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llvm-mirror/unittests/Analysis/LazyCallGraphTest.cpp

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[LCG] Add a unittest for the LazyCallGraph. I had a weak moment and resisted this for too long. Just with the basic testing here I was able to exercise the analysis in more detail and sift out both type signature bugs in the API and a bug in the DFS numbering. All of these are fixed here as well. The unittests will be much more important for the mutation support where it is necessary to craft minimal mutations and then inspect the state of the graph. There is just no way to do that with a standard FileCheck test. However, unittesting these kinds of analyses is really quite easy, especially as they're designed with the new pass manager where there is essentially no infrastructure required to rig up the core logic and exercise it at an API level. As a minor aside about the DFS numbering bug, the DFS numbering used in LCG is a bit unusual. Rather than numbering from 0, we number from 1, and use 0 as the sentinel "unvisited" state. Other implementations often use '-1' for this, but I find it easier to deal with 0 and it shouldn't make any real difference provided someone doesn't write silly bugs like forgetting to actually initialize the DFS numbering. Oops. ;] llvm-svn: 206954
2014-04-23 10:08:49 +02:00
//===- LazyCallGraphTest.cpp - Unit tests for the lazy CG analysis --------===//
//
// The LLVM Compiler Infrastructure
//
// This file is distributed under the University of Illinois Open Source
// License. See LICENSE.TXT for details.
//
//===----------------------------------------------------------------------===//
#include "llvm/Analysis/LazyCallGraph.h"
#include "llvm/AsmParser/Parser.h"
[LCG] Add the necessary functionality to the LazyCallGraph to support inlining. The basic inlining operation makes the following changes to the call graph: 1) Add edges that were previously transitive edges. This is always trivial and this patch gives the LCG helper methods to make this more convenient. 2) Remove the inlined edge. We had existing support for this, but it contained bugs that needed to be fixed. Testing in the same pattern as the inliner exposes these bugs very nicely. 3) Delete a function when it becomes dead because it is internal and all calls have been inlined. The LCG had no support at all for this operation, so this adds that support. Two unittests have been added that exercise this specific mutation pattern to the call graph. They were extremely effective in uncovering bugs. Sadly, a large fraction of the code here is just to implement those unit tests, but I think they're paying for themselves. =] This was split out of a patch that actually uses the routines to implement inlining in the new pass manager in order to isolate (with unit tests) the logic that was entirely within the LCG. Many thanks for the careful review from folks! There will be a few minor follow-up patches based on the comments in the review as well. Differential Revision: https://reviews.llvm.org/D24225 llvm-svn: 283982
2016-10-12 09:59:56 +02:00
#include "llvm/IR/Instructions.h"
[LCG] Add a unittest for the LazyCallGraph. I had a weak moment and resisted this for too long. Just with the basic testing here I was able to exercise the analysis in more detail and sift out both type signature bugs in the API and a bug in the DFS numbering. All of these are fixed here as well. The unittests will be much more important for the mutation support where it is necessary to craft minimal mutations and then inspect the state of the graph. There is just no way to do that with a standard FileCheck test. However, unittesting these kinds of analyses is really quite easy, especially as they're designed with the new pass manager where there is essentially no infrastructure required to rig up the core logic and exercise it at an API level. As a minor aside about the DFS numbering bug, the DFS numbering used in LCG is a bit unusual. Rather than numbering from 0, we number from 1, and use 0 as the sentinel "unvisited" state. Other implementations often use '-1' for this, but I find it easier to deal with 0 and it shouldn't make any real difference provided someone doesn't write silly bugs like forgetting to actually initialize the DFS numbering. Oops. ;] llvm-svn: 206954
2014-04-23 10:08:49 +02:00
#include "llvm/IR/Function.h"
#include "llvm/IR/LLVMContext.h"
#include "llvm/IR/Module.h"
#include "llvm/Support/ErrorHandling.h"
#include "llvm/Support/SourceMgr.h"
#include "gtest/gtest.h"
#include <memory>
using namespace llvm;
namespace {
Remove every uses of getGlobalContext() in LLVM (but the C API) At the same time, fixes InstructionsTest::CastInst unittest: yes you can leave the IR in an invalid state and exit when you don't destroy the context (like the global one), no longer now. This is the first part of http://reviews.llvm.org/D19094 From: Mehdi Amini <mehdi.amini@apple.com> llvm-svn: 266379
2016-04-14 23:59:01 +02:00
std::unique_ptr<Module> parseAssembly(LLVMContext &Context,
const char *Assembly) {
[LCG] Add a unittest for the LazyCallGraph. I had a weak moment and resisted this for too long. Just with the basic testing here I was able to exercise the analysis in more detail and sift out both type signature bugs in the API and a bug in the DFS numbering. All of these are fixed here as well. The unittests will be much more important for the mutation support where it is necessary to craft minimal mutations and then inspect the state of the graph. There is just no way to do that with a standard FileCheck test. However, unittesting these kinds of analyses is really quite easy, especially as they're designed with the new pass manager where there is essentially no infrastructure required to rig up the core logic and exercise it at an API level. As a minor aside about the DFS numbering bug, the DFS numbering used in LCG is a bit unusual. Rather than numbering from 0, we number from 1, and use 0 as the sentinel "unvisited" state. Other implementations often use '-1' for this, but I find it easier to deal with 0 and it shouldn't make any real difference provided someone doesn't write silly bugs like forgetting to actually initialize the DFS numbering. Oops. ;] llvm-svn: 206954
2014-04-23 10:08:49 +02:00
SMDiagnostic Error;
Remove every uses of getGlobalContext() in LLVM (but the C API) At the same time, fixes InstructionsTest::CastInst unittest: yes you can leave the IR in an invalid state and exit when you don't destroy the context (like the global one), no longer now. This is the first part of http://reviews.llvm.org/D19094 From: Mehdi Amini <mehdi.amini@apple.com> llvm-svn: 266379
2016-04-14 23:59:01 +02:00
std::unique_ptr<Module> M = parseAssemblyString(Assembly, Error, Context);
[LCG] Add a unittest for the LazyCallGraph. I had a weak moment and resisted this for too long. Just with the basic testing here I was able to exercise the analysis in more detail and sift out both type signature bugs in the API and a bug in the DFS numbering. All of these are fixed here as well. The unittests will be much more important for the mutation support where it is necessary to craft minimal mutations and then inspect the state of the graph. There is just no way to do that with a standard FileCheck test. However, unittesting these kinds of analyses is really quite easy, especially as they're designed with the new pass manager where there is essentially no infrastructure required to rig up the core logic and exercise it at an API level. As a minor aside about the DFS numbering bug, the DFS numbering used in LCG is a bit unusual. Rather than numbering from 0, we number from 1, and use 0 as the sentinel "unvisited" state. Other implementations often use '-1' for this, but I find it easier to deal with 0 and it shouldn't make any real difference provided someone doesn't write silly bugs like forgetting to actually initialize the DFS numbering. Oops. ;] llvm-svn: 206954
2014-04-23 10:08:49 +02:00
std::string ErrMsg;
raw_string_ostream OS(ErrMsg);
Error.print("", OS);
// A failure here means that the test itself is buggy.
