llvm-6502/include/llvm/Analysis/LazyCallGraph.h

429 lines
15 KiB
C
Raw Normal View History

[PM] Add a new "lazy" call graph analysis pass for the new pass manager. The primary motivation for this pass is to separate the call graph analysis used by the new pass manager's CGSCC pass management from the existing call graph analysis pass. That analysis pass is (somewhat unfortunately) over-constrained by the existing CallGraphSCCPassManager requirements. Those requirements make it *really* hard to cleanly layer the needed functionality for the new pass manager on top of the existing analysis. However, there are also a bunch of things that the pass manager would specifically benefit from doing differently from the existing call graph analysis, and this new implementation tries to address several of them: - Be lazy about scanning function definitions. The existing pass eagerly scans the entire module to build the initial graph. This new pass is significantly more lazy, and I plan to push this even further to maximize locality during CGSCC walks. - Don't use a single synthetic node to partition functions with an indirect call from functions whose address is taken. This node creates a huge choke-point which would preclude good parallelization across the fanout of the SCC graph when we got to the point of looking at such changes to LLVM. - Use a memory dense and lightweight representation of the call graph rather than value handles and tracking call instructions. This will require explicit update calls instead of some updates working transparently, but should end up being significantly more efficient. The explicit update calls ended up being needed in many cases for the existing call graph so we don't really lose anything. - Doesn't explicitly model SCCs and thus doesn't provide an "identity" for an SCC which is stable across updates. This is essential for the new pass manager to work correctly. - Only form the graph necessary for traversing all of the functions in an SCC friendly order. This is a much simpler graph structure and should be more memory dense. It does limit the ways in which it is appropriate to use this analysis. I wish I had a better name than "call graph". I've commented extensively this aspect. This is still very much a WIP, in fact it is really just the initial bits. But it is about the fourth version of the initial bits that I've implemented with each of the others running into really frustrating problms. This looks like it will actually work and I'd like to split the actual complexity across commits for the sake of my reviewers. =] The rest of the implementation along with lots of wiring will follow somewhat more rapidly now that there is a good path forward. Naturally, this doesn't impact any of the existing optimizer. This code is specific to the new pass manager. A bunch of thanks are deserved for the various folks that have helped with the design of this, especially Nick Lewycky who actually sat with me to go through the fundamentals of the final version here. git-svn-id: https://llvm.org/svn/llvm-project/llvm/trunk@200903 91177308-0d34-0410-b5e6-96231b3b80d8
2014-02-06 04:37:03 +00:00
//===- LazyCallGraph.h - Analysis of a Module's call graph ------*- C++ -*-===//
//
// The LLVM Compiler Infrastructure
//
// This file is distributed under the University of Illinois Open Source
// License. See LICENSE.TXT for details.
//
//===----------------------------------------------------------------------===//
/// \file
///
/// Implements a lazy call graph analysis and related passes for the new pass
/// manager.
///
/// NB: This is *not* a traditional call graph! It is a graph which models both
/// the current calls and potential calls. As a consequence there are many
/// edges in this call graph that do not correspond to a 'call' or 'invoke'
/// instruction.
///
/// The primary use cases of this graph analysis is to facilitate iterating
/// across the functions of a module in ways that ensure all callees are
/// visited prior to a caller (given any SCC constraints), or vice versa. As
/// such is it particularly well suited to organizing CGSCC optimizations such
/// as inlining, outlining, argument promotion, etc. That is its primary use
/// case and motivates the design. It may not be appropriate for other
/// purposes. The use graph of functions or some other conservative analysis of
/// call instructions may be interesting for optimizations and subsequent
/// analyses which don't work in the context of an overly specified
/// potential-call-edge graph.
///
/// To understand the specific rules and nature of this call graph analysis,
/// see the documentation of the \c LazyCallGraph below.
///
//===----------------------------------------------------------------------===//
#ifndef LLVM_ANALYSIS_LAZY_CALL_GRAPH
#define LLVM_ANALYSIS_LAZY_CALL_GRAPH
#include "llvm/ADT/DenseMap.h"
#include "llvm/ADT/PointerUnion.h"
#include "llvm/ADT/STLExtras.h"
[LCG] Add support for building persistent and connected SCCs to the LazyCallGraph. This is the start of the whole point of this different abstraction, but it is just the initial bits. Here is a run-down of what's going on here. I'm planning to incorporate some (or all) of this into comments going forward, hopefully with better editing and wording. =] The crux of the problem with the traditional way of building SCCs is that they are ephemeral. The new pass manager however really needs the ability to associate analysis passes and results of analysis passes with SCCs in order to expose these analysis passes to the SCC passes. Making this work is kind-of the whole point of the new pass manager. =] So, when we're building SCCs for the call graph, we actually want to build persistent nodes that stick around and can be reasoned about later. We'd also like the ability to walk the SCC graph in more complex ways than just the traditional postorder traversal of the current CGSCC walk. That means that in addition to being persistent, the SCCs need to be connected into a useful graph structure. However, we still want the SCCs to be formed lazily where possible. These constraints are quite hard to satisfy with the SCC iterator. Also, using that would bypass our ability to actually add data to the nodes of the call graph to facilite implementing the Tarjan walk. So I've re-implemented things in a more direct and embedded way. This immediately makes it easy to get the persistence and connectivity correct, and it also allows leveraging the existing nodes to simplify the algorithm. I've worked somewhat to make this implementation more closely follow the traditional paper's nomenclature and strategy, although it is still a bit obtuse because it isn't recursive, using an explicit stack and a tail call instead, and it is interruptable, resuming each time we need another SCC. The other tricky bit here, and what actually took almost all the time and trials and errors I spent building this, is exactly *what* graph structure to build for the SCCs. The naive thing to build is the call graph in its newly acyclic form. I wrote about 4 versions of this which did precisely this. Inevitably, when I experimented with them across various use cases, they became incredibly awkward. It was all implementable, but it felt like a complete wrong fit. Square peg, round hole. There were two overriding aspects that pushed me in a different direction: 1) We want to discover the SCC graph in a postorder fashion. That means the root node will be the *last* node we find. Using the call-SCC DAG as the graph structure of the SCCs results in an orphaned graph until we discover a root. 2) We will eventually want to walk the SCC graph in parallel, exploring distinct sub-graphs independently, and synchronizing at merge points. This again is not helped by the call-SCC DAG structure. The structure which, quite surprisingly, ended up being completely natural to use is the *inverse* of the call-SCC DAG. We add the leaf SCCs to the graph as "roots", and have edges to the caller SCCs. Once I switched to building this structure, everything just fell into place elegantly. Aside from general cleanups (there are FIXMEs and too few comments overall) that are still needed, the other missing piece of this is support for iterating across levels of the SCC graph. These will become useful for implementing #2, but they aren't an immediate priority. Once SCCs are in good shape, I'll be working on adding mutation support for incremental updates and adding the pass manager that this analysis enables. git-svn-id: https://llvm.org/svn/llvm-project/llvm/trunk@206581 91177308-0d34-0410-b5e6-96231b3b80d8
2014-04-18 10:50:32 +00:00
#include "llvm/ADT/SetVector.h"
#include "llvm/ADT/SmallPtrSet.h"
#include "llvm/ADT/SmallVector.h"
[LCG] Add support for building persistent and connected SCCs to the LazyCallGraph. This is the start of the whole point of this different abstraction, but it is just the initial bits. Here is a run-down of what's going on here. I'm planning to incorporate some (or all) of this into comments going forward, hopefully with better editing and wording. =] The crux of the problem with the traditional way of building SCCs is that they are ephemeral. The new pass manager however really needs the ability to associate analysis passes and results of analysis passes with SCCs in order to expose these analysis passes to the SCC passes. Making this work is kind-of the whole point of the new pass manager. =] So, when we're building SCCs for the call graph, we actually want to build persistent nodes that stick around and can be reasoned about later. We'd also like the ability to walk the SCC graph in more complex ways than just the traditional postorder traversal of the current CGSCC walk. That means that in addition to being persistent, the SCCs need to be connected into a useful graph structure. However, we still want the SCCs to be formed lazily where possible. These constraints are quite hard to satisfy with the SCC iterator. Also, using that would bypass our ability to actually add data to the nodes of the call graph to facilite implementing the Tarjan walk. So I've re-implemented things in a more direct and embedded way. This immediately makes it easy to get the persistence and connectivity correct, and it also allows leveraging the existing nodes to simplify the algorithm. I've worked somewhat to make this implementation more closely follow the traditional paper's nomenclature and strategy, although it is still a bit obtuse because it isn't recursive, using an explicit stack and a tail call instead, and it is interruptable, resuming each time we need another SCC. The other tricky bit here, and what actually took almost all the time and trials and errors I spent building this, is exactly *what* graph structure to build for the SCCs. The naive thing to build is the call graph in its newly acyclic form. I wrote about 4 versions of this which did precisely this. Inevitably, when I experimented with them across various use cases, they became incredibly awkward. It was all implementable, but it felt like a complete wrong fit. Square peg, round hole. There were two overriding aspects that pushed me in a different direction: 1) We want to discover the SCC graph in a postorder fashion. That means the root node will be the *last* node we find. Using the call-SCC DAG as the graph structure of the SCCs results in an orphaned graph until we discover a root. 2) We will eventually want to walk the SCC graph in parallel, exploring distinct sub-graphs independently, and synchronizing at merge points. This again is not helped by the call-SCC DAG structure. The structure which, quite surprisingly, ended up being completely natural to use is the *inverse* of the call-SCC DAG. We add the leaf SCCs to the graph as "roots", and have edges to the caller SCCs. Once I switched to building this structure, everything just fell into place elegantly. Aside from general cleanups (there are FIXMEs and too few comments overall) that are still needed, the other missing piece of this is support for iterating across levels of the SCC graph. These will become useful for implementing #2, but they aren't an immediate priority. Once SCCs are in good shape, I'll be working on adding mutation support for incremental updates and adding the pass manager that this analysis enables. git-svn-id: https://llvm.org/svn/llvm-project/llvm/trunk@206581 91177308-0d34-0410-b5e6-96231b3b80d8
