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

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[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. llvm-svn: 200903
2014-02-06 05:37:03 +01:00
//===- 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_LAZYCALLGRAPH_H
#define LLVM_ANALYSIS_LAZYCALLGRAPH_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. llvm-svn: 200903
2014-02-06 05:37:03 +01:00
#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. llvm-svn: 206581
2014-04-18 12:50:32 +02:00
#include "llvm/ADT/SetVector.h"
#include "llvm/ADT/SmallPtrSet.h"
#include "llvm/ADT/SmallVector.h"
#include "llvm/ADT/iterator.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. llvm-svn: 206581
2014-04-18 12:50:32 +02:00
#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. llvm-svn: 200903
2014-02-06 05:37:03 +01:00
#include "llvm/IR/BasicBlock.h"
#include "llvm/IR/Function.h"
#include "llvm/IR/Module.h"
#include "llvm/IR/PassManager.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. llvm-svn: 200903
2014-02-06 05:37:03 +01:00
#include "llvm/Support/Allocator.h"
#include "llvm/Support/raw_ostream.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. llvm-svn: 200903
2014-02-06 05:37:03 +01:00
#include <iterator>
[LCG] Construct an actual call graph with call-edge SCCs nested inside reference-edge SCCs. This essentially builds a more normal call graph as a subgraph of the "reference graph" that was the old model. This allows both to exist and the different use cases to use the aspect which addresses their needs. Specifically, the pass manager and other *ordering* constrained logic can use the reference graph to achieve conservative order of visit, while analyses reasoning about attributes and other properties derived from reachability can reason about the direct call graph. Note that this isn't necessarily complete: it doesn't model edges to declarations or indirect calls. Those can be found by scanning the instructions of the function if desirable, and in fact every user currently does this in order to handle things like calls to instrinsics. If useful, we could consider caching this information in the call graph to save the instruction scans, but currently that doesn't seem to be important. An important realization for why the representation chosen here works is that the call graph is a formal subset of the reference graph and thus both can live within the same data structure. All SCCs of the call graph are necessarily contained within an SCC of the reference graph, etc. The design is to build 'RefSCC's to model SCCs of the reference graph, and then within them more literal SCCs for the call graph. The formation of actual call edge SCCs is not done lazily, unlike reference edge 'RefSCC's. Instead, once a reference SCC is formed, it directly builds the call SCCs within it and stores them in a post-order sequence. This is used to provide a consistent platform for mutation and update of the graph. The post-order also allows for very efficient updates in common cases by bounding the number of nodes (and thus edges) considered. There is considerable common code that I'm still looking for the best way to factor out between the various DFS implementations here. So far, my attempts have made the code harder to read and understand despite reducing the duplication, which seems a poor tradeoff. I've not given up on figuring out the right way to do this, but I wanted to wait until I at least had the system working and tested to continue attempting to factor it differently. This also requires introducing several new algorithms in order to handle all of the incremental update scenarios for the more complex structure involving two edge colorings. I've tried to comment the algorithms sufficiently to make it clear how this is expected to work, but they may still need more extensive documentation. I know that there are some changes which are not strictly necessarily coupled here. The process of developing this started out with a very focused set of changes for the new structure of the graph and algorithms, but subsequent changes to bring the APIs and code into consistent and understandable patterns also ended up touching on other aspects. There was no good way to separate these out without causing *massive* merge conflicts. Ultimately, to a large degree this is a rewrite of most of the core algorithms in the LCG class and so I don't think it really matters much. Many thanks to the careful review by Sanjoy Das! Differential Revision: http://reviews.llvm.org/D16802 llvm-svn: 261040
2016-02-17 01:18:16 +01:00
#include <utility>
[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. llvm-svn: 200903
2014-02-06 05:37:03 +01:00
namespace llvm {
class PreservedAnalyses;
class raw_ostream;
/// A lazily constructed view of the call graph of a module.
[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. llvm-svn: 200903
2014-02-06 05:37:03 +01:00
///
/// 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. llvm-svn: 206581
2014-04-18 12:50:32 +02:00
class SCC;
[LCG] Construct an actual call graph with call-edge SCCs nested inside reference-edge SCCs. This essentially builds a more normal call graph as a subgraph of the "reference graph" that was the old model. This allows both to exist and the different use cases to use the aspect which addresses their needs. Specifically, the pass manager and other *ordering* constrained logic can use the reference graph to achieve conservative order of visit, while analyses reasoning about attributes and other properties derived from reachability can reason about the direct call graph. Note that this isn't necessarily complete: it doesn't model edges to declarations or indirect calls. Those can be found by scanning the instructions of the function if desirable, and in fact every user currently does this in order to handle things like calls to instrinsics. If useful, we could consider caching this information in the call graph to save the instruction scans, but currently that doesn't seem to be important. An important realization for why the representation chosen here works is that the call graph is a formal subset of the reference graph and thus both can live within the same data structure. All SCCs of the call graph are necessarily contained within an SCC of the reference graph, etc. The design is to build 'RefSCC's to model SCCs of the reference graph, and then within them more literal SCCs for the call graph. The formation of actual call edge SCCs is not done lazily, unlike reference edge 'RefSCC's. Instead, once a reference SCC is formed, it directly builds the call SCCs within it and stores them in a post-order sequence. This is used to provide a consistent platform for mutation and update of the graph. The post-order also allows for very efficient updates in common cases by bounding the number of nodes (and thus edges) considered. There is considerable common code that I'm still looking for the best way to factor out between the various DFS implementations here. So far, my attempts have made the code harder to read and understand despite reducing the duplication, which seems a poor tradeoff. I've not given up on figuring out the right way to do this, but I wanted to wait until I at least had the system working and tested to continue attempting to factor it differently. This also requires introducing several new algorithms in order to handle all of the incremental update scenarios for the more complex structure involving two edge colorings. I've tried to comment the algorithms sufficiently to make it clear how this is expected to work, but they may still need more extensive documentation. I know that there are some changes which are not strictly necessarily coupled here. The process of developing this started out with a very focused set of changes for the new structure of the graph and algorithms, but subsequent changes to bring the APIs and code into consistent and understandable patterns also ended up touching on other aspects. There was no good way to separate these out without causing *massive* merge conflicts. Ultimately, to a large degree this is a rewrite of most of the core algorithms in the LCG class and so I don't think it really matters much. Many thanks to the careful review by Sanjoy Das! Differential Revision: http://reviews.llvm.org/D16802 llvm-svn: 261040
2016-02-17 01:18:16 +01:00
class RefSCC;
class edge_iterator;
[LCG] Construct an actual call graph with call-edge SCCs nested inside reference-edge SCCs. This essentially builds a more normal call graph as a subgraph of the "reference graph" that was the old model. This allows both to exist and the different use cases to use the aspect which addresses their needs. Specifically, the pass manager and other *ordering* constrained logic can use the reference graph to achieve conservative order of visit, while analyses reasoning about attributes and other properties derived from reachability can reason about the direct call graph. Note that this isn't necessarily complete: it doesn't model edges to declarations or indirect calls. Those can be found by scanning the instructions of the function if desirable, and in fact every user currently does this in order to handle things like calls to instrinsics. If useful, we could consider caching this information in the call graph to save the instruction scans, but currently that doesn't seem to be important. An important realization for why the representation chosen here works is that the call graph is a formal subset of the reference graph and thus both can live within the same data structure. All SCCs of the call graph are necessarily contained within an SCC of the reference graph, etc. The design is to build 'RefSCC's to model SCCs of the reference graph, and then within them more literal SCCs for the call graph. The formation of actual call edge SCCs is not done lazily, unlike reference edge 'RefSCC's. Instead, once a reference SCC is formed, it directly builds the call SCCs within it and stores them in a post-order sequence. This is used to provide a consistent platform for mutation and update of the graph. The post-order also allows for very efficient updates in common cases by bounding the number of nodes (and thus edges) considered. There is considerable common code that I'm still looking for the best way to factor out between the various DFS implementations here. So far, my attempts have made the code harder to read and understand despite reducing the duplication, which seems a poor tradeoff. I've not given up on figuring out the right way to do this, but I wanted to wait until I at least had the system working and tested to continue attempting to factor it differently. This also requires introducing several new algorithms in order to handle all of the incremental update scenarios for the more complex structure involving two edge colorings. I've tried to comment the algorithms sufficiently to make it clear how this is expected to work, but they may still need more extensive documentation. I know that there are some changes which are not strictly necessarily coupled here. The process of developing this started out with a very focused set of changes for the new structure of the graph and algorithms, but subsequent changes to bring the APIs and code into consistent and understandable patterns also ended up touching on other aspects. There was no good way to separate these out without causing *massive* merge conflicts. Ultimately, to a large degree this is a rewrite of most of the core algorithms in the LCG class and so I don't think it really matters much. Many thanks to the careful review by Sanjoy Das! Differential Revision: http://reviews.llvm.org/D16802 llvm-svn: 261040
2016-02-17 01:18:16 +01:00
class call_edge_iterator;
/// A class used to represent edges in the call graph.
///
/// The lazy call graph models both *call* edges and *reference* edges. Call
/// edges are much what you would expect, and exist when there is a 'call' or
/// 'invoke' instruction of some function. Reference edges are also tracked
/// along side these, and exist whenever any instruction (transitively
/// through its operands) references a function. All call edges are
/// inherently reference edges, and so the reference graph forms a superset
/// of the formal call graph.
///
/// Furthermore, edges also may point to raw \c Function objects when those
/// functions have not been scanned and incorporated into the graph (yet).
/// This is one of the primary ways in which the graph can be lazy. When
/// functions are scanned and fully incorporated into the graph, all of the
/// edges referencing them are updated to point to the graph \c Node objects
/// instead of to the raw \c Function objects. This class even provides
/// methods to trigger this scan on-demand by attempting to get the target
/// node of the graph and providing a reference back to the graph in order to
/// lazily build it if necessary.
///
/// All of these forms of edges are fundamentally represented as outgoing
/// edges. The edges are stored in the source node and point at the target
/// node. This allows the edge structure itself to be a very compact data
/// structure: essentially a tagged pointer.
class Edge {
public:
/// The kind of edge in the graph.
enum Kind : bool { Ref = false, Call = true };
Edge();
explicit Edge(Function &F, Kind K);
explicit Edge(Node &N, Kind K);
/// Test whether the edge is null.
///
/// This happens when an edge has been deleted. We leave the edge objects
/// around but clear them.
operator bool() const;
[PM] Introduce basic update capabilities to the new PM's CGSCC pass manager, including both plumbing and logic to handle function pass updates. There are three fundamentally tied changes here: 1) Plumbing *some* mechanism for updating the CGSCC pass manager as the CG changes while passes are running. 2) Changing the CGSCC pass manager infrastructure to have support for the underlying graph to mutate mid-pass run. 3) Actually updating the CG after function passes run. I can separate them if necessary, but I think its really useful to have them together as the needs of #3 drove #2, and that in turn drove #1. The plumbing technique is to extend the "run" method signature with extra arguments. We provide the call graph that intrinsically is available as it is the basis of the pass manager's IR units, and an output parameter that records the results of updating the call graph during an SCC passes's run. Note that "...UpdateResult" isn't a *great* name here... suggestions very welcome. I tried a pretty frustrating number of different data structures and such for the innards of the update result. Every other one failed for one reason or another. Sometimes I just couldn't keep the layers of complexity right in my head. The thing that really worked was to just directly provide access to the underlying structures used to walk the call graph so that their updates could be informed by the *particular* nature of the change to the graph. The technique for how to make the pass management infrastructure cope with mutating graphs was also something that took a really, really large number of iterations to get to a place where I was happy. Here are some of the considerations that drove the design: - We operate at three levels within the infrastructure: RefSCC, SCC, and Node. In each case, we are working bottom up and so we want to continue to iterate on the "lowest" node as the graph changes. Look at how we iterate over nodes in an SCC running function passes as those function passes mutate the CG. We continue to iterate on the "lowest" SCC, which is the one that continues to contain the function just processed. - The call graph structure re-uses SCCs (and RefSCCs) during mutation events for the *highest* entry in the resulting new subgraph, not the lowest. This means that it is necessary to continually update the current SCC or RefSCC as it shifts. This is really surprising and subtle, and took a long time for me to work out. I actually tried changing the call graph to provide the opposite behavior, and it breaks *EVERYTHING*. The graph update algorithms are really deeply tied to this particualr pattern. - When SCCs or RefSCCs are split apart and refined and we continually re-pin our processing to the bottom one in the subgraph, we need to enqueue the newly formed SCCs and RefSCCs for subsequent processing. Queuing them presents a few challenges: 1) SCCs and RefSCCs use wildly different iteration strategies at a high level. We end up needing to converge them on worklist approaches that can be extended in order to be able to handle the mutations. 2) The order of the enqueuing need to remain bottom-up post-order so that we don't get surprising order of visitation for things like the inliner. 3) We need the worklists to have set semantics so we don't duplicate things endlessly. We don't need a *persistent* set though because we always keep processing the bottom node!!!! This is super, super surprising to me and took a long time to convince myself this is correct, but I'm pretty sure it is... Once we sink down to the bottom node, we can't re-split out the same node in any way, and the postorder of the current queue is fixed and unchanging. 4) We need to make sure that the "current" SCC or RefSCC actually gets enqueued here such that we re-visit it because we continue processing a *new*, *bottom* SCC/RefSCC. - We also need the ability to *skip* SCCs and RefSCCs that get merged into a larger component. We even need the ability to skip *nodes* from an SCC that are no longer part of that SCC. This led to the design you see in the patch which uses SetVector-based worklists. The RefSCC worklist is always empty until an update occurs and is just used to handle those RefSCCs created by updates as the others don't even exist yet and are formed on-demand during the bottom-up walk. The SCC worklist is pre-populated from the RefSCC, and we push new SCCs onto it and blacklist existing SCCs on it to get the desired processing. We then *directly* update these when updating the call graph as I was never able to find a satisfactory abstraction around the update strategy. Finally, we need to compute the updates for function passes. This is mostly used as an initial customer of all the update mechanisms to drive their design to at least cover some real set of use cases. There are a bunch of interesting things that came out of doing this: - It is really nice to do this a function at a time because that function is likely hot in the cache. This means we want even the function pass adaptor to support online updates to the call graph! - To update the call graph after arbitrary function pass mutations is quite hard. We have to build a fairly comprehensive set of data structures and then process them. Fortunately, some of this code is related to the code for building the cal graph in the first place. Unfortunately, very little of it makes any sense to share because the nature of what we're doing is so very different. I've factored out the one part that made sense at least. - We need to transfer these updates into the various structures for the CGSCC pass manager. Once those were more sanely worked out, this became relatively easier. But some of those needs necessitated changes to the LazyCallGraph interface to make it significantly easier to extract the changed SCCs from an update operation. - We also need to update the CGSCC analysis manager as the shape of the graph changes. When an SCC is merged away we need to clear analyses associated with it from the analysis manager which we didn't have support for in the analysis manager infrsatructure. New SCCs are easy! But then we have the case that the original SCC has its shape changed but remains in the call graph. There we need to *invalidate* the analyses associated with it. - We also need to invalidate analyses after we *finish* processing an SCC. But the analyses we need to invalidate here are *only those for the newly updated SCC*!!! Because we only continue processing the bottom SCC, if we split SCCs apart the original one gets invalidated once when its shape changes and is not processed farther so its analyses will be correct. It is the bottom SCC which continues being processed and needs to have the "normal" invalidation done based on the preserved analyses set. All of this is mostly background and context for the changes here. Many thanks to all the reviewers who helped here. Especially Sanjoy who caught several interesting bugs in the graph algorithms, David, Sean, and others who all helped with feedback. Differential Revision: http://reviews.llvm.org/D21464 llvm-svn: 279618
2016-08-24 11:37:14 +02:00
/// Returnss the \c Kind of the edge.
Kind getKind() const;
/// Test whether the edge represents a direct call to a function.
///
/// This requires that the edge is not null.
bool isCall() const;
/// Get the function referenced by this edge.
///
/// This requires that the edge is not null, but will succeed whether we
/// have built a graph node for the function yet or not.
Function &getFunction() const;
/// Get the call graph node referenced by this edge if one exists.
///
/// This requires that the edge is not null. If we have built a graph node
/// for the function this edge points to, this will return that node,
/// otherwise it will return null.
Node *getNode() const;
/// Get the call graph node for this edge, building it if necessary.
///
/// This requires that the edge is not null. If we have not yet built
/// a graph node for the function this edge points to, this will first ask
/// the graph to build that node, inserting it into all the relevant
/// structures.
Node &getNode(LazyCallGraph &G);
private:
[LCG] Construct an actual call graph with call-edge SCCs nested inside reference-edge SCCs. This essentially builds a more normal call graph as a subgraph of the "reference graph" that was the old model. This allows both to exist and the different use cases to use the aspect which addresses their needs. Specifically, the pass manager and other *ordering* constrained logic can use the reference graph to achieve conservative order of visit, while analyses reasoning about attributes and other properties derived from reachability can reason about the direct call graph. Note that this isn't necessarily complete: it doesn't model edges to declarations or indirect calls. Those can be found by scanning the instructions of the function if desirable, and in fact every user currently does this in order to handle things like calls to instrinsics. If useful, we could consider caching this information in the call graph to save the instruction scans, but currently that doesn't seem to be important. An important realization for why the representation chosen here works is that the call graph is a formal subset of the reference graph and thus both can live within the same data structure. All SCCs of the call graph are necessarily contained within an SCC of the reference graph, etc. The design is to build 'RefSCC's to model SCCs of the reference graph, and then within them more literal SCCs for the call graph. The formation of actual call edge SCCs is not done lazily, unlike reference edge 'RefSCC's. Instead, once a reference SCC is formed, it directly builds the call SCCs within it and stores them in a post-order sequence. This is used to provide a consistent platform for mutation and update of the graph. The post-order also allows for very efficient updates in common cases by bounding the number of nodes (and thus edges) considered. There is considerable common code that I'm still looking for the best way to factor out between the various DFS implementations here. So far, my attempts have made the code harder to read and understand despite reducing the duplication, which seems a poor tradeoff. I've not given up on figuring out the right way to do this, but I wanted to wait until I at least had the system working and tested to continue attempting to factor it differently. This also requires introducing several new algorithms in order to handle all of the incremental update scenarios for the more complex structure involving two edge colorings. I've tried to comment the algorithms sufficiently to make it clear how this is expected to work, but they may still need more extensive documentation. I know that there are some changes which are not strictly necessarily coupled here. The process of developing this started out with a very focused set of changes for the new structure of the graph and algorithms, but subsequent changes to bring the APIs and code into consistent and understandable patterns also ended up touching on other aspects. There was no good way to separate these out without causing *massive* merge conflicts. Ultimately, to a large degree this is a rewrite of most of the core algorithms in the LCG class and so I don't think it really matters much. Many thanks to the careful review by Sanjoy Das! Differential Revision: http://reviews.llvm.org/D16802 llvm-svn: 261040
2016-02-17 01:18:16 +01:00
friend class LazyCallGraph::Node;
friend class LazyCallGraph::RefSCC;
[LCG] Construct an actual call graph with call-edge SCCs nested inside reference-edge SCCs. This essentially builds a more normal call graph as a subgraph of the "reference graph" that was the old model. This allows both to exist and the different use cases to use the aspect which addresses their needs. Specifically, the pass manager and other *ordering* constrained logic can use the reference graph to achieve conservative order of visit, while analyses reasoning about attributes and other properties derived from reachability can reason about the direct call graph. Note that this isn't necessarily complete: it doesn't model edges to declarations or indirect calls. Those can be found by scanning the instructions of the function if desirable, and in fact every user currently does this in order to handle things like calls to instrinsics. If useful, we could consider caching this information in the call graph to save the instruction scans, but currently that doesn't seem to be important. An important realization for why the representation chosen here works is that the call graph is a formal subset of the reference graph and thus both can live within the same data structure. All SCCs of the call graph are necessarily contained within an SCC of the reference graph, etc. The design is to build 'RefSCC's to model SCCs of the reference graph, and then within them more literal SCCs for the call graph. The formation of actual call edge SCCs is not done lazily, unlike reference edge 'RefSCC's. Instead, once a reference SCC is formed, it directly builds the call SCCs within it and stores them in a post-order sequence. This is used to provide a consistent platform for mutation and update of the graph. The post-order also allows for very efficient updates in common cases by bounding the number of nodes (and thus edges) considered. There is considerable common code that I'm still looking for the best way to factor out between the various DFS implementations here. So far, my attempts have made the code harder to read and understand despite reducing the duplication, which seems a poor tradeoff. I've not given up on figuring out the right way to do this, but I wanted to wait until I at least had the system working and tested to continue attempting to factor it differently. This also requires introducing several new algorithms in order to handle all of the incremental update scenarios for the more complex structure involving two edge colorings. I've tried to comment the algorithms sufficiently to make it clear how this is expected to work, but they may still need more extensive documentation. I know that there are some changes which are not strictly necessarily coupled here. The process of developing this started out with a very focused set of changes for the new structure of the graph and algorithms, but subsequent changes to bring the APIs and code into consistent and understandable patterns also ended up touching on other aspects. There was no good way to separate these out without causing *massive* merge conflicts. Ultimately, to a large degree this is a rewrite of most of the core algorithms in the LCG class and so I don't think it really matters much. Many thanks to the careful review by Sanjoy Das! Differential Revision: http://reviews.llvm.org/D16802 llvm-svn: 261040
2016-02-17 01:18:16 +01:00
PointerIntPair<PointerUnion<Function *, Node *>, 1, Kind> Value;
[LCG] Construct an actual call graph with call-edge SCCs nested inside reference-edge SCCs. This essentially builds a more normal call graph as a subgraph of the "reference graph" that was the old model. This allows both to exist and the different use cases to use the aspect which addresses their needs. Specifically, the pass manager and other *ordering* constrained logic can use the reference graph to achieve conservative order of visit, while analyses reasoning about attributes and other properties derived from reachability can reason about the direct call graph. Note that this isn't necessarily complete: it doesn't model edges to declarations or indirect calls. Those can be found by scanning the instructions of the function if desirable, and in fact every user currently does this in order to handle things like calls to instrinsics. If useful, we could consider caching this information in the call graph to save the instruction scans, but currently that doesn't seem to be important. An important realization for why the representation chosen here works is that the call graph is a formal subset of the reference graph and thus both can live within the same data structure. All SCCs of the call graph are necessarily contained within an SCC of the reference graph, etc. The design is to build 'RefSCC's to model SCCs of the reference graph, and then within them more literal SCCs for the call graph. The formation of actual call edge SCCs is not done lazily, unlike reference edge 'RefSCC's. Instead, once a reference SCC is formed, it directly builds the call SCCs within it and stores them in a post-order sequence. This is used to provide a consistent platform for mutation and update of the graph. The post-order also allows for very efficient updates in common cases by bounding the number of nodes (and thus edges) considered. There is considerable common code that I'm still looking for the best way to factor out between the various DFS implementations here. So far, my attempts have made the code harder to read and understand despite reducing the duplication, which seems a poor tradeoff. I've not given up on figuring out the right way to do this, but I wanted to wait until I at least had the system working and tested to continue attempting to factor it differently. This also requires introducing several new algorithms in order to handle all of the incremental update scenarios for the more complex structure involving two edge colorings. I've tried to comment the algorithms sufficiently to make it clear how this is expected to work, but they may still need more extensive documentation. I know that there are some changes which are not strictly necessarily coupled here. The process of developing this started out with a very focused set of changes for the new structure of the graph and algorithms, but subsequent changes to bring the APIs and code into consistent and understandable patterns also ended up touching on other aspects. There was no good way to separate these out without causing *massive* merge conflicts. Ultimately, to a large degree this is a rewrite of most of the core algorithms in the LCG class and so I don't think it really matters much. Many thanks to the careful review by Sanjoy Das! Differential Revision: http://reviews.llvm.org/D16802 llvm-svn: 261040
2016-02-17 01:18:16 +01:00
void setKind(Kind K) { Value.setInt(K); }
};
typedef SmallVector<Edge, 4> EdgeVectorT;
typedef SmallVectorImpl<Edge> EdgeVectorImplT;
[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. llvm-svn: 200903
2014-02-06 05:37:03 +01:00
/// 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. llvm-svn: 206581
2014-04-18 12:50:32 +02:00
friend class LazyCallGraph::SCC;
friend class LazyCallGraph::RefSCC;
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. llvm-svn: 206581
2014-04-18 12:50:32 +02:00
// We provide for the DFS numbering and Tarjan walk lowlink numbers to be
[LCG] Construct an actual call graph with call-edge SCCs nested inside reference-edge SCCs. This essentially builds a more normal call graph as a subgraph of the "reference graph" that was the old model. This allows both to exist and the different use cases to use the aspect which addresses their needs. Specifically, the pass manager and other *ordering* constrained logic can use the reference graph to achieve conservative order of visit, while analyses reasoning about attributes and other properties derived from reachability can reason about the direct call graph. Note that this isn't necessarily complete: it doesn't model edges to declarations or indirect calls. Those can be found by scanning the instructions of the function if desirable, and in fact every user currently does this in order to handle things like calls to instrinsics. If useful, we could consider caching this information in the call graph to save the instruction scans, but currently that doesn't seem to be important. An important realization for why the representation chosen here works is that the call graph is a formal subset of the reference graph and thus both can live within the same data structure. All SCCs of the call graph are necessarily contained within an SCC of the reference graph, etc. The design is to build 'RefSCC's to model SCCs of the reference graph, and then within them more literal SCCs for the call graph. The formation of actual call edge SCCs is not done lazily, unlike reference edge 'RefSCC's. Instead, once a reference SCC is formed, it directly builds the call SCCs within it and stores them in a post-order sequence. This is used to provide a consistent platform for mutation and update of the graph. The post-order also allows for very efficient updates in common cases by bounding the number of nodes (and thus edges) considered. There is considerable common code that I'm still looking for the best way to factor out between the various DFS implementations here. So far, my attempts have made the code harder to read and understand despite reducing the duplication, which seems a poor tradeoff. I've not given up on figuring out the right way to do this, but I wanted to wait until I at least had the system working and tested to continue attempting to factor it differently. This also requires introducing several new algorithms in order to handle all of the incremental update scenarios for the more complex structure involving two edge colorings. I've tried to comment the algorithms sufficiently to make it clear how this is expected to work, but they may still need more extensive documentation. I know that there are some changes which are not strictly necessarily coupled here. The process of developing this started out with a very focused set of changes for the new structure of the graph and algorithms, but subsequent changes to bring the APIs and code into consistent and understandable patterns also ended up touching on other aspects. There was no good way to separate these out without causing *massive* merge conflicts. Ultimately, to a large degree this is a rewrite of most of the core algorithms in the LCG class and so I don't think it really matters much. Many thanks to the careful review by Sanjoy Das! Differential Revision: http://reviews.llvm.org/D16802 llvm-svn: 261040
2016-02-17 01:18:16 +01:00
// stored directly within the node. These are both '-1' when nodes are part
// of an SCC (or RefSCC), or '0' when not yet reached in a DFS walk.