Modernize the .ll parsing interface. * Use StringRef instead of std::string& * Return a std::unique_ptr<Module> instead of taking an optional module to write to (was not really used). * Use current comment style. * Use current naming convention. llvm-svn: 215989
2014-08-19 18:58:54 +02:00
if (!M)
[LCG] Add a unittest for the LazyCallGraph. I had a weak moment and resisted this for too long. Just with the basic testing here I was able to exercise the analysis in more detail and sift out both type signature bugs in the API and a bug in the DFS numbering. All of these are fixed here as well. The unittests will be much more important for the mutation support where it is necessary to craft minimal mutations and then inspect the state of the graph. There is just no way to do that with a standard FileCheck test. However, unittesting these kinds of analyses is really quite easy, especially as they're designed with the new pass manager where there is essentially no infrastructure required to rig up the core logic and exercise it at an API level. As a minor aside about the DFS numbering bug, the DFS numbering used in LCG is a bit unusual. Rather than numbering from 0, we number from 1, and use 0 as the sentinel "unvisited" state. Other implementations often use '-1' for this, but I find it easier to deal with 0 and it shouldn't make any real difference provided someone doesn't write silly bugs like forgetting to actually initialize the DFS numbering. Oops. ;] llvm-svn: 206954
2014-04-23 10:08:49 +02:00
report_fatal_error(OS.str().c_str());
return M;
}
Silence gcc's -Wcomment gcc's (4.7, I think) -Wcomment warning is not "as smart" as clang's and warns even if the line right after the backslash-newline sequence only has a line comment that starts at the beginning of the line. llvm-svn: 220360
2014-10-22 04:16:06 +02:00
/*
IR forming a call graph with a diamond of triangle-shaped SCCs:
d1
/ \
d3--d2
/ \
b1 c1
/ \ / \
b3--b2 c3--c2
\ /
a1
/ \
a3--a2
All call edges go up between SCCs, and clockwise around the SCC.
*/
[LCG] Add a unittest for the LazyCallGraph. I had a weak moment and resisted this for too long. Just with the basic testing here I was able to exercise the analysis in more detail and sift out both type signature bugs in the API and a bug in the DFS numbering. All of these are fixed here as well. The unittests will be much more important for the mutation support where it is necessary to craft minimal mutations and then inspect the state of the graph. There is just no way to do that with a standard FileCheck test. However, unittesting these kinds of analyses is really quite easy, especially as they're designed with the new pass manager where there is essentially no infrastructure required to rig up the core logic and exercise it at an API level. As a minor aside about the DFS numbering bug, the DFS numbering used in LCG is a bit unusual. Rather than numbering from 0, we number from 1, and use 0 as the sentinel "unvisited" state. Other implementations often use '-1' for this, but I find it easier to deal with 0 and it shouldn't make any real difference provided someone doesn't write silly bugs like forgetting to actually initialize the DFS numbering. Oops. ;] llvm-svn: 206954
2014-04-23 10:08:49 +02:00
static const char DiamondOfTriangles[] =
"define void @a1() {\n"
"entry:\n"
" call void @a2()\n"
" call void @b2()\n"
" call void @c3()\n"
" ret void\n"
"}\n"
"define void @a2() {\n"
"entry:\n"
" call void @a3()\n"
" ret void\n"
"}\n"
"define void @a3() {\n"
"entry:\n"
" call void @a1()\n"
" ret void\n"
"}\n"
"define void @b1() {\n"
"entry:\n"
" call void @b2()\n"
" call void @d3()\n"
" ret void\n"
"}\n"
"define void @b2() {\n"
"entry:\n"
" call void @b3()\n"
" ret void\n"
"}\n"
"define void @b3() {\n"
"entry:\n"
" call void @b1()\n"
" ret void\n"
"}\n"
"define void @c1() {\n"
"entry:\n"
" call void @c2()\n"
" call void @d2()\n"
" ret void\n"
"}\n"
"define void @c2() {\n"
"entry:\n"
" call void @c3()\n"
" ret void\n"
"}\n"
"define void @c3() {\n"
"entry:\n"
" call void @c1()\n"
" ret void\n"
"}\n"
"define void @d1() {\n"
"entry:\n"
" call void @d2()\n"
" ret void\n"
"}\n"
"define void @d2() {\n"
"entry:\n"
" call void @d3()\n"
" ret void\n"
"}\n"
"define void @d3() {\n"
"entry:\n"
" call void @d1()\n"
" ret void\n"
"}\n";
[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
/*
IR forming a reference graph with a diamond of triangle-shaped RefSCCs
d1
/ \
d3--d2
/ \
b1 c1
/ \ / \
b3--b2 c3--c2
\ /
a1
/ \
a3--a2
All call edges go up between RefSCCs, and clockwise around the RefSCC.