2014-04-18 10:50:32 +00:00
#include "llvm/ADT/iterator_range.h"
[PM] Add a new "lazy" call graph analysis pass for the new pass manager. The primary motivation for this pass is to separate the call graph analysis used by the new pass manager's CGSCC pass management from the existing call graph analysis pass. That analysis pass is (somewhat unfortunately) over-constrained by the existing CallGraphSCCPassManager requirements. Those requirements make it *really* hard to cleanly layer the needed functionality for the new pass manager on top of the existing analysis. However, there are also a bunch of things that the pass manager would specifically benefit from doing differently from the existing call graph analysis, and this new implementation tries to address several of them: - Be lazy about scanning function definitions. The existing pass eagerly scans the entire module to build the initial graph. This new pass is significantly more lazy, and I plan to push this even further to maximize locality during CGSCC walks. - Don't use a single synthetic node to partition functions with an indirect call from functions whose address is taken. This node creates a huge choke-point which would preclude good parallelization across the fanout of the SCC graph when we got to the point of looking at such changes to LLVM. - Use a memory dense and lightweight representation of the call graph rather than value handles and tracking call instructions. This will require explicit update calls instead of some updates working transparently, but should end up being significantly more efficient. The explicit update calls ended up being needed in many cases for the existing call graph so we don't really lose anything. - Doesn't explicitly model SCCs and thus doesn't provide an "identity" for an SCC which is stable across updates. This is essential for the new pass manager to work correctly. - Only form the graph necessary for traversing all of the functions in an SCC friendly order. This is a much simpler graph structure and should be more memory dense. It does limit the ways in which it is appropriate to use this analysis. I wish I had a better name than "call graph". I've commented extensively this aspect. This is still very much a WIP, in fact it is really just the initial bits. But it is about the fourth version of the initial bits that I've implemented with each of the others running into really frustrating problms. This looks like it will actually work and I'd like to split the actual complexity across commits for the sake of my reviewers. =] The rest of the implementation along with lots of wiring will follow somewhat more rapidly now that there is a good path forward. Naturally, this doesn't impact any of the existing optimizer. This code is specific to the new pass manager. A bunch of thanks are deserved for the various folks that have helped with the design of this, especially Nick Lewycky who actually sat with me to go through the fundamentals of the final version here. git-svn-id: https://llvm.org/svn/llvm-project/llvm/trunk@200903 91177308-0d34-0410-b5e6-96231b3b80d8
2014-02-06 04:37:03 +00:00
#include "llvm/IR/BasicBlock.h"
#include "llvm/IR/Function.h"
#include "llvm/IR/Module.h"
[PM] Add a new "lazy" call graph analysis pass for the new pass manager. The primary motivation for this pass is to separate the call graph analysis used by the new pass manager's CGSCC pass management from the existing call graph analysis pass. That analysis pass is (somewhat unfortunately) over-constrained by the existing CallGraphSCCPassManager requirements. Those requirements make it *really* hard to cleanly layer the needed functionality for the new pass manager on top of the existing analysis. However, there are also a bunch of things that the pass manager would specifically benefit from doing differently from the existing call graph analysis, and this new implementation tries to address several of them: - Be lazy about scanning function definitions. The existing pass eagerly scans the entire module to build the initial graph. This new pass is significantly more lazy, and I plan to push this even further to maximize locality during CGSCC walks. - Don't use a single synthetic node to partition functions with an indirect call from functions whose address is taken. This node creates a huge choke-point which would preclude good parallelization across the fanout of the SCC graph when we got to the point of looking at such changes to LLVM. - Use a memory dense and lightweight representation of the call graph rather than value handles and tracking call instructions. This will require explicit update calls instead of some updates working transparently, but should end up being significantly more efficient. The explicit update calls ended up being needed in many cases for the existing call graph so we don't really lose anything. - Doesn't explicitly model SCCs and thus doesn't provide an "identity" for an SCC which is stable across updates. This is essential for the new pass manager to work correctly. - Only form the graph necessary for traversing all of the functions in an SCC friendly order. This is a much simpler graph structure and should be more memory dense. It does limit the ways in which it is appropriate to use this analysis. I wish I had a better name than "call graph". I've commented extensively this aspect. This is still very much a WIP, in fact it is really just the initial bits. But it is about the fourth version of the initial bits that I've implemented with each of the others running into really frustrating problms. This looks like it will actually work and I'd like to split the actual complexity across commits for the sake of my reviewers. =] The rest of the implementation along with lots of wiring will follow somewhat more rapidly now that there is a good path forward. Naturally, this doesn't impact any of the existing optimizer. This code is specific to the new pass manager. A bunch of thanks are deserved for the various folks that have helped with the design of this, especially Nick Lewycky who actually sat with me to go through the fundamentals of the final version here. git-svn-id: https://llvm.org/svn/llvm-project/llvm/trunk@200903 91177308-0d34-0410-b5e6-96231b3b80d8
2014-02-06 04:37:03 +00:00
#include "llvm/Support/Allocator.h"
#include <iterator>
namespace llvm {
class ModuleAnalysisManager;
class PreservedAnalyses;
class raw_ostream;
/// \brief A lazily constructed view of the call graph of a module.
///
/// With the edges of this graph, the motivating constraint that we are
/// attempting to maintain is that function-local optimization, CGSCC-local
/// optimizations, and optimizations transforming a pair of functions connected
/// by an edge in the graph, do not invalidate a bottom-up traversal of the SCC
/// DAG. That is, no optimizations will delete, remove, or add an edge such
/// that functions already visited in a bottom-up order of the SCC DAG are no
/// longer valid to have visited, or such that functions not yet visited in
/// a bottom-up order of the SCC DAG are not required to have already been
/// visited.
///
/// Within this constraint, the desire is to minimize the merge points of the
/// SCC DAG. The greater the fanout of the SCC DAG and the fewer merge points
/// in the SCC DAG, the more independence there is in optimizing within it.
/// There is a strong desire to enable parallelization of optimizations over
/// the call graph, and both limited fanout and merge points will (artificially
/// in some cases) limit the scaling of such an effort.
///
/// To this end, graph represents both direct and any potential resolution to
/// an indirect call edge. Another way to think about it is that it represents
/// both the direct call edges and any direct call edges that might be formed
/// through static optimizations. Specifically, it considers taking the address
/// of a function to be an edge in the call graph because this might be
/// forwarded to become a direct call by some subsequent function-local
/// optimization. The result is that the graph closely follows the use-def
/// edges for functions. Walking "up" the graph can be done by looking at all
/// of the uses of a function.
///
/// The roots of the call graph are the external functions and functions
/// escaped into global variables. Those functions can be called from outside
/// of the module or via unknowable means in the IR -- we may not be able to
/// form even a potential call edge from a function body which may dynamically
/// load the function and call it.
///
/// This analysis still requires updates to remain valid after optimizations
/// which could potentially change the set of potential callees. The
/// constraints it operates under only make the traversal order remain valid.
///
/// The entire analysis must be re-computed if full interprocedural
/// optimizations run at any point. For example, globalopt completely
/// invalidates the information in this analysis.
///
/// FIXME: This class is named LazyCallGraph in a lame attempt to distinguish
/// it from the existing CallGraph. At some point, it is expected that this
/// will be the only call graph and it will be renamed accordingly.