[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. llvm-svn: 206581
2014-04-18 12:50:32 +02:00
int DFSNumber;
int LowLink;
mutable EdgeVectorT Edges;
[LCG] Construct an actual call graph with call-edge SCCs nested inside reference-edge SCCs. This essentially builds a more normal call graph as a subgraph of the "reference graph" that was the old model. This allows both to exist and the different use cases to use the aspect which addresses their needs. Specifically, the pass manager and other *ordering* constrained logic can use the reference graph to achieve conservative order of visit, while analyses reasoning about attributes and other properties derived from reachability can reason about the direct call graph. Note that this isn't necessarily complete: it doesn't model edges to declarations or indirect calls. Those can be found by scanning the instructions of the function if desirable, and in fact every user currently does this in order to handle things like calls to instrinsics. If useful, we could consider caching this information in the call graph to save the instruction scans, but currently that doesn't seem to be important. An important realization for why the representation chosen here works is that the call graph is a formal subset of the reference graph and thus both can live within the same data structure. All SCCs of the call graph are necessarily contained within an SCC of the reference graph, etc. The design is to build 'RefSCC's to model SCCs of the reference graph, and then within them more literal SCCs for the call graph. The formation of actual call edge SCCs is not done lazily, unlike reference edge 'RefSCC's. Instead, once a reference SCC is formed, it directly builds the call SCCs within it and stores them in a post-order sequence. This is used to provide a consistent platform for mutation and update of the graph. The post-order also allows for very efficient updates in common cases by bounding the number of nodes (and thus edges) considered. There is considerable common code that I'm still looking for the best way to factor out between the various DFS implementations here. So far, my attempts have made the code harder to read and understand despite reducing the duplication, which seems a poor tradeoff. I've not given up on figuring out the right way to do this, but I wanted to wait until I at least had the system working and tested to continue attempting to factor it differently. This also requires introducing several new algorithms in order to handle all of the incremental update scenarios for the more complex structure involving two edge colorings. I've tried to comment the algorithms sufficiently to make it clear how this is expected to work, but they may still need more extensive documentation. I know that there are some changes which are not strictly necessarily coupled here. The process of developing this started out with a very focused set of changes for the new structure of the graph and algorithms, but subsequent changes to bring the APIs and code into consistent and understandable patterns also ended up touching on other aspects. There was no good way to separate these out without causing *massive* merge conflicts. Ultimately, to a large degree this is a rewrite of most of the core algorithms in the LCG class and so I don't think it really matters much. Many thanks to the careful review by Sanjoy Das! Differential Revision: http://reviews.llvm.org/D16802 llvm-svn: 261040
2016-02-17 01:18:16 +01:00
DenseMap<Function *, int> EdgeIndexMap;
/// Basic constructor implements the scanning of F into Edges and
/// EdgeIndexMap.
Node(LazyCallGraph &G, Function &F);
/// Internal helper to insert an edge to a function.
void insertEdgeInternal(Function &ChildF, Edge::Kind EK);
/// Internal helper to insert an edge to a node.
void insertEdgeInternal(Node &ChildN, Edge::Kind EK);
[LCG] Construct an actual call graph with call-edge SCCs nested inside reference-edge SCCs. This essentially builds a more normal call graph as a subgraph of the "reference graph" that was the old model. This allows both to exist and the different use cases to use the aspect which addresses their needs. Specifically, the pass manager and other *ordering* constrained logic can use the reference graph to achieve conservative order of visit, while analyses reasoning about attributes and other properties derived from reachability can reason about the direct call graph. Note that this isn't necessarily complete: it doesn't model edges to declarations or indirect calls. Those can be found by scanning the instructions of the function if desirable, and in fact every user currently does this in order to handle things like calls to instrinsics. If useful, we could consider caching this information in the call graph to save the instruction scans, but currently that doesn't seem to be important. An important realization for why the representation chosen here works is that the call graph is a formal subset of the reference graph and thus both can live within the same data structure. All SCCs of the call graph are necessarily contained within an SCC of the reference graph, etc. The design is to build 'RefSCC's to model SCCs of the reference graph, and then within them more literal SCCs for the call graph. The formation of actual call edge SCCs is not done lazily, unlike reference edge 'RefSCC's. Instead, once a reference SCC is formed, it directly builds the call SCCs within it and stores them in a post-order sequence. This is used to provide a consistent platform for mutation and update of the graph. The post-order also allows for very efficient updates in common cases by bounding the number of nodes (and thus edges) considered. There is considerable common code that I'm still looking for the best way to factor out between the various DFS implementations here. So far, my attempts have made the code harder to read and understand despite reducing the duplication, which seems a poor tradeoff. I've not given up on figuring out the right way to do this, but I wanted to wait until I at least had the system working and tested to continue attempting to factor it differently. This also requires introducing several new algorithms in order to handle all of the incremental update scenarios for the more complex structure involving two edge colorings. I've tried to comment the algorithms sufficiently to make it clear how this is expected to work, but they may still need more extensive documentation. I know that there are some changes which are not strictly necessarily coupled here. The process of developing this started out with a very focused set of changes for the new structure of the graph and algorithms, but subsequent changes to bring the APIs and code into consistent and understandable patterns also ended up touching on other aspects. There was no good way to separate these out without causing *massive* merge conflicts. Ultimately, to a large degree this is a rewrite of most of the core algorithms in the LCG class and so I don't think it really matters much. Many thanks to the careful review by Sanjoy Das! Differential Revision: http://reviews.llvm.org/D16802 llvm-svn: 261040
2016-02-17 01:18:16 +01:00
/// Internal helper to change an edge kind.
void setEdgeKind(Function &ChildF, Edge::Kind EK);
/// Internal helper to remove the edge to the given function.
void removeEdgeInternal(Function &ChildF);
void clear() {
Edges.clear();
EdgeIndexMap.clear();
}
/// Print the name of this node's function.
friend raw_ostream &operator<<(raw_ostream &OS, const Node &N) {
return OS << N.F.getName();
}
/// Dump the name of this node's function to stderr.
void dump() const;
public:
LazyCallGraph &getGraph() const { return *G; }
Function &getFunction() const { return F; }
edge_iterator begin() const {
return edge_iterator(Edges.begin(), Edges.end());
}
edge_iterator end() const { return edge_iterator(Edges.end(), Edges.end()); }
[LCG] Construct an actual call graph with call-edge SCCs nested inside reference-edge SCCs. This essentially builds a more normal call graph as a subgraph of the "reference graph" that was the old model. This allows both to exist and the different use cases to use the aspect which addresses their needs. Specifically, the pass manager and other *ordering* constrained logic can use the reference graph to achieve conservative order of visit, while analyses reasoning about attributes and other properties derived from reachability can reason about the direct call graph. Note that this isn't necessarily complete: it doesn't model edges to declarations or indirect calls. Those can be found by scanning the instructions of the function if desirable, and in fact every user currently does this in order to handle things like calls to instrinsics. If useful, we could consider caching this information in the call graph to save the instruction scans, but currently that doesn't seem to be important. An important realization for why the representation chosen here works is that the call graph is a formal subset of the reference graph and thus both can live within the same data structure. All SCCs of the call graph are necessarily contained within an SCC of the reference graph, etc. The design is to build 'RefSCC's to model SCCs of the reference graph, and then within them more literal SCCs for the call graph. The formation of actual call edge SCCs is not done lazily, unlike reference edge 'RefSCC's. Instead, once a reference SCC is formed, it directly builds the call SCCs within it and stores them in a post-order sequence. This is used to provide a consistent platform for mutation and update of the graph. The post-order also allows for very efficient updates in common cases by bounding the number of nodes (and thus edges) considered. There is considerable common code that I'm still looking for the best way to factor out between the various DFS implementations here. So far, my attempts have made the code harder to read and understand despite reducing the duplication, which seems a poor tradeoff. I've not given up on figuring out the right way to do this, but I wanted to wait until I at least had the system working and tested to continue attempting to factor it differently. This also requires introducing several new algorithms in order to handle all of the incremental update scenarios for the more complex structure involving two edge colorings. I've tried to comment the algorithms sufficiently to make it clear how this is expected to work, but they may still need more extensive documentation. I know that there are some changes which are not strictly necessarily coupled here. The process of developing this started out with a very focused set of changes for the new structure of the graph and algorithms, but subsequent changes to bring the APIs and code into consistent and understandable patterns also ended up touching on other aspects. There was no good way to separate these out without causing *massive* merge conflicts. Ultimately, to a large degree this is a rewrite of most of the core algorithms in the LCG class and so I don't think it really matters much. Many thanks to the careful review by Sanjoy Das! Differential Revision: http://reviews.llvm.org/D16802 llvm-svn: 261040
2016-02-17 01:18:16 +01:00
const Edge &operator[](int i) const { return Edges[i]; }
const Edge &operator[](Function &F) const {
assert(EdgeIndexMap.find(&F) != EdgeIndexMap.end() && "No such edge!");
return Edges[EdgeIndexMap.find(&F)->second];
}
const Edge &operator[](Node &N) const { return (*this)[N.getFunction()]; }
[PM] Introduce basic update capabilities to the new PM's CGSCC pass manager, including both plumbing and logic to handle function pass updates. There are three fundamentally tied changes here: 1) Plumbing *some* mechanism for updating the CGSCC pass manager as the CG changes while passes are running. 2) Changing the CGSCC pass manager infrastructure to have support for the underlying graph to mutate mid-pass run. 3) Actually updating the CG after function passes run. I can separate them if necessary, but I think its really useful to have them together as the needs of #3 drove #2, and that in turn drove #1. The plumbing technique is to extend the "run" method signature with extra arguments. We provide the call graph that intrinsically is available as it is the basis of the pass manager's IR units, and an output parameter that records the results of updating the call graph during an SCC passes's run. Note that "...UpdateResult" isn't a *great* name here... suggestions very welcome. I tried a pretty frustrating number of different data structures and such for the innards of the update result. Every other one failed for one reason or another. Sometimes I just couldn't keep the layers of complexity right in my head. The thing that really worked was to just directly provide access to the underlying structures used to walk the call graph so that their updates could be informed by the *particular* nature of the change to the graph. The technique for how to make the pass management infrastructure cope with mutating graphs was also something that took a really, really large number of iterations to get to a place where I was happy. Here are some of the considerations that drove the design: - We operate at three levels within the infrastructure: RefSCC, SCC, and Node. In each case, we are working bottom up and so we want to continue to iterate on the "lowest" node as the graph changes. Look at how we iterate over nodes in an SCC running function passes as those function passes mutate the CG. We continue to iterate on the "lowest" SCC, which is the one that continues to contain the function just processed. - The call graph structure re-uses SCCs (and RefSCCs) during mutation events for the *highest* entry in the resulting new subgraph, not the lowest. This means that it is necessary to continually update the current SCC or RefSCC as it shifts. This is really surprising and subtle, and took a long time for me to work out. I actually tried changing the call graph to provide the opposite behavior, and it breaks *EVERYTHING*. The graph update algorithms are really deeply tied to this particualr pattern. - When SCCs or RefSCCs are split apart and refined and we continually re-pin our processing to the bottom one in the subgraph, we need to enqueue the newly formed SCCs and RefSCCs for subsequent processing. Queuing them presents a few challenges: 1) SCCs and RefSCCs use wildly different iteration strategies at a high level. We end up needing to converge them on worklist approaches that can be extended in order to be able to handle the mutations. 2) The order of the enqueuing need to remain bottom-up post-order so that we don't get surprising order of visitation for things like the inliner. 3) We need the worklists to have set semantics so we don't duplicate things endlessly. We don't need a *persistent* set though because we always keep processing the bottom node!!!! This is super, super surprising to me and took a long time to convince myself this is correct, but I'm pretty sure it is... Once we sink down to the bottom node, we can't re-split out the same node in any way, and the postorder of the current queue is fixed and unchanging. 4) We need to make sure that the "current" SCC or RefSCC actually gets enqueued here such that we re-visit it because we continue processing a *new*, *bottom* SCC/RefSCC. - We also need the ability to *skip* SCCs and RefSCCs that get merged into a larger component. We even need the ability to skip *nodes* from an SCC that are no longer part of that SCC. This led to the design you see in the patch which uses SetVector-based worklists. The RefSCC worklist is always empty until an update occurs and is just used to handle those RefSCCs created by updates as the others don't even exist yet and are formed on-demand during the bottom-up walk. The SCC worklist is pre-populated from the RefSCC, and we push new SCCs onto it and blacklist existing SCCs on it to get the desired processing. We then *directly* update these when updating the call graph as I was never able to find a satisfactory abstraction around the update strategy. Finally, we need to compute the updates for function passes. This is mostly used as an initial customer of all the update mechanisms to drive their design to at least cover some real set of use cases. There are a bunch of interesting things that came out of doing this: - It is really nice to do this a function at a time because that function is likely hot in the cache. This means we want even the function pass adaptor to support online updates to the call graph! - To update the call graph after arbitrary function pass mutations is quite hard. We have to build a fairly comprehensive set of data structures and then process them. Fortunately, some of this code is related to the code for building the cal graph in the first place. Unfortunately, very little of it makes any sense to share because the nature of what we're doing is so very different. I've factored out the one part that made sense at least. - We need to transfer these updates into the various structures for the CGSCC pass manager. Once those were more sanely worked out, this became relatively easier. But some of those needs necessitated changes to the LazyCallGraph interface to make it significantly easier to extract the changed SCCs from an update operation. - We also need to update the CGSCC analysis manager as the shape of the graph changes. When an SCC is merged away we need to clear analyses associated with it from the analysis manager which we didn't have support for in the analysis manager infrsatructure. New SCCs are easy! But then we have the case that the original SCC has its shape changed but remains in the call graph. There we need to *invalidate* the analyses associated with it. - We also need to invalidate analyses after we *finish* processing an SCC. But the analyses we need to invalidate here are *only those for the newly updated SCC*!!! Because we only continue processing the bottom SCC, if we split SCCs apart the original one gets invalidated once when its shape changes and is not processed farther so its analyses will be correct. It is the bottom SCC which continues being processed and needs to have the "normal" invalidation done based on the preserved analyses set. All of this is mostly background and context for the changes here. Many thanks to all the reviewers who helped here. Especially Sanjoy who caught several interesting bugs in the graph algorithms, David, Sean, and others who all helped with feedback. Differential Revision: http://reviews.llvm.org/D21464 llvm-svn: 279618
2016-08-24 11:37:14 +02:00
const Edge *lookup(Function &F) const {
auto EI = EdgeIndexMap.find(&F);
return EI != EdgeIndexMap.end() ? &Edges[EI->second] : nullptr;
}
[LCG] Construct an actual call graph with call-edge SCCs nested inside reference-edge SCCs. This essentially builds a more normal call graph as a subgraph of the "reference graph" that was the old model. This allows both to exist and the different use cases to use the aspect which addresses their needs. Specifically, the pass manager and other *ordering* constrained logic can use the reference graph to achieve conservative order of visit, while analyses reasoning about attributes and other properties derived from reachability can reason about the direct call graph. Note that this isn't necessarily complete: it doesn't model edges to declarations or indirect calls. Those can be found by scanning the instructions of the function if desirable, and in fact every user currently does this in order to handle things like calls to instrinsics. If useful, we could consider caching this information in the call graph to save the instruction scans, but currently that doesn't seem to be important. An important realization for why the representation chosen here works is that the call graph is a formal subset of the reference graph and thus both can live within the same data structure. All SCCs of the call graph are necessarily contained within an SCC of the reference graph, etc. The design is to build 'RefSCC's to model SCCs of the reference graph, and then within them more literal SCCs for the call graph. The formation of actual call edge SCCs is not done lazily, unlike reference edge 'RefSCC's. Instead, once a reference SCC is formed, it directly builds the call SCCs within it and stores them in a post-order sequence. This is used to provide a consistent platform for mutation and update of the graph. The post-order also allows for very efficient updates in common cases by bounding the number of nodes (and thus edges) considered. There is considerable common code that I'm still looking for the best way to factor out between the various DFS implementations here. So far, my attempts have made the code harder to read and understand despite reducing the duplication, which seems a poor tradeoff. I've not given up on figuring out the right way to do this, but I wanted to wait until I at least had the system working and tested to continue attempting to factor it differently. This also requires introducing several new algorithms in order to handle all of the incremental update scenarios for the more complex structure involving two edge colorings. I've tried to comment the algorithms sufficiently to make it clear how this is expected to work, but they may still need more extensive documentation. I know that there are some changes which are not strictly necessarily coupled here. The process of developing this started out with a very focused set of changes for the new structure of the graph and algorithms, but subsequent changes to bring the APIs and code into consistent and understandable patterns also ended up touching on other aspects. There was no good way to separate these out without causing *massive* merge conflicts. Ultimately, to a large degree this is a rewrite of most of the core algorithms in the LCG class and so I don't think it really matters much. Many thanks to the careful review by Sanjoy Das! Differential Revision: http://reviews.llvm.org/D16802 llvm-svn: 261040
2016-02-17 01:18:16 +01:00
call_edge_iterator call_begin() const {
return call_edge_iterator(Edges.begin(), Edges.end());
}
call_edge_iterator call_end() const {
return call_edge_iterator(Edges.end(), Edges.end());
}
iterator_range<call_edge_iterator> calls() const {
return make_range(call_begin(), call_end());
}
/// Equality is defined as address equality.
bool operator==(const Node &N) const { return this == &N; }
bool operator!=(const Node &N) const { return !operator==(N); }
};
/// 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 edge_iterator
: public iterator_adaptor_base<edge_iterator, EdgeVectorImplT::iterator,
std::forward_iterator_tag> {
friend class LazyCallGraph;
friend class LazyCallGraph::Node;
EdgeVectorImplT::iterator E;
// Build the iterator for a specific position in the edge list.
edge_iterator(EdgeVectorImplT::iterator BaseI,
EdgeVectorImplT::iterator E)
: iterator_adaptor_base(BaseI), E(E) {
while (I != E && !*I)
++I;
}
public:
edge_iterator() {}
using iterator_adaptor_base::operator++;
edge_iterator &operator++() {
do {
++I;
} while (I != E && !*I);
return *this;
}
};
/// A lazy iterator over specifically call edges.
///
/// This has the same iteration properties as the \c edge_iterator, but
/// restricts itself to edges which represent actual calls.
class call_edge_iterator
: public iterator_adaptor_base<call_edge_iterator,
EdgeVectorImplT::iterator,
std::forward_iterator_tag> {
friend class LazyCallGraph;
friend class LazyCallGraph::Node;
EdgeVectorImplT::iterator E;
/// Advance the iterator to the next valid, call edge.
void advanceToNextEdge() {
while (I != E && (!*I || !I->isCall()))
++I;
}
// Build the iterator for a specific position in the edge list.
call_edge_iterator(EdgeVectorImplT::iterator BaseI,
EdgeVectorImplT::iterator E)
: iterator_adaptor_base(BaseI), E(E) {
advanceToNextEdge();
}
public:
call_edge_iterator() {}
using iterator_adaptor_base::operator++;
call_edge_iterator &operator++() {
++I;
advanceToNextEdge();
return *this;
}
};
/// An SCC of the call graph.
[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. llvm-svn: 206581
2014-04-18 12:50:32 +02:00
///
[LCG] Construct an actual call graph with call-edge SCCs nested inside reference-edge SCCs. This essentially builds a more normal call graph as a subgraph of the "reference graph" that was the old model. This allows both to exist and the different use cases to use the aspect which addresses their needs. Specifically, the pass manager and other *ordering* constrained logic can use the reference graph to achieve conservative order of visit, while analyses reasoning about attributes and other properties derived from reachability can reason about the direct call graph. Note that this isn't necessarily complete: it doesn't model edges to declarations or indirect calls. Those can be found by scanning the instructions of the function if desirable, and in fact every user currently does this in order to handle things like calls to instrinsics. If useful, we could consider caching this information in the call graph to save the instruction scans, but currently that doesn't seem to be important. An important realization for why the representation chosen here works is that the call graph is a formal subset of the reference graph and thus both can live within the same data structure. All SCCs of the call graph are necessarily contained within an SCC of the reference graph, etc. The design is to build 'RefSCC's to model SCCs of the reference graph, and then within them more literal SCCs for the call graph. The formation of actual call edge SCCs is not done lazily, unlike reference edge 'RefSCC's. Instead, once a reference SCC is formed, it directly builds the call SCCs within it and stores them in a post-order sequence. This is used to provide a consistent platform for mutation and update of the graph. The post-order also allows for very efficient updates in common cases by bounding the number of nodes (and thus edges) considered. There is considerable common code that I'm still looking for the best way to factor out between the various DFS implementations here. So far, my attempts have made the code harder to read and understand despite reducing the duplication, which seems a poor tradeoff. I've not given up on figuring out the right way to do this, but I wanted to wait until I at least had the system working and tested to continue attempting to factor it differently. This also requires introducing several new algorithms in order to handle all of the incremental update scenarios for the more complex structure involving two edge colorings. I've tried to comment the algorithms sufficiently to make it clear how this is expected to work, but they may still need more extensive documentation. I know that there are some changes which are not strictly necessarily coupled here. The process of developing this started out with a very focused set of changes for the new structure of the graph and algorithms, but subsequent changes to bring the APIs and code into consistent and understandable patterns also ended up touching on other aspects. There was no good way to separate these out without causing *massive* merge conflicts. Ultimately, to a large degree this is a rewrite of most of the core algorithms in the LCG class and so I don't think it really matters much. Many thanks to the careful review by Sanjoy Das! Differential Revision: http://reviews.llvm.org/D16802 llvm-svn: 261040
2016-02-17 01:18:16 +01:00
/// This represents a Strongly Connected Component of the direct call graph
/// -- ignoring indirect calls and function references. It stores this as
[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. llvm-svn: 206581
2014-04-18 12:50:32 +02:00
/// a collection of call graph nodes. While the order of nodes in the SCC is
/// stable, it is not any particular order.
[LCG] Construct an actual call graph with call-edge SCCs nested inside reference-edge SCCs. This essentially builds a more normal call graph as a subgraph of the "reference graph" that was the old model. This allows both to exist and the different use cases to use the aspect which addresses their needs. Specifically, the pass manager and other *ordering* constrained logic can use the reference graph to achieve conservative order of visit, while analyses reasoning about attributes and other properties derived from reachability can reason about the direct call graph. Note that this isn't necessarily complete: it doesn't model edges to declarations or indirect calls. Those can be found by scanning the instructions of the function if desirable, and in fact every user currently does this in order to handle things like calls to instrinsics. If useful, we could consider caching this information in the call graph to save the instruction scans, but currently that doesn't seem to be important. An important realization for why the representation chosen here works is that the call graph is a formal subset of the reference graph and thus both can live within the same data structure. All SCCs of the call graph are necessarily contained within an SCC of the reference graph, etc. The design is to build 'RefSCC's to model SCCs of the reference graph, and then within them more literal SCCs for the call graph. The formation of actual call edge SCCs is not done lazily, unlike reference edge 'RefSCC's. Instead, once a reference SCC is formed, it directly builds the call SCCs within it and stores them in a post-order sequence. This is used to provide a consistent platform for mutation and update of the graph. The post-order also allows for very efficient updates in common cases by bounding the number of nodes (and thus edges) considered. There is considerable common code that I'm still looking for the best way to factor out between the various DFS implementations here. So far, my attempts have made the code harder to read and understand despite reducing the duplication, which seems a poor tradeoff. I've not given up on figuring out the right way to do this, but I wanted to wait until I at least had the system working and tested to continue attempting to factor it differently. This also requires introducing several new algorithms in order to handle all of the incremental update scenarios for the more complex structure involving two edge colorings. I've tried to comment the algorithms sufficiently to make it clear how this is expected to work, but they may still need more extensive documentation. I know that there are some changes which are not strictly necessarily coupled here. The process of developing this started out with a very focused set of changes for the new structure of the graph and algorithms, but subsequent changes to bring the APIs and code into consistent and understandable patterns also ended up touching on other aspects. There was no good way to separate these out without causing *massive* merge conflicts. Ultimately, to a large degree this is a rewrite of most of the core algorithms in the LCG class and so I don't think it really matters much. Many thanks to the careful review by Sanjoy Das! Differential Revision: http://reviews.llvm.org/D16802 llvm-svn: 261040
2016-02-17 01:18:16 +01:00
///
/// The SCCs are nested within a \c RefSCC, see below for details about that
/// outer structure. SCCs do not support mutation of the call graph, that
/// must be done through the containing \c RefSCC in order to fully reason
/// about the ordering and connections of the graph.
