalized probability and frequency analyses. These can model static heuristics, code annotation derived heuristics as well as eventual profile loading. By basing the optimization on the analysis interface it can work from any (or a combination) of these inputs. 3) It uses a more aggressive algorithm, both building chains from tho bottom up to maximize benefit, and using an SCC-based walk to layout chains of blocks in a profitable ordering without O(N^2) iterations which the old pass involves. The pass is currently gated behind a flag, and not enabled by default because it still needs to grow some important features. Most notably, it needs to support loop aligning and careful layout of loop structures much as done by hand currently in CodePlacementOpt. Once it supports these, and has sufficient testing and quality tuning, it should replace both of these passes. Thanks to Nick Lewycky and Richard Smith for help authoring & debugging this, and to Jakob, Andy, Eric, Jim, and probably a few others I'm forgetting for reviewing and answering all my questions. Writing a backend pass is *sooo* much better now than it used to be. =D git-svn-id: https://llvm.org/svn/llvm-project/llvm/trunk@142641 91177308-0d34-0410-b5e6-96231b3b80d8
2011-10-21 06:46:38 +00:00
Completely re-write the algorithm behind MachineBlockPlacement based on discussions with Andy. Fundamentally, the previous algorithm is both counter productive on several fronts and prioritizing things which aren't necessarily the most important: static branch prediction. The new algorithm uses the existing loop CFG structure information to walk through the CFG itself to layout blocks. It coalesces adjacent blocks within the loop where the CFG allows based on the most likely path taken. Finally, it topologically orders the block chains that have been formed. This allows it to choose a (mostly) topologically valid ordering which still priorizes fallthrough within the structural constraints. As a final twist in the algorithm, it does violate the CFG when it discovers a "hot" edge, that is an edge that is more than 4x hotter than the competing edges in the CFG. These are forcibly merged into a fallthrough chain. Future transformations that need te be added are rotation of loop exit conditions to be fallthrough, and better isolation of cold block chains. I'm also planning on adding statistics to model how well the algorithm does at laying out blocks based on the probabilities it receives. The old tests mostly still pass, and I have some new tests to add, but the nested loops are still behaving very strangely. This almost seems like working-as-intended as it rotated the exit branch to be fallthrough, but I'm not convinced this is actually the best layout. It is well supported by the probabilities for loops we currently get, but those are pretty broken for nested loops, so this may change later. git-svn-id: https://llvm.org/svn/llvm-project/llvm/trunk@142743 91177308-0d34-0410-b5e6-96231b3b80d8
2011-10-23 09:18:45 +00:00
// Update the incoming blocks to point to this chain, and add them to the
// chain structure.
for (BlockChain::iterator BI = Chain->begin(), BE = Chain->end();
BI != BE; ++BI) {
Blocks.push_back(*BI);
assert(BlockToChain[*BI] == Chain && "Incoming blocks not in chain");
BlockToChain[*BI] = this;
}
Implement a block placement pass based on the branch probability and block frequency analyses. This differs substantially from the existing block-placement pass in LLVM: 1) It operates on the Machine-IR in the CodeGen layer. This exposes much more (and more precise) information and opportunities. Also, the results are more stable due to fewer transforms ocurring after the pass runs. 2) It uses the generalized probability and frequency analyses. These can model static heuristics, code annotation derived heuristics as well as eventual profile loading. By basing the optimization on the analysis interface it can work from any (or a combination) of these inputs. 3) It uses a more aggressive algorithm, both building chains from tho bottom up to maximize benefit, and using an SCC-based walk to layout chains of blocks in a profitable ordering without O(N^2) iterations which the old pass involves. The pass is currently gated behind a flag, and not enabled by default because it still needs to grow some important features. Most notably, it needs to support loop aligning and careful layout of loop structures much as done by hand currently in CodePlacementOpt. Once it supports these, and has sufficient testing and quality tuning, it should replace both of these passes. Thanks to Nick Lewycky and Richard Smith for help authoring & debugging this, and to Jakob, Andy, Eric, Jim, and probably a few others I'm forgetting for reviewing and answering all my questions. Writing a backend pass is *sooo* much better now than it used to be. =D git-svn-id: https://llvm.org/svn/llvm-project/llvm/trunk@142641 91177308-0d34-0410-b5e6-96231b3b80d8
2011-10-21 06:46:38 +00:00
}
};
}
namespace {
class MachineBlockPlacement : public MachineFunctionPass {
Completely re-write the algorithm behind MachineBlockPlacement based on discussions with Andy. Fundamentally, the previous algorithm is both counter productive on several fronts and prioritizing things which aren't necessarily the most important: static branch prediction. The new algorithm uses the existing loop CFG structure information to walk through the CFG itself to layout blocks. It coalesces adjacent blocks within the loop where the CFG allows based on the most likely path taken. Finally, it topologically orders the block chains that have been formed. This allows it to choose a (mostly) topologically valid ordering which still priorizes fallthrough within the structural constraints. As a final twist in the algorithm, it does violate the CFG when it discovers a "hot" edge, that is an edge that is more than 4x hotter than the competing edges in the CFG. These are forcibly merged into a fallthrough chain. Future transformations that need te be added are rotation of loop exit conditions to be fallthrough, and better isolation of cold block chains. I'm also planning on adding statistics to model how well the algorithm does at laying out blocks based on the probabilities it receives. The old tests mostly still pass, and I have some new tests to add, but the nested loops are still behaving very strangely. This almost seems like working-as-intended as it rotated the exit branch to be fallthrough, but I'm not convinced this is actually the best layout. It is well supported by the probabilities for loops we currently get, but those are pretty broken for nested loops, so this may change later. git-svn-id: https://llvm.org/svn/llvm-project/llvm/trunk@142743 91177308-0d34-0410-b5e6-96231b3b80d8
2011-10-23 09:18:45 +00:00
/// \brief A typedef for a block filter set.
typedef SmallPtrSet<MachineBasicBlock *, 16> BlockFilterSet;
Implement a block placement pass based on the branch probability and block frequency analyses. This differs substantially from the existing block-placement pass in LLVM: 1) It operates on the Machine-IR in the CodeGen layer. This exposes much more (and more precise) information and opportunities. Also, the results are more stable due to fewer transforms ocurring after the pass runs. 2) It uses the generalized probability and frequency analyses. These can model static heuristics, code annotation derived heuristics as well as eventual profile loading. By basing the optimization on the analysis interface it can work from any (or a combination) of these inputs. 3) It uses a more aggressive algorithm, both building chains from tho bottom up to maximize benefit, and using an SCC-based walk to layout chains of blocks in a profitable ordering without O(N^2) iterations which the old pass involves. The pass is currently gated behind a flag, and not enabled by default because it still needs to grow some important features. Most notably, it needs to support loop aligning and careful layout of loop structures much as done by hand currently in CodePlacementOpt. Once it supports these, and has sufficient testing and quality tuning, it should replace both of these passes. Thanks to Nick Lewycky and Richard Smith for help authoring & debugging this, and to Jakob, Andy, Eric, Jim, and probably a few others I'm forgetting for reviewing and answering all my questions. Writing a backend pass is *sooo* much better now than it used to be. =D git-svn-id: https://llvm.org/svn/llvm-project/llvm/trunk@142641 91177308-0d34-0410-b5e6-96231b3b80d8
2011-10-21 06:46:38 +00:00
/// \brief A handle to the branch probability pass.
const MachineBranchProbabilityInfo *MBPI;
/// \brief A handle to the function-wide block frequency pass.
const MachineBlockFrequencyInfo *MBFI;
/// \brief A handle to the loop info.
const MachineLoopInfo *MLI;
Implement a block placement pass based on the branch probability and block frequency analyses. This differs substantially from the existing block-placement pass in LLVM: 1) It operates on the Machine-IR in the CodeGen layer. This exposes much more (and more precise) information and opportunities. Also, the results are more stable due to fewer transforms ocurring after the pass runs. 2) It uses the generalized probability and frequency analyses. These can model static heuristics, code annotation derived heuristics as well as eventual profile loading. By basing the optimization on the analysis interface it can work from any (or a combination) of these inputs. 3) It uses a more aggressive algorithm, both building chains from tho bottom up to maximize benefit, and using an SCC-based walk to layout chains of blocks in a profitable ordering without O(N^2) iterations which the old pass involves. The pass is currently gated behind a flag, and not enabled by default because it still needs to grow some important features. Most notably, it needs to support loop aligning and careful layout of loop structures much as done by hand currently in CodePlacementOpt. Once it supports these, and has sufficient testing and quality tuning, it should replace both of these passes. Thanks to Nick Lewycky and Richard Smith for help authoring & debugging this, and to Jakob, Andy, Eric, Jim, and probably a few others I'm forgetting for reviewing and answering all my questions. Writing a backend pass is *sooo* much better now than it used to be. =D git-svn-id: https://llvm.org/svn/llvm-project/llvm/trunk@142641 91177308-0d34-0410-b5e6-96231b3b80d8
2011-10-21 06:46:38 +00:00
/// \brief A handle to the target's instruction info.
const TargetInstrInfo *TII;
/// \brief A handle to the target's lowering info.
const TargetLowering *TLI;
Implement a block placement pass based on the branch probability and block frequency analyses. This differs substantially from the existing block-placement pass in LLVM: 1) It operates on the Machine-IR in the CodeGen layer. This exposes much more (and more precise) information and opportunities. Also, the results are more stable due to fewer transforms ocurring after the pass runs. 2) It uses the generalized probability and frequency analyses. These can model static heuristics, code annotation derived heuristics as well as eventual profile loading. By basing the optimization on the analysis interface it can work from any (or a combination) of these inputs. 3) It uses a more aggressive algorithm, both building chains from tho bottom up to maximize benefit, and using an SCC-based walk to layout chains of blocks in a profitable ordering without O(N^2) iterations which the old pass involves. The pass is currently gated behind a flag, and not enabled by default because it still needs to grow some important features. Most notably, it needs to support loop aligning and careful layout of loop structures much as done by hand currently in CodePlacementOpt. Once it supports these, and has sufficient testing and quality tuning, it should replace both of these passes. Thanks to Nick Lewycky and Richard Smith for help authoring & debugging this, and to Jakob, Andy, Eric, Jim, and probably a few others I'm forgetting for reviewing and answering all my questions. Writing a backend pass is *sooo* much better now than it used to be. =D git-svn-id: https://llvm.org/svn/llvm-project/llvm/trunk@142641 91177308-0d34-0410-b5e6-96231b3b80d8
2011-10-21 06:46:38 +00:00
/// \brief Allocator and owner of BlockChain structures.