*/
static const char DiamondOfTrianglesRefGraph[] =
"define void @a1() {\n"
"entry:\n"
" %a = alloca void ()*\n"
" store void ()* @a2, void ()** %a\n"
" store void ()* @b2, void ()** %a\n"
" store void ()* @c3, void ()** %a\n"
" ret void\n"
"}\n"
"define void @a2() {\n"
"entry:\n"
" %a = alloca void ()*\n"
" store void ()* @a3, void ()** %a\n"
" ret void\n"
"}\n"
"define void @a3() {\n"
"entry:\n"
" %a = alloca void ()*\n"
" store void ()* @a1, void ()** %a\n"
" ret void\n"
"}\n"
"define void @b1() {\n"
"entry:\n"
" %a = alloca void ()*\n"
" store void ()* @b2, void ()** %a\n"
" store void ()* @d3, void ()** %a\n"
" ret void\n"
"}\n"
"define void @b2() {\n"
"entry:\n"
" %a = alloca void ()*\n"
" store void ()* @b3, void ()** %a\n"
" ret void\n"
"}\n"
"define void @b3() {\n"
"entry:\n"
" %a = alloca void ()*\n"
" store void ()* @b1, void ()** %a\n"
" ret void\n"
"}\n"
"define void @c1() {\n"
"entry:\n"
" %a = alloca void ()*\n"
" store void ()* @c2, void ()** %a\n"
" store void ()* @d2, void ()** %a\n"
" ret void\n"
"}\n"
"define void @c2() {\n"
"entry:\n"
" %a = alloca void ()*\n"
" store void ()* @c3, void ()** %a\n"
" ret void\n"
"}\n"
"define void @c3() {\n"
"entry:\n"
" %a = alloca void ()*\n"
" store void ()* @c1, void ()** %a\n"
" ret void\n"
"}\n"
"define void @d1() {\n"
"entry:\n"
" %a = alloca void ()*\n"
" store void ()* @d2, void ()** %a\n"
" ret void\n"
"}\n"
"define void @d2() {\n"
"entry:\n"
" %a = alloca void ()*\n"
" store void ()* @d3, void ()** %a\n"
" ret void\n"
"}\n"
"define void @d3() {\n"
"entry:\n"
" %a = alloca void ()*\n"
" store void ()* @d1, void ()** %a\n"
" ret void\n"
"}\n";
[LCG] Add a unittest for the LazyCallGraph. I had a weak moment and resisted this for too long. Just with the basic testing here I was able to exercise the analysis in more detail and sift out both type signature bugs in the API and a bug in the DFS numbering. All of these are fixed here as well. The unittests will be much more important for the mutation support where it is necessary to craft minimal mutations and then inspect the state of the graph. There is just no way to do that with a standard FileCheck test. However, unittesting these kinds of analyses is really quite easy, especially as they're designed with the new pass manager where there is essentially no infrastructure required to rig up the core logic and exercise it at an API level. As a minor aside about the DFS numbering bug, the DFS numbering used in LCG is a bit unusual. Rather than numbering from 0, we number from 1, and use 0 as the sentinel "unvisited" state. Other implementations often use '-1' for this, but I find it easier to deal with 0 and it shouldn't make any real difference provided someone doesn't write silly bugs like forgetting to actually initialize the DFS numbering. Oops. ;] llvm-svn: 206954
2014-04-23 10:08:49 +02:00
TEST(LazyCallGraphTest, BasicGraphFormation) {
Remove every uses of getGlobalContext() in LLVM (but the C API) At the same time, fixes InstructionsTest::CastInst unittest: yes you can leave the IR in an invalid state and exit when you don't destroy the context (like the global one), no longer now. This is the first part of http://reviews.llvm.org/D19094 From: Mehdi Amini <mehdi.amini@apple.com> llvm-svn: 266379
2016-04-14 23:59:01 +02:00
LLVMContext Context;
std::unique_ptr<Module> M = parseAssembly(Context, DiamondOfTriangles);
[LCG] Add a unittest for the LazyCallGraph. I had a weak moment and resisted this for too long. Just with the basic testing here I was able to exercise the analysis in more detail and sift out both type signature bugs in the API and a bug in the DFS numbering. All of these are fixed here as well. The unittests will be much more important for the mutation support where it is necessary to craft minimal mutations and then inspect the state of the graph. There is just no way to do that with a standard FileCheck test. However, unittesting these kinds of analyses is really quite easy, especially as they're designed with the new pass manager where there is essentially no infrastructure required to rig up the core logic and exercise it at an API level. As a minor aside about the DFS numbering bug, the DFS numbering used in LCG is a bit unusual. Rather than numbering from 0, we number from 1, and use 0 as the sentinel "unvisited" state. Other implementations often use '-1' for this, but I find it easier to deal with 0 and it shouldn't make any real difference provided someone doesn't write silly bugs like forgetting to actually initialize the DFS numbering. Oops. ;] llvm-svn: 206954
2014-04-23 10:08:49 +02:00
LazyCallGraph CG(*M);
// The order of the entry nodes should be stable w.r.t. the source order of
// the IR, and everything in our module is an entry node, so just directly
// build variables for each node.