class LazyCallGraph {
public:
class Node;
[LCG] Add support for building persistent and connected SCCs to the LazyCallGraph. This is the start of the whole point of this different abstraction, but it is just the initial bits. Here is a run-down of what's going on here. I'm planning to incorporate some (or all) of this into comments going forward, hopefully with better editing and wording. =] The crux of the problem with the traditional way of building SCCs is that they are ephemeral. The new pass manager however really needs the ability to associate analysis passes and results of analysis passes with SCCs in order to expose these analysis passes to the SCC passes. Making this work is kind-of the whole point of the new pass manager. =] So, when we're building SCCs for the call graph, we actually want to build persistent nodes that stick around and can be reasoned about later. We'd also like the ability to walk the SCC graph in more complex ways than just the traditional postorder traversal of the current CGSCC walk. That means that in addition to being persistent, the SCCs need to be connected into a useful graph structure. However, we still want the SCCs to be formed lazily where possible. These constraints are quite hard to satisfy with the SCC iterator. Also, using that would bypass our ability to actually add data to the nodes of the call graph to facilite implementing the Tarjan walk. So I've re-implemented things in a more direct and embedded way. This immediately makes it easy to get the persistence and connectivity correct, and it also allows leveraging the existing nodes to simplify the algorithm. I've worked somewhat to make this implementation more closely follow the traditional paper's nomenclature and strategy, although it is still a bit obtuse because it isn't recursive, using an explicit stack and a tail call instead, and it is interruptable, resuming each time we need another SCC. The other tricky bit here, and what actually took almost all the time and trials and errors I spent building this, is exactly *what* graph structure to build for the SCCs. The naive thing to build is the call graph in its newly acyclic form. I wrote about 4 versions of this which did precisely this. Inevitably, when I experimented with them across various use cases, they became incredibly awkward. It was all implementable, but it felt like a complete wrong fit. Square peg, round hole. There were two overriding aspects that pushed me in a different direction: 1) We want to discover the SCC graph in a postorder fashion. That means the root node will be the *last* node we find. Using the call-SCC DAG as the graph structure of the SCCs results in an orphaned graph until we discover a root. 2) We will eventually want to walk the SCC graph in parallel, exploring distinct sub-graphs independently, and synchronizing at merge points. This again is not helped by the call-SCC DAG structure. The structure which, quite surprisingly, ended up being completely natural to use is the *inverse* of the call-SCC DAG. We add the leaf SCCs to the graph as "roots", and have edges to the caller SCCs. Once I switched to building this structure, everything just fell into place elegantly. Aside from general cleanups (there are FIXMEs and too few comments overall) that are still needed, the other missing piece of this is support for iterating across levels of the SCC graph. These will become useful for implementing #2, but they aren't an immediate priority. Once SCCs are in good shape, I'll be working on adding mutation support for incremental updates and adding the pass manager that this analysis enables. git-svn-id: https://llvm.org/svn/llvm-project/llvm/trunk@206581 91177308-0d34-0410-b5e6-96231b3b80d8
2014-04-18 10:50:32 +00:00
class SCC;
[PM] Add a new "lazy" call graph analysis pass for the new pass manager. The primary motivation for this pass is to separate the call graph analysis used by the new pass manager's CGSCC pass management from the existing call graph analysis pass. That analysis pass is (somewhat unfortunately) over-constrained by the existing CallGraphSCCPassManager requirements. Those requirements make it *really* hard to cleanly layer the needed functionality for the new pass manager on top of the existing analysis. However, there are also a bunch of things that the pass manager would specifically benefit from doing differently from the existing call graph analysis, and this new implementation tries to address several of them: - Be lazy about scanning function definitions. The existing pass eagerly scans the entire module to build the initial graph. This new pass is significantly more lazy, and I plan to push this even further to maximize locality during CGSCC walks. - Don't use a single synthetic node to partition functions with an indirect call from functions whose address is taken. This node creates a huge choke-point which would preclude good parallelization across the fanout of the SCC graph when we got to the point of looking at such changes to LLVM. - Use a memory dense and lightweight representation of the call graph rather than value handles and tracking call instructions. This will require explicit update calls instead of some updates working transparently, but should end up being significantly more efficient. The explicit update calls ended up being needed in many cases for the existing call graph so we don't really lose anything. - Doesn't explicitly model SCCs and thus doesn't provide an "identity" for an SCC which is stable across updates. This is essential for the new pass manager to work correctly. - Only form the graph necessary for traversing all of the functions in an SCC friendly order. This is a much simpler graph structure and should be more memory dense. It does limit the ways in which it is appropriate to use this analysis. I wish I had a better name than "call graph". I've commented extensively this aspect. This is still very much a WIP, in fact it is really just the initial bits. But it is about the fourth version of the initial bits that I've implemented with each of the others running into really frustrating problms. This looks like it will actually work and I'd like to split the actual complexity across commits for the sake of my reviewers. =] The rest of the implementation along with lots of wiring will follow somewhat more rapidly now that there is a good path forward. Naturally, this doesn't impact any of the existing optimizer. This code is specific to the new pass manager. A bunch of thanks are deserved for the various folks that have helped with the design of this, especially Nick Lewycky who actually sat with me to go through the fundamentals of the final version here. git-svn-id: https://llvm.org/svn/llvm-project/llvm/trunk@200903 91177308-0d34-0410-b5e6-96231b3b80d8
2014-02-06 04:37:03 +00:00
typedef SmallVector<PointerUnion<Function *, Node *>, 4> NodeVectorT;
typedef SmallVectorImpl<PointerUnion<Function *, Node *>> NodeVectorImplT;
[PM] Add a new "lazy" call graph analysis pass for the new pass manager. The primary motivation for this pass is to separate the call graph analysis used by the new pass manager's CGSCC pass management from the existing call graph analysis pass. That analysis pass is (somewhat unfortunately) over-constrained by the existing CallGraphSCCPassManager requirements. Those requirements make it *really* hard to cleanly layer the needed functionality for the new pass manager on top of the existing analysis. However, there are also a bunch of things that the pass manager would specifically benefit from doing differently from the existing call graph analysis, and this new implementation tries to address several of them: - Be lazy about scanning function definitions. The existing pass eagerly scans the entire module to build the initial graph. This new pass is significantly more lazy, and I plan to push this even further to maximize locality during CGSCC walks. - Don't use a single synthetic node to partition functions with an indirect call from functions whose address is taken. This node creates a huge choke-point which would preclude good parallelization across the fanout of the SCC graph when we got to the point of looking at such changes to LLVM. - Use a memory dense and lightweight representation of the call graph rather than value handles and tracking call instructions. This will require explicit update calls instead of some updates working transparently, but should end up being significantly more efficient. The explicit update calls ended up being needed in many cases for the existing call graph so we don't really lose anything. - Doesn't explicitly model SCCs and thus doesn't provide an "identity" for an SCC which is stable across updates. This is essential for the new pass manager to work correctly. - Only form the graph necessary for traversing all of the functions in an SCC friendly order. This is a much simpler graph structure and should be more memory dense. It does limit the ways in which it is appropriate to use this analysis. I wish I had a better name than "call graph". I've commented extensively this aspect. This is still very much a WIP, in fact it is really just the initial bits. But it is about the fourth version of the initial bits that I've implemented with each of the others running into really frustrating problms. This looks like it will actually work and I'd like to split the actual complexity across commits for the sake of my reviewers. =] The rest of the implementation along with lots of wiring will follow somewhat more rapidly now that there is a good path forward. Naturally, this doesn't impact any of the existing optimizer. This code is specific to the new pass manager. A bunch of thanks are deserved for the various folks that have helped with the design of this, especially Nick Lewycky who actually sat with me to go through the fundamentals of the final version here. git-svn-id: https://llvm.org/svn/llvm-project/llvm/trunk@200903 91177308-0d34-0410-b5e6-96231b3b80d8
2014-02-06 04:37:03 +00:00
/// \brief A lazy iterator used for both the entry nodes and child nodes.
///
/// When this iterator is dereferenced, if not yet available, a function will
/// be scanned for "calls" or uses of functions and its child information
/// will be constructed. All of these results are accumulated and cached in
/// the graph.
class iterator : public std::iterator<std::bidirectional_iterator_tag, Node *,
ptrdiff_t, Node *, Node *> {
friend class LazyCallGraph;
friend class LazyCallGraph::Node;
typedef std::iterator<std::bidirectional_iterator_tag, Node *, ptrdiff_t,
Node *, Node *> BaseT;
/// \brief Nonce type to select the constructor for the end iterator.
struct IsAtEndT {};
LazyCallGraph *G;
[PM] Add a new "lazy" call graph analysis pass for the new pass manager. The primary motivation for this pass is to separate the call graph analysis used by the new pass manager's CGSCC pass management from the existing call graph analysis pass. That analysis pass is (somewhat unfortunately) over-constrained by the existing CallGraphSCCPassManager requirements. Those requirements make it *really* hard to cleanly layer the needed functionality for the new pass manager on top of the existing analysis. However, there are also a bunch of things that the pass manager would specifically benefit from doing differently from the existing call graph analysis, and this new implementation tries to address several of them: - Be lazy about scanning function definitions. The existing pass eagerly scans the entire module to build the initial graph. This new pass is significantly more lazy, and I plan to push this even further to maximize locality during CGSCC walks. - Don't use a single synthetic node to partition functions with an indirect call from functions whose address is taken. This node creates a huge choke-point which would preclude good parallelization across the fanout of the SCC graph when we got to the point of looking at such changes to LLVM. - Use a memory dense and lightweight representation of the call graph rather than value handles and tracking call instructions. This will require explicit update calls instead of some updates working transparently, but should end up being significantly more efficient. The explicit update calls ended up being needed in many cases for the existing call graph so we don't really lose anything. - Doesn't explicitly model SCCs and thus doesn't provide an "identity" for an SCC which is stable across updates. This is essential for the new pass manager to work correctly. - Only form the graph necessary for traversing all of the functions in an SCC friendly order. This is a much simpler graph structure and should be more memory dense. It does limit the ways in which it is appropriate to use this analysis. I wish I had a better name than "call graph". I've commented extensively this aspect. This is still very much a WIP, in fact it is really just the initial bits. But it is about the fourth version of the initial bits that I've implemented with each of the others running into really frustrating problms. This looks like it will actually work and I'd like to split the actual complexity across commits for the sake of my reviewers. =] The rest of the implementation along with lots of wiring will follow somewhat more rapidly now that there is a good path forward. Naturally, this doesn't impact any of the existing optimizer. This code is specific to the new pass manager. A bunch of thanks are deserved for the various folks that have helped with the design of this, especially Nick Lewycky who actually sat with me to go through the fundamentals of the final version here. git-svn-id: https://llvm.org/svn/llvm-project/llvm/trunk@200903 91177308-0d34-0410-b5e6-96231b3b80d8
2014-02-06 04:37:03 +00:00
NodeVectorImplT::iterator NI;
// Build the begin iterator for a node.
explicit iterator(LazyCallGraph &G, NodeVectorImplT &Nodes)
: G(&G), NI(Nodes.begin()) {}
[PM] Add a new "lazy" call graph analysis pass for the new pass manager. The primary motivation for this pass is to separate the call graph analysis used by the new pass manager's CGSCC pass management from the existing call graph analysis pass. That analysis pass is (somewhat unfortunately) over-constrained by the existing CallGraphSCCPassManager requirements. Those requirements make it *really* hard to cleanly layer the needed functionality for the new pass manager on top of the existing analysis. However, there are also a bunch of things that the pass manager would specifically benefit from doing differently from the existing call graph analysis, and this new implementation tries to address several of them: - Be lazy about scanning function definitions. The existing pass eagerly scans the entire module to build the initial graph. This new pass is significantly more lazy, and I plan to push this even further to maximize locality during CGSCC walks. - Don't use a single synthetic node to partition functions with an indirect call from functions whose address is taken. This node creates a huge choke-point which would preclude good parallelization across the fanout of the SCC graph when we got to the point of looking at such changes to LLVM. - Use a memory dense and lightweight representation of the call graph rather than value handles and tracking call instructions. This will require explicit update calls instead of some updates working transparently, but should end up being significantly more efficient. The explicit update calls ended up being needed in many cases for the existing call graph so we don't really lose anything. - Doesn't explicitly model SCCs and thus doesn't provide an "identity" for an SCC which is stable across updates. This is essential for the new pass manager to work correctly. - Only form the graph necessary for traversing all of the functions in an SCC friendly order. This is a much simpler graph structure and should be more memory dense. It does limit the ways in which it is appropriate to use this analysis. I wish I had a better name than "call graph". I've commented extensively this aspect. This is still very much a WIP, in fact it is really just the initial bits. But it is about the fourth version of the initial bits that I've implemented with each of the others running into really frustrating problms. This looks like it will actually work and I'd like to split the actual complexity across commits for the sake of my reviewers. =] The rest of the implementation along with lots of wiring will follow somewhat more rapidly now that there is a good path forward. Naturally, this doesn't impact any of the existing optimizer. This code is specific to the new pass manager. A bunch of thanks are deserved for the various folks that have helped with the design of this, especially Nick Lewycky who actually sat with me to go through the fundamentals of the final version here. git-svn-id: https://llvm.org/svn/llvm-project/llvm/trunk@200903 91177308-0d34-0410-b5e6-96231b3b80d8
2014-02-06 04:37:03 +00:00
// Build the end iterator for a node. This is selected purely by overload.