///
/// We build BlockChains lazily by merging together high probability BB
/// sequences acording to the "Algo2" in the paper mentioned at the top of
/// the file. To reduce malloc traffic, we allocate them using this slab-like
/// allocator, and destroy them after the pass completes.
SpecificBumpPtrAllocator<BlockChain> ChainAllocator;
/// \brief Function wide BasicBlock to BlockChain mapping.
///
/// This mapping allows efficiently moving from any given basic block to the
/// BlockChain it participates in, if any. We use it to, among other things,
/// allow implicitly defining edges between chains as the existing edges
/// between basic blocks.
DenseMap<MachineBasicBlock *, BlockChain *> BlockToChain;
BlockChain *CreateChain(MachineBasicBlock *BB);
Completely re-write the algorithm behind MachineBlockPlacement based on discussions with Andy. Fundamentally, the previous algorithm is both counter productive on several fronts and prioritizing things which aren't necessarily the most important: static branch prediction. The new algorithm uses the existing loop CFG structure information to walk through the CFG itself to layout blocks. It coalesces adjacent blocks within the loop where the CFG allows based on the most likely path taken. Finally, it topologically orders the block chains that have been formed. This allows it to choose a (mostly) topologically valid ordering which still priorizes fallthrough within the structural constraints. As a final twist in the algorithm, it does violate the CFG when it discovers a "hot" edge, that is an edge that is more than 4x hotter than the competing edges in the CFG. These are forcibly merged into a fallthrough chain. Future transformations that need te be added are rotation of loop exit conditions to be fallthrough, and better isolation of cold block chains. I'm also planning on adding statistics to model how well the algorithm does at laying out blocks based on the probabilities it receives. The old tests mostly still pass, and I have some new tests to add, but the nested loops are still behaving very strangely. This almost seems like working-as-intended as it rotated the exit branch to be fallthrough, but I'm not convinced this is actually the best layout. It is well supported by the probabilities for loops we currently get, but those are pretty broken for nested loops, so this may change later. git-svn-id: https://llvm.org/svn/llvm-project/llvm/trunk@142743 91177308-0d34-0410-b5e6-96231b3b80d8
2011-10-23 09:18:45 +00:00
void mergeSuccessor(MachineBasicBlock *BB, BlockChain *Chain,
BlockFilterSet *Filter = 0);
void buildLoopChains(MachineFunction &F, MachineLoop &L);
void buildCFGChains(MachineFunction &F);
void placeChainsTopologically(MachineFunction &F);
void AlignLoops(MachineFunction &F);
Implement a block placement pass based on the branch probability and block frequency analyses. This differs substantially from the existing block-placement pass in LLVM: 1) It operates on the Machine-IR in the CodeGen layer. This exposes much more (and more precise) information and opportunities. Also, the results are more stable due to fewer transforms ocurring after the pass runs. 2) It uses the generalized probability and frequency analyses. These can model static heuristics, code annotation derived heuristics as well as eventual profile loading. By basing the optimization on the analysis interface it can work from any (or a combination) of these inputs. 3) It uses a more aggressive algorithm, both building chains from tho bottom up to maximize benefit, and using an SCC-based walk to layout chains of blocks in a profitable ordering without O(N^2) iterations which the old pass involves. The pass is currently gated behind a flag, and not enabled by default because it still needs to grow some important features. Most notably, it needs to support loop aligning and careful layout of loop structures much as done by hand currently in CodePlacementOpt. Once it supports these, and has sufficient testing and quality tuning, it should replace both of these passes. Thanks to Nick Lewycky and Richard Smith for help authoring & debugging this, and to Jakob, Andy, Eric, Jim, and probably a few others I'm forgetting for reviewing and answering all my questions. Writing a backend pass is *sooo* much better now than it used to be. =D git-svn-id: https://llvm.org/svn/llvm-project/llvm/trunk@142641 91177308-0d34-0410-b5e6-96231b3b80d8
2011-10-21 06:46:38 +00:00
public:
static char ID; // Pass identification, replacement for typeid
MachineBlockPlacement() : MachineFunctionPass(ID) {
initializeMachineBlockPlacementPass(*PassRegistry::getPassRegistry());
}
bool runOnMachineFunction(MachineFunction &F);
void getAnalysisUsage(AnalysisUsage &AU) const {
AU.addRequired<MachineBranchProbabilityInfo>();
AU.addRequired<MachineBlockFrequencyInfo>();
AU.addRequired<MachineLoopInfo>();
Implement a block placement pass based on the branch probability and block frequency analyses. This differs substantially from the existing block-placement pass in LLVM: 1) It operates on the Machine-IR in the CodeGen layer. This exposes much more (and more precise) information and opportunities. Also, the results are more stable due to fewer transforms ocurring after the pass runs. 2) It uses the generalized probability and frequency analyses. These can model static heuristics, code annotation derived heuristics as well as eventual profile loading. By basing the optimization on the analysis interface it can work from any (or a combination) of these inputs. 3) It uses a more aggressive algorithm, both building chains from tho bottom up to maximize benefit, and using an SCC-based walk to layout chains of blocks in a profitable ordering without O(N^2) iterations which the old pass involves. The pass is currently gated behind a flag, and not enabled by default because it still needs to grow some important features. Most notably, it needs to support loop aligning and careful layout of loop structures much as done by hand currently in CodePlacementOpt. Once it supports these, and has sufficient testing and quality tuning, it should replace both of these passes. Thanks to Nick Lewycky and Richard Smith for help authoring & debugging this, and to Jakob, Andy, Eric, Jim, and probably a few others I'm forgetting for reviewing and answering all my questions. Writing a backend pass is *sooo* much better now than it used to be. =D git-svn-id: https://llvm.org/svn/llvm-project/llvm/trunk@142641 91177308-0d34-0410-b5e6-96231b3b80d8
2011-10-21 06:46:38 +00:00
MachineFunctionPass::getAnalysisUsage(AU);
}
const char *getPassName() const { return "Block Placement"; }
};
}
char MachineBlockPlacement::ID = 0;
INITIALIZE_PASS_BEGIN(MachineBlockPlacement, "block-placement2",
"Branch Probability Basic Block Placement", false, false)
INITIALIZE_PASS_DEPENDENCY(MachineBranchProbabilityInfo)
INITIALIZE_PASS_DEPENDENCY(MachineBlockFrequencyInfo)
INITIALIZE_PASS_DEPENDENCY(MachineLoopInfo)
Implement a block placement pass based on the branch probability and block frequency analyses. This differs substantially from the existing block-placement pass in LLVM: 1) It operates on the Machine-IR in the CodeGen layer. This exposes much more (and more precise) information and opportunities. Also, the results are more stable due to fewer transforms ocurring after the pass runs. 2) It uses the generalized probability and frequency analyses. These can model static heuristics, code annotation derived heuristics as well as eventual profile loading. By basing the optimization on the analysis interface it can work from any (or a combination) of these inputs. 3) It uses a more aggressive algorithm, both building chains from tho bottom up to maximize benefit, and using an SCC-based walk to layout chains of blocks in a profitable ordering without O(N^2) iterations which the old pass involves. The pass is currently gated behind a flag, and not enabled by default because it still needs to grow some important features. Most notably, it needs to support loop aligning and careful layout of loop structures much as done by hand currently in CodePlacementOpt. Once it supports these, and has sufficient testing and quality tuning, it should replace both of these passes. Thanks to Nick Lewycky and Richard Smith for help authoring & debugging this, and to Jakob, Andy, Eric, Jim, and probably a few others I'm forgetting for reviewing and answering all my questions. Writing a backend pass is *sooo* much better now than it used to be. =D git-svn-id: https://llvm.org/svn/llvm-project/llvm/trunk@142641 91177308-0d34-0410-b5e6-96231b3b80d8
2011-10-21 06:46:38 +00:00
INITIALIZE_PASS_END(MachineBlockPlacement, "block-placement2",
"Branch Probability Basic Block Placement", false, false)
FunctionPass *llvm::createMachineBlockPlacementPass() {
return new MachineBlockPlacement();
}
Completely re-write the algorithm behind MachineBlockPlacement based on discussions with Andy. Fundamentally, the previous algorithm is both counter productive on several fronts and prioritizing things which aren't necessarily the most important: static branch prediction. The new algorithm uses the existing loop CFG structure information to walk through the CFG itself to layout blocks. It coalesces adjacent blocks within the loop where the CFG allows based on the most likely path taken. Finally, it topologically orders the block chains that have been formed. This allows it to choose a (mostly) topologically valid ordering which still priorizes fallthrough within the structural constraints. As a final twist in the algorithm, it does violate the CFG when it discovers a "hot" edge, that is an edge that is more than 4x hotter than the competing edges in the CFG. These are forcibly merged into a fallthrough chain. Future transformations that need te be added are rotation of loop exit conditions to be fallthrough, and better isolation of cold block chains. I'm also planning on adding statistics to model how well the algorithm does at laying out blocks based on the probabilities it receives. The old tests mostly still pass, and I have some new tests to add, but the nested loops are still behaving very strangely. This almost seems like working-as-intended as it rotated the exit branch to be fallthrough, but I'm not convinced this is actually the best layout. It is well supported by the probabilities for loops we currently get, but those are pretty broken for nested loops, so this may change later. git-svn-id: https://llvm.org/svn/llvm-project/llvm/trunk@142743 91177308-0d34-0410-b5e6-96231b3b80d8
2011-10-23 09:18:45 +00:00
#ifndef NDEBUG
/// \brief Helper to print the name of a MBB.