auto I = CG.begin();
[LCG] Build an edge abstraction for the LazyCallGraph and use it to differentiate between indirect references to functions an direct calls. This doesn't do a whole lot yet other than change the print out produced by the analysis, but it lays the groundwork for a very major change I'm working on next: teaching the call graph to actually be a call graph, modeling *both* the indirect reference graph and the call graph simultaneously. More details on that in the next patch though. The rest of this is essentially a bunch of over-engineering that won't be interesting until the next patch. But this also isolates essentially all of the churn necessary to introduce the edge abstraction from the very important behavior change necessary in order to separately model the two graphs. So it should make review of the subsequent patch a bit easier at the cost of making this patch seem poorly motivated. ;] Differential Revision: http://reviews.llvm.org/D16038 llvm-svn: 259463
2016-02-02 04:57:13 +01:00
LazyCallGraph::Node &A1 = (I++)->getNode(CG);
[LCG] Switch the primary node iterator to be a *much* more normal C++ iterator, returning a Node by reference on dereference. llvm-svn: 207048
2014-04-24 01:34:48 +02:00
EXPECT_EQ("a1", A1.getFunction().getName());
[LCG] Build an edge abstraction for the LazyCallGraph and use it to differentiate between indirect references to functions an direct calls. This doesn't do a whole lot yet other than change the print out produced by the analysis, but it lays the groundwork for a very major change I'm working on next: teaching the call graph to actually be a call graph, modeling *both* the indirect reference graph and the call graph simultaneously. More details on that in the next patch though. The rest of this is essentially a bunch of over-engineering that won't be interesting until the next patch. But this also isolates essentially all of the churn necessary to introduce the edge abstraction from the very important behavior change necessary in order to separately model the two graphs. So it should make review of the subsequent patch a bit easier at the cost of making this patch seem poorly motivated. ;] Differential Revision: http://reviews.llvm.org/D16038 llvm-svn: 259463
2016-02-02 04:57:13 +01:00
LazyCallGraph::Node &A2 = (I++)->getNode(CG);
[LCG] Switch the primary node iterator to be a *much* more normal C++ iterator, returning a Node by reference on dereference. llvm-svn: 207048
2014-04-24 01:34:48 +02:00
EXPECT_EQ("a2", A2.getFunction().getName());
[LCG] Build an edge abstraction for the LazyCallGraph and use it to differentiate between indirect references to functions an direct calls. This doesn't do a whole lot yet other than change the print out produced by the analysis, but it lays the groundwork for a very major change I'm working on next: teaching the call graph to actually be a call graph, modeling *both* the indirect reference graph and the call graph simultaneously. More details on that in the next patch though. The rest of this is essentially a bunch of over-engineering that won't be interesting until the next patch. But this also isolates essentially all of the churn necessary to introduce the edge abstraction from the very important behavior change necessary in order to separately model the two graphs. So it should make review of the subsequent patch a bit easier at the cost of making this patch seem poorly motivated. ;] Differential Revision: http://reviews.llvm.org/D16038 llvm-svn: 259463
2016-02-02 04:57:13 +01:00
LazyCallGraph::Node &A3 = (I++)->getNode(CG);
[LCG] Switch the primary node iterator to be a *much* more normal C++ iterator, returning a Node by reference on dereference. llvm-svn: 207048
2014-04-24 01:34:48 +02:00
EXPECT_EQ("a3", A3.getFunction().getName());
[LCG] Build an edge abstraction for the LazyCallGraph and use it to differentiate between indirect references to functions an direct calls. This doesn't do a whole lot yet other than change the print out produced by the analysis, but it lays the groundwork for a very major change I'm working on next: teaching the call graph to actually be a call graph, modeling *both* the indirect reference graph and the call graph simultaneously. More details on that in the next patch though. The rest of this is essentially a bunch of over-engineering that won't be interesting until the next patch. But this also isolates essentially all of the churn necessary to introduce the edge abstraction from the very important behavior change necessary in order to separately model the two graphs. So it should make review of the subsequent patch a bit easier at the cost of making this patch seem poorly motivated. ;] Differential Revision: http://reviews.llvm.org/D16038 llvm-svn: 259463
2016-02-02 04:57:13 +01:00
LazyCallGraph::Node &B1 = (I++)->getNode(CG);
[LCG] Switch the primary node iterator to be a *much* more normal C++ iterator, returning a Node by reference on dereference. llvm-svn: 207048
2014-04-24 01:34:48 +02:00
EXPECT_EQ("b1", B1.getFunction().getName());
[LCG] Build an edge abstraction for the LazyCallGraph and use it to differentiate between indirect references to functions an direct calls. This doesn't do a whole lot yet other than change the print out produced by the analysis, but it lays the groundwork for a very major change I'm working on next: teaching the call graph to actually be a call graph, modeling *both* the indirect reference graph and the call graph simultaneously. More details on that in the next patch though. The rest of this is essentially a bunch of over-engineering that won't be interesting until the next patch. But this also isolates essentially all of the churn necessary to introduce the edge abstraction from the very important behavior change necessary in order to separately model the two graphs. So it should make review of the subsequent patch a bit easier at the cost of making this patch seem poorly motivated. ;] Differential Revision: http://reviews.llvm.org/D16038 llvm-svn: 259463
2016-02-02 04:57:13 +01:00
LazyCallGraph::Node &B2 = (I++)->getNode(CG);
[LCG] Switch the primary node iterator to be a *much* more normal C++ iterator, returning a Node by reference on dereference. llvm-svn: 207048
2014-04-24 01:34:48 +02:00
EXPECT_EQ("b2", B2.getFunction().getName());
[LCG] Build an edge abstraction for the LazyCallGraph and use it to differentiate between indirect references to functions an direct calls. This doesn't do a whole lot yet other than change the print out produced by the analysis, but it lays the groundwork for a very major change I'm working on next: teaching the call graph to actually be a call graph, modeling *both* the indirect reference graph and the call graph simultaneously. More details on that in the next patch though. The rest of this is essentially a bunch of over-engineering that won't be interesting until the next patch. But this also isolates essentially all of the churn necessary to introduce the edge abstraction from the very important behavior change necessary in order to separately model the two graphs. So it should make review of the subsequent patch a bit easier at the cost of making this patch seem poorly motivated. ;] Differential Revision: http://reviews.llvm.org/D16038 llvm-svn: 259463
2016-02-02 04:57:13 +01:00
LazyCallGraph::Node &B3 = (I++)->getNode(CG);
[LCG] Switch the primary node iterator to be a *much* more normal C++ iterator, returning a Node by reference on dereference. llvm-svn: 207048
2014-04-24 01:34:48 +02:00
EXPECT_EQ("b3", B3.getFunction().getName());
[LCG] Build an edge abstraction for the LazyCallGraph and use it to differentiate between indirect references to functions an direct calls. This doesn't do a whole lot yet other than change the print out produced by the analysis, but it lays the groundwork for a very major change I'm working on next: teaching the call graph to actually be a call graph, modeling *both* the indirect reference graph and the call graph simultaneously. More details on that in the next patch though. The rest of this is essentially a bunch of over-engineering that won't be interesting until the next patch. But this also isolates essentially all of the churn necessary to introduce the edge abstraction from the very important behavior change necessary in order to separately model the two graphs. So it should make review of the subsequent patch a bit easier at the cost of making this patch seem poorly motivated. ;] Differential Revision: http://reviews.llvm.org/D16038 llvm-svn: 259463