[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. llvm-svn: 206581
2014-04-18 12:50:32 +02:00
class SCC {
friend class LazyCallGraph;
friend class LazyCallGraph::Node;
[LCG] Construct an actual call graph with call-edge SCCs nested inside reference-edge SCCs. This essentially builds a more normal call graph as a subgraph of the "reference graph" that was the old model. This allows both to exist and the different use cases to use the aspect which addresses their needs. Specifically, the pass manager and other *ordering* constrained logic can use the reference graph to achieve conservative order of visit, while analyses reasoning about attributes and other properties derived from reachability can reason about the direct call graph. Note that this isn't necessarily complete: it doesn't model edges to declarations or indirect calls. Those can be found by scanning the instructions of the function if desirable, and in fact every user currently does this in order to handle things like calls to instrinsics. If useful, we could consider caching this information in the call graph to save the instruction scans, but currently that doesn't seem to be important. An important realization for why the representation chosen here works is that the call graph is a formal subset of the reference graph and thus both can live within the same data structure. All SCCs of the call graph are necessarily contained within an SCC of the reference graph, etc. The design is to build 'RefSCC's to model SCCs of the reference graph, and then within them more literal SCCs for the call graph. The formation of actual call edge SCCs is not done lazily, unlike reference edge 'RefSCC's. Instead, once a reference SCC is formed, it directly builds the call SCCs within it and stores them in a post-order sequence. This is used to provide a consistent platform for mutation and update of the graph. The post-order also allows for very efficient updates in common cases by bounding the number of nodes (and thus edges) considered. There is considerable common code that I'm still looking for the best way to factor out between the various DFS implementations here. So far, my attempts have made the code harder to read and understand despite reducing the duplication, which seems a poor tradeoff. I've not given up on figuring out the right way to do this, but I wanted to wait until I at least had the system working and tested to continue attempting to factor it differently. This also requires introducing several new algorithms in order to handle all of the incremental update scenarios for the more complex structure involving two edge colorings. I've tried to comment the algorithms sufficiently to make it clear how this is expected to work, but they may still need more extensive documentation. I know that there are some changes which are not strictly necessarily coupled here. The process of developing this started out with a very focused set of changes for the new structure of the graph and algorithms, but subsequent changes to bring the APIs and code into consistent and understandable patterns also ended up touching on other aspects. There was no good way to separate these out without causing *massive* merge conflicts. Ultimately, to a large degree this is a rewrite of most of the core algorithms in the LCG class and so I don't think it really matters much. Many thanks to the careful review by Sanjoy Das! Differential Revision: http://reviews.llvm.org/D16802 llvm-svn: 261040
2016-02-17 01:18:16 +01:00
RefSCC *OuterRefSCC;
[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. llvm-svn: 206581
2014-04-18 12:50:32 +02:00
SmallVector<Node *, 1> Nodes;
[LCG] Construct an actual call graph with call-edge SCCs nested inside reference-edge SCCs. This essentially builds a more normal call graph as a subgraph of the "reference graph" that was the old model. This allows both to exist and the different use cases to use the aspect which addresses their needs. Specifically, the pass manager and other *ordering* constrained logic can use the reference graph to achieve conservative order of visit, while analyses reasoning about attributes and other properties derived from reachability can reason about the direct call graph. Note that this isn't necessarily complete: it doesn't model edges to declarations or indirect calls. Those can be found by scanning the instructions of the function if desirable, and in fact every user currently does this in order to handle things like calls to instrinsics. If useful, we could consider caching this information in the call graph to save the instruction scans, but currently that doesn't seem to be important. An important realization for why the representation chosen here works is that the call graph is a formal subset of the reference graph and thus both can live within the same data structure. All SCCs of the call graph are necessarily contained within an SCC of the reference graph, etc. The design is to build 'RefSCC's to model SCCs of the reference graph, and then within them more literal SCCs for the call graph. The formation of actual call edge SCCs is not done lazily, unlike reference edge 'RefSCC's. Instead, once a reference SCC is formed, it directly builds the call SCCs within it and stores them in a post-order sequence. This is used to provide a consistent platform for mutation and update of the graph. The post-order also allows for very efficient updates in common cases by bounding the number of nodes (and thus edges) considered. There is considerable common code that I'm still looking for the best way to factor out between the various DFS implementations here. So far, my attempts have made the code harder to read and understand despite reducing the duplication, which seems a poor tradeoff. I've not given up on figuring out the right way to do this, but I wanted to wait until I at least had the system working and tested to continue attempting to factor it differently. This also requires introducing several new algorithms in order to handle all of the incremental update scenarios for the more complex structure involving two edge colorings. I've tried to comment the algorithms sufficiently to make it clear how this is expected to work, but they may still need more extensive documentation. I know that there are some changes which are not strictly necessarily coupled here. The process of developing this started out with a very focused set of changes for the new structure of the graph and algorithms, but subsequent changes to bring the APIs and code into consistent and understandable patterns also ended up touching on other aspects. There was no good way to separate these out without causing *massive* merge conflicts. Ultimately, to a large degree this is a rewrite of most of the core algorithms in the LCG class and so I don't think it really matters much. Many thanks to the careful review by Sanjoy Das! Differential Revision: http://reviews.llvm.org/D16802 llvm-svn: 261040
2016-02-17 01:18:16 +01:00
template <typename NodeRangeT>
SCC(RefSCC &OuterRefSCC, NodeRangeT &&Nodes)
: OuterRefSCC(&OuterRefSCC), Nodes(std::forward<NodeRangeT>(Nodes)) {}
[LCG] Construct an actual call graph with call-edge SCCs nested inside reference-edge SCCs. This essentially builds a more normal call graph as a subgraph of the "reference graph" that was the old model. This allows both to exist and the different use cases to use the aspect which addresses their needs. Specifically, the pass manager and other *ordering* constrained logic can use the reference graph to achieve conservative order of visit, while analyses reasoning about attributes and other properties derived from reachability can reason about the direct call graph. Note that this isn't necessarily complete: it doesn't model edges to declarations or indirect calls. Those can be found by scanning the instructions of the function if desirable, and in fact every user currently does this in order to handle things like calls to instrinsics. If useful, we could consider caching this information in the call graph to save the instruction scans, but currently that doesn't seem to be important. An important realization for why the representation chosen here works is that the call graph is a formal subset of the reference graph and thus both can live within the same data structure. All SCCs of the call graph are necessarily contained within an SCC of the reference graph, etc. The design is to build 'RefSCC's to model SCCs of the reference graph, and then within them more literal SCCs for the call graph. The formation of actual call edge SCCs is not done lazily, unlike reference edge 'RefSCC's. Instead, once a reference SCC is formed, it directly builds the call SCCs within it and stores them in a post-order sequence. This is used to provide a consistent platform for mutation and update of the graph. The post-order also allows for very efficient updates in common cases by bounding the number of nodes (and thus edges) considered. There is considerable common code that I'm still looking for the best way to factor out between the various DFS implementations here. So far, my attempts have made the code harder to read and understand despite reducing the duplication, which seems a poor tradeoff. I've not given up on figuring out the right way to do this, but I wanted to wait until I at least had the system working and tested to continue attempting to factor it differently. This also requires introducing several new algorithms in order to handle all of the incremental update scenarios for the more complex structure involving two edge colorings. I've tried to comment the algorithms sufficiently to make it clear how this is expected to work, but they may still need more extensive documentation. I know that there are some changes which are not strictly necessarily coupled here. The process of developing this started out with a very focused set of changes for the new structure of the graph and algorithms, but subsequent changes to bring the APIs and code into consistent and understandable patterns also ended up touching on other aspects. There was no good way to separate these out without causing *massive* merge conflicts. Ultimately, to a large degree this is a rewrite of most of the core algorithms in the LCG class and so I don't think it really matters much. Many thanks to the careful review by Sanjoy Das! Differential Revision: http://reviews.llvm.org/D16802 llvm-svn: 261040
2016-02-17 01:18:16 +01:00
void clear() {
OuterRefSCC = nullptr;
Nodes.clear();
}
[LCG] Add the first round of mutation support to the lazy call graph. This implements the core functionality necessary to remove an edge from the call graph and correctly update both the basic graph and the SCC structure. As part of that it has to run a tiny (in number of nodes) Tarjan-style DFS walk of an SCC being mutated to compute newly formed SCCs, etc. This is *very rough* and a WIP. I have a bunch of FIXMEs for code cleanup that will reduce the boilerplate in this change substantially. I also have a bunch of simplifications to various parts of both algorithms that I want to make, but first I'd like to have a more holistic picture. Ideally, I'd also like more testing. I'll probably add quite a few more unit tests as I go here to cover the various different aspects and corner cases of removing edges from the graph. Still, this is, so far, successfully updating the SCC graph in-place without disrupting the identity established for the existing SCCs even when we do challenging things like delete the critical edge that made an SCC cycle at all and have to reform things as a tree of smaller SCCs. Getting this to work is really critical for the new pass manager as it is going to associate significant state with the SCC instance and needs it to be stable. That is also the motivation behind the return of the newly formed SCCs. Eventually, I'll wire this all the way up to the public API so that the pass manager can use it to correctly re-enqueue newly formed SCCs into a fresh postorder traversal. llvm-svn: 206968
2014-04-23 13:03:03 +02:00
/// Print a short descrtiption useful for debugging or logging.
///
/// We print the function names in the SCC wrapped in '()'s and skipping
/// the middle functions if there are a large number.
//
// Note: this is defined inline to dodge issues with GCC's interpretation
// of enclosing namespaces for friend function declarations.
friend raw_ostream &operator<<(raw_ostream &OS, const SCC &C) {
OS << '(';
int i = 0;
for (LazyCallGraph::Node &N : C) {
if (i > 0)
OS << ", ";
// Elide the inner elements if there are too many.
if (i > 8) {
OS << "..., " << *C.Nodes.back();
break;
}
OS << N;
++i;
}
OS << ')';
return OS;
}
/// Dump a short description of this SCC to stderr.
void dump() const;
[LCG] Construct an actual call graph with call-edge SCCs nested inside reference-edge SCCs. This essentially builds a more normal call graph as a subgraph of the "reference graph" that was the old model. This allows both to exist and the different use cases to use the aspect which addresses their needs. Specifically, the pass manager and other *ordering* constrained logic can use the reference graph to achieve conservative order of visit, while analyses reasoning about attributes and other properties derived from reachability can reason about the direct call graph. Note that this isn't necessarily complete: it doesn't model edges to declarations or indirect calls. Those can be found by scanning the instructions of the function if desirable, and in fact every user currently does this in order to handle things like calls to instrinsics. If useful, we could consider caching this information in the call graph to save the instruction scans, but currently that doesn't seem to be important. An important realization for why the representation chosen here works is that the call graph is a formal subset of the reference graph and thus both can live within the same data structure. All SCCs of the call graph are necessarily contained within an SCC of the reference graph, etc. The design is to build 'RefSCC's to model SCCs of the reference graph, and then within them more literal SCCs for the call graph. The formation of actual call edge SCCs is not done lazily, unlike reference edge 'RefSCC's. Instead, once a reference SCC is formed, it directly builds the call SCCs within it and stores them in a post-order sequence. This is used to provide a consistent platform for mutation and update of the graph. The post-order also allows for very efficient updates in common cases by bounding the number of nodes (and thus edges) considered. There is considerable common code that I'm still looking for the best way to factor out between the various DFS implementations here. So far, my attempts have made the code harder to read and understand despite reducing the duplication, which seems a poor tradeoff. I've not given up on figuring out the right way to do this, but I wanted to wait until I at least had the system working and tested to continue attempting to factor it differently. This also requires introducing several new algorithms in order to handle all of the incremental update scenarios for the more complex structure involving two edge colorings. I've tried to comment the algorithms sufficiently to make it clear how this is expected to work, but they may still need more extensive documentation. I know that there are some changes which are not strictly necessarily coupled here. The process of developing this started out with a very focused set of changes for the new structure of the graph and algorithms, but subsequent changes to bring the APIs and code into consistent and understandable patterns also ended up touching on other aspects. There was no good way to separate these out without causing *massive* merge conflicts. Ultimately, to a large degree this is a rewrite of most of the core algorithms in the LCG class and so I don't think it really matters much. Many thanks to the careful review by Sanjoy Das! Differential Revision: http://reviews.llvm.org/D16802 llvm-svn: 261040
2016-02-17 01:18:16 +01:00
#ifndef NDEBUG
/// Verify invariants about the SCC.
///
/// This will attempt to validate all of the basic invariants within an
/// SCC, but not that it is a strongly connected componet per-se. Primarily
/// useful while building and updating the graph to check that basic
/// properties are in place rather than having inexplicable crashes later.
void verify();
#endif
[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. llvm-svn: 206581
2014-04-18 12:50:32 +02:00
public:
[LCG] Construct an actual call graph with call-edge SCCs nested inside reference-edge SCCs. This essentially builds a more normal call graph as a subgraph of the "reference graph" that was the old model. This allows both to exist and the different use cases to use the aspect which addresses their needs. Specifically, the pass manager and other *ordering* constrained logic can use the reference graph to achieve conservative order of visit, while analyses reasoning about attributes and other properties derived from reachability can reason about the direct call graph. Note that this isn't necessarily complete: it doesn't model edges to declarations or indirect calls. Those can be found by scanning the instructions of the function if desirable, and in fact every user currently does this in order to handle things like calls to instrinsics. If useful, we could consider caching this information in the call graph to save the instruction scans, but currently that doesn't seem to be important. An important realization for why the representation chosen here works is that the call graph is a formal subset of the reference graph and thus both can live within the same data structure. All SCCs of the call graph are necessarily contained within an SCC of the reference graph, etc. The design is to build 'RefSCC's to model SCCs of the reference graph, and then within them more literal SCCs for the call graph. The formation of actual call edge SCCs is not done lazily, unlike reference edge 'RefSCC's. Instead, once a reference SCC is formed, it directly builds the call SCCs within it and stores them in a post-order sequence. This is used to provide a consistent platform for mutation and update of the graph. The post-order also allows for very efficient updates in common cases by bounding the number of nodes (and thus edges) considered. There is considerable common code that I'm still looking for the best way to factor out between the various DFS implementations here. So far, my attempts have made the code harder to read and understand despite reducing the duplication, which seems a poor tradeoff. I've not given up on figuring out the right way to do this, but I wanted to wait until I at least had the system working and tested to continue attempting to factor it differently. This also requires introducing several new algorithms in order to handle all of the incremental update scenarios for the more complex structure involving two edge colorings. I've tried to comment the algorithms sufficiently to make it clear how this is expected to work, but they may still need more extensive documentation. I know that there are some changes which are not strictly necessarily coupled here. The process of developing this started out with a very focused set of changes for the new structure of the graph and algorithms, but subsequent changes to bring the APIs and code into consistent and understandable patterns also ended up touching on other aspects. There was no good way to separate these out without causing *massive* merge conflicts. Ultimately, to a large degree this is a rewrite of most of the core algorithms in the LCG class and so I don't think it really matters much. Many thanks to the careful review by Sanjoy Das! Differential Revision: http://reviews.llvm.org/D16802 llvm-svn: 261040
2016-02-17 01:18:16 +01:00
typedef pointee_iterator<SmallVectorImpl<Node *>::const_iterator> iterator;
[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. llvm-svn: 206581
2014-04-18 12:50:32 +02:00
iterator begin() const { return Nodes.begin(); }
iterator end() const { return Nodes.end(); }
[LCG] Construct an actual call graph with call-edge SCCs nested inside reference-edge SCCs. This essentially builds a more normal call graph as a subgraph of the "reference graph" that was the old model. This allows both to exist and the different use cases to use the aspect which addresses their needs. Specifically, the pass manager and other *ordering* constrained logic can use the reference graph to achieve conservative order of visit, while analyses reasoning about attributes and other properties derived from reachability can reason about the direct call graph. Note that this isn't necessarily complete: it doesn't model edges to declarations or indirect calls. Those can be found by scanning the instructions of the function if desirable, and in fact every user currently does this in order to handle things like calls to instrinsics. If useful, we could consider caching this information in the call graph to save the instruction scans, but currently that doesn't seem to be important. An important realization for why the representation chosen here works is that the call graph is a formal subset of the reference graph and thus both can live within the same data structure. All SCCs of the call graph are necessarily contained within an SCC of the reference graph, etc. The design is to build 'RefSCC's to model SCCs of the reference graph, and then within them more literal SCCs for the call graph. The formation of actual call edge SCCs is not done lazily, unlike reference edge 'RefSCC's. Instead, once a reference SCC is formed, it directly builds the call SCCs within it and stores them in a post-order sequence. This is used to provide a consistent platform for mutation and update of the graph. The post-order also allows for very efficient updates in common cases by bounding the number of nodes (and thus edges) considered. There is considerable common code that I'm still looking for the best way to factor out between the various DFS implementations here. So far, my attempts have made the code harder to read and understand despite reducing the duplication, which seems a poor tradeoff. I've not given up on figuring out the right way to do this, but I wanted to wait until I at least had the system working and tested to continue attempting to factor it differently. This also requires introducing several new algorithms in order to handle all of the incremental update scenarios for the more complex structure involving two edge colorings. I've tried to comment the algorithms sufficiently to make it clear how this is expected to work, but they may still need more extensive documentation. I know that there are some changes which are not strictly necessarily coupled here. The process of developing this started out with a very focused set of changes for the new structure of the graph and algorithms, but subsequent changes to bring the APIs and code into consistent and understandable patterns also ended up touching on other aspects. There was no good way to separate these out without causing *massive* merge conflicts. Ultimately, to a large degree this is a rewrite of most of the core algorithms in the LCG class and so I don't think it really matters much. Many thanks to the careful review by Sanjoy Das! Differential Revision: http://reviews.llvm.org/D16802 llvm-svn: 261040
2016-02-17 01:18:16 +01:00
int size() const { return Nodes.size(); }
RefSCC &getOuterRefSCC() const { return *OuterRefSCC; }
/// Provide a short name by printing this SCC to a std::string.
[LCG] Construct an actual call graph with call-edge SCCs nested inside reference-edge SCCs. This essentially builds a more normal call graph as a subgraph of the "reference graph" that was the old model. This allows both to exist and the different use cases to use the aspect which addresses their needs. Specifically, the pass manager and other *ordering* constrained logic can use the reference graph to achieve conservative order of visit, while analyses reasoning about attributes and other properties derived from reachability can reason about the direct call graph. Note that this isn't necessarily complete: it doesn't model edges to declarations or indirect calls. Those can be found by scanning the instructions of the function if desirable, and in fact every user currently does this in order to handle things like calls to instrinsics. If useful, we could consider caching this information in the call graph to save the instruction scans, but currently that doesn't seem to be important. An important realization for why the representation chosen here works is that the call graph is a formal subset of the reference graph and thus both can live within the same data structure. All SCCs of the call graph are necessarily contained within an SCC of the reference graph, etc. The design is to build 'RefSCC's to model SCCs of the reference graph, and then within them more literal SCCs for the call graph. The formation of actual call edge SCCs is not done lazily, unlike reference edge 'RefSCC's. Instead, once a reference SCC is formed, it directly builds the call SCCs within it and stores them in a post-order sequence. This is used to provide a consistent platform for mutation and update of the graph. The post-order also allows for very efficient updates in common cases by bounding the number of nodes (and thus edges) considered. There is considerable common code that I'm still looking for the best way to factor out between the various DFS implementations here. So far, my attempts have made the code harder to read and understand despite reducing the duplication, which seems a poor tradeoff. I've not given up on figuring out the right way to do this, but I wanted to wait until I at least had the system working and tested to continue attempting to factor it differently. This also requires introducing several new algorithms in order to handle all of the incremental update scenarios for the more complex structure involving two edge colorings. I've tried to comment the algorithms sufficiently to make it clear how this is expected to work, but they may still need more extensive documentation. I know that there are some changes which are not strictly necessarily coupled here. The process of developing this started out with a very focused set of changes for the new structure of the graph and algorithms, but subsequent changes to bring the APIs and code into consistent and understandable patterns also ended up touching on other aspects. There was no good way to separate these out without causing *massive* merge conflicts. Ultimately, to a large degree this is a rewrite of most of the core algorithms in the LCG class and so I don't think it really matters much. Many thanks to the careful review by Sanjoy Das! Differential Revision: http://reviews.llvm.org/D16802 llvm-svn: 261040
2016-02-17 01:18:16 +01:00
///
/// This copes with the fact that we don't have a name per-se for an SCC
/// while still making the use of this in debugging and logging useful.
std::string getName() const {
std::string Name;
raw_string_ostream OS(Name);
OS << *this;
OS.flush();
return Name;
}
[LCG] Construct an actual call graph with call-edge SCCs nested inside reference-edge SCCs. This essentially builds a more normal call graph as a subgraph of the "reference graph" that was the old model. This allows both to exist and the different use cases to use the aspect which addresses their needs. Specifically, the pass manager and other *ordering* constrained logic can use the reference graph to achieve conservative order of visit, while analyses reasoning about attributes and other properties derived from reachability can reason about the direct call graph. Note that this isn't necessarily complete: it doesn't model edges to declarations or indirect calls. Those can be found by scanning the instructions of the function if desirable, and in fact every user currently does this in order to handle things like calls to instrinsics. If useful, we could consider caching this information in the call graph to save the instruction scans, but currently that doesn't seem to be important. An important realization for why the representation chosen here works is that the call graph is a formal subset of the reference graph and thus both can live within the same data structure. All SCCs of the call graph are necessarily contained within an SCC of the reference graph, etc. The design is to build 'RefSCC's to model SCCs of the reference graph, and then within them more literal SCCs for the call graph. The formation of actual call edge SCCs is not done lazily, unlike reference edge 'RefSCC's. Instead, once a reference SCC is formed, it directly builds the call SCCs within it and stores them in a post-order sequence. This is used to provide a consistent platform for mutation and update of the graph. The post-order also allows for very efficient updates in common cases by bounding the number of nodes (and thus edges) considered. There is considerable common code that I'm still looking for the best way to factor out between the various DFS implementations here. So far, my attempts have made the code harder to read and understand despite reducing the duplication, which seems a poor tradeoff. I've not given up on figuring out the right way to do this, but I wanted to wait until I at least had the system working and tested to continue attempting to factor it differently. This also requires introducing several new algorithms in order to handle all of the incremental update scenarios for the more complex structure involving two edge colorings. I've tried to comment the algorithms sufficiently to make it clear how this is expected to work, but they may still need more extensive documentation. I know that there are some changes which are not strictly necessarily coupled here. The process of developing this started out with a very focused set of changes for the new structure of the graph and algorithms, but subsequent changes to bring the APIs and code into consistent and understandable patterns also ended up touching on other aspects. There was no good way to separate these out without causing *massive* merge conflicts. Ultimately, to a large degree this is a rewrite of most of the core algorithms in the LCG class and so I don't think it really matters much. Many thanks to the careful review by Sanjoy Das! Differential Revision: http://reviews.llvm.org/D16802 llvm-svn: 261040
2016-02-17 01:18:16 +01:00
};
/// A RefSCC of the call graph.
///
/// This models a Strongly Connected Component of function reference edges in
/// the call graph. As opposed to actual SCCs, these can be used to scope
/// subgraphs of the module which are independent from other subgraphs of the
/// module because they do not reference it in any way. This is also the unit
/// where we do mutation of the graph in order to restrict mutations to those
/// which don't violate this independence.
///
/// A RefSCC contains a DAG of actual SCCs. All the nodes within the RefSCC
/// are necessarily within some actual SCC that nests within it. Since
/// a direct call *is* a reference, there will always be at least one RefSCC
/// around any SCC.
class RefSCC {
friend class LazyCallGraph;
friend class LazyCallGraph::Node;
LazyCallGraph *G;
SmallPtrSet<RefSCC *, 1> Parents;
/// A postorder list of the inner SCCs.
SmallVector<SCC *, 4> SCCs;
/// A map from SCC to index in the postorder list.
SmallDenseMap<SCC *, int, 4> SCCIndices;
/// Fast-path constructor. RefSCCs should instead be constructed by calling
/// formRefSCCFast on the graph itself.
RefSCC(LazyCallGraph &G);
void clear() {
Parents.clear();
SCCs.clear();
SCCIndices.clear();
}
/// Print a short description useful for debugging or logging.
///
/// We print the SCCs wrapped in '[]'s and skipping the middle SCCs if
/// there are a large number.
//
// Note: this is defined inline to dodge issues with GCC's interpretation
// of enclosing namespaces for friend function declarations.
friend raw_ostream &operator<<(raw_ostream &OS, const RefSCC &RC) {
OS << '[';
int i = 0;
for (LazyCallGraph::SCC &C : RC) {
if (i > 0)
OS << ", ";
// Elide the inner elements if there are too many.
if (i > 4) {
OS << "..., " << *RC.SCCs.back();
break;
}
OS << C;
++i;
}
OS << ']';
return OS;
}
/// Dump a short description of this RefSCC to stderr.
void dump() const;
[LCG] Construct an actual call graph with call-edge SCCs nested inside reference-edge SCCs. This essentially builds a more normal call graph as a subgraph of the "reference graph" that was the old model. This allows both to exist and the different use cases to use the aspect which addresses their needs. Specifically, the pass manager and other *ordering* constrained logic can use the reference graph to achieve conservative order of visit, while analyses reasoning about attributes and other properties derived from reachability can reason about the direct call graph. Note that this isn't necessarily complete: it doesn't model edges to declarations or indirect calls. Those can be found by scanning the instructions of the function if desirable, and in fact every user currently does this in order to handle things like calls to instrinsics. If useful, we could consider caching this information in the call graph to save the instruction scans, but currently that doesn't seem to be important. An important realization for why the representation chosen here works is that the call graph is a formal subset of the reference graph and thus both can live within the same data structure. All SCCs of the call graph are necessarily contained within an SCC of the reference graph, etc. The design is to build 'RefSCC's to model SCCs of the reference graph, and then within them more literal SCCs for the call graph. The formation of actual call edge SCCs is not done lazily, unlike reference edge 'RefSCC's. Instead, once a reference SCC is formed, it directly builds the call SCCs within it and stores them in a post-order sequence. This is used to provide a consistent platform for mutation and update of the graph. The post-order also allows for very efficient updates in common cases by bounding the number of nodes (and thus edges) considered. There is considerable common code that I'm still looking for the best way to factor out between the various DFS implementations here. So far, my attempts have made the code harder to read and understand despite reducing the duplication, which seems a poor tradeoff. I've not given up on figuring out the right way to do this, but I wanted to wait until I at least had the system working and tested to continue attempting to factor it differently. This also requires introducing several new algorithms in order to handle all of the incremental update scenarios for the more complex structure involving two edge colorings. I've tried to comment the algorithms sufficiently to make it clear how this is expected to work, but they may still need more extensive documentation. I know that there are some changes which are not strictly necessarily coupled here. The process of developing this started out with a very focused set of changes for the new structure of the graph and algorithms, but subsequent changes to bring the APIs and code into consistent and understandable patterns also ended up touching on other aspects. There was no good way to separate these out without causing *massive* merge conflicts. Ultimately, to a large degree this is a rewrite of most of the core algorithms in the LCG class and so I don't think it really matters much. Many thanks to the careful review by Sanjoy Das! Differential Revision: http://reviews.llvm.org/D16802 llvm-svn: 261040
2016-02-17 01:18:16 +01:00
#ifndef NDEBUG
/// Verify invariants about the RefSCC and all its SCCs.
///
/// This will attempt to validate all of the invariants *within* the
/// RefSCC, but not that it is a strongly connected component of the larger
/// graph. This makes it useful even when partially through an update.
///
/// Invariants checked:
/// - SCCs and their indices match.
/// - The SCCs list is in fact in post-order.
void verify();
#endif
/// Handle any necessary parent set updates after inserting a trivial ref
/// or call edge.
void handleTrivialEdgeInsertion(Node &SourceN, Node &TargetN);
[LCG] Construct an actual call graph with call-edge SCCs nested inside reference-edge SCCs. This essentially builds a more normal call graph as a subgraph of the "reference graph" that was the old model. This allows both to exist and the different use cases to use the aspect which addresses their needs. Specifically, the pass manager and other *ordering* constrained logic can use the reference graph to achieve conservative order of visit, while analyses reasoning about attributes and other properties derived from reachability can reason about the direct call graph. Note that this isn't necessarily complete: it doesn't model edges to declarations or indirect calls. Those can be found by scanning the instructions of the function if desirable, and in fact every user currently does this in order to handle things like calls to instrinsics. If useful, we could consider caching this information in the call graph to save the instruction scans, but currently that doesn't seem to be important. An important realization for why the representation chosen here works is that the call graph is a formal subset of the reference graph and thus both can live within the same data structure. All SCCs of the call graph are necessarily contained within an SCC of the reference graph, etc. The design is to build 'RefSCC's to model SCCs of the reference graph, and then within them more literal SCCs for the call graph. The formation of actual call edge SCCs is not done lazily, unlike reference edge 'RefSCC's. Instead, once a reference SCC is formed, it directly builds the call SCCs within it and stores them in a post-order sequence. This is used to provide a consistent platform for mutation and update of the graph. The post-order also allows for very efficient updates in common cases by bounding the number of nodes (and thus edges) considered. There is considerable common code that I'm still looking for the best way to factor out between the various DFS implementations here. So far, my attempts have made the code harder to read and understand despite reducing the duplication, which seems a poor tradeoff. I've not given up on figuring out the right way to do this, but I wanted to wait until I at least had the system working and tested to continue attempting to factor it differently. This also requires introducing several new algorithms in order to handle all of the incremental update scenarios for the more complex structure involving two edge colorings. I've tried to comment the algorithms sufficiently to make it clear how this is expected to work, but they may still need more extensive documentation. I know that there are some changes which are not strictly necessarily coupled here. The process of developing this started out with a very focused set of changes for the new structure of the graph and algorithms, but subsequent changes to bring the APIs and code into consistent and understandable patterns also ended up touching on other aspects. There was no good way to separate these out without causing *massive* merge conflicts. Ultimately, to a large degree this is a rewrite of most of the core algorithms in the LCG class and so I don't think it really matters much. Many thanks to the careful review by Sanjoy Das! Differential Revision: http://reviews.llvm.org/D16802 llvm-svn: 261040
2016-02-17 01:18:16 +01:00
public:
typedef pointee_iterator<SmallVectorImpl<SCC *>::const_iterator> iterator;
typedef iterator_range<iterator> range;
typedef pointee_iterator<SmallPtrSetImpl<RefSCC *>::const_iterator>
parent_iterator;
iterator begin() const { return SCCs.begin(); }
iterator end() const { return SCCs.end(); }
ssize_t size() const { return SCCs.size(); }
SCC &operator[](int Idx) { return *SCCs[Idx]; }
iterator find(SCC &C) const {
return SCCs.begin() + SCCIndices.find(&C)->second;
}
parent_iterator parent_begin() const { return Parents.begin(); }
parent_iterator parent_end() const { return Parents.end(); }
iterator_range<parent_iterator> parents() const {
return make_range(parent_begin(), parent_end());
}
/// Test if this SCC is a parent of \a C.