///
/// Only used by debug logging.
static std::string getBlockName(MachineBasicBlock *BB) {
std::string Result;
raw_string_ostream OS(Result);
OS << "BB#" << BB->getNumber()
<< " (derived from LLVM BB '" << BB->getName() << "')";
OS.flush();
return Result;
}
Implement a block placement pass based on the branch probability and block frequency analyses. This differs substantially from the existing block-placement pass in LLVM: 1) It operates on the Machine-IR in the CodeGen layer. This exposes much more (and more precise) information and opportunities. Also, the results are more stable due to fewer transforms ocurring after the pass runs. 2) It uses the generalized probability and frequency analyses. These can model static heuristics, code annotation derived heuristics as well as eventual profile loading. By basing the optimization on the analysis interface it can work from any (or a combination) of these inputs. 3) It uses a more aggressive algorithm, both building chains from tho bottom up to maximize benefit, and using an SCC-based walk to layout chains of blocks in a profitable ordering without O(N^2) iterations which the old pass involves. The pass is currently gated behind a flag, and not enabled by default because it still needs to grow some important features. Most notably, it needs to support loop aligning and careful layout of loop structures much as done by hand currently in CodePlacementOpt. Once it supports these, and has sufficient testing and quality tuning, it should replace both of these passes. Thanks to Nick Lewycky and Richard Smith for help authoring & debugging this, and to Jakob, Andy, Eric, Jim, and probably a few others I'm forgetting for reviewing and answering all my questions. Writing a backend pass is *sooo* much better now than it used to be. =D git-svn-id: https://llvm.org/svn/llvm-project/llvm/trunk@142641 91177308-0d34-0410-b5e6-96231b3b80d8
2011-10-21 06:46:38 +00:00
Completely re-write the algorithm behind MachineBlockPlacement based on discussions with Andy. Fundamentally, the previous algorithm is both counter productive on several fronts and prioritizing things which aren't necessarily the most important: static branch prediction. The new algorithm uses the existing loop CFG structure information to walk through the CFG itself to layout blocks. It coalesces adjacent blocks within the loop where the CFG allows based on the most likely path taken. Finally, it topologically orders the block chains that have been formed. This allows it to choose a (mostly) topologically valid ordering which still priorizes fallthrough within the structural constraints. As a final twist in the algorithm, it does violate the CFG when it discovers a "hot" edge, that is an edge that is more than 4x hotter than the competing edges in the CFG. These are forcibly merged into a fallthrough chain. Future transformations that need te be added are rotation of loop exit conditions to be fallthrough, and better isolation of cold block chains. I'm also planning on adding statistics to model how well the algorithm does at laying out blocks based on the probabilities it receives. The old tests mostly still pass, and I have some new tests to add, but the nested loops are still behaving very strangely. This almost seems like working-as-intended as it rotated the exit branch to be fallthrough, but I'm not convinced this is actually the best layout. It is well supported by the probabilities for loops we currently get, but those are pretty broken for nested loops, so this may change later. git-svn-id: https://llvm.org/svn/llvm-project/llvm/trunk@142743 91177308-0d34-0410-b5e6-96231b3b80d8
2011-10-23 09:18:45 +00:00
/// \brief Helper to print the number of a MBB.
///
/// Only used by debug logging.
static std::string getBlockNum(MachineBasicBlock *BB) {
std::string Result;
raw_string_ostream OS(Result);
OS << "BB#" << BB->getNumber();
OS.flush();
return Result;
Implement a block placement pass based on the branch probability and block frequency analyses. This differs substantially from the existing block-placement pass in LLVM: 1) It operates on the Machine-IR in the CodeGen layer. This exposes much more (and more precise) information and opportunities. Also, the results are more stable due to fewer transforms ocurring after the pass runs. 2) It uses the generalized probability and frequency analyses. These can model static heuristics, code annotation derived heuristics as well as eventual profile loading. By basing the optimization on the analysis interface it can work from any (or a combination) of these inputs. 3) It uses a more aggressive algorithm, both building chains from tho bottom up to maximize benefit, and using an SCC-based walk to layout chains of blocks in a profitable ordering without O(N^2) iterations which the old pass involves. The pass is currently gated behind a flag, and not enabled by default because it still needs to grow some important features. Most notably, it needs to support loop aligning and careful layout of loop structures much as done by hand currently in CodePlacementOpt. Once it supports these, and has sufficient testing and quality tuning, it should replace both of these passes. Thanks to Nick Lewycky and Richard Smith for help authoring & debugging this, and to Jakob, Andy, Eric, Jim, and probably a few others I'm forgetting for reviewing and answering all my questions. Writing a backend pass is *sooo* much better now than it used to be. =D git-svn-id: https://llvm.org/svn/llvm-project/llvm/trunk@142641 91177308-0d34-0410-b5e6-96231b3b80d8
2011-10-21 06:46:38 +00:00
}
Completely re-write the algorithm behind MachineBlockPlacement based on discussions with Andy. Fundamentally, the previous algorithm is both counter productive on several fronts and prioritizing things which aren't necessarily the most important: static branch prediction. The new algorithm uses the existing loop CFG structure information to walk through the CFG itself to layout blocks. It coalesces adjacent blocks within the loop where the CFG allows based on the most likely path taken. Finally, it topologically orders the block chains that have been formed. This allows it to choose a (mostly) topologically valid ordering which still priorizes fallthrough within the structural constraints. As a final twist in the algorithm, it does violate the CFG when it discovers a "hot" edge, that is an edge that is more than 4x hotter than the competing edges in the CFG. These are forcibly merged into a fallthrough chain. Future transformations that need te be added are rotation of loop exit conditions to be fallthrough, and better isolation of cold block chains. I'm also planning on adding statistics to model how well the algorithm does at laying out blocks based on the probabilities it receives. The old tests mostly still pass, and I have some new tests to add, but the nested loops are still behaving very strangely. This almost seems like working-as-intended as it rotated the exit branch to be fallthrough, but I'm not convinced this is actually the best layout. It is well supported by the probabilities for loops we currently get, but those are pretty broken for nested loops, so this may change later. git-svn-id: https://llvm.org/svn/llvm-project/llvm/trunk@142743 91177308-0d34-0410-b5e6-96231b3b80d8
2011-10-23 09:18:45 +00:00
#endif
Implement a block placement pass based on the branch probability and block frequency analyses. This differs substantially from the existing block-placement pass in LLVM: 1) It operates on the Machine-IR in the CodeGen layer. This exposes much more (and more precise) information and opportunities. Also, the results are more stable due to fewer transforms ocurring after the pass runs. 2) It uses the generalized probability and frequency analyses. These can model static heuristics, code annotation derived heuristics as well as eventual profile loading. By basing the optimization on the analysis interface it can work from any (or a combination) of these inputs. 3) It uses a more aggressive algorithm, both building chains from tho bottom up to maximize benefit, and using an SCC-based walk to layout chains of blocks in a profitable ordering without O(N^2) iterations which the old pass involves. The pass is currently gated behind a flag, and not enabled by default because it still needs to grow some important features. Most notably, it needs to support loop aligning and careful layout of loop structures much as done by hand currently in CodePlacementOpt. Once it supports these, and has sufficient testing and quality tuning, it should replace both of these passes. Thanks to Nick Lewycky and Richard Smith for help authoring & debugging this, and to Jakob, Andy, Eric, Jim, and probably a few others I'm forgetting for reviewing and answering all my questions. Writing a backend pass is *sooo* much better now than it used to be. =D git-svn-id: https://llvm.org/svn/llvm-project/llvm/trunk@142641 91177308-0d34-0410-b5e6-96231b3b80d8
2011-10-21 06:46:38 +00:00
/// \brief Helper to create a new chain for a single BB.
///
/// Takes care of growing the Chains, setting up the BlockChain object, and any
/// debug checking logic.
/// \returns A pointer to the new BlockChain.
BlockChain *MachineBlockPlacement::CreateChain(MachineBasicBlock *BB) {
BlockChain *Chain =
new (ChainAllocator.Allocate()) BlockChain(BlockToChain, BB);
return Chain;
}
Completely re-write the algorithm behind MachineBlockPlacement based on discussions with Andy. Fundamentally, the previous algorithm is both counter productive on several fronts and prioritizing things which aren't necessarily the most important: static branch prediction. The new algorithm uses the existing loop CFG structure information to walk through the CFG itself to layout blocks. It coalesces adjacent blocks within the loop where the CFG allows based on the most likely path taken. Finally, it topologically orders the block chains that have been formed. This allows it to choose a (mostly) topologically valid ordering which still priorizes fallthrough within the structural constraints. As a final twist in the algorithm, it does violate the CFG when it discovers a "hot" edge, that is an edge that is more than 4x hotter than the competing edges in the CFG. These are forcibly merged into a fallthrough chain. Future transformations that need te be added are rotation of loop exit conditions to be fallthrough, and better isolation of cold block chains. I'm also planning on adding statistics to model how well the algorithm does at laying out blocks based on the probabilities it receives. The old tests mostly still pass, and I have some new tests to add, but the nested loops are still behaving very strangely. This almost seems like working-as-intended as it rotated the exit branch to be fallthrough, but I'm not convinced this is actually the best layout. It is well supported by the probabilities for loops we currently get, but those are pretty broken for nested loops, so this may change later. git-svn-id: https://llvm.org/svn/llvm-project/llvm/trunk@142743 91177308-0d34-0410-b5e6-96231b3b80d8
2011-10-23 09:18:45 +00:00
/// \brief Merge a chain with any viable successor.