2016-02-02 04:57:13 +01:00
LazyCallGraph::Node &C1 = (I++)->getNode(CG);
[LCG] Switch the primary node iterator to be a *much* more normal C++ iterator, returning a Node by reference on dereference. llvm-svn: 207048
2014-04-24 01:34:48 +02:00
EXPECT_EQ("c1", C1.getFunction().getName());
[LCG] Build an edge abstraction for the LazyCallGraph and use it to differentiate between indirect references to functions an direct calls. This doesn't do a whole lot yet other than change the print out produced by the analysis, but it lays the groundwork for a very major change I'm working on next: teaching the call graph to actually be a call graph, modeling *both* the indirect reference graph and the call graph simultaneously. More details on that in the next patch though. The rest of this is essentially a bunch of over-engineering that won't be interesting until the next patch. But this also isolates essentially all of the churn necessary to introduce the edge abstraction from the very important behavior change necessary in order to separately model the two graphs. So it should make review of the subsequent patch a bit easier at the cost of making this patch seem poorly motivated. ;] Differential Revision: http://reviews.llvm.org/D16038 llvm-svn: 259463
2016-02-02 04:57:13 +01:00
LazyCallGraph::Node &C2 = (I++)->getNode(CG);
[LCG] Switch the primary node iterator to be a *much* more normal C++ iterator, returning a Node by reference on dereference. llvm-svn: 207048
2014-04-24 01:34:48 +02:00
EXPECT_EQ("c2", C2.getFunction().getName());
[LCG] Build an edge abstraction for the LazyCallGraph and use it to differentiate between indirect references to functions an direct calls. This doesn't do a whole lot yet other than change the print out produced by the analysis, but it lays the groundwork for a very major change I'm working on next: teaching the call graph to actually be a call graph, modeling *both* the indirect reference graph and the call graph simultaneously. More details on that in the next patch though. The rest of this is essentially a bunch of over-engineering that won't be interesting until the next patch. But this also isolates essentially all of the churn necessary to introduce the edge abstraction from the very important behavior change necessary in order to separately model the two graphs. So it should make review of the subsequent patch a bit easier at the cost of making this patch seem poorly motivated. ;] Differential Revision: http://reviews.llvm.org/D16038 llvm-svn: 259463
2016-02-02 04:57:13 +01:00
LazyCallGraph::Node &C3 = (I++)->getNode(CG);
[LCG] Switch the primary node iterator to be a *much* more normal C++ iterator, returning a Node by reference on dereference. llvm-svn: 207048
2014-04-24 01:34:48 +02:00
EXPECT_EQ("c3", C3.getFunction().getName());
[LCG] Build an edge abstraction for the LazyCallGraph and use it to differentiate between indirect references to functions an direct calls. This doesn't do a whole lot yet other than change the print out produced by the analysis, but it lays the groundwork for a very major change I'm working on next: teaching the call graph to actually be a call graph, modeling *both* the indirect reference graph and the call graph simultaneously. More details on that in the next patch though. The rest of this is essentially a bunch of over-engineering that won't be interesting until the next patch. But this also isolates essentially all of the churn necessary to introduce the edge abstraction from the very important behavior change necessary in order to separately model the two graphs. So it should make review of the subsequent patch a bit easier at the cost of making this patch seem poorly motivated. ;] Differential Revision: http://reviews.llvm.org/D16038 llvm-svn: 259463
2016-02-02 04:57:13 +01:00
LazyCallGraph::Node &D1 = (I++)->getNode(CG);
[LCG] Switch the primary node iterator to be a *much* more normal C++ iterator, returning a Node by reference on dereference. llvm-svn: 207048
2014-04-24 01:34:48 +02:00
EXPECT_EQ("d1", D1.getFunction().getName());
[LCG] Build an edge abstraction for the LazyCallGraph and use it to differentiate between indirect references to functions an direct calls. This doesn't do a whole lot yet other than change the print out produced by the analysis, but it lays the groundwork for a very major change I'm working on next: teaching the call graph to actually be a call graph, modeling *both* the indirect reference graph and the call graph simultaneously. More details on that in the next patch though. The rest of this is essentially a bunch of over-engineering that won't be interesting until the next patch. But this also isolates essentially all of the churn necessary to introduce the edge abstraction from the very important behavior change necessary in order to separately model the two graphs. So it should make review of the subsequent patch a bit easier at the cost of making this patch seem poorly motivated. ;] Differential Revision: http://reviews.llvm.org/D16038 llvm-svn: 259463
2016-02-02 04:57:13 +01:00
LazyCallGraph::Node &D2 = (I++)->getNode(CG);
[LCG] Switch the primary node iterator to be a *much* more normal C++ iterator, returning a Node by reference on dereference. llvm-svn: 207048
2014-04-24 01:34:48 +02:00
EXPECT_EQ("d2", D2.getFunction().getName());
[LCG] Build an edge abstraction for the LazyCallGraph and use it to differentiate between indirect references to functions an direct calls. This doesn't do a whole lot yet other than change the print out produced by the analysis, but it lays the groundwork for a very major change I'm working on next: teaching the call graph to actually be a call graph, modeling *both* the indirect reference graph and the call graph simultaneously. More details on that in the next patch though. The rest of this is essentially a bunch of over-engineering that won't be interesting until the next patch. But this also isolates essentially all of the churn necessary to introduce the edge abstraction from the very important behavior change necessary in order to separately model the two graphs. So it should make review of the subsequent patch a bit easier at the cost of making this patch seem poorly motivated. ;] Differential Revision: http://reviews.llvm.org/D16038 llvm-svn: 259463
2016-02-02 04:57:13 +01:00
LazyCallGraph::Node &D3 = (I++)->getNode(CG);
[LCG] Switch the primary node iterator to be a *much* more normal C++ iterator, returning a Node by reference on dereference. llvm-svn: 207048
2014-04-24 01:34:48 +02:00
EXPECT_EQ("d3", D3.getFunction().getName());
[LCG] Add a unittest for the LazyCallGraph. I had a weak moment and resisted this for too long. Just with the basic testing here I was able to exercise the analysis in more detail and sift out both type signature bugs in the API and a bug in the DFS numbering. All of these are fixed here as well. The unittests will be much more important for the mutation support where it is necessary to craft minimal mutations and then inspect the state of the graph. There is just no way to do that with a standard FileCheck test. However, unittesting these kinds of analyses is really quite easy, especially as they're designed with the new pass manager where there is essentially no infrastructure required to rig up the core logic and exercise it at an API level. As a minor aside about the DFS numbering bug, the DFS numbering used in LCG is a bit unusual. Rather than numbering from 0, we number from 1, and use 0 as the sentinel "unvisited" state. Other implementations often use '-1' for this, but I find it easier to deal with 0 and it shouldn't make any real difference provided someone doesn't write silly bugs like forgetting to actually initialize the DFS numbering. Oops. ;] llvm-svn: 206954
2014-04-23 10:08:49 +02:00
EXPECT_EQ(CG.end(), I);
// Build vectors and sort them for the rest of the assertions to make them
// independent of order.