iterator(LazyCallGraph &G, NodeVectorImplT &Nodes, IsAtEndT /*Nonce*/)
: G(&G), NI(Nodes.end()) {}
[PM] Add a new "lazy" call graph analysis pass for the new pass manager. The primary motivation for this pass is to separate the call graph analysis used by the new pass manager's CGSCC pass management from the existing call graph analysis pass. That analysis pass is (somewhat unfortunately) over-constrained by the existing CallGraphSCCPassManager requirements. Those requirements make it *really* hard to cleanly layer the needed functionality for the new pass manager on top of the existing analysis. However, there are also a bunch of things that the pass manager would specifically benefit from doing differently from the existing call graph analysis, and this new implementation tries to address several of them: - Be lazy about scanning function definitions. The existing pass eagerly scans the entire module to build the initial graph. This new pass is significantly more lazy, and I plan to push this even further to maximize locality during CGSCC walks. - Don't use a single synthetic node to partition functions with an indirect call from functions whose address is taken. This node creates a huge choke-point which would preclude good parallelization across the fanout of the SCC graph when we got to the point of looking at such changes to LLVM. - Use a memory dense and lightweight representation of the call graph rather than value handles and tracking call instructions. This will require explicit update calls instead of some updates working transparently, but should end up being significantly more efficient. The explicit update calls ended up being needed in many cases for the existing call graph so we don't really lose anything. - Doesn't explicitly model SCCs and thus doesn't provide an "identity" for an SCC which is stable across updates. This is essential for the new pass manager to work correctly. - Only form the graph necessary for traversing all of the functions in an SCC friendly order. This is a much simpler graph structure and should be more memory dense. It does limit the ways in which it is appropriate to use this analysis. I wish I had a better name than "call graph". I've commented extensively this aspect. This is still very much a WIP, in fact it is really just the initial bits. But it is about the fourth version of the initial bits that I've implemented with each of the others running into really frustrating problms. This looks like it will actually work and I'd like to split the actual complexity across commits for the sake of my reviewers. =] The rest of the implementation along with lots of wiring will follow somewhat more rapidly now that there is a good path forward. Naturally, this doesn't impact any of the existing optimizer. This code is specific to the new pass manager. A bunch of thanks are deserved for the various folks that have helped with the design of this, especially Nick Lewycky who actually sat with me to go through the fundamentals of the final version here. git-svn-id: https://llvm.org/svn/llvm-project/llvm/trunk@200903 91177308-0d34-0410-b5e6-96231b3b80d8
2014-02-06 04:37:03 +00:00
public:
bool operator==(const iterator &Arg) { return NI == Arg.NI; }
bool operator!=(const iterator &Arg) { return !operator==(Arg); }
reference operator*() const {
if (NI->is<Node *>())
return NI->get<Node *>();
Function *F = NI->get<Function *>();
Node *ChildN = G->get(*F);
[PM] Add a new "lazy" call graph analysis pass for the new pass manager. The primary motivation for this pass is to separate the call graph analysis used by the new pass manager's CGSCC pass management from the existing call graph analysis pass. That analysis pass is (somewhat unfortunately) over-constrained by the existing CallGraphSCCPassManager requirements. Those requirements make it *really* hard to cleanly layer the needed functionality for the new pass manager on top of the existing analysis. However, there are also a bunch of things that the pass manager would specifically benefit from doing differently from the existing call graph analysis, and this new implementation tries to address several of them: - Be lazy about scanning function definitions. The existing pass eagerly scans the entire module to build the initial graph. This new pass is significantly more lazy, and I plan to push this even further to maximize locality during CGSCC walks. - Don't use a single synthetic node to partition functions with an indirect call from functions whose address is taken. This node creates a huge choke-point which would preclude good parallelization across the fanout of the SCC graph when we got to the point of looking at such changes to LLVM. - Use a memory dense and lightweight representation of the call graph rather than value handles and tracking call instructions. This will require explicit update calls instead of some updates working transparently, but should end up being significantly more efficient. The explicit update calls ended up being needed in many cases for the existing call graph so we don't really lose anything. - Doesn't explicitly model SCCs and thus doesn't provide an "identity" for an SCC which is stable across updates. This is essential for the new pass manager to work correctly. - Only form the graph necessary for traversing all of the functions in an SCC friendly order. This is a much simpler graph structure and should be more memory dense. It does limit the ways in which it is appropriate to use this analysis. I wish I had a better name than "call graph". I've commented extensively this aspect. This is still very much a WIP, in fact it is really just the initial bits. But it is about the fourth version of the initial bits that I've implemented with each of the others running into really frustrating problms. This looks like it will actually work and I'd like to split the actual complexity across commits for the sake of my reviewers. =] The rest of the implementation along with lots of wiring will follow somewhat more rapidly now that there is a good path forward. Naturally, this doesn't impact any of the existing optimizer. This code is specific to the new pass manager. A bunch of thanks are deserved for the various folks that have helped with the design of this, especially Nick Lewycky who actually sat with me to go through the fundamentals of the final version here. git-svn-id: https://llvm.org/svn/llvm-project/llvm/trunk@200903 91177308-0d34-0410-b5e6-96231b3b80d8
2014-02-06 04:37:03 +00:00
*NI = ChildN;
return ChildN;
}
pointer operator->() const { return operator*(); }
iterator &operator++() {
++NI;
return *this;
}
iterator operator++(int) {
iterator prev = *this;
++*this;
return prev;
}
iterator &operator--() {
--NI;
return *this;
}
iterator operator--(int) {
iterator next = *this;
--*this;
return next;
}
};
/// \brief A node in the call graph.
///
/// This represents a single node. It's primary roles are to cache the list of
/// callees, de-duplicate and provide fast testing of whether a function is
/// a callee, and facilitate iteration of child nodes in the graph.
class Node {
friend class LazyCallGraph;
[LCG] Add support for building persistent and connected SCCs to the LazyCallGraph. This is the start of the whole point of this different abstraction, but it is just the initial bits. Here is a run-down of what's going on here. I'm planning to incorporate some (or all) of this into comments going forward, hopefully with better editing and wording. =] The crux of the problem with the traditional way of building SCCs is that they are ephemeral. The new pass manager however really needs the ability to associate analysis passes and results of analysis passes with SCCs in order to expose these analysis passes to the SCC passes. Making this work is kind-of the whole point of the new pass manager. =] So, when we're building SCCs for the call graph, we actually want to build persistent nodes that stick around and can be reasoned about later. We'd also like the ability to walk the SCC graph in more complex ways than just the traditional postorder traversal of the current CGSCC walk. That means that in addition to being persistent, the SCCs need to be connected into a useful graph structure. However, we still want the SCCs to be formed lazily where possible. These constraints are quite hard to satisfy with the SCC iterator. Also, using that would bypass our ability to actually add data to the nodes of the call graph to facilite implementing the Tarjan walk. So I've re-implemented things in a more direct and embedded way. This immediately makes it easy to get the persistence and connectivity correct, and it also allows leveraging the existing nodes to simplify the algorithm. I've worked somewhat to make this implementation more closely follow the traditional paper's nomenclature and strategy, although it is still a bit obtuse because it isn't recursive, using an explicit stack and a tail call instead, and it is interruptable, resuming each time we need another SCC. The other tricky bit here, and what actually took almost all the time and trials and errors I spent building this, is exactly *what* graph structure to build for the SCCs. The naive thing to build is the call graph in its newly acyclic form. I wrote about 4 versions of this which did precisely this. Inevitably, when I experimented with them across various use cases, they became incredibly awkward. It was all implementable, but it felt like a complete wrong fit. Square peg, round hole. There were two overriding aspects that pushed me in a different direction: 1) We want to discover the SCC graph in a postorder fashion. That means the root node will be the *last* node we find. Using the call-SCC DAG as the graph structure of the SCCs results in an orphaned graph until we discover a root. 2) We will eventually want to walk the SCC graph in parallel, exploring distinct sub-graphs independently, and synchronizing at merge points. This again is not helped by the call-SCC DAG structure. The structure which, quite surprisingly, ended up being completely natural to use is the *inverse* of the call-SCC DAG. We add the leaf SCCs to the graph as "roots", and have edges to the caller SCCs. Once I switched to building this structure, everything just fell into place elegantly. Aside from general cleanups (there are FIXMEs and too few comments overall) that are still needed, the other missing piece of this is support for iterating across levels of the SCC graph. These will become useful for implementing #2, but they aren't an immediate priority. Once SCCs are in good shape, I'll be working on adding mutation support for incremental updates and adding the pass manager that this analysis enables. git-svn-id: https://llvm.org/svn/llvm-project/llvm/trunk@206581 91177308-0d34-0410-b5e6-96231b3b80d8
2014-04-18 10:50:32 +00:00
friend class LazyCallGraph::SCC;
LazyCallGraph *G;
Function &F;