Implement a block placement pass based on the branch probability and block frequency analyses. This differs substantially from the existing block-placement pass in LLVM: 1) It operates on the Machine-IR in the CodeGen layer. This exposes much more (and more precise) information and opportunities. Also, the results are more stable due to fewer transforms ocurring after the pass runs. 2) It uses the generalized probability and frequency analyses. These can model static heuristics, code annotation derived heuristics as well as eventual profile loading. By basing the optimization on the analysis interface it can work from any (or a combination) of these inputs. 3) It uses a more aggressive algorithm, both building chains from tho bottom up to maximize benefit, and using an SCC-based walk to layout chains of blocks in a profitable ordering without O(N^2) iterations which the old pass involves. The pass is currently gated behind a flag, and not enabled by default because it still needs to grow some important features. Most notably, it needs to support loop aligning and careful layout of loop structures much as done by hand currently in CodePlacementOpt. Once it supports these, and has sufficient testing and quality tuning, it should replace both of these passes. Thanks to Nick Lewycky and Richard Smith for help authoring & debugging this, and to Jakob, Andy, Eric, Jim, and probably a few others I'm forgetting for reviewing and answering all my questions. Writing a backend pass is *sooo* much better now than it used to be. =D git-svn-id: https://llvm.org/svn/llvm-project/llvm/trunk@142641 91177308-0d34-0410-b5e6-96231b3b80d8
2011-10-21 06:46:38 +00:00
///
Completely re-write the algorithm behind MachineBlockPlacement based on discussions with Andy. Fundamentally, the previous algorithm is both counter productive on several fronts and prioritizing things which aren't necessarily the most important: static branch prediction. The new algorithm uses the existing loop CFG structure information to walk through the CFG itself to layout blocks. It coalesces adjacent blocks within the loop where the CFG allows based on the most likely path taken. Finally, it topologically orders the block chains that have been formed. This allows it to choose a (mostly) topologically valid ordering which still priorizes fallthrough within the structural constraints. As a final twist in the algorithm, it does violate the CFG when it discovers a "hot" edge, that is an edge that is more than 4x hotter than the competing edges in the CFG. These are forcibly merged into a fallthrough chain. Future transformations that need te be added are rotation of loop exit conditions to be fallthrough, and better isolation of cold block chains. I'm also planning on adding statistics to model how well the algorithm does at laying out blocks based on the probabilities it receives. The old tests mostly still pass, and I have some new tests to add, but the nested loops are still behaving very strangely. This almost seems like working-as-intended as it rotated the exit branch to be fallthrough, but I'm not convinced this is actually the best layout. It is well supported by the probabilities for loops we currently get, but those are pretty broken for nested loops, so this may change later. git-svn-id: https://llvm.org/svn/llvm-project/llvm/trunk@142743 91177308-0d34-0410-b5e6-96231b3b80d8
2011-10-23 09:18:45 +00:00
/// This routine walks the predecessors of the current block, looking for
/// viable merge candidates. It has strict rules it uses to determine when
/// a predecessor can be merged with the current block, which center around
/// preserving the CFG structure. It performs the merge if any viable candidate
/// is found.
void MachineBlockPlacement::mergeSuccessor(MachineBasicBlock *BB,
BlockChain *Chain,
BlockFilterSet *Filter) {
assert(BB);
assert(Chain);
// If this block is not at the end of its chain, it cannot merge with any
// other chain.
if (Chain && *llvm::prior(Chain->end()) != BB)
return;
Implement a block placement pass based on the branch probability and block frequency analyses. This differs substantially from the existing block-placement pass in LLVM: 1) It operates on the Machine-IR in the CodeGen layer. This exposes much more (and more precise) information and opportunities. Also, the results are more stable due to fewer transforms ocurring after the pass runs. 2) It uses the generalized probability and frequency analyses. These can model static heuristics, code annotation derived heuristics as well as eventual profile loading. By basing the optimization on the analysis interface it can work from any (or a combination) of these inputs. 3) It uses a more aggressive algorithm, both building chains from tho bottom up to maximize benefit, and using an SCC-based walk to layout chains of blocks in a profitable ordering without O(N^2) iterations which the old pass involves. The pass is currently gated behind a flag, and not enabled by default because it still needs to grow some important features. Most notably, it needs to support loop aligning and careful layout of loop structures much as done by hand currently in CodePlacementOpt. Once it supports these, and has sufficient testing and quality tuning, it should replace both of these passes. Thanks to Nick Lewycky and Richard Smith for help authoring & debugging this, and to Jakob, Andy, Eric, Jim, and probably a few others I'm forgetting for reviewing and answering all my questions. Writing a backend pass is *sooo* much better now than it used to be. =D git-svn-id: https://llvm.org/svn/llvm-project/llvm/trunk@142641 91177308-0d34-0410-b5e6-96231b3b80d8
2011-10-21 06:46:38 +00:00
Completely re-write the algorithm behind MachineBlockPlacement based on discussions with Andy. Fundamentally, the previous algorithm is both counter productive on several fronts and prioritizing things which aren't necessarily the most important: static branch prediction. The new algorithm uses the existing loop CFG structure information to walk through the CFG itself to layout blocks. It coalesces adjacent blocks within the loop where the CFG allows based on the most likely path taken. Finally, it topologically orders the block chains that have been formed. This allows it to choose a (mostly) topologically valid ordering which still priorizes fallthrough within the structural constraints. As a final twist in the algorithm, it does violate the CFG when it discovers a "hot" edge, that is an edge that is more than 4x hotter than the competing edges in the CFG. These are forcibly merged into a fallthrough chain. Future transformations that need te be added are rotation of loop exit conditions to be fallthrough, and better isolation of cold block chains. I'm also planning on adding statistics to model how well the algorithm does at laying out blocks based on the probabilities it receives. The old tests mostly still pass, and I have some new tests to add, but the nested loops are still behaving very strangely. This almost seems like working-as-intended as it rotated the exit branch to be fallthrough, but I'm not convinced this is actually the best layout. It is well supported by the probabilities for loops we currently get, but those are pretty broken for nested loops, so this may change later. git-svn-id: https://llvm.org/svn/llvm-project/llvm/trunk@142743 91177308-0d34-0410-b5e6-96231b3b80d8
2011-10-23 09:18:45 +00:00
// Walk through the successors looking for the highest probability edge.
MachineBasicBlock *Successor = 0;
BranchProbability BestProb = BranchProbability::getZero();
Completely re-write the algorithm behind MachineBlockPlacement based on discussions with Andy. Fundamentally, the previous algorithm is both counter productive on several fronts and prioritizing things which aren't necessarily the most important: static branch prediction. The new algorithm uses the existing loop CFG structure information to walk through the CFG itself to layout blocks. It coalesces adjacent blocks within the loop where the CFG allows based on the most likely path taken. Finally, it topologically orders the block chains that have been formed. This allows it to choose a (mostly) topologically valid ordering which still priorizes fallthrough within the structural constraints. As a final twist in the algorithm, it does violate the CFG when it discovers a "hot" edge, that is an edge that is more than 4x hotter than the competing edges in the CFG. These are forcibly merged into a fallthrough chain. Future transformations that need te be added are rotation of loop exit conditions to be fallthrough, and better isolation of cold block chains. I'm also planning on adding statistics to model how well the algorithm does at laying out blocks based on the probabilities it receives. The old tests mostly still pass, and I have some new tests to add, but the nested loops are still behaving very strangely. This almost seems like working-as-intended as it rotated the exit branch to be fallthrough, but I'm not convinced this is actually the best layout. It is well supported by the probabilities for loops we currently get, but those are pretty broken for nested loops, so this may change later. git-svn-id: https://llvm.org/svn/llvm-project/llvm/trunk@142743 91177308-0d34-0410-b5e6-96231b3b80d8
2011-10-23 09:18:45 +00:00
DEBUG(dbgs() << "Attempting merge from: " << getBlockName(BB) << "\n");
for (MachineBasicBlock::succ_iterator SI = BB->succ_begin(),
SE = BB->succ_end();
SI != SE; ++SI) {
if (BB == *SI || (Filter && !Filter->count(*SI)))
continue;
BranchProbability SuccProb = MBPI->getEdgeProbability(BB, *SI);
DEBUG(dbgs() << " " << getBlockName(*SI) << " -> " << SuccProb << "\n");
if (!Successor || SuccProb > BestProb || (!(SuccProb < BestProb) &&
Completely re-write the algorithm behind MachineBlockPlacement based on discussions with Andy. Fundamentally, the previous algorithm is both counter productive on several fronts and prioritizing things which aren't necessarily the most important: static branch prediction. The new algorithm uses the existing loop CFG structure information to walk through the CFG itself to layout blocks. It coalesces adjacent blocks within the loop where the CFG allows based on the most likely path taken. Finally, it topologically orders the block chains that have been formed. This allows it to choose a (mostly) topologically valid ordering which still priorizes fallthrough within the structural constraints. As a final twist in the algorithm, it does violate the CFG when it discovers a "hot" edge, that is an edge that is more than 4x hotter than the competing edges in the CFG. These are forcibly merged into a fallthrough chain. Future transformations that need te be added are rotation of loop exit conditions to be fallthrough, and better isolation of cold block chains. I'm also planning on adding statistics to model how well the algorithm does at laying out blocks based on the probabilities it receives. The old tests mostly still pass, and I have some new tests to add, but the nested loops are still behaving very strangely. This almost seems like working-as-intended as it rotated the exit branch to be fallthrough, but I'm not convinced this is actually the best layout. It is well supported by the probabilities for loops we currently get, but those are pretty broken for nested loops, so this may change later. git-svn-id: https://llvm.org/svn/llvm-project/llvm/trunk@142743 91177308-0d34-0410-b5e6-96231b3b80d8
2011-10-23 09:18:45 +00:00
BB->isLayoutSuccessor(*SI))) {
Successor = *SI;
BestProb = SuccProb;
Implement a block placement pass based on the branch probability and block frequency analyses. This differs substantially from the existing block-placement pass in LLVM: 1) It operates on the Machine-IR in the CodeGen layer. This exposes much more (and more precise) information and opportunities. Also, the results are more stable due to fewer transforms ocurring after the pass runs. 2) It uses the generalized probability and frequency analyses. These can model static heuristics, code annotation derived heuristics as well as eventual profile loading. By basing the optimization on the analysis interface it can work from any (or a combination) of these inputs. 3) It uses a more aggressive algorithm, both building chains from tho bottom up to maximize benefit, and using an SCC-based walk to layout chains of blocks in a profitable ordering without O(N^2) iterations which the old pass involves. The pass is currently gated behind a flag, and not enabled by default because it still needs to grow some important features. Most notably, it needs to support loop aligning and careful layout of loop structures much as done by hand currently in CodePlacementOpt. Once it supports these, and has sufficient testing and quality tuning, it should replace both of these passes. Thanks to Nick Lewycky and Richard Smith for help authoring & debugging this, and to Jakob, Andy, Eric, Jim, and probably a few others I'm forgetting for reviewing and answering all my questions. Writing a backend pass is *sooo* much better now than it used to be. =D git-svn-id: https://llvm.org/svn/llvm-project/llvm/trunk@142641 91177308-0d34-0410-b5e6-96231b3b80d8
2011-10-21 06:46:38 +00:00
}
}