std::vector<std::string> Nodes;
[LCG] Build an edge abstraction for the LazyCallGraph and use it to differentiate between indirect references to functions an direct calls. This doesn't do a whole lot yet other than change the print out produced by the analysis, but it lays the groundwork for a very major change I'm working on next: teaching the call graph to actually be a call graph, modeling *both* the indirect reference graph and the call graph simultaneously. More details on that in the next patch though. The rest of this is essentially a bunch of over-engineering that won't be interesting until the next patch. But this also isolates essentially all of the churn necessary to introduce the edge abstraction from the very important behavior change necessary in order to separately model the two graphs. So it should make review of the subsequent patch a bit easier at the cost of making this patch seem poorly motivated. ;] Differential Revision: http://reviews.llvm.org/D16038 llvm-svn: 259463
2016-02-02 04:57:13 +01:00
for (LazyCallGraph::Edge &E : A1)
Nodes.push_back(E.getFunction().getName());
[LCG] Add a unittest for the LazyCallGraph. I had a weak moment and resisted this for too long. Just with the basic testing here I was able to exercise the analysis in more detail and sift out both type signature bugs in the API and a bug in the DFS numbering. All of these are fixed here as well. The unittests will be much more important for the mutation support where it is necessary to craft minimal mutations and then inspect the state of the graph. There is just no way to do that with a standard FileCheck test. However, unittesting these kinds of analyses is really quite easy, especially as they're designed with the new pass manager where there is essentially no infrastructure required to rig up the core logic and exercise it at an API level. As a minor aside about the DFS numbering bug, the DFS numbering used in LCG is a bit unusual. Rather than numbering from 0, we number from 1, and use 0 as the sentinel "unvisited" state. Other implementations often use '-1' for this, but I find it easier to deal with 0 and it shouldn't make any real difference provided someone doesn't write silly bugs like forgetting to actually initialize the DFS numbering. Oops. ;] llvm-svn: 206954
2014-04-23 10:08:49 +02:00
std::sort(Nodes.begin(), Nodes.end());
EXPECT_EQ("a2", Nodes[0]);
EXPECT_EQ("b2", Nodes[1]);
EXPECT_EQ("c3", Nodes[2]);
Nodes.clear();
[LCG] Switch the primary node iterator to be a *much* more normal C++ iterator, returning a Node by reference on dereference. llvm-svn: 207048
2014-04-24 01:34:48 +02:00
EXPECT_EQ(A2.end(), std::next(A2.begin()));
EXPECT_EQ("a3", A2.begin()->getFunction().getName());
EXPECT_EQ(A3.end(), std::next(A3.begin()));
EXPECT_EQ("a1", A3.begin()->getFunction().getName());
[LCG] Add a unittest for the LazyCallGraph. I had a weak moment and resisted this for too long. Just with the basic testing here I was able to exercise the analysis in more detail and sift out both type signature bugs in the API and a bug in the DFS numbering. All of these are fixed here as well. The unittests will be much more important for the mutation support where it is necessary to craft minimal mutations and then inspect the state of the graph. There is just no way to do that with a standard FileCheck test. However, unittesting these kinds of analyses is really quite easy, especially as they're designed with the new pass manager where there is essentially no infrastructure required to rig up the core logic and exercise it at an API level. As a minor aside about the DFS numbering bug, the DFS numbering used in LCG is a bit unusual. Rather than numbering from 0, we number from 1, and use 0 as the sentinel "unvisited" state. Other implementations often use '-1' for this, but I find it easier to deal with 0 and it shouldn't make any real difference provided someone doesn't write silly bugs like forgetting to actually initialize the DFS numbering. Oops. ;] llvm-svn: 206954
2014-04-23 10:08:49 +02:00
[LCG] Build an edge abstraction for the LazyCallGraph and use it to differentiate between indirect references to functions an direct calls. This doesn't do a whole lot yet other than change the print out produced by the analysis, but it lays the groundwork for a very major change I'm working on next: teaching the call graph to actually be a call graph, modeling *both* the indirect reference graph and the call graph simultaneously. More details on that in the next patch though. The rest of this is essentially a bunch of over-engineering that won't be interesting until the next patch. But this also isolates essentially all of the churn necessary to introduce the edge abstraction from the very important behavior change necessary in order to separately model the two graphs. So it should make review of the subsequent patch a bit easier at the cost of making this patch seem poorly motivated. ;] Differential Revision: http://reviews.llvm.org/D16038 llvm-svn: 259463
2016-02-02 04:57:13 +01:00
for (LazyCallGraph::Edge &E : B1)
Nodes.push_back(E.getFunction().getName());
[LCG] Add a unittest for the LazyCallGraph. I had a weak moment and resisted this for too long. Just with the basic testing here I was able to exercise the analysis in more detail and sift out both type signature bugs in the API and a bug in the DFS numbering. All of these are fixed here as well. The unittests will be much more important for the mutation support where it is necessary to craft minimal mutations and then inspect the state of the graph. There is just no way to do that with a standard FileCheck test. However, unittesting these kinds of analyses is really quite easy, especially as they're designed with the new pass manager where there is essentially no infrastructure required to rig up the core logic and exercise it at an API level. As a minor aside about the DFS numbering bug, the DFS numbering used in LCG is a bit unusual. Rather than numbering from 0, we number from 1, and use 0 as the sentinel "unvisited" state. Other implementations often use '-1' for this, but I find it easier to deal with 0 and it shouldn't make any real difference provided someone doesn't write silly bugs like forgetting to actually initialize the DFS numbering. Oops. ;] llvm-svn: 206954