[LCG] Add support for building persistent and connected SCCs to the LazyCallGraph. This is the start of the whole point of this different abstraction, but it is just the initial bits. Here is a run-down of what's going on here. I'm planning to incorporate some (or all) of this into comments going forward, hopefully with better editing and wording. =] The crux of the problem with the traditional way of building SCCs is that they are ephemeral. The new pass manager however really needs the ability to associate analysis passes and results of analysis passes with SCCs in order to expose these analysis passes to the SCC passes. Making this work is kind-of the whole point of the new pass manager. =] So, when we're building SCCs for the call graph, we actually want to build persistent nodes that stick around and can be reasoned about later. We'd also like the ability to walk the SCC graph in more complex ways than just the traditional postorder traversal of the current CGSCC walk. That means that in addition to being persistent, the SCCs need to be connected into a useful graph structure. However, we still want the SCCs to be formed lazily where possible. These constraints are quite hard to satisfy with the SCC iterator. Also, using that would bypass our ability to actually add data to the nodes of the call graph to facilite implementing the Tarjan walk. So I've re-implemented things in a more direct and embedded way. This immediately makes it easy to get the persistence and connectivity correct, and it also allows leveraging the existing nodes to simplify the algorithm. I've worked somewhat to make this implementation more closely follow the traditional paper's nomenclature and strategy, although it is still a bit obtuse because it isn't recursive, using an explicit stack and a tail call instead, and it is interruptable, resuming each time we need another SCC. The other tricky bit here, and what actually took almost all the time and trials and errors I spent building this, is exactly *what* graph structure to build for the SCCs. The naive thing to build is the call graph in its newly acyclic form. I wrote about 4 versions of this which did precisely this. Inevitably, when I experimented with them across various use cases, they became incredibly awkward. It was all implementable, but it felt like a complete wrong fit. Square peg, round hole. There were two overriding aspects that pushed me in a different direction: 1) We want to discover the SCC graph in a postorder fashion. That means the root node will be the *last* node we find. Using the call-SCC DAG as the graph structure of the SCCs results in an orphaned graph until we discover a root. 2) We will eventually want to walk the SCC graph in parallel, exploring distinct sub-graphs independently, and synchronizing at merge points. This again is not helped by the call-SCC DAG structure. The structure which, quite surprisingly, ended up being completely natural to use is the *inverse* of the call-SCC DAG. We add the leaf SCCs to the graph as "roots", and have edges to the caller SCCs. Once I switched to building this structure, everything just fell into place elegantly. Aside from general cleanups (there are FIXMEs and too few comments overall) that are still needed, the other missing piece of this is support for iterating across levels of the SCC graph. These will become useful for implementing #2, but they aren't an immediate priority. Once SCCs are in good shape, I'll be working on adding mutation support for incremental updates and adding the pass manager that this analysis enables. git-svn-id: https://llvm.org/svn/llvm-project/llvm/trunk@206581 91177308-0d34-0410-b5e6-96231b3b80d8
2014-04-18 10:50:32 +00:00
// We provide for the DFS numbering and Tarjan walk lowlink numbers to be
// stored directly within the node.
int DFSNumber;
int LowLink;
mutable NodeVectorT Callees;
DenseMap<Function *, size_t> CalleeIndexMap;
/// \brief Basic constructor implements the scanning of F into Callees and
/// CalleeIndexMap.
Node(LazyCallGraph &G, Function &F);
public:
typedef LazyCallGraph::iterator iterator;
Function &getFunction() const {
return F;
};
iterator begin() const { return iterator(*G, Callees); }
iterator end() const { return iterator(*G, Callees, iterator::IsAtEndT()); }
/// Equality is defined as address equality.
bool operator==(const Node &N) const { return this == &N; }
bool operator!=(const Node &N) const { return !operator==(N); }
};
[LCG] Add support for building persistent and connected SCCs to the LazyCallGraph. This is the start of the whole point of this different abstraction, but it is just the initial bits. Here is a run-down of what's going on here. I'm planning to incorporate some (or all) of this into comments going forward, hopefully with better editing and wording. =] The crux of the problem with the traditional way of building SCCs is that they are ephemeral. The new pass manager however really needs the ability to associate analysis passes and results of analysis passes with SCCs in order to expose these analysis passes to the SCC passes. Making this work is kind-of the whole point of the new pass manager. =] So, when we're building SCCs for the call graph, we actually want to build persistent nodes that stick around and can be reasoned about later. We'd also like the ability to walk the SCC graph in more complex ways than just the traditional postorder traversal of the current CGSCC walk. That means that in addition to being persistent, the SCCs need to be connected into a useful graph structure. However, we still want the SCCs to be formed lazily where possible. These constraints are quite hard to satisfy with the SCC iterator. Also, using that would bypass our ability to actually add data to the nodes of the call graph to facilite implementing the Tarjan walk. So I've re-implemented things in a more direct and embedded way. This immediately makes it easy to get the persistence and connectivity correct, and it also allows leveraging the existing nodes to simplify the algorithm. I've worked somewhat to make this implementation more closely follow the traditional paper's nomenclature and strategy, although it is still a bit obtuse because it isn't recursive, using an explicit stack and a tail call instead, and it is interruptable, resuming each time we need another SCC. The other tricky bit here, and what actually took almost all the time and trials and errors I spent building this, is exactly *what* graph structure to build for the SCCs. The naive thing to build is the call graph in its newly acyclic form. I wrote about 4 versions of this which did precisely this. Inevitably, when I experimented with them across various use cases, they became incredibly awkward. It was all implementable, but it felt like a complete wrong fit. Square peg, round hole. There were two overriding aspects that pushed me in a different direction: 1) We want to discover the SCC graph in a postorder fashion. That means the root node will be the *last* node we find. Using the call-SCC DAG as the graph structure of the SCCs results in an orphaned graph until we discover a root. 2) We will eventually want to walk the SCC graph in parallel, exploring distinct sub-graphs independently, and synchronizing at merge points. This again is not helped by the call-SCC DAG structure. The structure which, quite surprisingly, ended up being completely natural to use is the *inverse* of the call-SCC DAG. We add the leaf SCCs to the graph as "roots", and have edges to the caller SCCs. Once I switched to building this structure, everything just fell into place elegantly. Aside from general cleanups (there are FIXMEs and too few comments overall) that are still needed, the other missing piece of this is support for iterating across levels of the SCC graph. These will become useful for implementing #2, but they aren't an immediate priority. Once SCCs are in good shape, I'll be working on adding mutation support for incremental updates and adding the pass manager that this analysis enables. git-svn-id: https://llvm.org/svn/llvm-project/llvm/trunk@206581 91177308-0d34-0410-b5e6-96231b3b80d8
2014-04-18 10:50:32 +00:00
/// \brief An SCC of the call graph.
///
/// This represents a Strongly Connected Component of the call graph as
/// a collection of call graph nodes. While the order of nodes in the SCC is
/// stable, it is not any particular order.
class SCC {
friend class LazyCallGraph;
friend class LazyCallGraph::Node;
SmallSetVector<SCC *, 1> ParentSCCs;
SmallVector<Node *, 1> Nodes;
SmallPtrSet<Function *, 1> NodeSet;
SCC() {}
public:
typedef SmallVectorImpl<Node *>::const_iterator iterator;
iterator begin() const { return Nodes.begin(); }
iterator end() const { return Nodes.end(); }
};
/// \brief A post-order depth-first SCC iterator over the call graph.
///
/// This iterator triggers the Tarjan DFS-based formation of the SCC DAG for
/// the call graph, walking it lazily in depth-first post-order. That is, it
/// always visits SCCs for a callee prior to visiting the SCC for a caller
/// (when they are in different SCCs).
class postorder_scc_iterator
: public std::iterator<std::forward_iterator_tag, SCC *, ptrdiff_t, SCC *,
SCC *> {
friend class LazyCallGraph;
friend class LazyCallGraph::Node;
typedef std::iterator<std::forward_iterator_tag, SCC *, ptrdiff_t,
SCC *, SCC *> BaseT;
/// \brief Nonce type to select the constructor for the end iterator.
struct IsAtEndT {};
LazyCallGraph *G;
SCC *C;
// Build the begin iterator for a node.
postorder_scc_iterator(LazyCallGraph &G) : G(&G) {
C = G.getNextSCCInPostOrder();
}
// Build the end iterator for a node. This is selected purely by overload.
postorder_scc_iterator(LazyCallGraph &G, IsAtEndT /*Nonce*/)
: G(&G), C(nullptr) {}
public:
bool operator==(const postorder_scc_iterator &Arg) {
return G == Arg.G && C == Arg.C;
}
bool operator!=(const postorder_scc_iterator &Arg) {
return !operator==(Arg);
}
reference operator*() const { return C; }
pointer operator->() const { return operator*(); }
postorder_scc_iterator &operator++() {
C = G->getNextSCCInPostOrder();
return *this;
}
postorder_scc_iterator operator++(int) {
postorder_scc_iterator prev = *this;
++*this;
return prev;
}
};
[PM] Add a new "lazy" call graph analysis pass for the new pass manager. The primary motivation for this pass is to separate the call graph analysis used by the new pass manager's CGSCC pass management from the existing call graph analysis pass. That analysis pass is (somewhat unfortunately) over-constrained by the existing CallGraphSCCPassManager requirements. Those requirements make it *really* hard to cleanly layer the needed functionality for the new pass manager on top of the existing analysis. However, there are also a bunch of things that the pass manager would specifically benefit from doing differently from the existing call graph analysis, and this new implementation tries to address several of them: - Be lazy about scanning function definitions. The existing pass eagerly scans the entire module to build the initial graph. This new pass is significantly more lazy, and I plan to push this even further to maximize locality during CGSCC walks. - Don't use a single synthetic node to partition functions with an indirect call from functions whose address is taken. This node creates a huge choke-point which would preclude good parallelization across the fanout of the SCC graph when we got to the point of looking at such changes to LLVM. - Use a memory dense and lightweight representation of the call graph rather than value handles and tracking call instructions. This will require explicit update calls instead of some updates working transparently, but should end up being significantly more efficient. The explicit update calls ended up being needed in many cases for the existing call graph so we don't really lose anything. - Doesn't explicitly model SCCs and thus doesn't provide an "identity" for an SCC which is stable across updates. This is essential for the new pass manager to work correctly. - Only form the graph necessary for traversing all of the functions in an SCC friendly order. This is a much simpler graph structure and should be more memory dense. It does limit the ways in which it is appropriate to use this analysis. I wish I had a better name than "call graph". I've commented extensively this aspect. This is still very much a WIP, in fact it is really just the initial bits. But it is about the fourth version of the initial bits that I've implemented with each of the others running into really frustrating problms. This looks like it will actually work and I'd like to split the actual complexity across commits for the sake of my reviewers. =] The rest of the implementation along with lots of wiring will follow somewhat more rapidly now that there is a good path forward. Naturally, this doesn't impact any of the existing optimizer. This code is specific to the new pass manager. A bunch of thanks are deserved for the various folks that have helped with the design of this, especially Nick Lewycky who actually sat with me to go through the fundamentals of the final version here. git-svn-id: https://llvm.org/svn/llvm-project/llvm/trunk@200903 91177308-0d34-0410-b5e6-96231b3b80d8
2014-02-06 04:37:03 +00:00
/// \brief Construct a graph for the given module.