Completely re-write the algorithm behind MachineBlockPlacement based on discussions with Andy. Fundamentally, the previous algorithm is both counter productive on several fronts and prioritizing things which aren't necessarily the most important: static branch prediction. The new algorithm uses the existing loop CFG structure information to walk through the CFG itself to layout blocks. It coalesces adjacent blocks within the loop where the CFG allows based on the most likely path taken. Finally, it topologically orders the block chains that have been formed. This allows it to choose a (mostly) topologically valid ordering which still priorizes fallthrough within the structural constraints. As a final twist in the algorithm, it does violate the CFG when it discovers a "hot" edge, that is an edge that is more than 4x hotter than the competing edges in the CFG. These are forcibly merged into a fallthrough chain. Future transformations that need te be added are rotation of loop exit conditions to be fallthrough, and better isolation of cold block chains. I'm also planning on adding statistics to model how well the algorithm does at laying out blocks based on the probabilities it receives. The old tests mostly still pass, and I have some new tests to add, but the nested loops are still behaving very strangely. This almost seems like working-as-intended as it rotated the exit branch to be fallthrough, but I'm not convinced this is actually the best layout. It is well supported by the probabilities for loops we currently get, but those are pretty broken for nested loops, so this may change later. git-svn-id: https://llvm.org/svn/llvm-project/llvm/trunk@142743 91177308-0d34-0410-b5e6-96231b3b80d8
2011-10-23 09:18:45 +00:00
if (!Successor)
return;
Implement a block placement pass based on the branch probability and block frequency analyses. This differs substantially from the existing block-placement pass in LLVM: 1) It operates on the Machine-IR in the CodeGen layer. This exposes much more (and more precise) information and opportunities. Also, the results are more stable due to fewer transforms ocurring after the pass runs. 2) It uses the generalized probability and frequency analyses. These can model static heuristics, code annotation derived heuristics as well as eventual profile loading. By basing the optimization on the analysis interface it can work from any (or a combination) of these inputs. 3) It uses a more aggressive algorithm, both building chains from tho bottom up to maximize benefit, and using an SCC-based walk to layout chains of blocks in a profitable ordering without O(N^2) iterations which the old pass involves. The pass is currently gated behind a flag, and not enabled by default because it still needs to grow some important features. Most notably, it needs to support loop aligning and careful layout of loop structures much as done by hand currently in CodePlacementOpt. Once it supports these, and has sufficient testing and quality tuning, it should replace both of these passes. Thanks to Nick Lewycky and Richard Smith for help authoring & debugging this, and to Jakob, Andy, Eric, Jim, and probably a few others I'm forgetting for reviewing and answering all my questions. Writing a backend pass is *sooo* much better now than it used to be. =D git-svn-id: https://llvm.org/svn/llvm-project/llvm/trunk@142641 91177308-0d34-0410-b5e6-96231b3b80d8
2011-10-21 06:46:38 +00:00
Completely re-write the algorithm behind MachineBlockPlacement based on discussions with Andy. Fundamentally, the previous algorithm is both counter productive on several fronts and prioritizing things which aren't necessarily the most important: static branch prediction. The new algorithm uses the existing loop CFG structure information to walk through the CFG itself to layout blocks. It coalesces adjacent blocks within the loop where the CFG allows based on the most likely path taken. Finally, it topologically orders the block chains that have been formed. This allows it to choose a (mostly) topologically valid ordering which still priorizes fallthrough within the structural constraints. As a final twist in the algorithm, it does violate the CFG when it discovers a "hot" edge, that is an edge that is more than 4x hotter than the competing edges in the CFG. These are forcibly merged into a fallthrough chain. Future transformations that need te be added are rotation of loop exit conditions to be fallthrough, and better isolation of cold block chains. I'm also planning on adding statistics to model how well the algorithm does at laying out blocks based on the probabilities it receives. The old tests mostly still pass, and I have some new tests to add, but the nested loops are still behaving very strangely. This almost seems like working-as-intended as it rotated the exit branch to be fallthrough, but I'm not convinced this is actually the best layout. It is well supported by the probabilities for loops we currently get, but those are pretty broken for nested loops, so this may change later. git-svn-id: https://llvm.org/svn/llvm-project/llvm/trunk@142743 91177308-0d34-0410-b5e6-96231b3b80d8
2011-10-23 09:18:45 +00:00
// Grab a chain if it exists already for this successor and make sure the
// successor is at the start of the chain as we can't merge mid-chain. Also,
// if the successor chain is the same as our chain, we're already merged.
BlockChain *SuccChain = BlockToChain[Successor];
if (SuccChain && (SuccChain == Chain || Successor != *SuccChain->begin()))
return;
// We only merge chains across a CFG merge when the desired merge path is
// significantly hotter than the incoming edge. We define a hot edge more
// strictly than the BranchProbabilityInfo does, as the two predecessor
// blocks may have dramatically different incoming probabilities we need to
// account for. Therefor we use the "global" edge weight which is the
// branch's probability times the block frequency of the predecessor.
BlockFrequency MergeWeight = MBFI->getBlockFreq(BB);
MergeWeight *= MBPI->getEdgeProbability(BB, Successor);
// We only want to consider breaking the CFG when the merge weight is much
// higher (80% vs. 20%), so multiply it by 1/4. This will require the merged
// edge to be 4x more likely before we disrupt the CFG. This number matches
// the definition of "hot" in BranchProbabilityAnalysis (80% vs. 20%).
MergeWeight *= BranchProbability(1, 4);
for (MachineBasicBlock::pred_iterator PI = Successor->pred_begin(),
PE = Successor->pred_end();
PI != PE; ++PI) {
if (BB == *PI || Successor == *PI) continue;
BlockFrequency PredWeight = MBFI->getBlockFreq(*PI);
PredWeight *= MBPI->getEdgeProbability(*PI, Successor);
// Return on the first predecessor we find which outstrips our merge weight.
if (MergeWeight < PredWeight)
return;
DEBUG(dbgs() << "Breaking CFG edge!\n"
<< " Edge from " << getBlockNum(BB) << " to "
<< getBlockNum(Successor) << ": " << MergeWeight << "\n"
<< " vs. " << getBlockNum(BB) << " to "
<< getBlockNum(*PI) << ": " << PredWeight << "\n");
}
Implement a block placement pass based on the branch probability and block frequency analyses. This differs substantially from the existing block-placement pass in LLVM: 1) It operates on the Machine-IR in the CodeGen layer. This exposes much more (and more precise) information and opportunities. Also, the results are more stable due to fewer transforms ocurring after the pass runs. 2) It uses the generalized probability and frequency analyses. These can model static heuristics, code annotation derived heuristics as well as eventual profile loading. By basing the optimization on the analysis interface it can work from any (or a combination) of these inputs. 3) It uses a more aggressive algorithm, both building chains from tho bottom up to maximize benefit, and using an SCC-based walk to layout chains of blocks in a profitable ordering without O(N^2) iterations which the old pass involves. The pass is currently gated behind a flag, and not enabled by default because it still needs to grow some important features. Most notably, it needs to support loop aligning and careful layout of loop structures much as done by hand currently in CodePlacementOpt. Once it supports these, and has sufficient testing and quality tuning, it should replace both of these passes. Thanks to Nick Lewycky and Richard Smith for help authoring & debugging this, and to Jakob, Andy, Eric, Jim, and probably a few others I'm forgetting for reviewing and answering all my questions. Writing a backend pass is *sooo* much better now than it used to be. =D git-svn-id: https://llvm.org/svn/llvm-project/llvm/trunk@142641 91177308-0d34-0410-b5e6-96231b3b80d8
2011-10-21 06:46:38 +00:00
Completely re-write the algorithm behind MachineBlockPlacement based on discussions with Andy. Fundamentally, the previous algorithm is both counter productive on several fronts and prioritizing things which aren't necessarily the most important: static branch prediction. The new algorithm uses the existing loop CFG structure information to walk through the CFG itself to layout blocks. It coalesces adjacent blocks within the loop where the CFG allows based on the most likely path taken. Finally, it topologically orders the block chains that have been formed. This allows it to choose a (mostly) topologically valid ordering which still priorizes fallthrough within the structural constraints. As a final twist in the algorithm, it does violate the CFG when it discovers a "hot" edge, that is an edge that is more than 4x hotter than the competing edges in the CFG. These are forcibly merged into a fallthrough chain. Future transformations that need te be added are rotation of loop exit conditions to be fallthrough, and better isolation of cold block chains. I'm also planning on adding statistics to model how well the algorithm does at laying out blocks based on the probabilities it receives. The old tests mostly still pass, and I have some new tests to add, but the nested loops are still behaving very strangely. This almost seems like working-as-intended as it rotated the exit branch to be fallthrough, but I'm not convinced this is actually the best layout. It is well supported by the probabilities for loops we currently get, but those are pretty broken for nested loops, so this may change later. git-svn-id: https://llvm.org/svn/llvm-project/llvm/trunk@142743 91177308-0d34-0410-b5e6-96231b3b80d8
2011-10-23 09:18:45 +00:00
DEBUG(dbgs() << "Merging from " << getBlockNum(BB) << " to "
<< getBlockNum(Successor) << "\n");
Chain->merge(Successor, SuccChain);
}
Implement a block placement pass based on the branch probability and block frequency analyses. This differs substantially from the existing block-placement pass in LLVM: 1) It operates on the Machine-IR in the CodeGen layer. This exposes much more (and more precise) information and opportunities. Also, the results are more stable due to fewer transforms ocurring after the pass runs. 2) It uses the generalized probability and frequency analyses. These can model static heuristics, code annotation derived heuristics as well as eventual profile loading. By basing the optimization on the analysis interface it can work from any (or a combination) of these inputs. 3) It uses a more aggressive algorithm, both building chains from tho bottom up to maximize benefit, and using an SCC-based walk to layout chains of blocks in a profitable ordering without O(N^2) iterations which the old pass involves. The pass is currently gated behind a flag, and not enabled by default because it still needs to grow some important features. Most notably, it needs to support loop aligning and careful layout of loop structures much as done by hand currently in CodePlacementOpt. Once it supports these, and has sufficient testing and quality tuning, it should replace both of these passes. Thanks to Nick Lewycky and Richard Smith for help authoring & debugging this, and to Jakob, Andy, Eric, Jim, and probably a few others I'm forgetting for reviewing and answering all my questions. Writing a backend pass is *sooo* much better now than it used to be. =D git-svn-id: https://llvm.org/svn/llvm-project/llvm/trunk@142641 91177308-0d34-0410-b5e6-96231b3b80d8
2011-10-21 06:46:38 +00:00
Completely re-write the algorithm behind MachineBlockPlacement based on discussions with Andy. Fundamentally, the previous algorithm is both counter productive on several fronts and prioritizing things which aren't necessarily the most important: static branch prediction. The new algorithm uses the existing loop CFG structure information to walk through the CFG itself to layout blocks. It coalesces adjacent blocks within the loop where the CFG allows based on the most likely path taken. Finally, it topologically orders the block chains that have been formed. This allows it to choose a (mostly) topologically valid ordering which still priorizes fallthrough within the structural constraints. As a final twist in the algorithm, it does violate the CFG when it discovers a "hot" edge, that is an edge that is more than 4x hotter than the competing edges in the CFG. These are forcibly merged into a fallthrough chain. Future transformations that need te be added are rotation of loop exit conditions to be fallthrough, and better isolation of cold block chains. I'm also planning on adding statistics to model how well the algorithm does at laying out blocks based on the probabilities it receives. The old tests mostly still pass, and I have some new tests to add, but the nested loops are still behaving very strangely. This almost seems like working-as-intended as it rotated the exit branch to be fallthrough, but I'm not convinced this is actually the best layout. It is well supported by the probabilities for loops we currently get, but those are pretty broken for nested loops, so this may change later. git-svn-id: https://llvm.org/svn/llvm-project/llvm/trunk@142743 91177308-0d34-0410-b5e6-96231b3b80d8
2011-10-23 09:18:45 +00:00
/// \brief Forms basic block chains from the natural loop structures.