[LCG] Construct an actual call graph with call-edge SCCs nested inside reference-edge SCCs. This essentially builds a more normal call graph as a subgraph of the "reference graph" that was the old model. This allows both to exist and the different use cases to use the aspect which addresses their needs. Specifically, the pass manager and other *ordering* constrained logic can use the reference graph to achieve conservative order of visit, while analyses reasoning about attributes and other properties derived from reachability can reason about the direct call graph. Note that this isn't necessarily complete: it doesn't model edges to declarations or indirect calls. Those can be found by scanning the instructions of the function if desirable, and in fact every user currently does this in order to handle things like calls to instrinsics. If useful, we could consider caching this information in the call graph to save the instruction scans, but currently that doesn't seem to be important. An important realization for why the representation chosen here works is that the call graph is a formal subset of the reference graph and thus both can live within the same data structure. All SCCs of the call graph are necessarily contained within an SCC of the reference graph, etc. The design is to build 'RefSCC's to model SCCs of the reference graph, and then within them more literal SCCs for the call graph. The formation of actual call edge SCCs is not done lazily, unlike reference edge 'RefSCC's. Instead, once a reference SCC is formed, it directly builds the call SCCs within it and stores them in a post-order sequence. This is used to provide a consistent platform for mutation and update of the graph. The post-order also allows for very efficient updates in common cases by bounding the number of nodes (and thus edges) considered. There is considerable common code that I'm still looking for the best way to factor out between the various DFS implementations here. So far, my attempts have made the code harder to read and understand despite reducing the duplication, which seems a poor tradeoff. I've not given up on figuring out the right way to do this, but I wanted to wait until I at least had the system working and tested to continue attempting to factor it differently. This also requires introducing several new algorithms in order to handle all of the incremental update scenarios for the more complex structure involving two edge colorings. I've tried to comment the algorithms sufficiently to make it clear how this is expected to work, but they may still need more extensive documentation. I know that there are some changes which are not strictly necessarily coupled here. The process of developing this started out with a very focused set of changes for the new structure of the graph and algorithms, but subsequent changes to bring the APIs and code into consistent and understandable patterns also ended up touching on other aspects. There was no good way to separate these out without causing *massive* merge conflicts. Ultimately, to a large degree this is a rewrite of most of the core algorithms in the LCG class and so I don't think it really matters much. Many thanks to the careful review by Sanjoy Das! Differential Revision: http://reviews.llvm.org/D16802 llvm-svn: 261040
2016-02-17 01:18:16 +01:00
bool isParentOf(const RefSCC &C) const { return C.isChildOf(*this); }
[LCG] Construct an actual call graph with call-edge SCCs nested inside reference-edge SCCs. This essentially builds a more normal call graph as a subgraph of the "reference graph" that was the old model. This allows both to exist and the different use cases to use the aspect which addresses their needs. Specifically, the pass manager and other *ordering* constrained logic can use the reference graph to achieve conservative order of visit, while analyses reasoning about attributes and other properties derived from reachability can reason about the direct call graph. Note that this isn't necessarily complete: it doesn't model edges to declarations or indirect calls. Those can be found by scanning the instructions of the function if desirable, and in fact every user currently does this in order to handle things like calls to instrinsics. If useful, we could consider caching this information in the call graph to save the instruction scans, but currently that doesn't seem to be important. An important realization for why the representation chosen here works is that the call graph is a formal subset of the reference graph and thus both can live within the same data structure. All SCCs of the call graph are necessarily contained within an SCC of the reference graph, etc. The design is to build 'RefSCC's to model SCCs of the reference graph, and then within them more literal SCCs for the call graph. The formation of actual call edge SCCs is not done lazily, unlike reference edge 'RefSCC's. Instead, once a reference SCC is formed, it directly builds the call SCCs within it and stores them in a post-order sequence. This is used to provide a consistent platform for mutation and update of the graph. The post-order also allows for very efficient updates in common cases by bounding the number of nodes (and thus edges) considered. There is considerable common code that I'm still looking for the best way to factor out between the various DFS implementations here. So far, my attempts have made the code harder to read and understand despite reducing the duplication, which seems a poor tradeoff. I've not given up on figuring out the right way to do this, but I wanted to wait until I at least had the system working and tested to continue attempting to factor it differently. This also requires introducing several new algorithms in order to handle all of the incremental update scenarios for the more complex structure involving two edge colorings. I've tried to comment the algorithms sufficiently to make it clear how this is expected to work, but they may still need more extensive documentation. I know that there are some changes which are not strictly necessarily coupled here. The process of developing this started out with a very focused set of changes for the new structure of the graph and algorithms, but subsequent changes to bring the APIs and code into consistent and understandable patterns also ended up touching on other aspects. There was no good way to separate these out without causing *massive* merge conflicts. Ultimately, to a large degree this is a rewrite of most of the core algorithms in the LCG class and so I don't think it really matters much. Many thanks to the careful review by Sanjoy Das! Differential Revision: http://reviews.llvm.org/D16802 llvm-svn: 261040
2016-02-17 01:18:16 +01:00
/// Test if this RefSCC is an ancestor of \a C.
bool isAncestorOf(const RefSCC &C) const { return C.isDescendantOf(*this); }
[LCG] Construct an actual call graph with call-edge SCCs nested inside reference-edge SCCs. This essentially builds a more normal call graph as a subgraph of the "reference graph" that was the old model. This allows both to exist and the different use cases to use the aspect which addresses their needs. Specifically, the pass manager and other *ordering* constrained logic can use the reference graph to achieve conservative order of visit, while analyses reasoning about attributes and other properties derived from reachability can reason about the direct call graph. Note that this isn't necessarily complete: it doesn't model edges to declarations or indirect calls. Those can be found by scanning the instructions of the function if desirable, and in fact every user currently does this in order to handle things like calls to instrinsics. If useful, we could consider caching this information in the call graph to save the instruction scans, but currently that doesn't seem to be important. An important realization for why the representation chosen here works is that the call graph is a formal subset of the reference graph and thus both can live within the same data structure. All SCCs of the call graph are necessarily contained within an SCC of the reference graph, etc. The design is to build 'RefSCC's to model SCCs of the reference graph, and then within them more literal SCCs for the call graph. The formation of actual call edge SCCs is not done lazily, unlike reference edge 'RefSCC's. Instead, once a reference SCC is formed, it directly builds the call SCCs within it and stores them in a post-order sequence. This is used to provide a consistent platform for mutation and update of the graph. The post-order also allows for very efficient updates in common cases by bounding the number of nodes (and thus edges) considered. There is considerable common code that I'm still looking for the best way to factor out between the various DFS implementations here. So far, my attempts have made the code harder to read and understand despite reducing the duplication, which seems a poor tradeoff. I've not given up on figuring out the right way to do this, but I wanted to wait until I at least had the system working and tested to continue attempting to factor it differently. This also requires introducing several new algorithms in order to handle all of the incremental update scenarios for the more complex structure involving two edge colorings. I've tried to comment the algorithms sufficiently to make it clear how this is expected to work, but they may still need more extensive documentation. I know that there are some changes which are not strictly necessarily coupled here. The process of developing this started out with a very focused set of changes for the new structure of the graph and algorithms, but subsequent changes to bring the APIs and code into consistent and understandable patterns also ended up touching on other aspects. There was no good way to separate these out without causing *massive* merge conflicts. Ultimately, to a large degree this is a rewrite of most of the core algorithms in the LCG class and so I don't think it really matters much. Many thanks to the careful review by Sanjoy Das! Differential Revision: http://reviews.llvm.org/D16802 llvm-svn: 261040
2016-02-17 01:18:16 +01:00
/// Test if this RefSCC is a child of \a C.
bool isChildOf(const RefSCC &C) const {
return Parents.count(const_cast<RefSCC *>(&C));
}
[LCG] Construct an actual call graph with call-edge SCCs nested inside reference-edge SCCs. This essentially builds a more normal call graph as a subgraph of the "reference graph" that was the old model. This allows both to exist and the different use cases to use the aspect which addresses their needs. Specifically, the pass manager and other *ordering* constrained logic can use the reference graph to achieve conservative order of visit, while analyses reasoning about attributes and other properties derived from reachability can reason about the direct call graph. Note that this isn't necessarily complete: it doesn't model edges to declarations or indirect calls. Those can be found by scanning the instructions of the function if desirable, and in fact every user currently does this in order to handle things like calls to instrinsics. If useful, we could consider caching this information in the call graph to save the instruction scans, but currently that doesn't seem to be important. An important realization for why the representation chosen here works is that the call graph is a formal subset of the reference graph and thus both can live within the same data structure. All SCCs of the call graph are necessarily contained within an SCC of the reference graph, etc. The design is to build 'RefSCC's to model SCCs of the reference graph, and then within them more literal SCCs for the call graph. The formation of actual call edge SCCs is not done lazily, unlike reference edge 'RefSCC's. Instead, once a reference SCC is formed, it directly builds the call SCCs within it and stores them in a post-order sequence. This is used to provide a consistent platform for mutation and update of the graph. The post-order also allows for very efficient updates in common cases by bounding the number of nodes (and thus edges) considered. There is considerable common code that I'm still looking for the best way to factor out between the various DFS implementations here. So far, my attempts have made the code harder to read and understand despite reducing the duplication, which seems a poor tradeoff. I've not given up on figuring out the right way to do this, but I wanted to wait until I at least had the system working and tested to continue attempting to factor it differently. This also requires introducing several new algorithms in order to handle all of the incremental update scenarios for the more complex structure involving two edge colorings. I've tried to comment the algorithms sufficiently to make it clear how this is expected to work, but they may still need more extensive documentation. I know that there are some changes which are not strictly necessarily coupled here. The process of developing this started out with a very focused set of changes for the new structure of the graph and algorithms, but subsequent changes to bring the APIs and code into consistent and understandable patterns also ended up touching on other aspects. There was no good way to separate these out without causing *massive* merge conflicts. Ultimately, to a large degree this is a rewrite of most of the core algorithms in the LCG class and so I don't think it really matters much. Many thanks to the careful review by Sanjoy Das! Differential Revision: http://reviews.llvm.org/D16802 llvm-svn: 261040
2016-02-17 01:18:16 +01:00
/// Test if this RefSCC is a descendant of \a C.
bool isDescendantOf(const RefSCC &C) const;
/// Provide a short name by printing this SCC to a std::string.
///
/// This copes with the fact that we don't have a name per-se for an SCC
/// while still making the use of this in debugging and logging useful.
std::string getName() const {
std::string Name;
raw_string_ostream OS(Name);
OS << *this;
OS.flush();
return Name;
[LCG] Construct an actual call graph with call-edge SCCs nested inside reference-edge SCCs. This essentially builds a more normal call graph as a subgraph of the "reference graph" that was the old model. This allows both to exist and the different use cases to use the aspect which addresses their needs. Specifically, the pass manager and other *ordering* constrained logic can use the reference graph to achieve conservative order of visit, while analyses reasoning about attributes and other properties derived from reachability can reason about the direct call graph. Note that this isn't necessarily complete: it doesn't model edges to declarations or indirect calls. Those can be found by scanning the instructions of the function if desirable, and in fact every user currently does this in order to handle things like calls to instrinsics. If useful, we could consider caching this information in the call graph to save the instruction scans, but currently that doesn't seem to be important. An important realization for why the representation chosen here works is that the call graph is a formal subset of the reference graph and thus both can live within the same data structure. All SCCs of the call graph are necessarily contained within an SCC of the reference graph, etc. The design is to build 'RefSCC's to model SCCs of the reference graph, and then within them more literal SCCs for the call graph. The formation of actual call edge SCCs is not done lazily, unlike reference edge 'RefSCC's. Instead, once a reference SCC is formed, it directly builds the call SCCs within it and stores them in a post-order sequence. This is used to provide a consistent platform for mutation and update of the graph. The post-order also allows for very efficient updates in common cases by bounding the number of nodes (and thus edges) considered. There is considerable common code that I'm still looking for the best way to factor out between the various DFS implementations here. So far, my attempts have made the code harder to read and understand despite reducing the duplication, which seems a poor tradeoff. I've not given up on figuring out the right way to do this, but I wanted to wait until I at least had the system working and tested to continue attempting to factor it differently. This also requires introducing several new algorithms in order to handle all of the incremental update scenarios for the more complex structure involving two edge colorings. I've tried to comment the algorithms sufficiently to make it clear how this is expected to work, but they may still need more extensive documentation. I know that there are some changes which are not strictly necessarily coupled here. The process of developing this started out with a very focused set of changes for the new structure of the graph and algorithms, but subsequent changes to bring the APIs and code into consistent and understandable patterns also ended up touching on other aspects. There was no good way to separate these out without causing *massive* merge conflicts. Ultimately, to a large degree this is a rewrite of most of the core algorithms in the LCG class and so I don't think it really matters much. Many thanks to the careful review by Sanjoy Das! Differential Revision: http://reviews.llvm.org/D16802 llvm-svn: 261040
2016-02-17 01:18:16 +01:00
}
///@{
/// \name Mutation API
///
/// These methods provide the core API for updating the call graph in the
/// presence of a (potentially still in-flight) DFS-found SCCs.
///
/// Note that these methods sometimes have complex runtimes, so be careful
/// how you call them.
[LCG] Construct an actual call graph with call-edge SCCs nested inside reference-edge SCCs. This essentially builds a more normal call graph as a subgraph of the "reference graph" that was the old model. This allows both to exist and the different use cases to use the aspect which addresses their needs. Specifically, the pass manager and other *ordering* constrained logic can use the reference graph to achieve conservative order of visit, while analyses reasoning about attributes and other properties derived from reachability can reason about the direct call graph. Note that this isn't necessarily complete: it doesn't model edges to declarations or indirect calls. Those can be found by scanning the instructions of the function if desirable, and in fact every user currently does this in order to handle things like calls to instrinsics. If useful, we could consider caching this information in the call graph to save the instruction scans, but currently that doesn't seem to be important. An important realization for why the representation chosen here works is that the call graph is a formal subset of the reference graph and thus both can live within the same data structure. All SCCs of the call graph are necessarily contained within an SCC of the reference graph, etc. The design is to build 'RefSCC's to model SCCs of the reference graph, and then within them more literal SCCs for the call graph. The formation of actual call edge SCCs is not done lazily, unlike reference edge 'RefSCC's. Instead, once a reference SCC is formed, it directly builds the call SCCs within it and stores them in a post-order sequence. This is used to provide a consistent platform for mutation and update of the graph. The post-order also allows for very efficient updates in common cases by bounding the number of nodes (and thus edges) considered. There is considerable common code that I'm still looking for the best way to factor out between the various DFS implementations here. So far, my attempts have made the code harder to read and understand despite reducing the duplication, which seems a poor tradeoff. I've not given up on figuring out the right way to do this, but I wanted to wait until I at least had the system working and tested to continue attempting to factor it differently. This also requires introducing several new algorithms in order to handle all of the incremental update scenarios for the more complex structure involving two edge colorings. I've tried to comment the algorithms sufficiently to make it clear how this is expected to work, but they may still need more extensive documentation. I know that there are some changes which are not strictly necessarily coupled here. The process of developing this started out with a very focused set of changes for the new structure of the graph and algorithms, but subsequent changes to bring the APIs and code into consistent and understandable patterns also ended up touching on other aspects. There was no good way to separate these out without causing *massive* merge conflicts. Ultimately, to a large degree this is a rewrite of most of the core algorithms in the LCG class and so I don't think it really matters much. Many thanks to the careful review by Sanjoy Das! Differential Revision: http://reviews.llvm.org/D16802 llvm-svn: 261040
2016-02-17 01:18:16 +01:00
/// Make an existing internal ref edge into a call edge.
///
/// This may form a larger cycle and thus collapse SCCs into TargetN's SCC.
/// If that happens, the deleted SCC pointers are returned. These SCCs are
/// not in a valid state any longer but the pointers will remain valid
/// until destruction of the parent graph instance for the purpose of
/// clearing cached information.
///
/// After this operation, both SourceN's SCC and TargetN's SCC may move
/// position within this RefSCC's postorder list. Any SCCs merged are
/// merged into the TargetN's SCC in order to preserve reachability analyses
/// which took place on that SCC.
SmallVector<SCC *, 1> switchInternalEdgeToCall(Node &SourceN,
Node &TargetN);
/// Make an existing internal call edge into a ref edge.
///
/// If SourceN and TargetN are part of a single SCC, it may be split up due
/// to breaking a cycle in the call edges that formed it. If that happens,
/// then this routine will insert new SCCs into the postorder list *before*
/// the SCC of TargetN (previously the SCC of both). This preserves
/// postorder as the TargetN can reach all of the other nodes by definition
/// of previously being in a single SCC formed by the cycle from SourceN to
[PM] Introduce basic update capabilities to the new PM's CGSCC pass manager, including both plumbing and logic to handle function pass updates. There are three fundamentally tied changes here: 1) Plumbing *some* mechanism for updating the CGSCC pass manager as the CG changes while passes are running. 2) Changing the CGSCC pass manager infrastructure to have support for the underlying graph to mutate mid-pass run. 3) Actually updating the CG after function passes run. I can separate them if necessary, but I think its really useful to have them together as the needs of #3 drove #2, and that in turn drove #1. The plumbing technique is to extend the "run" method signature with extra arguments. We provide the call graph that intrinsically is available as it is the basis of the pass manager's IR units, and an output parameter that records the results of updating the call graph during an SCC passes's run. Note that "...UpdateResult" isn't a *great* name here... suggestions very welcome. I tried a pretty frustrating number of different data structures and such for the innards of the update result. Every other one failed for one reason or another. Sometimes I just couldn't keep the layers of complexity right in my head. The thing that really worked was to just directly provide access to the underlying structures used to walk the call graph so that their updates could be informed by the *particular* nature of the change to the graph. The technique for how to make the pass management infrastructure cope with mutating graphs was also something that took a really, really large number of iterations to get to a place where I was happy. Here are some of the considerations that drove the design: - We operate at three levels within the infrastructure: RefSCC, SCC, and Node. In each case, we are working bottom up and so we want to continue to iterate on the "lowest" node as the graph changes. Look at how we iterate over nodes in an SCC running function passes as those function passes mutate the CG. We continue to iterate on the "lowest" SCC, which is the one that continues to contain the function just processed. - The call graph structure re-uses SCCs (and RefSCCs) during mutation events for the *highest* entry in the resulting new subgraph, not the lowest. This means that it is necessary to continually update the current SCC or RefSCC as it shifts. This is really surprising and subtle, and took a long time for me to work out. I actually tried changing the call graph to provide the opposite behavior, and it breaks *EVERYTHING*. The graph update algorithms are really deeply tied to this particualr pattern. - When SCCs or RefSCCs are split apart and refined and we continually re-pin our processing to the bottom one in the subgraph, we need to enqueue the newly formed SCCs and RefSCCs for subsequent processing. Queuing them presents a few challenges: 1) SCCs and RefSCCs use wildly different iteration strategies at a high level. We end up needing to converge them on worklist approaches that can be extended in order to be able to handle the mutations. 2) The order of the enqueuing need to remain bottom-up post-order so that we don't get surprising order of visitation for things like the inliner. 3) We need the worklists to have set semantics so we don't duplicate things endlessly. We don't need a *persistent* set though because we always keep processing the bottom node!!!! This is super, super surprising to me and took a long time to convince myself this is correct, but I'm pretty sure it is... Once we sink down to the bottom node, we can't re-split out the same node in any way, and the postorder of the current queue is fixed and unchanging. 4) We need to make sure that the "current" SCC or RefSCC actually gets enqueued here such that we re-visit it because we continue processing a *new*, *bottom* SCC/RefSCC. - We also need the ability to *skip* SCCs and RefSCCs that get merged into a larger component. We even need the ability to skip *nodes* from an SCC that are no longer part of that SCC. This led to the design you see in the patch which uses SetVector-based worklists. The RefSCC worklist is always empty until an update occurs and is just used to handle those RefSCCs created by updates as the others don't even exist yet and are formed on-demand during the bottom-up walk. The SCC worklist is pre-populated from the RefSCC, and we push new SCCs onto it and blacklist existing SCCs on it to get the desired processing. We then *directly* update these when updating the call graph as I was never able to find a satisfactory abstraction around the update strategy. Finally, we need to compute the updates for function passes. This is mostly used as an initial customer of all the update mechanisms to drive their design to at least cover some real set of use cases. There are a bunch of interesting things that came out of doing this: - It is really nice to do this a function at a time because that function is likely hot in the cache. This means we want even the function pass adaptor to support online updates to the call graph! - To update the call graph after arbitrary function pass mutations is quite hard. We have to build a fairly comprehensive set of data structures and then process them. Fortunately, some of this code is related to the code for building the cal graph in the first place. Unfortunately, very little of it makes any sense to share because the nature of what we're doing is so very different. I've factored out the one part that made sense at least. - We need to transfer these updates into the various structures for the CGSCC pass manager. Once those were more sanely worked out, this became relatively easier. But some of those needs necessitated changes to the LazyCallGraph interface to make it significantly easier to extract the changed SCCs from an update operation. - We also need to update the CGSCC analysis manager as the shape of the graph changes. When an SCC is merged away we need to clear analyses associated with it from the analysis manager which we didn't have support for in the analysis manager infrsatructure. New SCCs are easy! But then we have the case that the original SCC has its shape changed but remains in the call graph. There we need to *invalidate* the analyses associated with it. - We also need to invalidate analyses after we *finish* processing an SCC. But the analyses we need to invalidate here are *only those for the newly updated SCC*!!! Because we only continue processing the bottom SCC, if we split SCCs apart the original one gets invalidated once when its shape changes and is not processed farther so its analyses will be correct. It is the bottom SCC which continues being processed and needs to have the "normal" invalidation done based on the preserved analyses set. All of this is mostly background and context for the changes here. Many thanks to all the reviewers who helped here. Especially Sanjoy who caught several interesting bugs in the graph algorithms, David, Sean, and others who all helped with feedback. Differential Revision: http://reviews.llvm.org/D21464 llvm-svn: 279618
2016-08-24 11:37:14 +02:00
/// TargetN.
///
/// The newly added SCCs are added *immediately* and contiguously
/// prior to the TargetN SCC and return the range covering the new SCCs in
/// the RefSCC's postorder sequence. You can directly iterate the returned
/// range to observe all of the new SCCs in postorder.
iterator_range<iterator> switchInternalEdgeToRef(Node &SourceN,
Node &TargetN);
[LCG] Construct an actual call graph with call-edge SCCs nested inside reference-edge SCCs. This essentially builds a more normal call graph as a subgraph of the "reference graph" that was the old model. This allows both to exist and the different use cases to use the aspect which addresses their needs. Specifically, the pass manager and other *ordering* constrained logic can use the reference graph to achieve conservative order of visit, while analyses reasoning about attributes and other properties derived from reachability can reason about the direct call graph. Note that this isn't necessarily complete: it doesn't model edges to declarations or indirect calls. Those can be found by scanning the instructions of the function if desirable, and in fact every user currently does this in order to handle things like calls to instrinsics. If useful, we could consider caching this information in the call graph to save the instruction scans, but currently that doesn't seem to be important. An important realization for why the representation chosen here works is that the call graph is a formal subset of the reference graph and thus both can live within the same data structure. All SCCs of the call graph are necessarily contained within an SCC of the reference graph, etc. The design is to build 'RefSCC's to model SCCs of the reference graph, and then within them more literal SCCs for the call graph. The formation of actual call edge SCCs is not done lazily, unlike reference edge 'RefSCC's. Instead, once a reference SCC is formed, it directly builds the call SCCs within it and stores them in a post-order sequence. This is used to provide a consistent platform for mutation and update of the graph. The post-order also allows for very efficient updates in common cases by bounding the number of nodes (and thus edges) considered. There is considerable common code that I'm still looking for the best way to factor out between the various DFS implementations here. So far, my attempts have made the code harder to read and understand despite reducing the duplication, which seems a poor tradeoff. I've not given up on figuring out the right way to do this, but I wanted to wait until I at least had the system working and tested to continue attempting to factor it differently. This also requires introducing several new algorithms in order to handle all of the incremental update scenarios for the more complex structure involving two edge colorings. I've tried to comment the algorithms sufficiently to make it clear how this is expected to work, but they may still need more extensive documentation. I know that there are some changes which are not strictly necessarily coupled here. The process of developing this started out with a very focused set of changes for the new structure of the graph and algorithms, but subsequent changes to bring the APIs and code into consistent and understandable patterns also ended up touching on other aspects. There was no good way to separate these out without causing *massive* merge conflicts. Ultimately, to a large degree this is a rewrite of most of the core algorithms in the LCG class and so I don't think it really matters much. Many thanks to the careful review by Sanjoy Das! Differential Revision: http://reviews.llvm.org/D16802 llvm-svn: 261040
2016-02-17 01:18:16 +01:00
/// Make an existing outgoing ref edge into a call edge.
///
/// Note that this is trivial as there are no cyclic impacts and there
/// remains a reference edge.
void switchOutgoingEdgeToCall(Node &SourceN, Node &TargetN);
/// Make an existing outgoing call edge into a ref edge.
///
[LCG] Construct an actual call graph with call-edge SCCs nested inside reference-edge SCCs. This essentially builds a more normal call graph as a subgraph of the "reference graph" that was the old model. This allows both to exist and the different use cases to use the aspect which addresses their needs. Specifically, the pass manager and other *ordering* constrained logic can use the reference graph to achieve conservative order of visit, while analyses reasoning about attributes and other properties derived from reachability can reason about the direct call graph. Note that this isn't necessarily complete: it doesn't model edges to declarations or indirect calls. Those can be found by scanning the instructions of the function if desirable, and in fact every user currently does this in order to handle things like calls to instrinsics. If useful, we could consider caching this information in the call graph to save the instruction scans, but currently that doesn't seem to be important. An important realization for why the representation chosen here works is that the call graph is a formal subset of the reference graph and thus both can live within the same data structure. All SCCs of the call graph are necessarily contained within an SCC of the reference graph, etc. The design is to build 'RefSCC's to model SCCs of the reference graph, and then within them more literal SCCs for the call graph. The formation of actual call edge SCCs is not done lazily, unlike reference edge 'RefSCC's. Instead, once a reference SCC is formed, it directly builds the call SCCs within it and stores them in a post-order sequence. This is used to provide a consistent platform for mutation and update of the graph. The post-order also allows for very efficient updates in common cases by bounding the number of nodes (and thus edges) considered. There is considerable common code that I'm still looking for the best way to factor out between the various DFS implementations here. So far, my attempts have made the code harder to read and understand despite reducing the duplication, which seems a poor tradeoff. I've not given up on figuring out the right way to do this, but I wanted to wait until I at least had the system working and tested to continue attempting to factor it differently. This also requires introducing several new algorithms in order to handle all of the incremental update scenarios for the more complex structure involving two edge colorings. I've tried to comment the algorithms sufficiently to make it clear how this is expected to work, but they may still need more extensive documentation. I know that there are some changes which are not strictly necessarily coupled here. The process of developing this started out with a very focused set of changes for the new structure of the graph and algorithms, but subsequent changes to bring the APIs and code into consistent and understandable patterns also ended up touching on other aspects. There was no good way to separate these out without causing *massive* merge conflicts. Ultimately, to a large degree this is a rewrite of most of the core algorithms in the LCG class and so I don't think it really matters much. Many thanks to the careful review by Sanjoy Das! Differential Revision: http://reviews.llvm.org/D16802 llvm-svn: 261040
2016-02-17 01:18:16 +01:00
/// This is trivial as there are no cyclic impacts and there remains
/// a reference edge.
void switchOutgoingEdgeToRef(Node &SourceN, Node &TargetN);
[LCG] Construct an actual call graph with call-edge SCCs nested inside reference-edge SCCs. This essentially builds a more normal call graph as a subgraph of the "reference graph" that was the old model. This allows both to exist and the different use cases to use the aspect which addresses their needs. Specifically, the pass manager and other *ordering* constrained logic can use the reference graph to achieve conservative order of visit, while analyses reasoning about attributes and other properties derived from reachability can reason about the direct call graph. Note that this isn't necessarily complete: it doesn't model edges to declarations or indirect calls. Those can be found by scanning the instructions of the function if desirable, and in fact every user currently does this in order to handle things like calls to instrinsics. If useful, we could consider caching this information in the call graph to save the instruction scans, but currently that doesn't seem to be important. An important realization for why the representation chosen here works is that the call graph is a formal subset of the reference graph and thus both can live within the same data structure. All SCCs of the call graph are necessarily contained within an SCC of the reference graph, etc. The design is to build 'RefSCC's to model SCCs of the reference graph, and then within them more literal SCCs for the call graph. The formation of actual call edge SCCs is not done lazily, unlike reference edge 'RefSCC's. Instead, once a reference SCC is formed, it directly builds the call SCCs within it and stores them in a post-order sequence. This is used to provide a consistent platform for mutation and update of the graph. The post-order also allows for very efficient updates in common cases by bounding the number of nodes (and thus edges) considered. There is considerable common code that I'm still looking for the best way to factor out between the various DFS implementations here. So far, my attempts have made the code harder to read and understand despite reducing the duplication, which seems a poor tradeoff. I've not given up on figuring out the right way to do this, but I wanted to wait until I at least had the system working and tested to continue attempting to factor it differently. This also requires introducing several new algorithms in order to handle all of the incremental update scenarios for the more complex structure involving two edge colorings. I've tried to comment the algorithms sufficiently to make it clear how this is expected to work, but they may still need more extensive documentation. I know that there are some changes which are not strictly necessarily coupled here. The process of developing this started out with a very focused set of changes for the new structure of the graph and algorithms, but subsequent changes to bring the APIs and code into consistent and understandable patterns also ended up touching on other aspects. There was no good way to separate these out without causing *massive* merge conflicts. Ultimately, to a large degree this is a rewrite of most of the core algorithms in the LCG class and so I don't think it really matters much. Many thanks to the careful review by Sanjoy Das! Differential Revision: http://reviews.llvm.org/D16802 llvm-svn: 261040
2016-02-17 01:18:16 +01:00
/// Insert a ref edge from one node in this RefSCC to another in this
/// RefSCC.