///
/// This sets up the graph and computes all of the entry points of the graph.
/// No function definitions are scanned until their nodes in the graph are
/// requested during traversal.
LazyCallGraph(Module &M);
LazyCallGraph(LazyCallGraph &&G);
LazyCallGraph &operator=(LazyCallGraph &&RHS);
[PM] Add a new "lazy" call graph analysis pass for the new pass manager. The primary motivation for this pass is to separate the call graph analysis used by the new pass manager's CGSCC pass management from the existing call graph analysis pass. That analysis pass is (somewhat unfortunately) over-constrained by the existing CallGraphSCCPassManager requirements. Those requirements make it *really* hard to cleanly layer the needed functionality for the new pass manager on top of the existing analysis. However, there are also a bunch of things that the pass manager would specifically benefit from doing differently from the existing call graph analysis, and this new implementation tries to address several of them: - Be lazy about scanning function definitions. The existing pass eagerly scans the entire module to build the initial graph. This new pass is significantly more lazy, and I plan to push this even further to maximize locality during CGSCC walks. - Don't use a single synthetic node to partition functions with an indirect call from functions whose address is taken. This node creates a huge choke-point which would preclude good parallelization across the fanout of the SCC graph when we got to the point of looking at such changes to LLVM. - Use a memory dense and lightweight representation of the call graph rather than value handles and tracking call instructions. This will require explicit update calls instead of some updates working transparently, but should end up being significantly more efficient. The explicit update calls ended up being needed in many cases for the existing call graph so we don't really lose anything. - Doesn't explicitly model SCCs and thus doesn't provide an "identity" for an SCC which is stable across updates. This is essential for the new pass manager to work correctly. - Only form the graph necessary for traversing all of the functions in an SCC friendly order. This is a much simpler graph structure and should be more memory dense. It does limit the ways in which it is appropriate to use this analysis. I wish I had a better name than "call graph". I've commented extensively this aspect. This is still very much a WIP, in fact it is really just the initial bits. But it is about the fourth version of the initial bits that I've implemented with each of the others running into really frustrating problms. This looks like it will actually work and I'd like to split the actual complexity across commits for the sake of my reviewers. =] The rest of the implementation along with lots of wiring will follow somewhat more rapidly now that there is a good path forward. Naturally, this doesn't impact any of the existing optimizer. This code is specific to the new pass manager. A bunch of thanks are deserved for the various folks that have helped with the design of this, especially Nick Lewycky who actually sat with me to go through the fundamentals of the final version here. git-svn-id: https://llvm.org/svn/llvm-project/llvm/trunk@200903 91177308-0d34-0410-b5e6-96231b3b80d8
2014-02-06 04:37:03 +00:00
iterator begin() { return iterator(*this, EntryNodes); }
iterator end() { return iterator(*this, EntryNodes, iterator::IsAtEndT()); }
[LCG] Add support for building persistent and connected SCCs to the LazyCallGraph. This is the start of the whole point of this different abstraction, but it is just the initial bits. Here is a run-down of what's going on here. I'm planning to incorporate some (or all) of this into comments going forward, hopefully with better editing and wording. =] The crux of the problem with the traditional way of building SCCs is that they are ephemeral. The new pass manager however really needs the ability to associate analysis passes and results of analysis passes with SCCs in order to expose these analysis passes to the SCC passes. Making this work is kind-of the whole point of the new pass manager. =] So, when we're building SCCs for the call graph, we actually want to build persistent nodes that stick around and can be reasoned about later. We'd also like the ability to walk the SCC graph in more complex ways than just the traditional postorder traversal of the current CGSCC walk. That means that in addition to being persistent, the SCCs need to be connected into a useful graph structure. However, we still want the SCCs to be formed lazily where possible. These constraints are quite hard to satisfy with the SCC iterator. Also, using that would bypass our ability to actually add data to the nodes of the call graph to facilite implementing the Tarjan walk. So I've re-implemented things in a more direct and embedded way. This immediately makes it easy to get the persistence and connectivity correct, and it also allows leveraging the existing nodes to simplify the algorithm. I've worked somewhat to make this implementation more closely follow the traditional paper's nomenclature and strategy, although it is still a bit obtuse because it isn't recursive, using an explicit stack and a tail call instead, and it is interruptable, resuming each time we need another SCC. The other tricky bit here, and what actually took almost all the time and trials and errors I spent building this, is exactly *what* graph structure to build for the SCCs. The naive thing to build is the call graph in its newly acyclic form. I wrote about 4 versions of this which did precisely this. Inevitably, when I experimented with them across various use cases, they became incredibly awkward. It was all implementable, but it felt like a complete wrong fit. Square peg, round hole. There were two overriding aspects that pushed me in a different direction: 1) We want to discover the SCC graph in a postorder fashion. That means the root node will be the *last* node we find. Using the call-SCC DAG as the graph structure of the SCCs results in an orphaned graph until we discover a root. 2) We will eventually want to walk the SCC graph in parallel, exploring distinct sub-graphs independently, and synchronizing at merge points. This again is not helped by the call-SCC DAG structure. The structure which, quite surprisingly, ended up being completely natural to use is the *inverse* of the call-SCC DAG. We add the leaf SCCs to the graph as "roots", and have edges to the caller SCCs. Once I switched to building this structure, everything just fell into place elegantly. Aside from general cleanups (there are FIXMEs and too few comments overall) that are still needed, the other missing piece of this is support for iterating across levels of the SCC graph. These will become useful for implementing #2, but they aren't an immediate priority. Once SCCs are in good shape, I'll be working on adding mutation support for incremental updates and adding the pass manager that this analysis enables. git-svn-id: https://llvm.org/svn/llvm-project/llvm/trunk@206581 91177308-0d34-0410-b5e6-96231b3b80d8
2014-04-18 10:50:32 +00:00
postorder_scc_iterator postorder_scc_begin() {
return postorder_scc_iterator(*this);
}
postorder_scc_iterator postorder_scc_end() {
return postorder_scc_iterator(*this, postorder_scc_iterator::IsAtEndT());
}
iterator_range<postorder_scc_iterator> postorder_sccs() {
return iterator_range<postorder_scc_iterator>(postorder_scc_begin(),
postorder_scc_end());
}
[PM] Add a new "lazy" call graph analysis pass for the new pass manager. The primary motivation for this pass is to separate the call graph analysis used by the new pass manager's CGSCC pass management from the existing call graph analysis pass. That analysis pass is (somewhat unfortunately) over-constrained by the existing CallGraphSCCPassManager requirements. Those requirements make it *really* hard to cleanly layer the needed functionality for the new pass manager on top of the existing analysis. However, there are also a bunch of things that the pass manager would specifically benefit from doing differently from the existing call graph analysis, and this new implementation tries to address several of them: - Be lazy about scanning function definitions. The existing pass eagerly scans the entire module to build the initial graph. This new pass is significantly more lazy, and I plan to push this even further to maximize locality during CGSCC walks. - Don't use a single synthetic node to partition functions with an indirect call from functions whose address is taken. This node creates a huge choke-point which would preclude good parallelization across the fanout of the SCC graph when we got to the point of looking at such changes to LLVM. - Use a memory dense and lightweight representation of the call graph rather than value handles and tracking call instructions. This will require explicit update calls instead of some updates working transparently, but should end up being significantly more efficient. The explicit update calls ended up being needed in many cases for the existing call graph so we don't really lose anything. - Doesn't explicitly model SCCs and thus doesn't provide an "identity" for an SCC which is stable across updates. This is essential for the new pass manager to work correctly. - Only form the graph necessary for traversing all of the functions in an SCC friendly order. This is a much simpler graph structure and should be more memory dense. It does limit the ways in which it is appropriate to use this analysis. I wish I had a better name than "call graph". I've commented extensively this aspect. This is still very much a WIP, in fact it is really just the initial bits. But it is about the fourth version of the initial bits that I've implemented with each of the others running into really frustrating problms. This looks like it will actually work and I'd like to split the actual complexity across commits for the sake of my reviewers. =] The rest of the implementation along with lots of wiring will follow somewhat more rapidly now that there is a good path forward. Naturally, this doesn't impact any of the existing optimizer. This code is specific to the new pass manager. A bunch of thanks are deserved for the various folks that have helped with the design of this, especially Nick Lewycky who actually sat with me to go through the fundamentals of the final version here. git-svn-id: https://llvm.org/svn/llvm-project/llvm/trunk@200903 91177308-0d34-0410-b5e6-96231b3b80d8
2014-02-06 04:37:03 +00:00
/// \brief Lookup a function in the graph which has already been scanned and
/// added.