///
/// These chains are designed to preserve the existing *structure* of the code
/// as much as possible. We can then stitch the chains together in a way which
/// both preserves the topological structure and minimizes taken conditional
/// branches.
void MachineBlockPlacement::buildLoopChains(MachineFunction &F, MachineLoop &L) {
// First recurse through any nested loops, building chains for those inner
// loops.
for (MachineLoop::iterator LI = L.begin(), LE = L.end(); LI != LE; ++LI)
buildLoopChains(F, **LI);
SmallPtrSet<MachineBasicBlock *, 16> LoopBlockSet(L.block_begin(),
L.block_end());
// Begin building up a set of chains of blocks within this loop which should
// remain contiguous. Some of the blocks already belong to a chain which
// represents an inner loop.
for (MachineLoop::block_iterator BI = L.block_begin(), BE = L.block_end();
BI != BE; ++BI) {
MachineBasicBlock *BB = *BI;
BlockChain *Chain = BlockToChain[BB];
if (!Chain) Chain = CreateChain(BB);
mergeSuccessor(BB, Chain, &LoopBlockSet);
}
}
Implement a block placement pass based on the branch probability and block frequency analyses. This differs substantially from the existing block-placement pass in LLVM: 1) It operates on the Machine-IR in the CodeGen layer. This exposes much more (and more precise) information and opportunities. Also, the results are more stable due to fewer transforms ocurring after the pass runs. 2) It uses the generalized probability and frequency analyses. These can model static heuristics, code annotation derived heuristics as well as eventual profile loading. By basing the optimization on the analysis interface it can work from any (or a combination) of these inputs. 3) It uses a more aggressive algorithm, both building chains from tho bottom up to maximize benefit, and using an SCC-based walk to layout chains of blocks in a profitable ordering without O(N^2) iterations which the old pass involves. The pass is currently gated behind a flag, and not enabled by default because it still needs to grow some important features. Most notably, it needs to support loop aligning and careful layout of loop structures much as done by hand currently in CodePlacementOpt. Once it supports these, and has sufficient testing and quality tuning, it should replace both of these passes. Thanks to Nick Lewycky and Richard Smith for help authoring & debugging this, and to Jakob, Andy, Eric, Jim, and probably a few others I'm forgetting for reviewing and answering all my questions. Writing a backend pass is *sooo* much better now than it used to be. =D git-svn-id: https://llvm.org/svn/llvm-project/llvm/trunk@142641 91177308-0d34-0410-b5e6-96231b3b80d8
2011-10-21 06:46:38 +00:00
Completely re-write the algorithm behind MachineBlockPlacement based on discussions with Andy. Fundamentally, the previous algorithm is both counter productive on several fronts and prioritizing things which aren't necessarily the most important: static branch prediction. The new algorithm uses the existing loop CFG structure information to walk through the CFG itself to layout blocks. It coalesces adjacent blocks within the loop where the CFG allows based on the most likely path taken. Finally, it topologically orders the block chains that have been formed. This allows it to choose a (mostly) topologically valid ordering which still priorizes fallthrough within the structural constraints. As a final twist in the algorithm, it does violate the CFG when it discovers a "hot" edge, that is an edge that is more than 4x hotter than the competing edges in the CFG. These are forcibly merged into a fallthrough chain. Future transformations that need te be added are rotation of loop exit conditions to be fallthrough, and better isolation of cold block chains. I'm also planning on adding statistics to model how well the algorithm does at laying out blocks based on the probabilities it receives. The old tests mostly still pass, and I have some new tests to add, but the nested loops are still behaving very strangely. This almost seems like working-as-intended as it rotated the exit branch to be fallthrough, but I'm not convinced this is actually the best layout. It is well supported by the probabilities for loops we currently get, but those are pretty broken for nested loops, so this may change later. git-svn-id: https://llvm.org/svn/llvm-project/llvm/trunk@142743 91177308-0d34-0410-b5e6-96231b3b80d8
2011-10-23 09:18:45 +00:00
void MachineBlockPlacement::buildCFGChains(MachineFunction &F) {
// First build any loop-based chains.
for (MachineLoopInfo::iterator LI = MLI->begin(), LE = MLI->end(); LI != LE;
++LI)
buildLoopChains(F, **LI);
// Now walk the blocks of the function forming chains where they don't
// violate any CFG structure.
for (MachineFunction::iterator BI = F.begin(), BE = F.end();
BI != BE; ++BI) {
MachineBasicBlock *BB = BI;
BlockChain *Chain = BlockToChain[BB];
if (!Chain) Chain = CreateChain(BB);
mergeSuccessor(BB, Chain);
Implement a block placement pass based on the branch probability and block frequency analyses. This differs substantially from the existing block-placement pass in LLVM: 1) It operates on the Machine-IR in the CodeGen layer. This exposes much more (and more precise) information and opportunities. Also, the results are more stable due to fewer transforms ocurring after the pass runs. 2) It uses the generalized probability and frequency analyses. These can model static heuristics, code annotation derived heuristics as well as eventual profile loading. By basing the optimization on the analysis interface it can work from any (or a combination) of these inputs. 3) It uses a more aggressive algorithm, both building chains from tho bottom up to maximize benefit, and using an SCC-based walk to layout chains of blocks in a profitable ordering without O(N^2) iterations which the old pass involves. The pass is currently gated behind a flag, and not enabled by default because it still needs to grow some important features. Most notably, it needs to support loop aligning and careful layout of loop structures much as done by hand currently in CodePlacementOpt. Once it supports these, and has sufficient testing and quality tuning, it should replace both of these passes. Thanks to Nick Lewycky and Richard Smith for help authoring & debugging this, and to Jakob, Andy, Eric, Jim, and probably a few others I'm forgetting for reviewing and answering all my questions. Writing a backend pass is *sooo* much better now than it used to be. =D git-svn-id: https://llvm.org/svn/llvm-project/llvm/trunk@142641 91177308-0d34-0410-b5e6-96231b3b80d8
2011-10-21 06:46:38 +00:00
}
}
Completely re-write the algorithm behind MachineBlockPlacement based on discussions with Andy. Fundamentally, the previous algorithm is both counter productive on several fronts and prioritizing things which aren't necessarily the most important: static branch prediction. The new algorithm uses the existing loop CFG structure information to walk through the CFG itself to layout blocks. It coalesces adjacent blocks within the loop where the CFG allows based on the most likely path taken. Finally, it topologically orders the block chains that have been formed. This allows it to choose a (mostly) topologically valid ordering which still priorizes fallthrough within the structural constraints. As a final twist in the algorithm, it does violate the CFG when it discovers a "hot" edge, that is an edge that is more than 4x hotter than the competing edges in the CFG. These are forcibly merged into a fallthrough chain. Future transformations that need te be added are rotation of loop exit conditions to be fallthrough, and better isolation of cold block chains. I'm also planning on adding statistics to model how well the algorithm does at laying out blocks based on the probabilities it receives. The old tests mostly still pass, and I have some new tests to add, but the nested loops are still behaving very strangely. This almost seems like working-as-intended as it rotated the exit branch to be fallthrough, but I'm not convinced this is actually the best layout. It is well supported by the probabilities for loops we currently get, but those are pretty broken for nested loops, so this may change later. git-svn-id: https://llvm.org/svn/llvm-project/llvm/trunk@142743 91177308-0d34-0410-b5e6-96231b3b80d8
2011-10-23 09:18:45 +00:00
void MachineBlockPlacement::placeChainsTopologically(MachineFunction &F) {
Implement a block placement pass based on the branch probability and block frequency analyses. This differs substantially from the existing block-placement pass in LLVM: 1) It operates on the Machine-IR in the CodeGen layer. This exposes much more (and more precise) information and opportunities. Also, the results are more stable due to fewer transforms ocurring after the pass runs. 2) It uses the generalized probability and frequency analyses. These can model static heuristics, code annotation derived heuristics as well as eventual profile loading. By basing the optimization on the analysis interface it can work from any (or a combination) of these inputs. 3) It uses a more aggressive algorithm, both building chains from tho bottom up to maximize benefit, and using an SCC-based walk to layout chains of blocks in a profitable ordering without O(N^2) iterations which the old pass involves. The pass is currently gated behind a flag, and not enabled by default because it still needs to grow some important features. Most notably, it needs to support loop aligning and careful layout of loop structures much as done by hand currently in CodePlacementOpt. Once it supports these, and has sufficient testing and quality tuning, it should replace both of these passes. Thanks to Nick Lewycky and Richard Smith for help authoring & debugging this, and to Jakob, Andy, Eric, Jim, and probably a few others I'm forgetting for reviewing and answering all my questions. Writing a backend pass is *sooo* much better now than it used to be. =D git-svn-id: https://llvm.org/svn/llvm-project/llvm/trunk@142641 91177308-0d34-0410-b5e6-96231b3b80d8