///
/// This is always a trivial operation as it doesn't change any part of the
/// graph structure besides connecting the two nodes.
///
/// Note that we don't support directly inserting internal *call* edges
/// because that could change the graph structure and requires returning
/// information about what became invalid. As a consequence, the pattern
/// should be to first insert the necessary ref edge, and then to switch it
/// to a call edge if needed and handle any invalidation that results. See
/// the \c switchInternalEdgeToCall routine for details.
void insertInternalRefEdge(Node &SourceN, Node &TargetN);
/// Insert an edge whose parent is in this RefSCC and child is in some
/// child RefSCC.
///
[LCG] Construct an actual call graph with call-edge SCCs nested inside reference-edge SCCs. This essentially builds a more normal call graph as a subgraph of the "reference graph" that was the old model. This allows both to exist and the different use cases to use the aspect which addresses their needs. Specifically, the pass manager and other *ordering* constrained logic can use the reference graph to achieve conservative order of visit, while analyses reasoning about attributes and other properties derived from reachability can reason about the direct call graph. Note that this isn't necessarily complete: it doesn't model edges to declarations or indirect calls. Those can be found by scanning the instructions of the function if desirable, and in fact every user currently does this in order to handle things like calls to instrinsics. If useful, we could consider caching this information in the call graph to save the instruction scans, but currently that doesn't seem to be important. An important realization for why the representation chosen here works is that the call graph is a formal subset of the reference graph and thus both can live within the same data structure. All SCCs of the call graph are necessarily contained within an SCC of the reference graph, etc. The design is to build 'RefSCC's to model SCCs of the reference graph, and then within them more literal SCCs for the call graph. The formation of actual call edge SCCs is not done lazily, unlike reference edge 'RefSCC's. Instead, once a reference SCC is formed, it directly builds the call SCCs within it and stores them in a post-order sequence. This is used to provide a consistent platform for mutation and update of the graph. The post-order also allows for very efficient updates in common cases by bounding the number of nodes (and thus edges) considered. There is considerable common code that I'm still looking for the best way to factor out between the various DFS implementations here. So far, my attempts have made the code harder to read and understand despite reducing the duplication, which seems a poor tradeoff. I've not given up on figuring out the right way to do this, but I wanted to wait until I at least had the system working and tested to continue attempting to factor it differently. This also requires introducing several new algorithms in order to handle all of the incremental update scenarios for the more complex structure involving two edge colorings. I've tried to comment the algorithms sufficiently to make it clear how this is expected to work, but they may still need more extensive documentation. I know that there are some changes which are not strictly necessarily coupled here. The process of developing this started out with a very focused set of changes for the new structure of the graph and algorithms, but subsequent changes to bring the APIs and code into consistent and understandable patterns also ended up touching on other aspects. There was no good way to separate these out without causing *massive* merge conflicts. Ultimately, to a large degree this is a rewrite of most of the core algorithms in the LCG class and so I don't think it really matters much. Many thanks to the careful review by Sanjoy Das! Differential Revision: http://reviews.llvm.org/D16802 llvm-svn: 261040
2016-02-17 01:18:16 +01:00
/// There must be an existing path from the \p SourceN to the \p TargetN.
/// This operation is inexpensive and does not change the set of SCCs and
/// RefSCCs in the graph.
void insertOutgoingEdge(Node &SourceN, Node &TargetN, Edge::Kind EK);
[LCG] Construct an actual call graph with call-edge SCCs nested inside reference-edge SCCs. This essentially builds a more normal call graph as a subgraph of the "reference graph" that was the old model. This allows both to exist and the different use cases to use the aspect which addresses their needs. Specifically, the pass manager and other *ordering* constrained logic can use the reference graph to achieve conservative order of visit, while analyses reasoning about attributes and other properties derived from reachability can reason about the direct call graph. Note that this isn't necessarily complete: it doesn't model edges to declarations or indirect calls. Those can be found by scanning the instructions of the function if desirable, and in fact every user currently does this in order to handle things like calls to instrinsics. If useful, we could consider caching this information in the call graph to save the instruction scans, but currently that doesn't seem to be important. An important realization for why the representation chosen here works is that the call graph is a formal subset of the reference graph and thus both can live within the same data structure. All SCCs of the call graph are necessarily contained within an SCC of the reference graph, etc. The design is to build 'RefSCC's to model SCCs of the reference graph, and then within them more literal SCCs for the call graph. The formation of actual call edge SCCs is not done lazily, unlike reference edge 'RefSCC's. Instead, once a reference SCC is formed, it directly builds the call SCCs within it and stores them in a post-order sequence. This is used to provide a consistent platform for mutation and update of the graph. The post-order also allows for very efficient updates in common cases by bounding the number of nodes (and thus edges) considered. There is considerable common code that I'm still looking for the best way to factor out between the various DFS implementations here. So far, my attempts have made the code harder to read and understand despite reducing the duplication, which seems a poor tradeoff. I've not given up on figuring out the right way to do this, but I wanted to wait until I at least had the system working and tested to continue attempting to factor it differently. This also requires introducing several new algorithms in order to handle all of the incremental update scenarios for the more complex structure involving two edge colorings. I've tried to comment the algorithms sufficiently to make it clear how this is expected to work, but they may still need more extensive documentation. I know that there are some changes which are not strictly necessarily coupled here. The process of developing this started out with a very focused set of changes for the new structure of the graph and algorithms, but subsequent changes to bring the APIs and code into consistent and understandable patterns also ended up touching on other aspects. There was no good way to separate these out without causing *massive* merge conflicts. Ultimately, to a large degree this is a rewrite of most of the core algorithms in the LCG class and so I don't think it really matters much. Many thanks to the careful review by Sanjoy Das! Differential Revision: http://reviews.llvm.org/D16802 llvm-svn: 261040
2016-02-17 01:18:16 +01:00
/// Insert an edge whose source is in a descendant RefSCC and target is in
/// this RefSCC.
///
[LCG] Construct an actual call graph with call-edge SCCs nested inside reference-edge SCCs. This essentially builds a more normal call graph as a subgraph of the "reference graph" that was the old model. This allows both to exist and the different use cases to use the aspect which addresses their needs. Specifically, the pass manager and other *ordering* constrained logic can use the reference graph to achieve conservative order of visit, while analyses reasoning about attributes and other properties derived from reachability can reason about the direct call graph. Note that this isn't necessarily complete: it doesn't model edges to declarations or indirect calls. Those can be found by scanning the instructions of the function if desirable, and in fact every user currently does this in order to handle things like calls to instrinsics. If useful, we could consider caching this information in the call graph to save the instruction scans, but currently that doesn't seem to be important. An important realization for why the representation chosen here works is that the call graph is a formal subset of the reference graph and thus both can live within the same data structure. All SCCs of the call graph are necessarily contained within an SCC of the reference graph, etc. The design is to build 'RefSCC's to model SCCs of the reference graph, and then within them more literal SCCs for the call graph. The formation of actual call edge SCCs is not done lazily, unlike reference edge 'RefSCC's. Instead, once a reference SCC is formed, it directly builds the call SCCs within it and stores them in a post-order sequence. This is used to provide a consistent platform for mutation and update of the graph. The post-order also allows for very efficient updates in common cases by bounding the number of nodes (and thus edges) considered. There is considerable common code that I'm still looking for the best way to factor out between the various DFS implementations here. So far, my attempts have made the code harder to read and understand despite reducing the duplication, which seems a poor tradeoff. I've not given up on figuring out the right way to do this, but I wanted to wait until I at least had the system working and tested to continue attempting to factor it differently. This also requires introducing several new algorithms in order to handle all of the incremental update scenarios for the more complex structure involving two edge colorings. I've tried to comment the algorithms sufficiently to make it clear how this is expected to work, but they may still need more extensive documentation. I know that there are some changes which are not strictly necessarily coupled here. The process of developing this started out with a very focused set of changes for the new structure of the graph and algorithms, but subsequent changes to bring the APIs and code into consistent and understandable patterns also ended up touching on other aspects. There was no good way to separate these out without causing *massive* merge conflicts. Ultimately, to a large degree this is a rewrite of most of the core algorithms in the LCG class and so I don't think it really matters much. Many thanks to the careful review by Sanjoy Das! Differential Revision: http://reviews.llvm.org/D16802 llvm-svn: 261040
2016-02-17 01:18:16 +01:00
/// There must be an existing path from the target to the source in this
/// case.
///
/// NB! This is has the potential to be a very expensive function. It
/// inherently forms a cycle in the prior RefSCC DAG and we have to merge
/// RefSCCs to resolve that cycle. But finding all of the RefSCCs which
/// participate in the cycle can in the worst case require traversing every
/// RefSCC in the graph. Every attempt is made to avoid that, but passes
/// must still exercise caution calling this routine repeatedly.
///
/// Also note that this can only insert ref edges. In order to insert
/// a call edge, first insert a ref edge and then switch it to a call edge.
/// These are intentionally kept as separate interfaces because each step
/// of the operation invalidates a different set of data structures.
///
/// This returns all the RefSCCs which were merged into the this RefSCC
/// (the target's). This allows callers to invalidate any cached
/// information.
///
/// FIXME: We could possibly optimize this quite a bit for cases where the
/// caller and callee are very nearby in the graph. See comments in the
/// implementation for details, but that use case might impact users.
[LCG] Construct an actual call graph with call-edge SCCs nested inside reference-edge SCCs. This essentially builds a more normal call graph as a subgraph of the "reference graph" that was the old model. This allows both to exist and the different use cases to use the aspect which addresses their needs. Specifically, the pass manager and other *ordering* constrained logic can use the reference graph to achieve conservative order of visit, while analyses reasoning about attributes and other properties derived from reachability can reason about the direct call graph. Note that this isn't necessarily complete: it doesn't model edges to declarations or indirect calls. Those can be found by scanning the instructions of the function if desirable, and in fact every user currently does this in order to handle things like calls to instrinsics. If useful, we could consider caching this information in the call graph to save the instruction scans, but currently that doesn't seem to be important. An important realization for why the representation chosen here works is that the call graph is a formal subset of the reference graph and thus both can live within the same data structure. All SCCs of the call graph are necessarily contained within an SCC of the reference graph, etc. The design is to build 'RefSCC's to model SCCs of the reference graph, and then within them more literal SCCs for the call graph. The formation of actual call edge SCCs is not done lazily, unlike reference edge 'RefSCC's. Instead, once a reference SCC is formed, it directly builds the call SCCs within it and stores them in a post-order sequence. This is used to provide a consistent platform for mutation and update of the graph. The post-order also allows for very efficient updates in common cases by bounding the number of nodes (and thus edges) considered. There is considerable common code that I'm still looking for the best way to factor out between the various DFS implementations here. So far, my attempts have made the code harder to read and understand despite reducing the duplication, which seems a poor tradeoff. I've not given up on figuring out the right way to do this, but I wanted to wait until I at least had the system working and tested to continue attempting to factor it differently. This also requires introducing several new algorithms in order to handle all of the incremental update scenarios for the more complex structure involving two edge colorings. I've tried to comment the algorithms sufficiently to make it clear how this is expected to work, but they may still need more extensive documentation. I know that there are some changes which are not strictly necessarily coupled here. The process of developing this started out with a very focused set of changes for the new structure of the graph and algorithms, but subsequent changes to bring the APIs and code into consistent and understandable patterns also ended up touching on other aspects. There was no good way to separate these out without causing *massive* merge conflicts. Ultimately, to a large degree this is a rewrite of most of the core algorithms in the LCG class and so I don't think it really matters much. Many thanks to the careful review by Sanjoy Das! Differential Revision: http://reviews.llvm.org/D16802 llvm-svn: 261040
2016-02-17 01:18:16 +01:00
SmallVector<RefSCC *, 1> insertIncomingRefEdge(Node &SourceN,
Node &TargetN);
/// Remove an edge whose source is in this RefSCC and target is *not*.
///
/// This removes an inter-RefSCC edge. All inter-RefSCC edges originating
/// from this SCC have been fully explored by any in-flight DFS graph
/// formation, so this is always safe to call once you have the source
/// RefSCC.
///
/// This operation does not change the cyclic structure of the graph and so
/// is very inexpensive. It may change the connectivity graph of the SCCs
/// though, so be careful calling this while iterating over them.
void removeOutgoingEdge(Node &SourceN, Node &TargetN);
/// Remove a ref edge which is entirely within this RefSCC.
///
/// Both the \a SourceN and the \a TargetN must be within this RefSCC.
/// Removing such an edge may break cycles that form this RefSCC and thus
/// this operation may change the RefSCC graph significantly. In
/// particular, this operation will re-form new RefSCCs based on the
/// remaining connectivity of the graph. The following invariants are
/// guaranteed to hold after calling this method:
///
/// 1) This RefSCC is still a RefSCC in the graph.
/// 2) This RefSCC will be the parent of any new RefSCCs. Thus, this RefSCC
/// is preserved as the root of any new RefSCC DAG formed.
/// 3) No RefSCC other than this RefSCC has its member set changed (this is
2014-05-15 03:52:21 +02:00
/// inherent in the definition of removing such an edge).
[LCG] Construct an actual call graph with call-edge SCCs nested inside reference-edge SCCs. This essentially builds a more normal call graph as a subgraph of the "reference graph" that was the old model. This allows both to exist and the different use cases to use the aspect which addresses their needs. Specifically, the pass manager and other *ordering* constrained logic can use the reference graph to achieve conservative order of visit, while analyses reasoning about attributes and other properties derived from reachability can reason about the direct call graph. Note that this isn't necessarily complete: it doesn't model edges to declarations or indirect calls. Those can be found by scanning the instructions of the function if desirable, and in fact every user currently does this in order to handle things like calls to instrinsics. If useful, we could consider caching this information in the call graph to save the instruction scans, but currently that doesn't seem to be important. An important realization for why the representation chosen here works is that the call graph is a formal subset of the reference graph and thus both can live within the same data structure. All SCCs of the call graph are necessarily contained within an SCC of the reference graph, etc. The design is to build 'RefSCC's to model SCCs of the reference graph, and then within them more literal SCCs for the call graph. The formation of actual call edge SCCs is not done lazily, unlike reference edge 'RefSCC's. Instead, once a reference SCC is formed, it directly builds the call SCCs within it and stores them in a post-order sequence. This is used to provide a consistent platform for mutation and update of the graph. The post-order also allows for very efficient updates in common cases by bounding the number of nodes (and thus edges) considered. There is considerable common code that I'm still looking for the best way to factor out between the various DFS implementations here. So far, my attempts have made the code harder to read and understand despite reducing the duplication, which seems a poor tradeoff. I've not given up on figuring out the right way to do this, but I wanted to wait until I at least had the system working and tested to continue attempting to factor it differently. This also requires introducing several new algorithms in order to handle all of the incremental update scenarios for the more complex structure involving two edge colorings. I've tried to comment the algorithms sufficiently to make it clear how this is expected to work, but they may still need more extensive documentation. I know that there are some changes which are not strictly necessarily coupled here. The process of developing this started out with a very focused set of changes for the new structure of the graph and algorithms, but subsequent changes to bring the APIs and code into consistent and understandable patterns also ended up touching on other aspects. There was no good way to separate these out without causing *massive* merge conflicts. Ultimately, to a large degree this is a rewrite of most of the core algorithms in the LCG class and so I don't think it really matters much. Many thanks to the careful review by Sanjoy Das! Differential Revision: http://reviews.llvm.org/D16802 llvm-svn: 261040
2016-02-17 01:18:16 +01:00
/// 4) All of the parent links of the RefSCC graph will be updated to
/// reflect the new RefSCC structure.
/// 5) All RefSCCs formed out of this RefSCC, excluding this RefSCC, will
/// be returned in post-order.
/// 6) The order of the RefSCCs in the vector will be a valid postorder
/// traversal of the new RefSCCs.
///
/// These invariants are very important to ensure that we can build
[LCG] Construct an actual call graph with call-edge SCCs nested inside reference-edge SCCs. This essentially builds a more normal call graph as a subgraph of the "reference graph" that was the old model. This allows both to exist and the different use cases to use the aspect which addresses their needs. Specifically, the pass manager and other *ordering* constrained logic can use the reference graph to achieve conservative order of visit, while analyses reasoning about attributes and other properties derived from reachability can reason about the direct call graph. Note that this isn't necessarily complete: it doesn't model edges to declarations or indirect calls. Those can be found by scanning the instructions of the function if desirable, and in fact every user currently does this in order to handle things like calls to instrinsics. If useful, we could consider caching this information in the call graph to save the instruction scans, but currently that doesn't seem to be important. An important realization for why the representation chosen here works is that the call graph is a formal subset of the reference graph and thus both can live within the same data structure. All SCCs of the call graph are necessarily contained within an SCC of the reference graph, etc. The design is to build 'RefSCC's to model SCCs of the reference graph, and then within them more literal SCCs for the call graph. The formation of actual call edge SCCs is not done lazily, unlike reference edge 'RefSCC's. Instead, once a reference SCC is formed, it directly builds the call SCCs within it and stores them in a post-order sequence. This is used to provide a consistent platform for mutation and update of the graph. The post-order also allows for very efficient updates in common cases by bounding the number of nodes (and thus edges) considered. There is considerable common code that I'm still looking for the best way to factor out between the various DFS implementations here. So far, my attempts have made the code harder to read and understand despite reducing the duplication, which seems a poor tradeoff. I've not given up on figuring out the right way to do this, but I wanted to wait until I at least had the system working and tested to continue attempting to factor it differently. This also requires introducing several new algorithms in order to handle all of the incremental update scenarios for the more complex structure involving two edge colorings. I've tried to comment the algorithms sufficiently to make it clear how this is expected to work, but they may still need more extensive documentation. I know that there are some changes which are not strictly necessarily coupled here. The process of developing this started out with a very focused set of changes for the new structure of the graph and algorithms, but subsequent changes to bring the APIs and code into consistent and understandable patterns also ended up touching on other aspects. There was no good way to separate these out without causing *massive* merge conflicts. Ultimately, to a large degree this is a rewrite of most of the core algorithms in the LCG class and so I don't think it really matters much. Many thanks to the careful review by Sanjoy Das! Differential Revision: http://reviews.llvm.org/D16802 llvm-svn: 261040
2016-02-17 01:18:16 +01:00
/// optimization pipelines on top of the CGSCC pass manager which
/// intelligently update the RefSCC graph without invalidating other parts
/// of the RefSCC graph.
///
/// Note that we provide no routine to remove a *call* edge. Instead, you
/// must first switch it to a ref edge using \c switchInternalEdgeToRef.
/// This split API is intentional as each of these two steps can invalidate
/// a different aspect of the graph structure and needs to have the
/// invalidation handled independently.
///
/// The runtime complexity of this method is, in the worst case, O(V+E)
[LCG] Construct an actual call graph with call-edge SCCs nested inside reference-edge SCCs. This essentially builds a more normal call graph as a subgraph of the "reference graph" that was the old model. This allows both to exist and the different use cases to use the aspect which addresses their needs. Specifically, the pass manager and other *ordering* constrained logic can use the reference graph to achieve conservative order of visit, while analyses reasoning about attributes and other properties derived from reachability can reason about the direct call graph. Note that this isn't necessarily complete: it doesn't model edges to declarations or indirect calls. Those can be found by scanning the instructions of the function if desirable, and in fact every user currently does this in order to handle things like calls to instrinsics. If useful, we could consider caching this information in the call graph to save the instruction scans, but currently that doesn't seem to be important. An important realization for why the representation chosen here works is that the call graph is a formal subset of the reference graph and thus both can live within the same data structure. All SCCs of the call graph are necessarily contained within an SCC of the reference graph, etc. The design is to build 'RefSCC's to model SCCs of the reference graph, and then within them more literal SCCs for the call graph. The formation of actual call edge SCCs is not done lazily, unlike reference edge 'RefSCC's. Instead, once a reference SCC is formed, it directly builds the call SCCs within it and stores them in a post-order sequence. This is used to provide a consistent platform for mutation and update of the graph. The post-order also allows for very efficient updates in common cases by bounding the number of nodes (and thus edges) considered. There is considerable common code that I'm still looking for the best way to factor out between the various DFS implementations here. So far, my attempts have made the code harder to read and understand despite reducing the duplication, which seems a poor tradeoff. I've not given up on figuring out the right way to do this, but I wanted to wait until I at least had the system working and tested to continue attempting to factor it differently. This also requires introducing several new algorithms in order to handle all of the incremental update scenarios for the more complex structure involving two edge colorings. I've tried to comment the algorithms sufficiently to make it clear how this is expected to work, but they may still need more extensive documentation. I know that there are some changes which are not strictly necessarily coupled here. The process of developing this started out with a very focused set of changes for the new structure of the graph and algorithms, but subsequent changes to bring the APIs and code into consistent and understandable patterns also ended up touching on other aspects. There was no good way to separate these out without causing *massive* merge conflicts. Ultimately, to a large degree this is a rewrite of most of the core algorithms in the LCG class and so I don't think it really matters much. Many thanks to the careful review by Sanjoy Das! Differential Revision: http://reviews.llvm.org/D16802 llvm-svn: 261040
2016-02-17 01:18:16 +01:00
/// where V is the number of nodes in this RefSCC and E is the number of
/// edges leaving the nodes in this RefSCC. Note that E includes both edges
/// within this RefSCC and edges from this RefSCC to child RefSCCs. Some
/// effort has been made to minimize the overhead of common cases such as
/// self-edges and edge removals which result in a spanning tree with no
/// more cycles. There are also detailed comments within the implementation
/// on techniques which could substantially improve this routine's
/// efficiency.
SmallVector<RefSCC *, 1> removeInternalRefEdge(Node &SourceN,
Node &TargetN);
/// A convenience wrapper around the above to handle trivial cases of
/// inserting a new call edge.
///
/// This is trivial whenever the target is in the same SCC as the source or
/// the edge is an outgoing edge to some descendant SCC. In these cases
/// there is no change to the cyclic structure of SCCs or RefSCCs.
///
/// To further make calling this convenient, it also handles inserting
/// already existing edges.
void insertTrivialCallEdge(Node &SourceN, Node &TargetN);
/// A convenience wrapper around the above to handle trivial cases of
/// inserting a new ref edge.
///
/// This is trivial whenever the target is in the same RefSCC as the source
/// or the edge is an outgoing edge to some descendant RefSCC. In these
/// cases there is no change to the cyclic structure of the RefSCCs.
///
/// To further make calling this convenient, it also handles inserting
/// already existing edges.
void insertTrivialRefEdge(Node &SourceN, Node &TargetN);
///@}
[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. llvm-svn: 206581
2014-04-18 12:50:32 +02:00
};
/// A post-order depth-first SCC iterator over the call graph.
[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. llvm-svn: 206581
2014-04-18 12:50:32 +02:00
///
/// 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).
[LCG] Construct an actual call graph with call-edge SCCs nested inside reference-edge SCCs. This essentially builds a more normal call graph as a subgraph of the "reference graph" that was the old model. This allows both to exist and the different use cases to use the aspect which addresses their needs. Specifically, the pass manager and other *ordering* constrained logic can use the reference graph to achieve conservative order of visit, while analyses reasoning about attributes and other properties derived from reachability can reason about the direct call graph. Note that this isn't necessarily complete: it doesn't model edges to declarations or indirect calls. Those can be found by scanning the instructions of the function if desirable, and in fact every user currently does this in order to handle things like calls to instrinsics. If useful, we could consider caching this information in the call graph to save the instruction scans, but currently that doesn't seem to be important. An important realization for why the representation chosen here works is that the call graph is a formal subset of the reference graph and thus both can live within the same data structure. All SCCs of the call graph are necessarily contained within an SCC of the reference graph, etc. The design is to build 'RefSCC's to model SCCs of the reference graph, and then within them more literal SCCs for the call graph. The formation of actual call edge SCCs is not done lazily, unlike reference edge 'RefSCC's. Instead, once a reference SCC is formed, it directly builds the call SCCs within it and stores them in a post-order sequence. This is used to provide a consistent platform for mutation and update of the graph. The post-order also allows for very efficient updates in common cases by bounding the number of nodes (and thus edges) considered. There is considerable common code that I'm still looking for the best way to factor out between the various DFS implementations here. So far, my attempts have made the code harder to read and understand despite reducing the duplication, which seems a poor tradeoff. I've not given up on figuring out the right way to do this, but I wanted to wait until I at least had the system working and tested to continue attempting to factor it differently. This also requires introducing several new algorithms in order to handle all of the incremental update scenarios for the more complex structure involving two edge colorings. I've tried to comment the algorithms sufficiently to make it clear how this is expected to work, but they may still need more extensive documentation. I know that there are some changes which are not strictly necessarily coupled here. The process of developing this started out with a very focused set of changes for the new structure of the graph and algorithms, but subsequent changes to bring the APIs and code into consistent and understandable patterns also ended up touching on other aspects. There was no good way to separate these out without causing *massive* merge conflicts. Ultimately, to a large degree this is a rewrite of most of the core algorithms in the LCG class and so I don't think it really matters much. Many thanks to the careful review by Sanjoy Das! Differential Revision: http://reviews.llvm.org/D16802 llvm-svn: 261040
2016-02-17 01:18:16 +01:00
class postorder_ref_scc_iterator
: public iterator_facade_base<postorder_ref_scc_iterator,
std::forward_iterator_tag, RefSCC> {
[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. llvm-svn: 206581
2014-04-18 12:50:32 +02:00
friend class LazyCallGraph;
friend class LazyCallGraph::Node;
/// Nonce type to select the constructor for the end iterator.