2014-04-23 10:08:49 +02:00
std::sort(Nodes.begin(), Nodes.end());
EXPECT_EQ("b2", Nodes[0]);
EXPECT_EQ("d3", Nodes[1]);
Nodes.clear();
[LCG] Switch the primary node iterator to be a *much* more normal C++ iterator, returning a Node by reference on dereference. llvm-svn: 207048
2014-04-24 01:34:48 +02:00
EXPECT_EQ(B2.end(), std::next(B2.begin()));
EXPECT_EQ("b3", B2.begin()->getFunction().getName());
EXPECT_EQ(B3.end(), std::next(B3.begin()));
EXPECT_EQ("b1", B3.begin()->getFunction().getName());
[LCG] Add a unittest for the LazyCallGraph. I had a weak moment and resisted this for too long. Just with the basic testing here I was able to exercise the analysis in more detail and sift out both type signature bugs in the API and a bug in the DFS numbering. All of these are fixed here as well. The unittests will be much more important for the mutation support where it is necessary to craft minimal mutations and then inspect the state of the graph. There is just no way to do that with a standard FileCheck test. However, unittesting these kinds of analyses is really quite easy, especially as they're designed with the new pass manager where there is essentially no infrastructure required to rig up the core logic and exercise it at an API level. As a minor aside about the DFS numbering bug, the DFS numbering used in LCG is a bit unusual. Rather than numbering from 0, we number from 1, and use 0 as the sentinel "unvisited" state. Other implementations often use '-1' for this, but I find it easier to deal with 0 and it shouldn't make any real difference provided someone doesn't write silly bugs like forgetting to actually initialize the DFS numbering. Oops. ;] llvm-svn: 206954
2014-04-23 10:08:49 +02:00
[LCG] Build an edge abstraction for the LazyCallGraph and use it to differentiate between indirect references to functions an direct calls. This doesn't do a whole lot yet other than change the print out produced by the analysis, but it lays the groundwork for a very major change I'm working on next: teaching the call graph to actually be a call graph, modeling *both* the indirect reference graph and the call graph simultaneously. More details on that in the next patch though. The rest of this is essentially a bunch of over-engineering that won't be interesting until the next patch. But this also isolates essentially all of the churn necessary to introduce the edge abstraction from the very important behavior change necessary in order to separately model the two graphs. So it should make review of the subsequent patch a bit easier at the cost of making this patch seem poorly motivated. ;] Differential Revision: http://reviews.llvm.org/D16038 llvm-svn: 259463
2016-02-02 04:57:13 +01:00
for (LazyCallGraph::Edge &E : C1)
Nodes.push_back(E.getFunction().getName());
[LCG] Add a unittest for the LazyCallGraph. I had a weak moment and resisted this for too long. Just with the basic testing here I was able to exercise the analysis in more detail and sift out both type signature bugs in the API and a bug in the DFS numbering. All of these are fixed here as well. The unittests will be much more important for the mutation support where it is necessary to craft minimal mutations and then inspect the state of the graph. There is just no way to do that with a standard FileCheck test. However, unittesting these kinds of analyses is really quite easy, especially as they're designed with the new pass manager where there is essentially no infrastructure required to rig up the core logic and exercise it at an API level. As a minor aside about the DFS numbering bug, the DFS numbering used in LCG is a bit unusual. Rather than numbering from 0, we number from 1, and use 0 as the sentinel "unvisited" state. Other implementations often use '-1' for this, but I find it easier to deal with 0 and it shouldn't make any real difference provided someone doesn't write silly bugs like forgetting to actually initialize the DFS numbering. Oops. ;] llvm-svn: 206954
2014-04-23 10:08:49 +02:00
std::sort(Nodes.begin(), Nodes.end());
EXPECT_EQ("c2", Nodes[0]);
EXPECT_EQ("d2", Nodes[1]);
Nodes.clear();
[LCG] Switch the primary node iterator to be a *much* more normal C++ iterator, returning a Node by reference on dereference. llvm-svn: 207048
2014-04-24 01:34:48 +02:00
EXPECT_EQ(C2.end(), std::next(C2.begin()));
EXPECT_EQ("c3", C2.begin()->getFunction().getName());
EXPECT_EQ(C3.end(), std::next(C3.begin()));
EXPECT_EQ("c1", C3.begin()->getFunction().getName());
EXPECT_EQ(D1.end(), std::next(D1.begin()));
EXPECT_EQ("d2", D1.begin()->getFunction().getName());
EXPECT_EQ(D2.end(), std::next(D2.begin()));
EXPECT_EQ("d3", D2.begin()->getFunction().getName());
EXPECT_EQ(D3.end(), std::next(D3.begin()));
EXPECT_EQ("d1", D3.begin()->getFunction().getName());
[LCG] Add a unittest for the LazyCallGraph. I had a weak moment and resisted this for too long. Just with the basic testing here I was able to exercise the analysis in more detail and sift out both type signature bugs in the API and a bug in the DFS numbering. All of these are fixed here as well. The unittests will be much more important for the mutation support where it is necessary to craft minimal mutations and then inspect the state of the graph. There is just no way to do that with a standard FileCheck test. However, unittesting these kinds of analyses is really quite easy, especially as they're designed with the new pass manager where there is essentially no infrastructure required to rig up the core logic and exercise it at an API level. As a minor aside about the DFS numbering bug, the DFS numbering used in LCG is a bit unusual. Rather than numbering from 0, we number from 1, and use 0 as the sentinel "unvisited" state. Other implementations often use '-1' for this, but I find it easier to deal with 0 and it shouldn't make any real difference provided someone doesn't write silly bugs like forgetting to actually initialize the DFS numbering. Oops. ;] llvm-svn: 206954
2014-04-23 10:08:49 +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
// Now lets look at the RefSCCs and SCCs.