Node *lookup(const Function &F) const { return NodeMap.lookup(&F); }
/// \brief Get a graph node for a given function, scanning it to populate the
/// graph data as necessary.
Node *get(Function &F) {
Node *&N = NodeMap[&F];
if (N)
return N;
return insertInto(F, N);
}
private:
/// \brief Allocator that holds all the call graph nodes.
SpecificBumpPtrAllocator<Node> BPA;
/// \brief Maps function->node for fast lookup.
DenseMap<const Function *, Node *> NodeMap;
/// \brief The entry nodes to the graph.
///
/// These nodes are reachable through "external" means. Put another way, they
/// escape at the module scope.
NodeVectorT EntryNodes;
/// \brief Map of the entry nodes in the graph to their indices in
/// \c EntryNodes.
DenseMap<Function *, size_t> EntryIndexMap;
[PM] Add a new "lazy" call graph analysis pass for the new pass manager. The primary motivation for this pass is to separate the call graph analysis used by the new pass manager's CGSCC pass management from the existing call graph analysis pass. That analysis pass is (somewhat unfortunately) over-constrained by the existing CallGraphSCCPassManager requirements. Those requirements make it *really* hard to cleanly layer the needed functionality for the new pass manager on top of the existing analysis. However, there are also a bunch of things that the pass manager would specifically benefit from doing differently from the existing call graph analysis, and this new implementation tries to address several of them: - Be lazy about scanning function definitions. The existing pass eagerly scans the entire module to build the initial graph. This new pass is significantly more lazy, and I plan to push this even further to maximize locality during CGSCC walks. - Don't use a single synthetic node to partition functions with an indirect call from functions whose address is taken. This node creates a huge choke-point which would preclude good parallelization across the fanout of the SCC graph when we got to the point of looking at such changes to LLVM. - Use a memory dense and lightweight representation of the call graph rather than value handles and tracking call instructions. This will require explicit update calls instead of some updates working transparently, but should end up being significantly more efficient. The explicit update calls ended up being needed in many cases for the existing call graph so we don't really lose anything. - Doesn't explicitly model SCCs and thus doesn't provide an "identity" for an SCC which is stable across updates. This is essential for the new pass manager to work correctly. - Only form the graph necessary for traversing all of the functions in an SCC friendly order. This is a much simpler graph structure and should be more memory dense. It does limit the ways in which it is appropriate to use this analysis. I wish I had a better name than "call graph". I've commented extensively this aspect. This is still very much a WIP, in fact it is really just the initial bits. But it is about the fourth version of the initial bits that I've implemented with each of the others running into really frustrating problms. This looks like it will actually work and I'd like to split the actual complexity across commits for the sake of my reviewers. =] The rest of the implementation along with lots of wiring will follow somewhat more rapidly now that there is a good path forward. Naturally, this doesn't impact any of the existing optimizer. This code is specific to the new pass manager. A bunch of thanks are deserved for the various folks that have helped with the design of this, especially Nick Lewycky who actually sat with me to go through the fundamentals of the final version here. git-svn-id: https://llvm.org/svn/llvm-project/llvm/trunk@200903 91177308-0d34-0410-b5e6-96231b3b80d8
2014-02-06 04:37:03 +00:00
[LCG] Add support for building persistent and connected SCCs to the LazyCallGraph. This is the start of the whole point of this different abstraction, but it is just the initial bits. Here is a run-down of what's going on here. I'm planning to incorporate some (or all) of this into comments going forward, hopefully with better editing and wording. =] The crux of the problem with the traditional way of building SCCs is that they are ephemeral. The new pass manager however really needs the ability to associate analysis passes and results of analysis passes with SCCs in order to expose these analysis passes to the SCC passes. Making this work is kind-of the whole point of the new pass manager. =] So, when we're building SCCs for the call graph, we actually want to build persistent nodes that stick around and can be reasoned about later. We'd also like the ability to walk the SCC graph in more complex ways than just the traditional postorder traversal of the current CGSCC walk. That means that in addition to being persistent, the SCCs need to be connected into a useful graph structure. However, we still want the SCCs to be formed lazily where possible. These constraints are quite hard to satisfy with the SCC iterator. Also, using that would bypass our ability to actually add data to the nodes of the call graph to facilite implementing the Tarjan walk. So I've re-implemented things in a more direct and embedded way. This immediately makes it easy to get the persistence and connectivity correct, and it also allows leveraging the existing nodes to simplify the algorithm. I've worked somewhat to make this implementation more closely follow the traditional paper's nomenclature and strategy, although it is still a bit obtuse because it isn't recursive, using an explicit stack and a tail call instead, and it is interruptable, resuming each time we need another SCC. The other tricky bit here, and what actually took almost all the time and trials and errors I spent building this, is exactly *what* graph structure to build for the SCCs. The naive thing to build is the call graph in its newly acyclic form. I wrote about 4 versions of this which did precisely this. Inevitably, when I experimented with them across various use cases, they became incredibly awkward. It was all implementable, but it felt like a complete wrong fit. Square peg, round hole. There were two overriding aspects that pushed me in a different direction: 1) We want to discover the SCC graph in a postorder fashion. That means the root node will be the *last* node we find. Using the call-SCC DAG as the graph structure of the SCCs results in an orphaned graph until we discover a root. 2) We will eventually want to walk the SCC graph in parallel, exploring distinct sub-graphs independently, and synchronizing at merge points. This again is not helped by the call-SCC DAG structure. The structure which, quite surprisingly, ended up being completely natural to use is the *inverse* of the call-SCC DAG. We add the leaf SCCs to the graph as "roots", and have edges to the caller SCCs. Once I switched to building this structure, everything just fell into place elegantly. Aside from general cleanups (there are FIXMEs and too few comments overall) that are still needed, the other missing piece of this is support for iterating across levels of the SCC graph. These will become useful for implementing #2, but they aren't an immediate priority. Once SCCs are in good shape, I'll be working on adding mutation support for incremental updates and adding the pass manager that this analysis enables. git-svn-id: https://llvm.org/svn/llvm-project/llvm/trunk@206581 91177308-0d34-0410-b5e6-96231b3b80d8
2014-04-18 10:50:32 +00:00
/// \brief Allocator that holds all the call graph SCCs.
SpecificBumpPtrAllocator<SCC> SCCBPA;
/// \brief Maps Function -> SCC for fast lookup.
DenseMap<const Function *, SCC *> SCCMap;
/// \brief The leaf SCCs of the graph.
///
/// These are all of the SCCs which have no children.
SmallVector<SCC *, 4> LeafSCCs;
/// \brief Stack of nodes not-yet-processed into SCCs.
SmallVector<std::pair<Node *, iterator>, 4> DFSStack;
/// \brief Set of entry nodes not-yet-processed into SCCs.
SmallSetVector<Function *, 4> SCCEntryNodes;
/// \brief Counter for the next DFS number to assign.
int NextDFSNumber;
[PM] Add a new "lazy" call graph analysis pass for the new pass manager. The primary motivation for this pass is to separate the call graph analysis used by the new pass manager's CGSCC pass management from the existing call graph analysis pass. That analysis pass is (somewhat unfortunately) over-constrained by the existing CallGraphSCCPassManager requirements. Those requirements make it *really* hard to cleanly layer the needed functionality for the new pass manager on top of the existing analysis. However, there are also a bunch of things that the pass manager would specifically benefit from doing differently from the existing call graph analysis, and this new implementation tries to address several of them: - Be lazy about scanning function definitions. The existing pass eagerly scans the entire module to build the initial graph. This new pass is significantly more lazy, and I plan to push this even further to maximize locality during CGSCC walks. - Don't use a single synthetic node to partition functions with an indirect call from functions whose address is taken. This node creates a huge choke-point which would preclude good parallelization across the fanout of the SCC graph when we got to the point of looking at such changes to LLVM. - Use a memory dense and lightweight representation of the call graph rather than value handles and tracking call instructions. This will require explicit update calls instead of some updates working transparently, but should end up being significantly more efficient. The explicit update calls ended up being needed in many cases for the existing call graph so we don't really lose anything. - Doesn't explicitly model SCCs and thus doesn't provide an "identity" for an SCC which is stable across updates. This is essential for the new pass manager to work correctly. - Only form the graph necessary for traversing all of the functions in an SCC friendly order. This is a much simpler graph structure and should be more memory dense. It does limit the ways in which it is appropriate to use this analysis. I wish I had a better name than "call graph". I've commented extensively this aspect. This is still very much a WIP, in fact it is really just the initial bits. But it is about the fourth version of the initial bits that I've implemented with each of the others running into really frustrating problms. This looks like it will actually work and I'd like to split the actual complexity across commits for the sake of my reviewers. =] The rest of the implementation along with lots of wiring will follow somewhat more rapidly now that there is a good path forward. Naturally, this doesn't impact any of the existing optimizer. This code is specific to the new pass manager. A bunch of thanks are deserved for the various folks that have helped with the design of this, especially Nick Lewycky who actually sat with me to go through the fundamentals of the final version here. git-svn-id: https://llvm.org/svn/llvm-project/llvm/trunk@200903 91177308-0d34-0410-b5e6-96231b3b80d8
2014-02-06 04:37:03 +00:00
/// \brief Helper to insert a new function, with an already looked-up entry in
/// the NodeMap.