[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. llvm-svn: 206581
2014-04-18 12:50:32 +02:00
struct IsAtEndT {};
LazyCallGraph *G;
[LCG] Redesign the lazy post-order iteration mechanism for the LazyCallGraph to support repeated, stable iterations, even in the face of graph updates. This is particularly important to allow the CGSCC pass manager to walk the RefSCCs (and thus everything else) in a module more than once. Lots of unittests and other tests were hard or impossible to write because repeated CGSCC pass managers which didn't invalidate the LazyCallGraph would conclude the module was empty after the first one. =[ Really, really bad. The interesting thing is that in many ways this simplifies the code. We can now re-use the same code for handling reference edge insertion updates of the RefSCC graph as we use for handling call edge insertion updates of the SCC graph. Outside of adapting to the shared logic for this (which isn't trivial, but is *much* simpler than the DFS it replaces!), the new code involves putting newly created RefSCCs when deleting a reference edge into the cached list in the correct way, and to re-formulate the iterator to be stable and effective even in the face of these kinds of updates. I've updated the unittests for the LazyCallGraph to re-iterate the postorder sequence and verify that this all works. We even check for using alternating iterators to trigger the lazy formation of RefSCCs after mutation has occured. It's worth noting that there are a reasonable number of likely simplifications we can make past this. It isn't clear that we need to keep the "LeafRefSCCs" around any more. But I've not removed that mostly because I want this to be a more isolated change. Differential Revision: https://reviews.llvm.org/D24219 llvm-svn: 281716
2016-09-16 12:20:17 +02:00
RefSCC *RC;
[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. llvm-svn: 206581
2014-04-18 12:50:32 +02:00
[LCG] Redesign the lazy post-order iteration mechanism for the LazyCallGraph to support repeated, stable iterations, even in the face of graph updates. This is particularly important to allow the CGSCC pass manager to walk the RefSCCs (and thus everything else) in a module more than once. Lots of unittests and other tests were hard or impossible to write because repeated CGSCC pass managers which didn't invalidate the LazyCallGraph would conclude the module was empty after the first one. =[ Really, really bad. The interesting thing is that in many ways this simplifies the code. We can now re-use the same code for handling reference edge insertion updates of the RefSCC graph as we use for handling call edge insertion updates of the SCC graph. Outside of adapting to the shared logic for this (which isn't trivial, but is *much* simpler than the DFS it replaces!), the new code involves putting newly created RefSCCs when deleting a reference edge into the cached list in the correct way, and to re-formulate the iterator to be stable and effective even in the face of these kinds of updates. I've updated the unittests for the LazyCallGraph to re-iterate the postorder sequence and verify that this all works. We even check for using alternating iterators to trigger the lazy formation of RefSCCs after mutation has occured. It's worth noting that there are a reasonable number of likely simplifications we can make past this. It isn't clear that we need to keep the "LeafRefSCCs" around any more. But I've not removed that mostly because I want this to be a more isolated change. Differential Revision: https://reviews.llvm.org/D24219 llvm-svn: 281716
2016-09-16 12:20:17 +02:00
/// Build the begin iterator for a node.
postorder_ref_scc_iterator(LazyCallGraph &G) : G(&G), RC(getRC(G, 0)) {}
[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. llvm-svn: 206581
2014-04-18 12:50:32 +02:00
[LCG] Redesign the lazy post-order iteration mechanism for the LazyCallGraph to support repeated, stable iterations, even in the face of graph updates. This is particularly important to allow the CGSCC pass manager to walk the RefSCCs (and thus everything else) in a module more than once. Lots of unittests and other tests were hard or impossible to write because repeated CGSCC pass managers which didn't invalidate the LazyCallGraph would conclude the module was empty after the first one. =[ Really, really bad. The interesting thing is that in many ways this simplifies the code. We can now re-use the same code for handling reference edge insertion updates of the RefSCC graph as we use for handling call edge insertion updates of the SCC graph. Outside of adapting to the shared logic for this (which isn't trivial, but is *much* simpler than the DFS it replaces!), the new code involves putting newly created RefSCCs when deleting a reference edge into the cached list in the correct way, and to re-formulate the iterator to be stable and effective even in the face of these kinds of updates. I've updated the unittests for the LazyCallGraph to re-iterate the postorder sequence and verify that this all works. We even check for using alternating iterators to trigger the lazy formation of RefSCCs after mutation has occured. It's worth noting that there are a reasonable number of likely simplifications we can make past this. It isn't clear that we need to keep the "LeafRefSCCs" around any more. But I've not removed that mostly because I want this to be a more isolated change. Differential Revision: https://reviews.llvm.org/D24219 llvm-svn: 281716
2016-09-16 12:20:17 +02:00
/// Build the end iterator for a node. This is selected purely by overload.
[LCG] Construct an actual call graph with call-edge SCCs nested inside reference-edge SCCs. This essentially builds a more normal call graph as a subgraph of the "reference graph" that was the old model. This allows both to exist and the different use cases to use the aspect which addresses their needs. Specifically, the pass manager and other *ordering* constrained logic can use the reference graph to achieve conservative order of visit, while analyses reasoning about attributes and other properties derived from reachability can reason about the direct call graph. Note that this isn't necessarily complete: it doesn't model edges to declarations or indirect calls. Those can be found by scanning the instructions of the function if desirable, and in fact every user currently does this in order to handle things like calls to instrinsics. If useful, we could consider caching this information in the call graph to save the instruction scans, but currently that doesn't seem to be important. An important realization for why the representation chosen here works is that the call graph is a formal subset of the reference graph and thus both can live within the same data structure. All SCCs of the call graph are necessarily contained within an SCC of the reference graph, etc. The design is to build 'RefSCC's to model SCCs of the reference graph, and then within them more literal SCCs for the call graph. The formation of actual call edge SCCs is not done lazily, unlike reference edge 'RefSCC's. Instead, once a reference SCC is formed, it directly builds the call SCCs within it and stores them in a post-order sequence. This is used to provide a consistent platform for mutation and update of the graph. The post-order also allows for very efficient updates in common cases by bounding the number of nodes (and thus edges) considered. There is considerable common code that I'm still looking for the best way to factor out between the various DFS implementations here. So far, my attempts have made the code harder to read and understand despite reducing the duplication, which seems a poor tradeoff. I've not given up on figuring out the right way to do this, but I wanted to wait until I at least had the system working and tested to continue attempting to factor it differently. This also requires introducing several new algorithms in order to handle all of the incremental update scenarios for the more complex structure involving two edge colorings. I've tried to comment the algorithms sufficiently to make it clear how this is expected to work, but they may still need more extensive documentation. I know that there are some changes which are not strictly necessarily coupled here. The process of developing this started out with a very focused set of changes for the new structure of the graph and algorithms, but subsequent changes to bring the APIs and code into consistent and understandable patterns also ended up touching on other aspects. There was no good way to separate these out without causing *massive* merge conflicts. Ultimately, to a large degree this is a rewrite of most of the core algorithms in the LCG class and so I don't think it really matters much. Many thanks to the careful review by Sanjoy Das! Differential Revision: http://reviews.llvm.org/D16802 llvm-svn: 261040
2016-02-17 01:18:16 +01:00
postorder_ref_scc_iterator(LazyCallGraph &G, IsAtEndT /*Nonce*/)
[LCG] Redesign the lazy post-order iteration mechanism for the LazyCallGraph to support repeated, stable iterations, even in the face of graph updates. This is particularly important to allow the CGSCC pass manager to walk the RefSCCs (and thus everything else) in a module more than once. Lots of unittests and other tests were hard or impossible to write because repeated CGSCC pass managers which didn't invalidate the LazyCallGraph would conclude the module was empty after the first one. =[ Really, really bad. The interesting thing is that in many ways this simplifies the code. We can now re-use the same code for handling reference edge insertion updates of the RefSCC graph as we use for handling call edge insertion updates of the SCC graph. Outside of adapting to the shared logic for this (which isn't trivial, but is *much* simpler than the DFS it replaces!), the new code involves putting newly created RefSCCs when deleting a reference edge into the cached list in the correct way, and to re-formulate the iterator to be stable and effective even in the face of these kinds of updates. I've updated the unittests for the LazyCallGraph to re-iterate the postorder sequence and verify that this all works. We even check for using alternating iterators to trigger the lazy formation of RefSCCs after mutation has occured. It's worth noting that there are a reasonable number of likely simplifications we can make past this. It isn't clear that we need to keep the "LeafRefSCCs" around any more. But I've not removed that mostly because I want this to be a more isolated change. Differential Revision: https://reviews.llvm.org/D24219 llvm-svn: 281716
2016-09-16 12:20:17 +02:00
: G(&G), RC(nullptr) {}
/// Get the post-order RefSCC at the given index of the postorder walk,
/// populating it if necessary.
static RefSCC *getRC(LazyCallGraph &G, int Index) {
if (Index == (int)G.PostOrderRefSCCs.size())
if (!G.buildNextRefSCCInPostOrder())
// We're at the end.
return nullptr;
assert(Index < (int)G.PostOrderRefSCCs.size() &&
"Built the next post-order RefSCC without growing list!");
return G.PostOrderRefSCCs[Index];
}
[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. llvm-svn: 206581
2014-04-18 12:50:32 +02:00
public:
[LCG] Construct an actual call graph with call-edge SCCs nested inside reference-edge SCCs. This essentially builds a more normal call graph as a subgraph of the "reference graph" that was the old model. This allows both to exist and the different use cases to use the aspect which addresses their needs. Specifically, the pass manager and other *ordering* constrained logic can use the reference graph to achieve conservative order of visit, while analyses reasoning about attributes and other properties derived from reachability can reason about the direct call graph. Note that this isn't necessarily complete: it doesn't model edges to declarations or indirect calls. Those can be found by scanning the instructions of the function if desirable, and in fact every user currently does this in order to handle things like calls to instrinsics. If useful, we could consider caching this information in the call graph to save the instruction scans, but currently that doesn't seem to be important. An important realization for why the representation chosen here works is that the call graph is a formal subset of the reference graph and thus both can live within the same data structure. All SCCs of the call graph are necessarily contained within an SCC of the reference graph, etc. The design is to build 'RefSCC's to model SCCs of the reference graph, and then within them more literal SCCs for the call graph. The formation of actual call edge SCCs is not done lazily, unlike reference edge 'RefSCC's. Instead, once a reference SCC is formed, it directly builds the call SCCs within it and stores them in a post-order sequence. This is used to provide a consistent platform for mutation and update of the graph. The post-order also allows for very efficient updates in common cases by bounding the number of nodes (and thus edges) considered. There is considerable common code that I'm still looking for the best way to factor out between the various DFS implementations here. So far, my attempts have made the code harder to read and understand despite reducing the duplication, which seems a poor tradeoff. I've not given up on figuring out the right way to do this, but I wanted to wait until I at least had the system working and tested to continue attempting to factor it differently. This also requires introducing several new algorithms in order to handle all of the incremental update scenarios for the more complex structure involving two edge colorings. I've tried to comment the algorithms sufficiently to make it clear how this is expected to work, but they may still need more extensive documentation. I know that there are some changes which are not strictly necessarily coupled here. The process of developing this started out with a very focused set of changes for the new structure of the graph and algorithms, but subsequent changes to bring the APIs and code into consistent and understandable patterns also ended up touching on other aspects. There was no good way to separate these out without causing *massive* merge conflicts. Ultimately, to a large degree this is a rewrite of most of the core algorithms in the LCG class and so I don't think it really matters much. Many thanks to the careful review by Sanjoy Das! Differential Revision: http://reviews.llvm.org/D16802 llvm-svn: 261040
2016-02-17 01:18:16 +01:00
bool operator==(const postorder_ref_scc_iterator &Arg) const {
[LCG] Redesign the lazy post-order iteration mechanism for the LazyCallGraph to support repeated, stable iterations, even in the face of graph updates. This is particularly important to allow the CGSCC pass manager to walk the RefSCCs (and thus everything else) in a module more than once. Lots of unittests and other tests were hard or impossible to write because repeated CGSCC pass managers which didn't invalidate the LazyCallGraph would conclude the module was empty after the first one. =[ Really, really bad. The interesting thing is that in many ways this simplifies the code. We can now re-use the same code for handling reference edge insertion updates of the RefSCC graph as we use for handling call edge insertion updates of the SCC graph. Outside of adapting to the shared logic for this (which isn't trivial, but is *much* simpler than the DFS it replaces!), the new code involves putting newly created RefSCCs when deleting a reference edge into the cached list in the correct way, and to re-formulate the iterator to be stable and effective even in the face of these kinds of updates. I've updated the unittests for the LazyCallGraph to re-iterate the postorder sequence and verify that this all works. We even check for using alternating iterators to trigger the lazy formation of RefSCCs after mutation has occured. It's worth noting that there are a reasonable number of likely simplifications we can make past this. It isn't clear that we need to keep the "LeafRefSCCs" around any more. But I've not removed that mostly because I want this to be a more isolated change. Differential Revision: https://reviews.llvm.org/D24219 llvm-svn: 281716
2016-09-16 12:20:17 +02:00
return G == Arg.G && RC == Arg.RC;
[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. llvm-svn: 206581
2014-04-18 12:50:32 +02:00
}
[LCG] Redesign the lazy post-order iteration mechanism for the LazyCallGraph to support repeated, stable iterations, even in the face of graph updates. This is particularly important to allow the CGSCC pass manager to walk the RefSCCs (and thus everything else) in a module more than once. Lots of unittests and other tests were hard or impossible to write because repeated CGSCC pass managers which didn't invalidate the LazyCallGraph would conclude the module was empty after the first one. =[ Really, really bad. The interesting thing is that in many ways this simplifies the code. We can now re-use the same code for handling reference edge insertion updates of the RefSCC graph as we use for handling call edge insertion updates of the SCC graph. Outside of adapting to the shared logic for this (which isn't trivial, but is *much* simpler than the DFS it replaces!), the new code involves putting newly created RefSCCs when deleting a reference edge into the cached list in the correct way, and to re-formulate the iterator to be stable and effective even in the face of these kinds of updates. I've updated the unittests for the LazyCallGraph to re-iterate the postorder sequence and verify that this all works. We even check for using alternating iterators to trigger the lazy formation of RefSCCs after mutation has occured. It's worth noting that there are a reasonable number of likely simplifications we can make past this. It isn't clear that we need to keep the "LeafRefSCCs" around any more. But I've not removed that mostly because I want this to be a more isolated change. Differential Revision: https://reviews.llvm.org/D24219 llvm-svn: 281716
2016-09-16 12:20:17 +02:00
reference operator*() const { return *RC; }
[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. llvm-svn: 206581
2014-04-18 12:50:32 +02:00
using iterator_facade_base::operator++;
[LCG] Construct an actual call graph with call-edge SCCs nested inside reference-edge SCCs. This essentially builds a more normal call graph as a subgraph of the "reference graph" that was the old model. This allows both to exist and the different use cases to use the aspect which addresses their needs. Specifically, the pass manager and other *ordering* constrained logic can use the reference graph to achieve conservative order of visit, while analyses reasoning about attributes and other properties derived from reachability can reason about the direct call graph. Note that this isn't necessarily complete: it doesn't model edges to declarations or indirect calls. Those can be found by scanning the instructions of the function if desirable, and in fact every user currently does this in order to handle things like calls to instrinsics. If useful, we could consider caching this information in the call graph to save the instruction scans, but currently that doesn't seem to be important. An important realization for why the representation chosen here works is that the call graph is a formal subset of the reference graph and thus both can live within the same data structure. All SCCs of the call graph are necessarily contained within an SCC of the reference graph, etc. The design is to build 'RefSCC's to model SCCs of the reference graph, and then within them more literal SCCs for the call graph. The formation of actual call edge SCCs is not done lazily, unlike reference edge 'RefSCC's. Instead, once a reference SCC is formed, it directly builds the call SCCs within it and stores them in a post-order sequence. This is used to provide a consistent platform for mutation and update of the graph. The post-order also allows for very efficient updates in common cases by bounding the number of nodes (and thus edges) considered. There is considerable common code that I'm still looking for the best way to factor out between the various DFS implementations here. So far, my attempts have made the code harder to read and understand despite reducing the duplication, which seems a poor tradeoff. I've not given up on figuring out the right way to do this, but I wanted to wait until I at least had the system working and tested to continue attempting to factor it differently. This also requires introducing several new algorithms in order to handle all of the incremental update scenarios for the more complex structure involving two edge colorings. I've tried to comment the algorithms sufficiently to make it clear how this is expected to work, but they may still need more extensive documentation. I know that there are some changes which are not strictly necessarily coupled here. The process of developing this started out with a very focused set of changes for the new structure of the graph and algorithms, but subsequent changes to bring the APIs and code into consistent and understandable patterns also ended up touching on other aspects. There was no good way to separate these out without causing *massive* merge conflicts. Ultimately, to a large degree this is a rewrite of most of the core algorithms in the LCG class and so I don't think it really matters much. Many thanks to the careful review by Sanjoy Das! Differential Revision: http://reviews.llvm.org/D16802 llvm-svn: 261040
2016-02-17 01:18:16 +01:00
postorder_ref_scc_iterator &operator++() {
[LCG] Redesign the lazy post-order iteration mechanism for the LazyCallGraph to support repeated, stable iterations, even in the face of graph updates. This is particularly important to allow the CGSCC pass manager to walk the RefSCCs (and thus everything else) in a module more than once. Lots of unittests and other tests were hard or impossible to write because repeated CGSCC pass managers which didn't invalidate the LazyCallGraph would conclude the module was empty after the first one. =[ Really, really bad. The interesting thing is that in many ways this simplifies the code. We can now re-use the same code for handling reference edge insertion updates of the RefSCC graph as we use for handling call edge insertion updates of the SCC graph. Outside of adapting to the shared logic for this (which isn't trivial, but is *much* simpler than the DFS it replaces!), the new code involves putting newly created RefSCCs when deleting a reference edge into the cached list in the correct way, and to re-formulate the iterator to be stable and effective even in the face of these kinds of updates. I've updated the unittests for the LazyCallGraph to re-iterate the postorder sequence and verify that this all works. We even check for using alternating iterators to trigger the lazy formation of RefSCCs after mutation has occured. It's worth noting that there are a reasonable number of likely simplifications we can make past this. It isn't clear that we need to keep the "LeafRefSCCs" around any more. But I've not removed that mostly because I want this to be a more isolated change. Differential Revision: https://reviews.llvm.org/D24219 llvm-svn: 281716
2016-09-16 12:20:17 +02:00
assert(RC && "Cannot increment the end iterator!");
RC = getRC(*G, G->RefSCCIndices.find(RC)->second + 1);
[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. llvm-svn: 206581
2014-04-18 12:50:32 +02:00
return *this;
}
};
/// Construct a graph for the given module.
[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. llvm-svn: 200903
2014-02-06 05:37:03 +01:00
///
/// 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);
edge_iterator begin() {
return edge_iterator(EntryEdges.begin(), EntryEdges.end());
}
edge_iterator end() {
return edge_iterator(EntryEdges.end(), EntryEdges.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. llvm-svn: 200903
2014-02-06 05:37:03 +01:00
[LCG] Construct an actual call graph with call-edge SCCs nested inside reference-edge SCCs. This essentially builds a more normal call graph as a subgraph of the "reference graph" that was the old model. This allows both to exist and the different use cases to use the aspect which addresses their needs. Specifically, the pass manager and other *ordering* constrained logic can use the reference graph to achieve conservative order of visit, while analyses reasoning about attributes and other properties derived from reachability can reason about the direct call graph. Note that this isn't necessarily complete: it doesn't model edges to declarations or indirect calls. Those can be found by scanning the instructions of the function if desirable, and in fact every user currently does this in order to handle things like calls to instrinsics. If useful, we could consider caching this information in the call graph to save the instruction scans, but currently that doesn't seem to be important. An important realization for why the representation chosen here works is that the call graph is a formal subset of the reference graph and thus both can live within the same data structure. All SCCs of the call graph are necessarily contained within an SCC of the reference graph, etc. The design is to build 'RefSCC's to model SCCs of the reference graph, and then within them more literal SCCs for the call graph. The formation of actual call edge SCCs is not done lazily, unlike reference edge 'RefSCC's. Instead, once a reference SCC is formed, it directly builds the call SCCs within it and stores them in a post-order sequence. This is used to provide a consistent platform for mutation and update of the graph. The post-order also allows for very efficient updates in common cases by bounding the number of nodes (and thus edges) considered. There is considerable common code that I'm still looking for the best way to factor out between the various DFS implementations here. So far, my attempts have made the code harder to read and understand despite reducing the duplication, which seems a poor tradeoff. I've not given up on figuring out the right way to do this, but I wanted to wait until I at least had the system working and tested to continue attempting to factor it differently. This also requires introducing several new algorithms in order to handle all of the incremental update scenarios for the more complex structure involving two edge colorings. I've tried to comment the algorithms sufficiently to make it clear how this is expected to work, but they may still need more extensive documentation. I know that there are some changes which are not strictly necessarily coupled here. The process of developing this started out with a very focused set of changes for the new structure of the graph and algorithms, but subsequent changes to bring the APIs and code into consistent and understandable patterns also ended up touching on other aspects. There was no good way to separate these out without causing *massive* merge conflicts. Ultimately, to a large degree this is a rewrite of most of the core algorithms in the LCG class and so I don't think it really matters much. Many thanks to the careful review by Sanjoy Das! Differential Revision: http://reviews.llvm.org/D16802 llvm-svn: 261040
2016-02-17 01:18:16 +01:00
postorder_ref_scc_iterator postorder_ref_scc_begin() {
return postorder_ref_scc_iterator(*this);
[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. llvm-svn: 206581
2014-04-18 12:50:32 +02:00
}
[LCG] Construct an actual call graph with call-edge SCCs nested inside reference-edge SCCs. This essentially builds a more normal call graph as a subgraph of the "reference graph" that was the old model. This allows both to exist and the different use cases to use the aspect which addresses their needs. Specifically, the pass manager and other *ordering* constrained logic can use the reference graph to achieve conservative order of visit, while analyses reasoning about attributes and other properties derived from reachability can reason about the direct call graph. Note that this isn't necessarily complete: it doesn't model edges to declarations or indirect calls. Those can be found by scanning the instructions of the function if desirable, and in fact every user currently does this in order to handle things like calls to instrinsics. If useful, we could consider caching this information in the call graph to save the instruction scans, but currently that doesn't seem to be important. An important realization for why the representation chosen here works is that the call graph is a formal subset of the reference graph and thus both can live within the same data structure. All SCCs of the call graph are necessarily contained within an SCC of the reference graph, etc. The design is to build 'RefSCC's to model SCCs of the reference graph, and then within them more literal SCCs for the call graph. The formation of actual call edge SCCs is not done lazily, unlike reference edge 'RefSCC's. Instead, once a reference SCC is formed, it directly builds the call SCCs within it and stores them in a post-order sequence. This is used to provide a consistent platform for mutation and update of the graph. The post-order also allows for very efficient updates in common cases by bounding the number of nodes (and thus edges) considered. There is considerable common code that I'm still looking for the best way to factor out between the various DFS implementations here. So far, my attempts have made the code harder to read and understand despite reducing the duplication, which seems a poor tradeoff. I've not given up on figuring out the right way to do this, but I wanted to wait until I at least had the system working and tested to continue attempting to factor it differently. This also requires introducing several new algorithms in order to handle all of the incremental update scenarios for the more complex structure involving two edge colorings. I've tried to comment the algorithms sufficiently to make it clear how this is expected to work, but they may still need more extensive documentation. I know that there are some changes which are not strictly necessarily coupled here. The process of developing this started out with a very focused set of changes for the new structure of the graph and algorithms, but subsequent changes to bring the APIs and code into consistent and understandable patterns also ended up touching on other aspects. There was no good way to separate these out without causing *massive* merge conflicts. Ultimately, to a large degree this is a rewrite of most of the core algorithms in the LCG class and so I don't think it really matters much. Many thanks to the careful review by Sanjoy Das! Differential Revision: http://reviews.llvm.org/D16802 llvm-svn: 261040
2016-02-17 01:18:16 +01:00
postorder_ref_scc_iterator postorder_ref_scc_end() {
return postorder_ref_scc_iterator(*this,
postorder_ref_scc_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. llvm-svn: 206581
2014-04-18 12:50:32 +02:00
}
[LCG] Construct an actual call graph with call-edge SCCs nested inside reference-edge SCCs. This essentially builds a more normal call graph as a subgraph of the "reference graph" that was the old model. This allows both to exist and the different use cases to use the aspect which addresses their needs. Specifically, the pass manager and other *ordering* constrained logic can use the reference graph to achieve conservative order of visit, while analyses reasoning about attributes and other properties derived from reachability can reason about the direct call graph. Note that this isn't necessarily complete: it doesn't model edges to declarations or indirect calls. Those can be found by scanning the instructions of the function if desirable, and in fact every user currently does this in order to handle things like calls to instrinsics. If useful, we could consider caching this information in the call graph to save the instruction scans, but currently that doesn't seem to be important. An important realization for why the representation chosen here works is that the call graph is a formal subset of the reference graph and thus both can live within the same data structure. All SCCs of the call graph are necessarily contained within an SCC of the reference graph, etc. The design is to build 'RefSCC's to model SCCs of the reference graph, and then within them more literal SCCs for the call graph. The formation of actual call edge SCCs is not done lazily, unlike reference edge 'RefSCC's. Instead, once a reference SCC is formed, it directly builds the call SCCs within it and stores them in a post-order sequence. This is used to provide a consistent platform for mutation and update of the graph. The post-order also allows for very efficient updates in common cases by bounding the number of nodes (and thus edges) considered. There is considerable common code that I'm still looking for the best way to factor out between the various DFS implementations here. So far, my attempts have made the code harder to read and understand despite reducing the duplication, which seems a poor tradeoff. I've not given up on figuring out the right way to do this, but I wanted to wait until I at least had the system working and tested to continue attempting to factor it differently. This also requires introducing several new algorithms in order to handle all of the incremental update scenarios for the more complex structure involving two edge colorings. I've tried to comment the algorithms sufficiently to make it clear how this is expected to work, but they may still need more extensive documentation. I know that there are some changes which are not strictly necessarily coupled here. The process of developing this started out with a very focused set of changes for the new structure of the graph and algorithms, but subsequent changes to bring the APIs and code into consistent and understandable patterns also ended up touching on other aspects. There was no good way to separate these out without causing *massive* merge conflicts. Ultimately, to a large degree this is a rewrite of most of the core algorithms in the LCG class and so I don't think it really matters much. Many thanks to the careful review by Sanjoy Das! Differential Revision: http://reviews.llvm.org/D16802 llvm-svn: 261040
2016-02-17 01:18:16 +01:00
iterator_range<postorder_ref_scc_iterator> postorder_ref_sccs() {
return make_range(postorder_ref_scc_begin(), postorder_ref_scc_end());
[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. llvm-svn: 206581
2014-04-18 12:50:32 +02:00
}
/// Lookup a function in the graph which has already been scanned and added.
[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. llvm-svn: 200903
2014-02-06 05:37:03 +01:00
Node *lookup(const Function &F) const { return NodeMap.lookup(&F); }
/// Lookup a function's SCC in the graph.
///
/// \returns null if the function hasn't been assigned an SCC via the SCC
/// iterator walk.
SCC *lookupSCC(Node &N) const { return SCCMap.lookup(&N); }
[LCG] Construct an actual call graph with call-edge SCCs nested inside reference-edge SCCs. This essentially builds a more normal call graph as a subgraph of the "reference graph" that was the old model. This allows both to exist and the different use cases to use the aspect which addresses their needs. Specifically, the pass manager and other *ordering* constrained logic can use the reference graph to achieve conservative order of visit, while analyses reasoning about attributes and other properties derived from reachability can reason about the direct call graph. Note that this isn't necessarily complete: it doesn't model edges to declarations or indirect calls. Those can be found by scanning the instructions of the function if desirable, and in fact every user currently does this in order to handle things like calls to instrinsics. If useful, we could consider caching this information in the call graph to save the instruction scans, but currently that doesn't seem to be important. An important realization for why the representation chosen here works is that the call graph is a formal subset of the reference graph and thus both can live within the same data structure. All SCCs of the call graph are necessarily contained within an SCC of the reference graph, etc. The design is to build 'RefSCC's to model SCCs of the reference graph, and then within them more literal SCCs for the call graph. The formation of actual call edge SCCs is not done lazily, unlike reference edge 'RefSCC's. Instead, once a reference SCC is formed, it directly builds the call SCCs within it and stores them in a post-order sequence. This is used to provide a consistent platform for mutation and update of the graph. The post-order also allows for very efficient updates in common cases by bounding the number of nodes (and thus edges) considered. There is considerable common code that I'm still looking for the best way to factor out between the various DFS implementations here. So far, my attempts have made the code harder to read and understand despite reducing the duplication, which seems a poor tradeoff. I've not given up on figuring out the right way to do this, but I wanted to wait until I at least had the system working and tested to continue attempting to factor it differently. This also requires introducing several new algorithms in order to handle all of the incremental update scenarios for the more complex structure involving two edge colorings. I've tried to comment the algorithms sufficiently to make it clear how this is expected to work, but they may still need more extensive documentation. I know that there are some changes which are not strictly necessarily coupled here. The process of developing this started out with a very focused set of changes for the new structure of the graph and algorithms, but subsequent changes to bring the APIs and code into consistent and understandable patterns also ended up touching on other aspects. There was no good way to separate these out without causing *massive* merge conflicts. Ultimately, to a large degree this is a rewrite of most of the core algorithms in the LCG class and so I don't think it really matters much. Many thanks to the careful review by Sanjoy Das! Differential Revision: http://reviews.llvm.org/D16802 llvm-svn: 261040
2016-02-17 01:18:16 +01:00
/// Lookup a function's RefSCC in the graph.