2011-10-21 06:46:38 +00:00
MachineBasicBlock *EntryB = &F.front();
assert(BlockToChain[EntryB] && "Missing chain for entry block");
assert(*BlockToChain[EntryB]->begin() == EntryB &&
Implement a block placement pass based on the branch probability and block frequency analyses. This differs substantially from the existing block-placement pass in LLVM: 1) It operates on the Machine-IR in the CodeGen layer. This exposes much more (and more precise) information and opportunities. Also, the results are more stable due to fewer transforms ocurring after the pass runs. 2) It uses the generalized probability and frequency analyses. These can model static heuristics, code annotation derived heuristics as well as eventual profile loading. By basing the optimization on the analysis interface it can work from any (or a combination) of these inputs. 3) It uses a more aggressive algorithm, both building chains from tho bottom up to maximize benefit, and using an SCC-based walk to layout chains of blocks in a profitable ordering without O(N^2) iterations which the old pass involves. The pass is currently gated behind a flag, and not enabled by default because it still needs to grow some important features. Most notably, it needs to support loop aligning and careful layout of loop structures much as done by hand currently in CodePlacementOpt. Once it supports these, and has sufficient testing and quality tuning, it should replace both of these passes. Thanks to Nick Lewycky and Richard Smith for help authoring & debugging this, and to Jakob, Andy, Eric, Jim, and probably a few others I'm forgetting for reviewing and answering all my questions. Writing a backend pass is *sooo* much better now than it used to be. =D git-svn-id: https://llvm.org/svn/llvm-project/llvm/trunk@142641 91177308-0d34-0410-b5e6-96231b3b80d8
2011-10-21 06:46:38 +00:00
"Entry block is not the head of the entry block chain");
Completely re-write the algorithm behind MachineBlockPlacement based on discussions with Andy. Fundamentally, the previous algorithm is both counter productive on several fronts and prioritizing things which aren't necessarily the most important: static branch prediction. The new algorithm uses the existing loop CFG structure information to walk through the CFG itself to layout blocks. It coalesces adjacent blocks within the loop where the CFG allows based on the most likely path taken. Finally, it topologically orders the block chains that have been formed. This allows it to choose a (mostly) topologically valid ordering which still priorizes fallthrough within the structural constraints. As a final twist in the algorithm, it does violate the CFG when it discovers a "hot" edge, that is an edge that is more than 4x hotter than the competing edges in the CFG. These are forcibly merged into a fallthrough chain. Future transformations that need te be added are rotation of loop exit conditions to be fallthrough, and better isolation of cold block chains. I'm also planning on adding statistics to model how well the algorithm does at laying out blocks based on the probabilities it receives. The old tests mostly still pass, and I have some new tests to add, but the nested loops are still behaving very strangely. This almost seems like working-as-intended as it rotated the exit branch to be fallthrough, but I'm not convinced this is actually the best layout. It is well supported by the probabilities for loops we currently get, but those are pretty broken for nested loops, so this may change later. git-svn-id: https://llvm.org/svn/llvm-project/llvm/trunk@142743 91177308-0d34-0410-b5e6-96231b3b80d8
2011-10-23 09:18:45 +00:00
// Walk the blocks in RPO, and insert each block for a chain in order the
// first time we see that chain.
MachineFunction::iterator InsertPos = F.begin();
SmallPtrSet<BlockChain *, 16> VisitedChains;
ReversePostOrderTraversal<MachineBasicBlock *> RPOT(EntryB);
typedef ReversePostOrderTraversal<MachineBasicBlock *>::rpo_iterator
rpo_iterator;
for (rpo_iterator I = RPOT.begin(), E = RPOT.end(); I != E; ++I) {
BlockChain *Chain = BlockToChain[*I];
assert(Chain);
if(!VisitedChains.insert(Chain))
Implement a block placement pass based on the branch probability and block frequency analyses. This differs substantially from the existing block-placement pass in LLVM: 1) It operates on the Machine-IR in the CodeGen layer. This exposes much more (and more precise) information and opportunities. Also, the results are more stable due to fewer transforms ocurring after the pass runs. 2) It uses the generalized probability and frequency analyses. These can model static heuristics, code annotation derived heuristics as well as eventual profile loading. By basing the optimization on the analysis interface it can work from any (or a combination) of these inputs. 3) It uses a more aggressive algorithm, both building chains from tho bottom up to maximize benefit, and using an SCC-based walk to layout chains of blocks in a profitable ordering without O(N^2) iterations which the old pass involves. The pass is currently gated behind a flag, and not enabled by default because it still needs to grow some important features. Most notably, it needs to support loop aligning and careful layout of loop structures much as done by hand currently in CodePlacementOpt. Once it supports these, and has sufficient testing and quality tuning, it should replace both of these passes. Thanks to Nick Lewycky and Richard Smith for help authoring & debugging this, and to Jakob, Andy, Eric, Jim, and probably a few others I'm forgetting for reviewing and answering all my questions. Writing a backend pass is *sooo* much better now than it used to be. =D git-svn-id: https://llvm.org/svn/llvm-project/llvm/trunk@142641 91177308-0d34-0410-b5e6-96231b3b80d8
2011-10-21 06:46:38 +00:00
continue;
Completely re-write the algorithm behind MachineBlockPlacement based on discussions with Andy. Fundamentally, the previous algorithm is both counter productive on several fronts and prioritizing things which aren't necessarily the most important: static branch prediction. The new algorithm uses the existing loop CFG structure information to walk through the CFG itself to layout blocks. It coalesces adjacent blocks within the loop where the CFG allows based on the most likely path taken. Finally, it topologically orders the block chains that have been formed. This allows it to choose a (mostly) topologically valid ordering which still priorizes fallthrough within the structural constraints. As a final twist in the algorithm, it does violate the CFG when it discovers a "hot" edge, that is an edge that is more than 4x hotter than the competing edges in the CFG. These are forcibly merged into a fallthrough chain. Future transformations that need te be added are rotation of loop exit conditions to be fallthrough, and better isolation of cold block chains. I'm also planning on adding statistics to model how well the algorithm does at laying out blocks based on the probabilities it receives. The old tests mostly still pass, and I have some new tests to add, but the nested loops are still behaving very strangely. This almost seems like working-as-intended as it rotated the exit branch to be fallthrough, but I'm not convinced this is actually the best layout. It is well supported by the probabilities for loops we currently get, but those are pretty broken for nested loops, so this may change later. git-svn-id: https://llvm.org/svn/llvm-project/llvm/trunk@142743 91177308-0d34-0410-b5e6-96231b3b80d8
2011-10-23 09:18:45 +00:00
for (BlockChain::iterator BI = Chain->begin(), BE = Chain->end(); BI != BE;
++BI) {
DEBUG(dbgs() << (BI == Chain->begin() ? "Placing chain "
: " ... ")
<< getBlockName(*BI) << "\n");
if (InsertPos != MachineFunction::iterator(*BI))
F.splice(InsertPos, *BI);
else
++InsertPos;
Implement a block placement pass based on the branch probability and block frequency analyses. This differs substantially from the existing block-placement pass in LLVM: 1) It operates on the Machine-IR in the CodeGen layer. This exposes much more (and more precise) information and opportunities. Also, the results are more stable due to fewer transforms ocurring after the pass runs. 2) It uses the generalized probability and frequency analyses. These can model static heuristics, code annotation derived heuristics as well as eventual profile loading. By basing the optimization on the analysis interface it can work from any (or a combination) of these inputs. 3) It uses a more aggressive algorithm, both building chains from tho bottom up to maximize benefit, and using an SCC-based walk to layout chains of blocks in a profitable ordering without O(N^2) iterations which the old pass involves. The pass is currently gated behind a flag, and not enabled by default because it still needs to grow some important features. Most notably, it needs to support loop aligning and careful layout of loop structures much as done by hand currently in CodePlacementOpt. Once it supports these, and has sufficient testing and quality tuning, it should replace both of these passes. Thanks to Nick Lewycky and Richard Smith for help authoring & debugging this, and to Jakob, Andy, Eric, Jim, and probably a few others I'm forgetting for reviewing and answering all my questions. Writing a backend pass is *sooo* much better now than it used to be. =D git-svn-id: https://llvm.org/svn/llvm-project/llvm/trunk@142641 91177308-0d34-0410-b5e6-96231b3b80d8
2011-10-21 06:46:38 +00:00
}
}
// Now that every block is in its final position, update all of the
// terminators.
SmallVector<MachineOperand, 4> Cond; // For AnalyzeBranch.
for (MachineFunction::iterator FI = F.begin(), FE = F.end(); FI != FE; ++FI) {
// FIXME: It would be awesome of updateTerminator would just return rather
// than assert when the branch cannot be analyzed in order to remove this
// boiler plate.