Node *insertInto(Function &F, Node *&MappedN);
/// \brief Helper to update pointers back to the graph object during moves.
void updateGraphPtrs();
[LCG] Add support for building persistent and connected SCCs to the LazyCallGraph. This is the start of the whole point of this different abstraction, but it is just the initial bits. Here is a run-down of what's going on here. I'm planning to incorporate some (or all) of this into comments going forward, hopefully with better editing and wording. =] The crux of the problem with the traditional way of building SCCs is that they are ephemeral. The new pass manager however really needs the ability to associate analysis passes and results of analysis passes with SCCs in order to expose these analysis passes to the SCC passes. Making this work is kind-of the whole point of the new pass manager. =] So, when we're building SCCs for the call graph, we actually want to build persistent nodes that stick around and can be reasoned about later. We'd also like the ability to walk the SCC graph in more complex ways than just the traditional postorder traversal of the current CGSCC walk. That means that in addition to being persistent, the SCCs need to be connected into a useful graph structure. However, we still want the SCCs to be formed lazily where possible. These constraints are quite hard to satisfy with the SCC iterator. Also, using that would bypass our ability to actually add data to the nodes of the call graph to facilite implementing the Tarjan walk. So I've re-implemented things in a more direct and embedded way. This immediately makes it easy to get the persistence and connectivity correct, and it also allows leveraging the existing nodes to simplify the algorithm. I've worked somewhat to make this implementation more closely follow the traditional paper's nomenclature and strategy, although it is still a bit obtuse because it isn't recursive, using an explicit stack and a tail call instead, and it is interruptable, resuming each time we need another SCC. The other tricky bit here, and what actually took almost all the time and trials and errors I spent building this, is exactly *what* graph structure to build for the SCCs. The naive thing to build is the call graph in its newly acyclic form. I wrote about 4 versions of this which did precisely this. Inevitably, when I experimented with them across various use cases, they became incredibly awkward. It was all implementable, but it felt like a complete wrong fit. Square peg, round hole. There were two overriding aspects that pushed me in a different direction: 1) We want to discover the SCC graph in a postorder fashion. That means the root node will be the *last* node we find. Using the call-SCC DAG as the graph structure of the SCCs results in an orphaned graph until we discover a root. 2) We will eventually want to walk the SCC graph in parallel, exploring distinct sub-graphs independently, and synchronizing at merge points. This again is not helped by the call-SCC DAG structure. The structure which, quite surprisingly, ended up being completely natural to use is the *inverse* of the call-SCC DAG. We add the leaf SCCs to the graph as "roots", and have edges to the caller SCCs. Once I switched to building this structure, everything just fell into place elegantly. Aside from general cleanups (there are FIXMEs and too few comments overall) that are still needed, the other missing piece of this is support for iterating across levels of the SCC graph. These will become useful for implementing #2, but they aren't an immediate priority. Once SCCs are in good shape, I'll be working on adding mutation support for incremental updates and adding the pass manager that this analysis enables. git-svn-id: https://llvm.org/svn/llvm-project/llvm/trunk@206581 91177308-0d34-0410-b5e6-96231b3b80d8
2014-04-18 10:50:32 +00:00
/// \brief Helper to form a new SCC out of the top of a DFSStack-like
/// structure.
SCC *formSCCFromDFSStack(
SmallVectorImpl<std::pair<Node *, Node::iterator>> &DFSStack);
[LCG] Add support for building persistent and connected SCCs to the LazyCallGraph. This is the start of the whole point of this different abstraction, but it is just the initial bits. Here is a run-down of what's going on here. I'm planning to incorporate some (or all) of this into comments going forward, hopefully with better editing and wording. =] The crux of the problem with the traditional way of building SCCs is that they are ephemeral. The new pass manager however really needs the ability to associate analysis passes and results of analysis passes with SCCs in order to expose these analysis passes to the SCC passes. Making this work is kind-of the whole point of the new pass manager. =] So, when we're building SCCs for the call graph, we actually want to build persistent nodes that stick around and can be reasoned about later. We'd also like the ability to walk the SCC graph in more complex ways than just the traditional postorder traversal of the current CGSCC walk. That means that in addition to being persistent, the SCCs need to be connected into a useful graph structure. However, we still want the SCCs to be formed lazily where possible. These constraints are quite hard to satisfy with the SCC iterator. Also, using that would bypass our ability to actually add data to the nodes of the call graph to facilite implementing the Tarjan walk. So I've re-implemented things in a more direct and embedded way. This immediately makes it easy to get the persistence and connectivity correct, and it also allows leveraging the existing nodes to simplify the algorithm. I've worked somewhat to make this implementation more closely follow the traditional paper's nomenclature and strategy, although it is still a bit obtuse because it isn't recursive, using an explicit stack and a tail call instead, and it is interruptable, resuming each time we need another SCC. The other tricky bit here, and what actually took almost all the time and trials and errors I spent building this, is exactly *what* graph structure to build for the SCCs. The naive thing to build is the call graph in its newly acyclic form. I wrote about 4 versions of this which did precisely this. Inevitably, when I experimented with them across various use cases, they became incredibly awkward. It was all implementable, but it felt like a complete wrong fit. Square peg, round hole. There were two overriding aspects that pushed me in a different direction: 1) We want to discover the SCC graph in a postorder fashion. That means the root node will be the *last* node we find. Using the call-SCC DAG as the graph structure of the SCCs results in an orphaned graph until we discover a root. 2) We will eventually want to walk the SCC graph in parallel, exploring distinct sub-graphs independently, and synchronizing at merge points. This again is not helped by the call-SCC DAG structure. The structure which, quite surprisingly, ended up being completely natural to use is the *inverse* of the call-SCC DAG. We add the leaf SCCs to the graph as "roots", and have edges to the caller SCCs. Once I switched to building this structure, everything just fell into place elegantly. Aside from general cleanups (there are FIXMEs and too few comments overall) that are still needed, the other missing piece of this is support for iterating across levels of the SCC graph. These will become useful for implementing #2, but they aren't an immediate priority. Once SCCs are in good shape, I'll be working on adding mutation support for incremental updates and adding the pass manager that this analysis enables. git-svn-id: https://llvm.org/svn/llvm-project/llvm/trunk@206581 91177308-0d34-0410-b5e6-96231b3b80d8
2014-04-18 10:50:32 +00:00
/// \brief Retrieve the next node in the post-order SCC walk of the call graph.
SCC *getNextSCCInPostOrder();
[PM] Add a new "lazy" call graph analysis pass for the new pass manager. The primary motivation for this pass is to separate the call graph analysis used by the new pass manager's CGSCC pass management from the existing call graph analysis pass. That analysis pass is (somewhat unfortunately) over-constrained by the existing CallGraphSCCPassManager requirements. Those requirements make it *really* hard to cleanly layer the needed functionality for the new pass manager on top of the existing analysis. However, there are also a bunch of things that the pass manager would specifically benefit from doing differently from the existing call graph analysis, and this new implementation tries to address several of them: - Be lazy about scanning function definitions. The existing pass eagerly scans the entire module to build the initial graph. This new pass is significantly more lazy, and I plan to push this even further to maximize locality during CGSCC walks. - Don't use a single synthetic node to partition functions with an indirect call from functions whose address is taken. This node creates a huge choke-point which would preclude good parallelization across the fanout of the SCC graph when we got to the point of looking at such changes to LLVM. - Use a memory dense and lightweight representation of the call graph rather than value handles and tracking call instructions. This will require explicit update calls instead of some updates working transparently, but should end up being significantly more efficient. The explicit update calls ended up being needed in many cases for the existing call graph so we don't really lose anything. - Doesn't explicitly model SCCs and thus doesn't provide an "identity" for an SCC which is stable across updates. This is essential for the new pass manager to work correctly. - Only form the graph necessary for traversing all of the functions in an SCC friendly order. This is a much simpler graph structure and should be more memory dense. It does limit the ways in which it is appropriate to use this analysis. I wish I had a better name than "call graph". I've commented extensively this aspect. This is still very much a WIP, in fact it is really just the initial bits. But it is about the fourth version of the initial bits that I've implemented with each of the others running into really frustrating problms. This looks like it will actually work and I'd like to split the actual complexity across commits for the sake of my reviewers. =] The rest of the implementation along with lots of wiring will follow somewhat more rapidly now that there is a good path forward. Naturally, this doesn't impact any of the existing optimizer. This code is specific to the new pass manager. A bunch of thanks are deserved for the various folks that have helped with the design of this, especially Nick Lewycky who actually sat with me to go through the fundamentals of the final version here. git-svn-id: https://llvm.org/svn/llvm-project/llvm/trunk@200903 91177308-0d34-0410-b5e6-96231b3b80d8
2014-02-06 04:37:03 +00:00
};
// Provide GraphTraits specializations for call graphs.
template <> struct GraphTraits<LazyCallGraph::Node *> {
typedef LazyCallGraph::Node NodeType;
typedef LazyCallGraph::iterator ChildIteratorType;
static NodeType *getEntryNode(NodeType *N) { return N; }
static ChildIteratorType child_begin(NodeType *N) { return N->begin(); }
static ChildIteratorType child_end(NodeType *N) { return N->end(); }
};
template <> struct GraphTraits<LazyCallGraph *> {
typedef LazyCallGraph::Node NodeType;
typedef LazyCallGraph::iterator ChildIteratorType;
static NodeType *getEntryNode(NodeType *N) { return N; }
static ChildIteratorType child_begin(NodeType *N) { return N->begin(); }
static ChildIteratorType child_end(NodeType *N) { return N->end(); }
};
/// \brief An analysis pass which computes the call graph for a module.
class LazyCallGraphAnalysis {
public:
/// \brief Inform generic clients of the result type.
typedef LazyCallGraph Result;
static void *ID() { return (void *)&PassID; }
/// \brief Compute the \c LazyCallGraph for a the module \c M.
///
/// This just builds the set of entry points to the call graph. The rest is
/// built lazily as it is walked.
LazyCallGraph run(Module *M) { return LazyCallGraph(*M); }
private:
static char PassID;
};
/// \brief A pass which prints the call graph to a \c raw_ostream.
///
/// This is primarily useful for testing the analysis.
class LazyCallGraphPrinterPass {
raw_ostream &OS;
public:
explicit LazyCallGraphPrinterPass(raw_ostream &OS);
PreservedAnalyses run(Module *M, ModuleAnalysisManager *AM);
static StringRef name() { return "LazyCallGraphPrinterPass"; }
};
}
#endif