///
/// \returns null if the function hasn't been assigned a RefSCC via the
/// RefSCC iterator walk.
RefSCC *lookupRefSCC(Node &N) const {
if (SCC *C = lookupSCC(N))
return &C->getOuterRefSCC();
return nullptr;
}
/// Get a graph node for a given function, scanning it to populate the graph
/// data as necessary.
Node &get(Function &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. llvm-svn: 200903
2014-02-06 05:37:03 +01:00
Node *&N = NodeMap[&F];
if (N)
return *N;
[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. llvm-svn: 200903
2014-02-06 05:37:03 +01:00
return insertInto(F, N);
}
///@{
/// \name Pre-SCC Mutation API
///
/// These methods are only valid to call prior to forming any SCCs for this
/// call graph. They can be used to update the core node-graph during
/// a node-based inorder traversal that precedes any SCC-based traversal.
///
/// Once you begin manipulating a call graph's SCCs, most mutation of the
/// graph must be performed via a RefSCC method. There are some exceptions
/// below.
/// Update the call graph after inserting a new edge.
void insertEdge(Node &Caller, Function &Callee, Edge::Kind EK);
/// Update the call graph after inserting a new edge.
void insertEdge(Function &Caller, Function &Callee, Edge::Kind EK) {
return insertEdge(get(Caller), Callee, EK);
}
/// Update the call graph after deleting an edge.
[LCG] Add the first round of mutation support to the lazy call graph. This implements the core functionality necessary to remove an edge from the call graph and correctly update both the basic graph and the SCC structure. As part of that it has to run a tiny (in number of nodes) Tarjan-style DFS walk of an SCC being mutated to compute newly formed SCCs, etc. This is *very rough* and a WIP. I have a bunch of FIXMEs for code cleanup that will reduce the boilerplate in this change substantially. I also have a bunch of simplifications to various parts of both algorithms that I want to make, but first I'd like to have a more holistic picture. Ideally, I'd also like more testing. I'll probably add quite a few more unit tests as I go here to cover the various different aspects and corner cases of removing edges from the graph. Still, this is, so far, successfully updating the SCC graph in-place without disrupting the identity established for the existing SCCs even when we do challenging things like delete the critical edge that made an SCC cycle at all and have to reform things as a tree of smaller SCCs. Getting this to work is really critical for the new pass manager as it is going to associate significant state with the SCC instance and needs it to be stable. That is also the motivation behind the return of the newly formed SCCs. Eventually, I'll wire this all the way up to the public API so that the pass manager can use it to correctly re-enqueue newly formed SCCs into a fresh postorder traversal. llvm-svn: 206968
2014-04-23 13:03:03 +02:00
void removeEdge(Node &Caller, Function &Callee);
/// Update the call graph after deleting an edge.
[LCG] Add the first round of mutation support to the lazy call graph. This implements the core functionality necessary to remove an edge from the call graph and correctly update both the basic graph and the SCC structure. As part of that it has to run a tiny (in number of nodes) Tarjan-style DFS walk of an SCC being mutated to compute newly formed SCCs, etc. This is *very rough* and a WIP. I have a bunch of FIXMEs for code cleanup that will reduce the boilerplate in this change substantially. I also have a bunch of simplifications to various parts of both algorithms that I want to make, but first I'd like to have a more holistic picture. Ideally, I'd also like more testing. I'll probably add quite a few more unit tests as I go here to cover the various different aspects and corner cases of removing edges from the graph. Still, this is, so far, successfully updating the SCC graph in-place without disrupting the identity established for the existing SCCs even when we do challenging things like delete the critical edge that made an SCC cycle at all and have to reform things as a tree of smaller SCCs. Getting this to work is really critical for the new pass manager as it is going to associate significant state with the SCC instance and needs it to be stable. That is also the motivation behind the return of the newly formed SCCs. Eventually, I'll wire this all the way up to the public API so that the pass manager can use it to correctly re-enqueue newly formed SCCs into a fresh postorder traversal. llvm-svn: 206968
2014-04-23 13:03:03 +02:00
void removeEdge(Function &Caller, Function &Callee) {
return removeEdge(get(Caller), Callee);
[LCG] Add the first round of mutation support to the lazy call graph. This implements the core functionality necessary to remove an edge from the call graph and correctly update both the basic graph and the SCC structure. As part of that it has to run a tiny (in number of nodes) Tarjan-style DFS walk of an SCC being mutated to compute newly formed SCCs, etc. This is *very rough* and a WIP. I have a bunch of FIXMEs for code cleanup that will reduce the boilerplate in this change substantially. I also have a bunch of simplifications to various parts of both algorithms that I want to make, but first I'd like to have a more holistic picture. Ideally, I'd also like more testing. I'll probably add quite a few more unit tests as I go here to cover the various different aspects and corner cases of removing edges from the graph. Still, this is, so far, successfully updating the SCC graph in-place without disrupting the identity established for the existing SCCs even when we do challenging things like delete the critical edge that made an SCC cycle at all and have to reform things as a tree of smaller SCCs. Getting this to work is really critical for the new pass manager as it is going to associate significant state with the SCC instance and needs it to be stable. That is also the motivation behind the return of the newly formed SCCs. Eventually, I'll wire this all the way up to the public API so that the pass manager can use it to correctly re-enqueue newly formed SCCs into a fresh postorder traversal. llvm-svn: 206968
2014-04-23 13:03:03 +02:00
}
///@}
///@{
/// \name General Mutation API
///
/// There are a very limited set of mutations allowed on the graph as a whole
/// once SCCs have started to be formed. These routines have strict contracts
/// but may be called at any point.
/// Remove a dead function from the call graph (typically to delete it).
///
/// Note that the function must have an empty use list, and the call graph
/// must be up-to-date prior to calling this. That means it is by itself in
/// a maximal SCC which is by itself in a maximal RefSCC, etc. No structural
/// changes result from calling this routine other than potentially removing
/// entry points into the call graph.
///
/// If SCC formation has begun, this function must not be part of the current
/// DFS in order to call this safely. Typically, the function will have been
/// fully visited by the DFS prior to calling this routine.
void removeDeadFunction(Function &F);
///@}
[PM] Introduce basic update capabilities to the new PM's CGSCC pass manager, including both plumbing and logic to handle function pass updates. There are three fundamentally tied changes here: 1) Plumbing *some* mechanism for updating the CGSCC pass manager as the CG changes while passes are running. 2) Changing the CGSCC pass manager infrastructure to have support for the underlying graph to mutate mid-pass run. 3) Actually updating the CG after function passes run. I can separate them if necessary, but I think its really useful to have them together as the needs of #3 drove #2, and that in turn drove #1. The plumbing technique is to extend the "run" method signature with extra arguments. We provide the call graph that intrinsically is available as it is the basis of the pass manager's IR units, and an output parameter that records the results of updating the call graph during an SCC passes's run. Note that "...UpdateResult" isn't a *great* name here... suggestions very welcome. I tried a pretty frustrating number of different data structures and such for the innards of the update result. Every other one failed for one reason or another. Sometimes I just couldn't keep the layers of complexity right in my head. The thing that really worked was to just directly provide access to the underlying structures used to walk the call graph so that their updates could be informed by the *particular* nature of the change to the graph. The technique for how to make the pass management infrastructure cope with mutating graphs was also something that took a really, really large number of iterations to get to a place where I was happy. Here are some of the considerations that drove the design: - We operate at three levels within the infrastructure: RefSCC, SCC, and Node. In each case, we are working bottom up and so we want to continue to iterate on the "lowest" node as the graph changes. Look at how we iterate over nodes in an SCC running function passes as those function passes mutate the CG. We continue to iterate on the "lowest" SCC, which is the one that continues to contain the function just processed. - The call graph structure re-uses SCCs (and RefSCCs) during mutation events for the *highest* entry in the resulting new subgraph, not the lowest. This means that it is necessary to continually update the current SCC or RefSCC as it shifts. This is really surprising and subtle, and took a long time for me to work out. I actually tried changing the call graph to provide the opposite behavior, and it breaks *EVERYTHING*. The graph update algorithms are really deeply tied to this particualr pattern. - When SCCs or RefSCCs are split apart and refined and we continually re-pin our processing to the bottom one in the subgraph, we need to enqueue the newly formed SCCs and RefSCCs for subsequent processing. Queuing them presents a few challenges: 1) SCCs and RefSCCs use wildly different iteration strategies at a high level. We end up needing to converge them on worklist approaches that can be extended in order to be able to handle the mutations. 2) The order of the enqueuing need to remain bottom-up post-order so that we don't get surprising order of visitation for things like the inliner. 3) We need the worklists to have set semantics so we don't duplicate things endlessly. We don't need a *persistent* set though because we always keep processing the bottom node!!!! This is super, super surprising to me and took a long time to convince myself this is correct, but I'm pretty sure it is... Once we sink down to the bottom node, we can't re-split out the same node in any way, and the postorder of the current queue is fixed and unchanging. 4) We need to make sure that the "current" SCC or RefSCC actually gets enqueued here such that we re-visit it because we continue processing a *new*, *bottom* SCC/RefSCC. - We also need the ability to *skip* SCCs and RefSCCs that get merged into a larger component. We even need the ability to skip *nodes* from an SCC that are no longer part of that SCC. This led to the design you see in the patch which uses SetVector-based worklists. The RefSCC worklist is always empty until an update occurs and is just used to handle those RefSCCs created by updates as the others don't even exist yet and are formed on-demand during the bottom-up walk. The SCC worklist is pre-populated from the RefSCC, and we push new SCCs onto it and blacklist existing SCCs on it to get the desired processing. We then *directly* update these when updating the call graph as I was never able to find a satisfactory abstraction around the update strategy. Finally, we need to compute the updates for function passes. This is mostly used as an initial customer of all the update mechanisms to drive their design to at least cover some real set of use cases. There are a bunch of interesting things that came out of doing this: - It is really nice to do this a function at a time because that function is likely hot in the cache. This means we want even the function pass adaptor to support online updates to the call graph! - To update the call graph after arbitrary function pass mutations is quite hard. We have to build a fairly comprehensive set of data structures and then process them. Fortunately, some of this code is related to the code for building the cal graph in the first place. Unfortunately, very little of it makes any sense to share because the nature of what we're doing is so very different. I've factored out the one part that made sense at least. - We need to transfer these updates into the various structures for the CGSCC pass manager. Once those were more sanely worked out, this became relatively easier. But some of those needs necessitated changes to the LazyCallGraph interface to make it significantly easier to extract the changed SCCs from an update operation. - We also need to update the CGSCC analysis manager as the shape of the graph changes. When an SCC is merged away we need to clear analyses associated with it from the analysis manager which we didn't have support for in the analysis manager infrsatructure. New SCCs are easy! But then we have the case that the original SCC has its shape changed but remains in the call graph. There we need to *invalidate* the analyses associated with it. - We also need to invalidate analyses after we *finish* processing an SCC. But the analyses we need to invalidate here are *only those for the newly updated SCC*!!! Because we only continue processing the bottom SCC, if we split SCCs apart the original one gets invalidated once when its shape changes and is not processed farther so its analyses will be correct. It is the bottom SCC which continues being processed and needs to have the "normal" invalidation done based on the preserved analyses set. All of this is mostly background and context for the changes here. Many thanks to all the reviewers who helped here. Especially Sanjoy who caught several interesting bugs in the graph algorithms, David, Sean, and others who all helped with feedback. Differential Revision: http://reviews.llvm.org/D21464 llvm-svn: 279618
2016-08-24 11:37:14 +02:00
///@{
/// \name Static helpers for code doing updates to the call graph.
///
/// These helpers are used to implement parts of the call graph but are also
/// useful to code doing updates or otherwise wanting to walk the IR in the
/// same patterns as when we build the call graph.
template <typename CallbackT>
static void visitReferences(SmallVectorImpl<Constant *> &Worklist,
SmallPtrSetImpl<Constant *> &Visited,
CallbackT Callback) {
while (!Worklist.empty()) {
Constant *C = Worklist.pop_back_val();
if (Function *F = dyn_cast<Function>(C)) {
Callback(*F);
continue;
}
for (Value *Op : C->operand_values())
if (Visited.insert(cast<Constant>(Op)).second)
Worklist.push_back(cast<Constant>(Op));
}
///@}
}
[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. llvm-svn: 200903
2014-02-06 05:37:03 +01:00
private:
[LCG] Construct an actual call graph with call-edge SCCs nested inside reference-edge SCCs. This essentially builds a more normal call graph as a subgraph of the "reference graph" that was the old model. This allows both to exist and the different use cases to use the aspect which addresses their needs. Specifically, the pass manager and other *ordering* constrained logic can use the reference graph to achieve conservative order of visit, while analyses reasoning about attributes and other properties derived from reachability can reason about the direct call graph. Note that this isn't necessarily complete: it doesn't model edges to declarations or indirect calls. Those can be found by scanning the instructions of the function if desirable, and in fact every user currently does this in order to handle things like calls to instrinsics. If useful, we could consider caching this information in the call graph to save the instruction scans, but currently that doesn't seem to be important. An important realization for why the representation chosen here works is that the call graph is a formal subset of the reference graph and thus both can live within the same data structure. All SCCs of the call graph are necessarily contained within an SCC of the reference graph, etc. The design is to build 'RefSCC's to model SCCs of the reference graph, and then within them more literal SCCs for the call graph. The formation of actual call edge SCCs is not done lazily, unlike reference edge 'RefSCC's. Instead, once a reference SCC is formed, it directly builds the call SCCs within it and stores them in a post-order sequence. This is used to provide a consistent platform for mutation and update of the graph. The post-order also allows for very efficient updates in common cases by bounding the number of nodes (and thus edges) considered. There is considerable common code that I'm still looking for the best way to factor out between the various DFS implementations here. So far, my attempts have made the code harder to read and understand despite reducing the duplication, which seems a poor tradeoff. I've not given up on figuring out the right way to do this, but I wanted to wait until I at least had the system working and tested to continue attempting to factor it differently. This also requires introducing several new algorithms in order to handle all of the incremental update scenarios for the more complex structure involving two edge colorings. I've tried to comment the algorithms sufficiently to make it clear how this is expected to work, but they may still need more extensive documentation. I know that there are some changes which are not strictly necessarily coupled here. The process of developing this started out with a very focused set of changes for the new structure of the graph and algorithms, but subsequent changes to bring the APIs and code into consistent and understandable patterns also ended up touching on other aspects. There was no good way to separate these out without causing *massive* merge conflicts. Ultimately, to a large degree this is a rewrite of most of the core algorithms in the LCG class and so I don't think it really matters much. Many thanks to the careful review by Sanjoy Das! Differential Revision: http://reviews.llvm.org/D16802 llvm-svn: 261040
2016-02-17 01:18:16 +01:00
typedef SmallVectorImpl<Node *>::reverse_iterator node_stack_iterator;
typedef iterator_range<node_stack_iterator> node_stack_range;
/// Allocator that holds all the call graph nodes.
[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. llvm-svn: 200903
2014-02-06 05:37:03 +01:00
SpecificBumpPtrAllocator<Node> BPA;
/// Maps function->node for fast lookup.
[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. llvm-svn: 200903
2014-02-06 05:37:03 +01:00
DenseMap<const Function *, Node *> NodeMap;
/// The entry nodes to the graph.
[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. llvm-svn: 200903
2014-02-06 05:37:03 +01:00
///
/// These nodes are reachable through "external" means. Put another way, they
/// escape at the module scope.
EdgeVectorT EntryEdges;
[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. llvm-svn: 200903
2014-02-06 05:37:03 +01:00
/// Map of the entry nodes in the graph to their indices in \c EntryEdges.
[LCG] Construct an actual call graph with call-edge SCCs nested inside reference-edge SCCs. This essentially builds a more normal call graph as a subgraph of the "reference graph" that was the old model. This allows both to exist and the different use cases to use the aspect which addresses their needs. Specifically, the pass manager and other *ordering* constrained logic can use the reference graph to achieve conservative order of visit, while analyses reasoning about attributes and other properties derived from reachability can reason about the direct call graph. Note that this isn't necessarily complete: it doesn't model edges to declarations or indirect calls. Those can be found by scanning the instructions of the function if desirable, and in fact every user currently does this in order to handle things like calls to instrinsics. If useful, we could consider caching this information in the call graph to save the instruction scans, but currently that doesn't seem to be important. An important realization for why the representation chosen here works is that the call graph is a formal subset of the reference graph and thus both can live within the same data structure. All SCCs of the call graph are necessarily contained within an SCC of the reference graph, etc. The design is to build 'RefSCC's to model SCCs of the reference graph, and then within them more literal SCCs for the call graph. The formation of actual call edge SCCs is not done lazily, unlike reference edge 'RefSCC's. Instead, once a reference SCC is formed, it directly builds the call SCCs within it and stores them in a post-order sequence. This is used to provide a consistent platform for mutation and update of the graph. The post-order also allows for very efficient updates in common cases by bounding the number of nodes (and thus edges) considered. There is considerable common code that I'm still looking for the best way to factor out between the various DFS implementations here. So far, my attempts have made the code harder to read and understand despite reducing the duplication, which seems a poor tradeoff. I've not given up on figuring out the right way to do this, but I wanted to wait until I at least had the system working and tested to continue attempting to factor it differently. This also requires introducing several new algorithms in order to handle all of the incremental update scenarios for the more complex structure involving two edge colorings. I've tried to comment the algorithms sufficiently to make it clear how this is expected to work, but they may still need more extensive documentation. I know that there are some changes which are not strictly necessarily coupled here. The process of developing this started out with a very focused set of changes for the new structure of the graph and algorithms, but subsequent changes to bring the APIs and code into consistent and understandable patterns also ended up touching on other aspects. There was no good way to separate these out without causing *massive* merge conflicts. Ultimately, to a large degree this is a rewrite of most of the core algorithms in the LCG class and so I don't think it really matters much. Many thanks to the careful review by Sanjoy Das! Differential Revision: http://reviews.llvm.org/D16802 llvm-svn: 261040
2016-02-17 01:18:16 +01:00
DenseMap<Function *, int> 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. llvm-svn: 200903
2014-02-06 05:37:03 +01:00
/// Allocator that holds all the call graph SCCs.
[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. llvm-svn: 206581
2014-04-18 12:50:32 +02:00
SpecificBumpPtrAllocator<SCC> SCCBPA;
/// Maps Function -> SCC for fast lookup.
DenseMap<Node *, SCC *> SCCMap;
[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. llvm-svn: 206581
2014-04-18 12:50:32 +02:00
[LCG] Construct an actual call graph with call-edge SCCs nested inside reference-edge SCCs. This essentially builds a more normal call graph as a subgraph of the "reference graph" that was the old model. This allows both to exist and the different use cases to use the aspect which addresses their needs. Specifically, the pass manager and other *ordering* constrained logic can use the reference graph to achieve conservative order of visit, while analyses reasoning about attributes and other properties derived from reachability can reason about the direct call graph. Note that this isn't necessarily complete: it doesn't model edges to declarations or indirect calls. Those can be found by scanning the instructions of the function if desirable, and in fact every user currently does this in order to handle things like calls to instrinsics. If useful, we could consider caching this information in the call graph to save the instruction scans, but currently that doesn't seem to be important. An important realization for why the representation chosen here works is that the call graph is a formal subset of the reference graph and thus both can live within the same data structure. All SCCs of the call graph are necessarily contained within an SCC of the reference graph, etc. The design is to build 'RefSCC's to model SCCs of the reference graph, and then within them more literal SCCs for the call graph. The formation of actual call edge SCCs is not done lazily, unlike reference edge 'RefSCC's. Instead, once a reference SCC is formed, it directly builds the call SCCs within it and stores them in a post-order sequence. This is used to provide a consistent platform for mutation and update of the graph. The post-order also allows for very efficient updates in common cases by bounding the number of nodes (and thus edges) considered. There is considerable common code that I'm still looking for the best way to factor out between the various DFS implementations here. So far, my attempts have made the code harder to read and understand despite reducing the duplication, which seems a poor tradeoff. I've not given up on figuring out the right way to do this, but I wanted to wait until I at least had the system working and tested to continue attempting to factor it differently. This also requires introducing several new algorithms in order to handle all of the incremental update scenarios for the more complex structure involving two edge colorings. I've tried to comment the algorithms sufficiently to make it clear how this is expected to work, but they may still need more extensive documentation. I know that there are some changes which are not strictly necessarily coupled here. The process of developing this started out with a very focused set of changes for the new structure of the graph and algorithms, but subsequent changes to bring the APIs and code into consistent and understandable patterns also ended up touching on other aspects. There was no good way to separate these out without causing *massive* merge conflicts. Ultimately, to a large degree this is a rewrite of most of the core algorithms in the LCG class and so I don't think it really matters much. Many thanks to the careful review by Sanjoy Das! Differential Revision: http://reviews.llvm.org/D16802 llvm-svn: 261040
2016-02-17 01:18:16 +01:00
/// Allocator that holds all the call graph RefSCCs.
SpecificBumpPtrAllocator<RefSCC> RefSCCBPA;
[LCG] Redesign the lazy post-order iteration mechanism for the LazyCallGraph to support repeated, stable iterations, even in the face of graph updates. This is particularly important to allow the CGSCC pass manager to walk the RefSCCs (and thus everything else) in a module more than once. Lots of unittests and other tests were hard or impossible to write because repeated CGSCC pass managers which didn't invalidate the LazyCallGraph would conclude the module was empty after the first one. =[ Really, really bad. The interesting thing is that in many ways this simplifies the code. We can now re-use the same code for handling reference edge insertion updates of the RefSCC graph as we use for handling call edge insertion updates of the SCC graph. Outside of adapting to the shared logic for this (which isn't trivial, but is *much* simpler than the DFS it replaces!), the new code involves putting newly created RefSCCs when deleting a reference edge into the cached list in the correct way, and to re-formulate the iterator to be stable and effective even in the face of these kinds of updates. I've updated the unittests for the LazyCallGraph to re-iterate the postorder sequence and verify that this all works. We even check for using alternating iterators to trigger the lazy formation of RefSCCs after mutation has occured. It's worth noting that there are a reasonable number of likely simplifications we can make past this. It isn't clear that we need to keep the "LeafRefSCCs" around any more. But I've not removed that mostly because I want this to be a more isolated change. Differential Revision: https://reviews.llvm.org/D24219 llvm-svn: 281716
2016-09-16 12:20:17 +02:00
/// The post-order sequence of RefSCCs.
///
/// This list is lazily formed the first time we walk the graph.
SmallVector<RefSCC *, 16> PostOrderRefSCCs;
/// A map from RefSCC to the index for it in the postorder sequence of
/// RefSCCs.
DenseMap<RefSCC *, int> RefSCCIndices;
[LCG] Construct an actual call graph with call-edge SCCs nested inside reference-edge SCCs. This essentially builds a more normal call graph as a subgraph of the "reference graph" that was the old model. This allows both to exist and the different use cases to use the aspect which addresses their needs. Specifically, the pass manager and other *ordering* constrained logic can use the reference graph to achieve conservative order of visit, while analyses reasoning about attributes and other properties derived from reachability can reason about the direct call graph. Note that this isn't necessarily complete: it doesn't model edges to declarations or indirect calls. Those can be found by scanning the instructions of the function if desirable, and in fact every user currently does this in order to handle things like calls to instrinsics. If useful, we could consider caching this information in the call graph to save the instruction scans, but currently that doesn't seem to be important. An important realization for why the representation chosen here works is that the call graph is a formal subset of the reference graph and thus both can live within the same data structure. All SCCs of the call graph are necessarily contained within an SCC of the reference graph, etc. The design is to build 'RefSCC's to model SCCs of the reference graph, and then within them more literal SCCs for the call graph. The formation of actual call edge SCCs is not done lazily, unlike reference edge 'RefSCC's. Instead, once a reference SCC is formed, it directly builds the call SCCs within it and stores them in a post-order sequence. This is used to provide a consistent platform for mutation and update of the graph. The post-order also allows for very efficient updates in common cases by bounding the number of nodes (and thus edges) considered. There is considerable common code that I'm still looking for the best way to factor out between the various DFS implementations here. So far, my attempts have made the code harder to read and understand despite reducing the duplication, which seems a poor tradeoff. I've not given up on figuring out the right way to do this, but I wanted to wait until I at least had the system working and tested to continue attempting to factor it differently. This also requires introducing several new algorithms in order to handle all of the incremental update scenarios for the more complex structure involving two edge colorings. I've tried to comment the algorithms sufficiently to make it clear how this is expected to work, but they may still need more extensive documentation. I know that there are some changes which are not strictly necessarily coupled here. The process of developing this started out with a very focused set of changes for the new structure of the graph and algorithms, but subsequent changes to bring the APIs and code into consistent and understandable patterns also ended up touching on other aspects. There was no good way to separate these out without causing *massive* merge conflicts. Ultimately, to a large degree this is a rewrite of most of the core algorithms in the LCG class and so I don't think it really matters much. Many thanks to the careful review by Sanjoy Das! Differential Revision: http://reviews.llvm.org/D16802 llvm-svn: 261040
2016-02-17 01:18:16 +01:00
/// The leaf RefSCCs of the graph.
[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. llvm-svn: 206581
2014-04-18 12:50:32 +02:00
///
[LCG] Construct an actual call graph with call-edge SCCs nested inside reference-edge SCCs. This essentially builds a more normal call graph as a subgraph of the "reference graph" that was the old model. This allows both to exist and the different use cases to use the aspect which addresses their needs. Specifically, the pass manager and other *ordering* constrained logic can use the reference graph to achieve conservative order of visit, while analyses reasoning about attributes and other properties derived from reachability can reason about the direct call graph. Note that this isn't necessarily complete: it doesn't model edges to declarations or indirect calls. Those can be found by scanning the instructions of the function if desirable, and in fact every user currently does this in order to handle things like calls to instrinsics. If useful, we could consider caching this information in the call graph to save the instruction scans, but currently that doesn't seem to be important. An important realization for why the representation chosen here works is that the call graph is a formal subset of the reference graph and thus both can live within the same data structure. All SCCs of the call graph are necessarily contained within an SCC of the reference graph, etc. The design is to build 'RefSCC's to model SCCs of the reference graph, and then within them more literal SCCs for the call graph. The formation of actual call edge SCCs is not done lazily, unlike reference edge 'RefSCC's. Instead, once a reference SCC is formed, it directly builds the call SCCs within it and stores them in a post-order sequence. This is used to provide a consistent platform for mutation and update of the graph. The post-order also allows for very efficient updates in common cases by bounding the number of nodes (and thus edges) considered. There is considerable common code that I'm still looking for the best way to factor out between the various DFS implementations here. So far, my attempts have made the code harder to read and understand despite reducing the duplication, which seems a poor tradeoff. I've not given up on figuring out the right way to do this, but I wanted to wait until I at least had the system working and tested to continue attempting to factor it differently. This also requires introducing several new algorithms in order to handle all of the incremental update scenarios for the more complex structure involving two edge colorings. I've tried to comment the algorithms sufficiently to make it clear how this is expected to work, but they may still need more extensive documentation. I know that there are some changes which are not strictly necessarily coupled here. The process of developing this started out with a very focused set of changes for the new structure of the graph and algorithms, but subsequent changes to bring the APIs and code into consistent and understandable patterns also ended up touching on other aspects. There was no good way to separate these out without causing *massive* merge conflicts. Ultimately, to a large degree this is a rewrite of most of the core algorithms in the LCG class and so I don't think it really matters much. Many thanks to the careful review by Sanjoy Das! Differential Revision: http://reviews.llvm.org/D16802 llvm-svn: 261040
2016-02-17 01:18:16 +01:00
/// These are all of the RefSCCs which have no children.