Cond.clear();
MachineBasicBlock *TBB = 0, *FBB = 0; // For AnalyzeBranch.
if (!TII->AnalyzeBranch(*FI, TBB, FBB, Cond))
FI->updateTerminator();
}
}
/// \brief Recursive helper to align a loop and any nested loops.
static void AlignLoop(MachineFunction &F, MachineLoop *L, unsigned Align) {
// Recurse through nested loops.
for (MachineLoop::iterator I = L->begin(), E = L->end(); I != E; ++I)
AlignLoop(F, *I, Align);
L->getTopBlock()->setAlignment(Align);
}
/// \brief Align loop headers to target preferred alignments.
void MachineBlockPlacement::AlignLoops(MachineFunction &F) {
if (F.getFunction()->hasFnAttr(Attribute::OptimizeForSize))
return;
unsigned Align = TLI->getPrefLoopAlignment();
if (!Align)
return; // Don't care about loop alignment.
for (MachineLoopInfo::iterator I = MLI->begin(), E = MLI->end(); I != E; ++I)
AlignLoop(F, *I, Align);
}
Implement a block placement pass based on the branch probability and block frequency analyses. This differs substantially from the existing block-placement pass in LLVM: 1) It operates on the Machine-IR in the CodeGen layer. This exposes much more (and more precise) information and opportunities. Also, the results are more stable due to fewer transforms ocurring after the pass runs. 2) It uses the generalized probability and frequency analyses. These can model static heuristics, code annotation derived heuristics as well as eventual profile loading. By basing the optimization on the analysis interface it can work from any (or a combination) of these inputs. 3) It uses a more aggressive algorithm, both building chains from tho bottom up to maximize benefit, and using an SCC-based walk to layout chains of blocks in a profitable ordering without O(N^2) iterations which the old pass involves. The pass is currently gated behind a flag, and not enabled by default because it still needs to grow some important features. Most notably, it needs to support loop aligning and careful layout of loop structures much as done by hand currently in CodePlacementOpt. Once it supports these, and has sufficient testing and quality tuning, it should replace both of these passes. Thanks to Nick Lewycky and Richard Smith for help authoring & debugging this, and to Jakob, Andy, Eric, Jim, and probably a few others I'm forgetting for reviewing and answering all my questions. Writing a backend pass is *sooo* much better now than it used to be. =D git-svn-id: https://llvm.org/svn/llvm-project/llvm/trunk@142641 91177308-0d34-0410-b5e6-96231b3b80d8
2011-10-21 06:46:38 +00:00
bool MachineBlockPlacement::runOnMachineFunction(MachineFunction &F) {
// Check for single-block functions and skip them.
if (llvm::next(F.begin()) == F.end())
return false;
MBPI = &getAnalysis<MachineBranchProbabilityInfo>();
MBFI = &getAnalysis<MachineBlockFrequencyInfo>();
MLI = &getAnalysis<MachineLoopInfo>();
Implement a block placement pass based on the branch probability and block frequency analyses. This differs substantially from the existing block-placement pass in LLVM: 1) It operates on the Machine-IR in the CodeGen layer. This exposes much more (and more precise) information and opportunities. Also, the results are more stable due to fewer transforms ocurring after the pass runs. 2) It uses the generalized probability and frequency analyses. These can model static heuristics, code annotation derived heuristics as well as eventual profile loading. By basing the optimization on the analysis interface it can work from any (or a combination) of these inputs. 3) It uses a more aggressive algorithm, both building chains from tho bottom up to maximize benefit, and using an SCC-based walk to layout chains of blocks in a profitable ordering without O(N^2) iterations which the old pass involves. The pass is currently gated behind a flag, and not enabled by default because it still needs to grow some important features. Most notably, it needs to support loop aligning and careful layout of loop structures much as done by hand currently in CodePlacementOpt. Once it supports these, and has sufficient testing and quality tuning, it should replace both of these passes. Thanks to Nick Lewycky and Richard Smith for help authoring & debugging this, and to Jakob, Andy, Eric, Jim, and probably a few others I'm forgetting for reviewing and answering all my questions. Writing a backend pass is *sooo* much better now than it used to be. =D git-svn-id: https://llvm.org/svn/llvm-project/llvm/trunk@142641 91177308-0d34-0410-b5e6-96231b3b80d8
2011-10-21 06:46:38 +00:00
TII = F.getTarget().getInstrInfo();
TLI = F.getTarget().getTargetLowering();
Implement a block placement pass based on the branch probability and block frequency analyses. This differs substantially from the existing block-placement pass in LLVM: 1) It operates on the Machine-IR in the CodeGen layer. This exposes much more (and more precise) information and opportunities. Also, the results are more stable due to fewer transforms ocurring after the pass runs. 2) It uses the generalized probability and frequency analyses. These can model static heuristics, code annotation derived heuristics as well as eventual profile loading. By basing the optimization on the analysis interface it can work from any (or a combination) of these inputs. 3) It uses a more aggressive algorithm, both building chains from tho bottom up to maximize benefit, and using an SCC-based walk to layout chains of blocks in a profitable ordering without O(N^2) iterations which the old pass involves. The pass is currently gated behind a flag, and not enabled by default because it still needs to grow some important features. Most notably, it needs to support loop aligning and careful layout of loop structures much as done by hand currently in CodePlacementOpt. Once it supports these, and has sufficient testing and quality tuning, it should replace both of these passes. Thanks to Nick Lewycky and Richard Smith for help authoring & debugging this, and to Jakob, Andy, Eric, Jim, and probably a few others I'm forgetting for reviewing and answering all my questions. Writing a backend pass is *sooo* much better now than it used to be. =D git-svn-id: https://llvm.org/svn/llvm-project/llvm/trunk@142641 91177308-0d34-0410-b5e6-96231b3b80d8
2011-10-21 06:46:38 +00:00
assert(BlockToChain.empty());
Completely re-write the algorithm behind MachineBlockPlacement based on discussions with Andy. Fundamentally, the previous algorithm is both counter productive on several fronts and prioritizing things which aren't necessarily the most important: static branch prediction. The new algorithm uses the existing loop CFG structure information to walk through the CFG itself to layout blocks. It coalesces adjacent blocks within the loop where the CFG allows based on the most likely path taken. Finally, it topologically orders the block chains that have been formed. This allows it to choose a (mostly) topologically valid ordering which still priorizes fallthrough within the structural constraints. As a final twist in the algorithm, it does violate the CFG when it discovers a "hot" edge, that is an edge that is more than 4x hotter than the competing edges in the CFG. These are forcibly merged into a fallthrough chain. Future transformations that need te be added are rotation of loop exit conditions to be fallthrough, and better isolation of cold block chains. I'm also planning on adding statistics to model how well the algorithm does at laying out blocks based on the probabilities it receives. The old tests mostly still pass, and I have some new tests to add, but the nested loops are still behaving very strangely. This almost seems like working-as-intended as it rotated the exit branch to be fallthrough, but I'm not convinced this is actually the best layout. It is well supported by the probabilities for loops we currently get, but those are pretty broken for nested loops, so this may change later. git-svn-id: https://llvm.org/svn/llvm-project/llvm/trunk@142743 91177308-0d34-0410-b5e6-96231b3b80d8
2011-10-23 09:18:45 +00:00
buildCFGChains(F);
placeChainsTopologically(F);
AlignLoops(F);
Implement a block placement pass based on the branch probability and block frequency analyses. This differs substantially from the existing block-placement pass in LLVM: 1) It operates on the Machine-IR in the CodeGen layer. This exposes much more (and more precise) information and opportunities. Also, the results are more stable due to fewer transforms ocurring after the pass runs. 2) It uses the generalized probability and frequency analyses. These can model static heuristics, code annotation derived heuristics as well as eventual profile loading. By basing the optimization on the analysis interface it can work from any (or a combination) of these inputs. 3) It uses a more aggressive algorithm, both building chains from tho bottom up to maximize benefit, and using an SCC-based walk to layout chains of blocks in a profitable ordering without O(N^2) iterations which the old pass involves. The pass is currently gated behind a flag, and not enabled by default because it still needs to grow some important features. Most notably, it needs to support loop aligning and careful layout of loop structures much as done by hand currently in CodePlacementOpt. Once it supports these, and has sufficient testing and quality tuning, it should replace both of these passes. Thanks to Nick Lewycky and Richard Smith for help authoring & debugging this, and to Jakob, Andy, Eric, Jim, and probably a few others I'm forgetting for reviewing and answering all my questions. Writing a backend pass is *sooo* much better now than it used to be. =D git-svn-id: https://llvm.org/svn/llvm-project/llvm/trunk@142641 91177308-0d34-0410-b5e6-96231b3b80d8
2011-10-21 06:46:38 +00:00
BlockToChain.clear();
// We always return true as we have no way to track whether the final order
// differs from the original order.
return true;
}
namespace {
/// \brief A pass to compute block placement statistics.
///
/// A separate pass to compute interesting statistics for evaluating block
/// placement. This is separate from the actual placement pass so that they can
/// be computed in the absense of any placement transformations or when using
/// alternative placement strategies.
class MachineBlockPlacementStats : public MachineFunctionPass {
/// \brief A handle to the branch probability pass.
const MachineBranchProbabilityInfo *MBPI;
/// \brief A handle to the function-wide block frequency pass.
const MachineBlockFrequencyInfo *MBFI;
public:
static char ID; // Pass identification, replacement for typeid
MachineBlockPlacementStats() : MachineFunctionPass(ID) {
initializeMachineBlockPlacementStatsPass(*PassRegistry::getPassRegistry());
}
bool runOnMachineFunction(MachineFunction &F);
void getAnalysisUsage(AnalysisUsage &AU) const {
AU.addRequired<MachineBranchProbabilityInfo>();
AU.addRequired<MachineBlockFrequencyInfo>();
AU.setPreservesAll();
MachineFunctionPass::getAnalysisUsage(AU);
}
const char *getPassName() const { return "Block Placement Stats"; }
};
}
char MachineBlockPlacementStats::ID = 0;
INITIALIZE_PASS_BEGIN(MachineBlockPlacementStats, "block-placement-stats",
"Basic Block Placement Stats", false, false)
INITIALIZE_PASS_DEPENDENCY(MachineBranchProbabilityInfo)
INITIALIZE_PASS_DEPENDENCY(MachineBlockFrequencyInfo)
INITIALIZE_PASS_END(MachineBlockPlacementStats, "block-placement-stats",
"Basic Block Placement Stats", false, false)
FunctionPass *llvm::createMachineBlockPlacementStatsPass() {
return new MachineBlockPlacementStats();
}
bool MachineBlockPlacementStats::runOnMachineFunction(MachineFunction &F) {
// Check for single-block functions and skip them.
if (llvm::next(F.begin()) == F.end())
return false;
MBPI = &getAnalysis<MachineBranchProbabilityInfo>();
MBFI = &getAnalysis<MachineBlockFrequencyInfo>();
for (MachineFunction::iterator I = F.begin(), E = F.end(); I != E; ++I) {
BlockFrequency BlockFreq = MBFI->getBlockFreq(I);
Statistic &NumBranches = (I->succ_size() > 1) ? NumCondBranches
: NumUncondBranches;
Statistic &BranchTakenFreq = (I->succ_size() > 1) ? CondBranchTakenFreq
: UncondBranchTakenFreq;
for (MachineBasicBlock::succ_iterator SI = I->succ_begin(),
SE = I->succ_end();
SI != SE; ++SI) {
// Skip if this successor is a fallthrough.
if (I->isLayoutSuccessor(*SI))
continue;
BlockFrequency EdgeFreq = BlockFreq * MBPI->getEdgeProbability(I, *SI);
++NumBranches;
BranchTakenFreq += EdgeFreq.getFrequency();
}
}
return false;
}