SmallVector<RefSCC *, 4> LeafRefSCCs;
[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. llvm-svn: 206581
2014-04-18 12:50:32 +02:00
/// Stack of nodes in the DFS walk.
SmallVector<std::pair<Node *, edge_iterator>, 4> 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. llvm-svn: 206581
2014-04-18 12:50:32 +02:00
[LCG] Construct an actual call graph with call-edge SCCs nested inside reference-edge SCCs. This essentially builds a more normal call graph as a subgraph of the "reference graph" that was the old model. This allows both to exist and the different use cases to use the aspect which addresses their needs. Specifically, the pass manager and other *ordering* constrained logic can use the reference graph to achieve conservative order of visit, while analyses reasoning about attributes and other properties derived from reachability can reason about the direct call graph. Note that this isn't necessarily complete: it doesn't model edges to declarations or indirect calls. Those can be found by scanning the instructions of the function if desirable, and in fact every user currently does this in order to handle things like calls to instrinsics. If useful, we could consider caching this information in the call graph to save the instruction scans, but currently that doesn't seem to be important. An important realization for why the representation chosen here works is that the call graph is a formal subset of the reference graph and thus both can live within the same data structure. All SCCs of the call graph are necessarily contained within an SCC of the reference graph, etc. The design is to build 'RefSCC's to model SCCs of the reference graph, and then within them more literal SCCs for the call graph. The formation of actual call edge SCCs is not done lazily, unlike reference edge 'RefSCC's. Instead, once a reference SCC is formed, it directly builds the call SCCs within it and stores them in a post-order sequence. This is used to provide a consistent platform for mutation and update of the graph. The post-order also allows for very efficient updates in common cases by bounding the number of nodes (and thus edges) considered. There is considerable common code that I'm still looking for the best way to factor out between the various DFS implementations here. So far, my attempts have made the code harder to read and understand despite reducing the duplication, which seems a poor tradeoff. I've not given up on figuring out the right way to do this, but I wanted to wait until I at least had the system working and tested to continue attempting to factor it differently. This also requires introducing several new algorithms in order to handle all of the incremental update scenarios for the more complex structure involving two edge colorings. I've tried to comment the algorithms sufficiently to make it clear how this is expected to work, but they may still need more extensive documentation. I know that there are some changes which are not strictly necessarily coupled here. The process of developing this started out with a very focused set of changes for the new structure of the graph and algorithms, but subsequent changes to bring the APIs and code into consistent and understandable patterns also ended up touching on other aspects. There was no good way to separate these out without causing *massive* merge conflicts. Ultimately, to a large degree this is a rewrite of most of the core algorithms in the LCG class and so I don't think it really matters much. Many thanks to the careful review by Sanjoy Das! Differential Revision: http://reviews.llvm.org/D16802 llvm-svn: 261040
2016-02-17 01:18:16 +01:00
/// Set of entry nodes not-yet-processed into RefSCCs.
SmallVector<Function *, 4> RefSCCEntryNodes;
[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. llvm-svn: 206581
2014-04-18 12:50:32 +02:00
/// Stack of nodes the DFS has walked but not yet put into a SCC.
[LCG] Construct an actual call graph with call-edge SCCs nested inside reference-edge SCCs. This essentially builds a more normal call graph as a subgraph of the "reference graph" that was the old model. This allows both to exist and the different use cases to use the aspect which addresses their needs. Specifically, the pass manager and other *ordering* constrained logic can use the reference graph to achieve conservative order of visit, while analyses reasoning about attributes and other properties derived from reachability can reason about the direct call graph. Note that this isn't necessarily complete: it doesn't model edges to declarations or indirect calls. Those can be found by scanning the instructions of the function if desirable, and in fact every user currently does this in order to handle things like calls to instrinsics. If useful, we could consider caching this information in the call graph to save the instruction scans, but currently that doesn't seem to be important. An important realization for why the representation chosen here works is that the call graph is a formal subset of the reference graph and thus both can live within the same data structure. All SCCs of the call graph are necessarily contained within an SCC of the reference graph, etc. The design is to build 'RefSCC's to model SCCs of the reference graph, and then within them more literal SCCs for the call graph. The formation of actual call edge SCCs is not done lazily, unlike reference edge 'RefSCC's. Instead, once a reference SCC is formed, it directly builds the call SCCs within it and stores them in a post-order sequence. This is used to provide a consistent platform for mutation and update of the graph. The post-order also allows for very efficient updates in common cases by bounding the number of nodes (and thus edges) considered. There is considerable common code that I'm still looking for the best way to factor out between the various DFS implementations here. So far, my attempts have made the code harder to read and understand despite reducing the duplication, which seems a poor tradeoff. I've not given up on figuring out the right way to do this, but I wanted to wait until I at least had the system working and tested to continue attempting to factor it differently. This also requires introducing several new algorithms in order to handle all of the incremental update scenarios for the more complex structure involving two edge colorings. I've tried to comment the algorithms sufficiently to make it clear how this is expected to work, but they may still need more extensive documentation. I know that there are some changes which are not strictly necessarily coupled here. The process of developing this started out with a very focused set of changes for the new structure of the graph and algorithms, but subsequent changes to bring the APIs and code into consistent and understandable patterns also ended up touching on other aspects. There was no good way to separate these out without causing *massive* merge conflicts. Ultimately, to a large degree this is a rewrite of most of the core algorithms in the LCG class and so I don't think it really matters much. Many thanks to the careful review by Sanjoy Das! Differential Revision: http://reviews.llvm.org/D16802 llvm-svn: 261040
2016-02-17 01:18:16 +01:00
SmallVector<Node *, 4> PendingRefSCCStack;
/// Counter for the next DFS number to assign.
[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. llvm-svn: 206581
2014-04-18 12:50:32 +02:00
int NextDFSNumber;
/// Helper to insert a new function, with an already looked-up entry in
[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. llvm-svn: 200903
2014-02-06 05:37:03 +01:00
/// the NodeMap.
Node &insertInto(Function &F, Node *&MappedN);
[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. llvm-svn: 200903
2014-02-06 05:37:03 +01:00
/// 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. llvm-svn: 206581
2014-04-18 12:50:32 +02:00
[LCG] Construct an actual call graph with call-edge SCCs nested inside reference-edge SCCs. This essentially builds a more normal call graph as a subgraph of the "reference graph" that was the old model. This allows both to exist and the different use cases to use the aspect which addresses their needs. Specifically, the pass manager and other *ordering* constrained logic can use the reference graph to achieve conservative order of visit, while analyses reasoning about attributes and other properties derived from reachability can reason about the direct call graph. Note that this isn't necessarily complete: it doesn't model edges to declarations or indirect calls. Those can be found by scanning the instructions of the function if desirable, and in fact every user currently does this in order to handle things like calls to instrinsics. If useful, we could consider caching this information in the call graph to save the instruction scans, but currently that doesn't seem to be important. An important realization for why the representation chosen here works is that the call graph is a formal subset of the reference graph and thus both can live within the same data structure. All SCCs of the call graph are necessarily contained within an SCC of the reference graph, etc. The design is to build 'RefSCC's to model SCCs of the reference graph, and then within them more literal SCCs for the call graph. The formation of actual call edge SCCs is not done lazily, unlike reference edge 'RefSCC's. Instead, once a reference SCC is formed, it directly builds the call SCCs within it and stores them in a post-order sequence. This is used to provide a consistent platform for mutation and update of the graph. The post-order also allows for very efficient updates in common cases by bounding the number of nodes (and thus edges) considered. There is considerable common code that I'm still looking for the best way to factor out between the various DFS implementations here. So far, my attempts have made the code harder to read and understand despite reducing the duplication, which seems a poor tradeoff. I've not given up on figuring out the right way to do this, but I wanted to wait until I at least had the system working and tested to continue attempting to factor it differently. This also requires introducing several new algorithms in order to handle all of the incremental update scenarios for the more complex structure involving two edge colorings. I've tried to comment the algorithms sufficiently to make it clear how this is expected to work, but they may still need more extensive documentation. I know that there are some changes which are not strictly necessarily coupled here. The process of developing this started out with a very focused set of changes for the new structure of the graph and algorithms, but subsequent changes to bring the APIs and code into consistent and understandable patterns also ended up touching on other aspects. There was no good way to separate these out without causing *massive* merge conflicts. Ultimately, to a large degree this is a rewrite of most of the core algorithms in the LCG class and so I don't think it really matters much. Many thanks to the careful review by Sanjoy Das! Differential Revision: http://reviews.llvm.org/D16802 llvm-svn: 261040
2016-02-17 01:18:16 +01:00
/// Allocates an SCC and constructs it using the graph allocator.
///
/// The arguments are forwarded to the constructor.
template <typename... Ts> SCC *createSCC(Ts &&... Args) {
return new (SCCBPA.Allocate()) SCC(std::forward<Ts>(Args)...);
}
/// Allocates a RefSCC and constructs it using the graph allocator.
///
/// The arguments are forwarded to the constructor.
template <typename... Ts> RefSCC *createRefSCC(Ts &&... Args) {
return new (RefSCCBPA.Allocate()) RefSCC(std::forward<Ts>(Args)...);
}
/// Build the SCCs for a RefSCC out of a list of nodes.
void buildSCCs(RefSCC &RC, node_stack_range Nodes);
/// Connect a RefSCC into the larger graph.
///
/// This walks the edges to connect the RefSCC to its children's parent set,
/// and updates the root leaf list.
void connectRefSCC(RefSCC &RC);
[LCG] Redesign the lazy post-order iteration mechanism for the LazyCallGraph to support repeated, stable iterations, even in the face of graph updates. This is particularly important to allow the CGSCC pass manager to walk the RefSCCs (and thus everything else) in a module more than once. Lots of unittests and other tests were hard or impossible to write because repeated CGSCC pass managers which didn't invalidate the LazyCallGraph would conclude the module was empty after the first one. =[ Really, really bad. The interesting thing is that in many ways this simplifies the code. We can now re-use the same code for handling reference edge insertion updates of the RefSCC graph as we use for handling call edge insertion updates of the SCC graph. Outside of adapting to the shared logic for this (which isn't trivial, but is *much* simpler than the DFS it replaces!), the new code involves putting newly created RefSCCs when deleting a reference edge into the cached list in the correct way, and to re-formulate the iterator to be stable and effective even in the face of these kinds of updates. I've updated the unittests for the LazyCallGraph to re-iterate the postorder sequence and verify that this all works. We even check for using alternating iterators to trigger the lazy formation of RefSCCs after mutation has occured. It's worth noting that there are a reasonable number of likely simplifications we can make past this. It isn't clear that we need to keep the "LeafRefSCCs" around any more. But I've not removed that mostly because I want this to be a more isolated change. Differential Revision: https://reviews.llvm.org/D24219 llvm-svn: 281716
2016-09-16 12:20:17 +02:00
/// Get the index of a RefSCC within the postorder traversal.
///
/// Requires that this RefSCC is a valid one in the (perhaps partial)
/// postorder traversed part of the graph.
int getRefSCCIndex(RefSCC &RC) {
auto IndexIt = RefSCCIndices.find(&RC);
assert(IndexIt != RefSCCIndices.end() && "RefSCC doesn't have an index!");
assert(PostOrderRefSCCs[IndexIt->second] == &RC &&
"Index does not point back at RC!");
return IndexIt->second;
}
/// Builds the next node in the post-order RefSCC walk of the call graph and
/// appends it to the \c PostOrderRefSCCs vector.
///
/// Returns true if a new RefSCC was successfully constructed, and false if
/// there are no more RefSCCs to build in the graph.
bool buildNextRefSCCInPostOrder();
[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. llvm-svn: 200903
2014-02-06 05:37:03 +01:00
};
inline LazyCallGraph::Edge::Edge() : Value() {}
inline LazyCallGraph::Edge::Edge(Function &F, Kind K) : Value(&F, K) {}
inline LazyCallGraph::Edge::Edge(Node &N, Kind K) : Value(&N, K) {}
inline LazyCallGraph::Edge::operator bool() const {
return !Value.getPointer().isNull();
}
[PM] Introduce basic update capabilities to the new PM's CGSCC pass manager, including both plumbing and logic to handle function pass updates. There are three fundamentally tied changes here: 1) Plumbing *some* mechanism for updating the CGSCC pass manager as the CG changes while passes are running. 2) Changing the CGSCC pass manager infrastructure to have support for the underlying graph to mutate mid-pass run. 3) Actually updating the CG after function passes run. I can separate them if necessary, but I think its really useful to have them together as the needs of #3 drove #2, and that in turn drove #1. The plumbing technique is to extend the "run" method signature with extra arguments. We provide the call graph that intrinsically is available as it is the basis of the pass manager's IR units, and an output parameter that records the results of updating the call graph during an SCC passes's run. Note that "...UpdateResult" isn't a *great* name here... suggestions very welcome. I tried a pretty frustrating number of different data structures and such for the innards of the update result. Every other one failed for one reason or another. Sometimes I just couldn't keep the layers of complexity right in my head. The thing that really worked was to just directly provide access to the underlying structures used to walk the call graph so that their updates could be informed by the *particular* nature of the change to the graph. The technique for how to make the pass management infrastructure cope with mutating graphs was also something that took a really, really large number of iterations to get to a place where I was happy. Here are some of the considerations that drove the design: - We operate at three levels within the infrastructure: RefSCC, SCC, and Node. In each case, we are working bottom up and so we want to continue to iterate on the "lowest" node as the graph changes. Look at how we iterate over nodes in an SCC running function passes as those function passes mutate the CG. We continue to iterate on the "lowest" SCC, which is the one that continues to contain the function just processed. - The call graph structure re-uses SCCs (and RefSCCs) during mutation events for the *highest* entry in the resulting new subgraph, not the lowest. This means that it is necessary to continually update the current SCC or RefSCC as it shifts. This is really surprising and subtle, and took a long time for me to work out. I actually tried changing the call graph to provide the opposite behavior, and it breaks *EVERYTHING*. The graph update algorithms are really deeply tied to this particualr pattern. - When SCCs or RefSCCs are split apart and refined and we continually re-pin our processing to the bottom one in the subgraph, we need to enqueue the newly formed SCCs and RefSCCs for subsequent processing. Queuing them presents a few challenges: 1) SCCs and RefSCCs use wildly different iteration strategies at a high level. We end up needing to converge them on worklist approaches that can be extended in order to be able to handle the mutations. 2) The order of the enqueuing need to remain bottom-up post-order so that we don't get surprising order of visitation for things like the inliner. 3) We need the worklists to have set semantics so we don't duplicate things endlessly. We don't need a *persistent* set though because we always keep processing the bottom node!!!! This is super, super surprising to me and took a long time to convince myself this is correct, but I'm pretty sure it is... Once we sink down to the bottom node, we can't re-split out the same node in any way, and the postorder of the current queue is fixed and unchanging. 4) We need to make sure that the "current" SCC or RefSCC actually gets enqueued here such that we re-visit it because we continue processing a *new*, *bottom* SCC/RefSCC. - We also need the ability to *skip* SCCs and RefSCCs that get merged into a larger component. We even need the ability to skip *nodes* from an SCC that are no longer part of that SCC. This led to the design you see in the patch which uses SetVector-based worklists. The RefSCC worklist is always empty until an update occurs and is just used to handle those RefSCCs created by updates as the others don't even exist yet and are formed on-demand during the bottom-up walk. The SCC worklist is pre-populated from the RefSCC, and we push new SCCs onto it and blacklist existing SCCs on it to get the desired processing. We then *directly* update these when updating the call graph as I was never able to find a satisfactory abstraction around the update strategy. Finally, we need to compute the updates for function passes. This is mostly used as an initial customer of all the update mechanisms to drive their design to at least cover some real set of use cases. There are a bunch of interesting things that came out of doing this: - It is really nice to do this a function at a time because that function is likely hot in the cache. This means we want even the function pass adaptor to support online updates to the call graph! - To update the call graph after arbitrary function pass mutations is quite hard. We have to build a fairly comprehensive set of data structures and then process them. Fortunately, some of this code is related to the code for building the cal graph in the first place. Unfortunately, very little of it makes any sense to share because the nature of what we're doing is so very different. I've factored out the one part that made sense at least. - We need to transfer these updates into the various structures for the CGSCC pass manager. Once those were more sanely worked out, this became relatively easier. But some of those needs necessitated changes to the LazyCallGraph interface to make it significantly easier to extract the changed SCCs from an update operation. - We also need to update the CGSCC analysis manager as the shape of the graph changes. When an SCC is merged away we need to clear analyses associated with it from the analysis manager which we didn't have support for in the analysis manager infrsatructure. New SCCs are easy! But then we have the case that the original SCC has its shape changed but remains in the call graph. There we need to *invalidate* the analyses associated with it. - We also need to invalidate analyses after we *finish* processing an SCC. But the analyses we need to invalidate here are *only those for the newly updated SCC*!!! Because we only continue processing the bottom SCC, if we split SCCs apart the original one gets invalidated once when its shape changes and is not processed farther so its analyses will be correct. It is the bottom SCC which continues being processed and needs to have the "normal" invalidation done based on the preserved analyses set. All of this is mostly background and context for the changes here. Many thanks to all the reviewers who helped here. Especially Sanjoy who caught several interesting bugs in the graph algorithms, David, Sean, and others who all helped with feedback. Differential Revision: http://reviews.llvm.org/D21464 llvm-svn: 279618
2016-08-24 11:37:14 +02:00
inline LazyCallGraph::Edge::Kind LazyCallGraph::Edge::getKind() const {
assert(*this && "Queried a null edge!");
return Value.getInt();
}
inline bool LazyCallGraph::Edge::isCall() const {
assert(*this && "Queried a null edge!");
[PM] Introduce basic update capabilities to the new PM's CGSCC pass manager, including both plumbing and logic to handle function pass updates. There are three fundamentally tied changes here: 1) Plumbing *some* mechanism for updating the CGSCC pass manager as the CG changes while passes are running. 2) Changing the CGSCC pass manager infrastructure to have support for the underlying graph to mutate mid-pass run. 3) Actually updating the CG after function passes run. I can separate them if necessary, but I think its really useful to have them together as the needs of #3 drove #2, and that in turn drove #1. The plumbing technique is to extend the "run" method signature with extra arguments. We provide the call graph that intrinsically is available as it is the basis of the pass manager's IR units, and an output parameter that records the results of updating the call graph during an SCC passes's run. Note that "...UpdateResult" isn't a *great* name here... suggestions very welcome. I tried a pretty frustrating number of different data structures and such for the innards of the update result. Every other one failed for one reason or another. Sometimes I just couldn't keep the layers of complexity right in my head. The thing that really worked was to just directly provide access to the underlying structures used to walk the call graph so that their updates could be informed by the *particular* nature of the change to the graph. The technique for how to make the pass management infrastructure cope with mutating graphs was also something that took a really, really large number of iterations to get to a place where I was happy. Here are some of the considerations that drove the design: - We operate at three levels within the infrastructure: RefSCC, SCC, and Node. In each case, we are working bottom up and so we want to continue to iterate on the "lowest" node as the graph changes. Look at how we iterate over nodes in an SCC running function passes as those function passes mutate the CG. We continue to iterate on the "lowest" SCC, which is the one that continues to contain the function just processed. - The call graph structure re-uses SCCs (and RefSCCs) during mutation events for the *highest* entry in the resulting new subgraph, not the lowest. This means that it is necessary to continually update the current SCC or RefSCC as it shifts. This is really surprising and subtle, and took a long time for me to work out. I actually tried changing the call graph to provide the opposite behavior, and it breaks *EVERYTHING*. The graph update algorithms are really deeply tied to this particualr pattern. - When SCCs or RefSCCs are split apart and refined and we continually re-pin our processing to the bottom one in the subgraph, we need to enqueue the newly formed SCCs and RefSCCs for subsequent processing. Queuing them presents a few challenges: 1) SCCs and RefSCCs use wildly different iteration strategies at a high level. We end up needing to converge them on worklist approaches that can be extended in order to be able to handle the mutations. 2) The order of the enqueuing need to remain bottom-up post-order so that we don't get surprising order of visitation for things like the inliner. 3) We need the worklists to have set semantics so we don't duplicate things endlessly. We don't need a *persistent* set though because we always keep processing the bottom node!!!! This is super, super surprising to me and took a long time to convince myself this is correct, but I'm pretty sure it is... Once we sink down to the bottom node, we can't re-split out the same node in any way, and the postorder of the current queue is fixed and unchanging. 4) We need to make sure that the "current" SCC or RefSCC actually gets enqueued here such that we re-visit it because we continue processing a *new*, *bottom* SCC/RefSCC. - We also need the ability to *skip* SCCs and RefSCCs that get merged into a larger component. We even need the ability to skip *nodes* from an SCC that are no longer part of that SCC. This led to the design you see in the patch which uses SetVector-based worklists. The RefSCC worklist is always empty until an update occurs and is just used to handle those RefSCCs created by updates as the others don't even exist yet and are formed on-demand during the bottom-up walk. The SCC worklist is pre-populated from the RefSCC, and we push new SCCs onto it and blacklist existing SCCs on it to get the desired processing. We then *directly* update these when updating the call graph as I was never able to find a satisfactory abstraction around the update strategy. Finally, we need to compute the updates for function passes. This is mostly used as an initial customer of all the update mechanisms to drive their design to at least cover some real set of use cases. There are a bunch of interesting things that came out of doing this: - It is really nice to do this a function at a time because that function is likely hot in the cache. This means we want even the function pass adaptor to support online updates to the call graph! - To update the call graph after arbitrary function pass mutations is quite hard. We have to build a fairly comprehensive set of data structures and then process them. Fortunately, some of this code is related to the code for building the cal graph in the first place. Unfortunately, very little of it makes any sense to share because the nature of what we're doing is so very different. I've factored out the one part that made sense at least. - We need to transfer these updates into the various structures for the CGSCC pass manager. Once those were more sanely worked out, this became relatively easier. But some of those needs necessitated changes to the LazyCallGraph interface to make it significantly easier to extract the changed SCCs from an update operation. - We also need to update the CGSCC analysis manager as the shape of the graph changes. When an SCC is merged away we need to clear analyses associated with it from the analysis manager which we didn't have support for in the analysis manager infrsatructure. New SCCs are easy! But then we have the case that the original SCC has its shape changed but remains in the call graph. There we need to *invalidate* the analyses associated with it. - We also need to invalidate analyses after we *finish* processing an SCC. But the analyses we need to invalidate here are *only those for the newly updated SCC*!!! Because we only continue processing the bottom SCC, if we split SCCs apart the original one gets invalidated once when its shape changes and is not processed farther so its analyses will be correct. It is the bottom SCC which continues being processed and needs to have the "normal" invalidation done based on the preserved analyses set. All of this is mostly background and context for the changes here. Many thanks to all the reviewers who helped here. Especially Sanjoy who caught several interesting bugs in the graph algorithms, David, Sean, and others who all helped with feedback. Differential Revision: http://reviews.llvm.org/D21464 llvm-svn: 279618
2016-08-24 11:37:14 +02:00
return getKind() == Call;
}
inline Function &LazyCallGraph::Edge::getFunction() const {
assert(*this && "Queried a null edge!");
auto P = Value.getPointer();
if (auto *F = P.dyn_cast<Function *>())
return *F;
return P.get<Node *>()->getFunction();
}
inline LazyCallGraph::Node *LazyCallGraph::Edge::getNode() const {
assert(*this && "Queried a null edge!");
auto P = Value.getPointer();
if (auto *N = P.dyn_cast<Node *>())
return N;
return nullptr;
}
inline LazyCallGraph::Node &LazyCallGraph::Edge::getNode(LazyCallGraph &G) {
assert(*this && "Queried a null edge!");
auto P = Value.getPointer();
if (auto *N = P.dyn_cast<Node *>())
return *N;
Node &N = G.get(*P.get<Function *>());
Value.setPointer(&N);
return N;
}
[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. llvm-svn: 200903
2014-02-06 05:37:03 +01:00
// Provide GraphTraits specializations for call graphs.
template <> struct GraphTraits<LazyCallGraph::Node *> {
typedef LazyCallGraph::Node *NodeRef;
typedef LazyCallGraph::edge_iterator ChildIteratorType;
[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. llvm-svn: 200903
2014-02-06 05:37:03 +01:00
static NodeRef getEntryNode(NodeRef N) { return N; }
static ChildIteratorType child_begin(NodeRef N) { return N->begin(); }
static ChildIteratorType child_end(NodeRef N) { return N->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. llvm-svn: 200903
2014-02-06 05:37:03 +01:00
};
template <> struct GraphTraits<LazyCallGraph *> {
typedef LazyCallGraph::Node *NodeRef;
typedef LazyCallGraph::edge_iterator ChildIteratorType;
[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. llvm-svn: 200903
2014-02-06 05:37:03 +01:00
static NodeRef getEntryNode(NodeRef N) { return N; }
static ChildIteratorType child_begin(NodeRef N) { return N->begin(); }
static ChildIteratorType child_end(NodeRef N) { return N->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. llvm-svn: 200903
2014-02-06 05:37:03 +01:00
};
/// An analysis pass which computes the call graph for a module.
class LazyCallGraphAnalysis : public AnalysisInfoMixin<LazyCallGraphAnalysis> {
friend AnalysisInfoMixin<LazyCallGraphAnalysis>;
static char PassID;
public:
/// Inform generic clients of the result type.
[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. llvm-svn: 200903
2014-02-06 05:37:03 +01:00
typedef LazyCallGraph Result;
/// Compute the \c LazyCallGraph for the module \c M.
[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. llvm-svn: 200903
2014-02-06 05:37:03 +01:00
///
/// 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, ModuleAnalysisManager &) {
return LazyCallGraph(M);
}
[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. llvm-svn: 200903
2014-02-06 05:37:03 +01:00
};
/// A pass which prints the call graph to a \c raw_ostream.
[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. llvm-svn: 200903
2014-02-06 05:37:03 +01:00
///
/// This is primarily useful for testing the analysis.
class LazyCallGraphPrinterPass
: public PassInfoMixin<LazyCallGraphPrinterPass> {
[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. llvm-svn: 200903
2014-02-06 05:37:03 +01:00
raw_ostream &OS;
public:
explicit LazyCallGraphPrinterPass(raw_ostream &OS);
PreservedAnalyses run(Module &M, ModuleAnalysisManager &AM);
[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. llvm-svn: 200903
2014-02-06 05:37:03 +01:00
};
/// A pass which prints the call graph as a DOT file to a \c raw_ostream.
///
/// This is primarily useful for visualization purposes.
class LazyCallGraphDOTPrinterPass
: public PassInfoMixin<LazyCallGraphDOTPrinterPass> {
raw_ostream &OS;
public:
explicit LazyCallGraphDOTPrinterPass(raw_ostream &OS);
PreservedAnalyses run(Module &M, ModuleAnalysisManager &AM);
};
}
[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. llvm-svn: 200903
2014-02-06 05:37:03 +01:00
#endif