auto J = CG.postorder_ref_scc_begin();
[LCG] Add a unittest for the LazyCallGraph. I had a weak moment and resisted this for too long. Just with the basic testing here I was able to exercise the analysis in more detail and sift out both type signature bugs in the API and a bug in the DFS numbering. All of these are fixed here as well. The unittests will be much more important for the mutation support where it is necessary to craft minimal mutations and then inspect the state of the graph. There is just no way to do that with a standard FileCheck test. However, unittesting these kinds of analyses is really quite easy, especially as they're designed with the new pass manager where there is essentially no infrastructure required to rig up the core logic and exercise it at an API level. As a minor aside about the DFS numbering bug, the DFS numbering used in LCG is a bit unusual. Rather than numbering from 0, we number from 1, and use 0 as the sentinel "unvisited" state. Other implementations often use '-1' for this, but I find it easier to deal with 0 and it shouldn't make any real difference provided someone doesn't write silly bugs like forgetting to actually initialize the DFS numbering. Oops. ;] llvm-svn: 206954
2014-04-23 10:08:49 +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
LazyCallGraph::RefSCC &D = *J++;
ASSERT_EQ(1, D.size());
for (LazyCallGraph::Node &N : *D.begin())
Nodes.push_back(N.getFunction().getName());
[LCG] Add a unittest for the LazyCallGraph. I had a weak moment and resisted this for too long. Just with the basic testing here I was able to exercise the analysis in more detail and sift out both type signature bugs in the API and a bug in the DFS numbering. All of these are fixed here as well. The unittests will be much more important for the mutation support where it is necessary to craft minimal mutations and then inspect the state of the graph. There is just no way to do that with a standard FileCheck test. However, unittesting these kinds of analyses is really quite easy, especially as they're designed with the new pass manager where there is essentially no infrastructure required to rig up the core logic and exercise it at an API level. As a minor aside about the DFS numbering bug, the DFS numbering used in LCG is a bit unusual. Rather than numbering from 0, we number from 1, and use 0 as the sentinel "unvisited" state. Other implementations often use '-1' for this, but I find it easier to deal with 0 and it shouldn't make any real difference provided someone doesn't write silly bugs like forgetting to actually initialize the DFS numbering. Oops. ;] llvm-svn: 206954
2014-04-23 10:08:49 +02:00
std::sort(Nodes.begin(), Nodes.end());
[LCG] Re-order expectations to provide more useful output when debugging an issue. This way you see that the number of nodes was wrong before a crash due to accessing too many nodes. llvm-svn: 207094
2014-04-24 11:59:56 +02:00
EXPECT_EQ(3u, Nodes.size());
[LCG] Add a unittest for the LazyCallGraph. I had a weak moment and resisted this for too long. Just with the basic testing here I was able to exercise the analysis in more detail and sift out both type signature bugs in the API and a bug in the DFS numbering. All of these are fixed here as well. The unittests will be much more important for the mutation support where it is necessary to craft minimal mutations and then inspect the state of the graph. There is just no way to do that with a standard FileCheck test. However, unittesting these kinds of analyses is really quite easy, especially as they're designed with the new pass manager where there is essentially no infrastructure required to rig up the core logic and exercise it at an API level. As a minor aside about the DFS numbering bug, the DFS numbering used in LCG is a bit unusual. Rather than numbering from 0, we number from 1, and use 0 as the sentinel "unvisited" state. Other implementations often use '-1' for this, but I find it easier to deal with 0 and it shouldn't make any real difference provided someone doesn't write silly bugs like forgetting to actually initialize the DFS numbering. Oops. ;] llvm-svn: 206954
2014-04-23 10:08:49 +02:00
EXPECT_EQ("d1", Nodes[0]);
EXPECT_EQ("d2", Nodes[1]);
EXPECT_EQ("d3", Nodes[2]);
Nodes.clear();
[LCG] Add some basic methods for querying the parent/child relationships of SCCs in the SCC DAG. Exercise them in the big graph test case. These will be especially useful for establishing invariants in insertion logic. llvm-svn: 207749
2014-05-01 14:12:42 +02:00
EXPECT_FALSE(D.isParentOf(D));
EXPECT_FALSE(D.isChildOf(D));
EXPECT_FALSE(D.isAncestorOf(D));
EXPECT_FALSE(D.isDescendantOf(D));
[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
EXPECT_EQ(&D, &*CG.postorder_ref_scc_begin());
[LCG] Add a unittest for the LazyCallGraph. I had a weak moment and resisted this for too long. Just with the basic testing here I was able to exercise the analysis in more detail and sift out both type signature bugs in the API and a bug in the DFS numbering. All of these are fixed here as well. The unittests will be much more important for the mutation support where it is necessary to craft minimal mutations and then inspect the state of the graph. There is just no way to do that with a standard FileCheck test. However, unittesting these kinds of analyses is really quite easy, especially as they're designed with the new pass manager where there is essentially no infrastructure required to rig up the core logic and exercise it at an API level. As a minor aside about the DFS numbering bug, the DFS numbering used in LCG is a bit unusual. Rather than numbering from 0, we number from 1, and use 0 as the sentinel "unvisited" state. Other implementations often use '-1' for this, but I find it easier to deal with 0 and it shouldn't make any real difference provided someone doesn't write silly bugs like forgetting to actually initialize the DFS numbering. Oops. ;] llvm-svn: 206954
2014-04-23 10:08:49 +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
LazyCallGraph::RefSCC &C = *J++;
ASSERT_EQ(1, C.size());
for (LazyCallGraph::Node &N : *C.begin())
Nodes.push_back(N.getFunction().getName());
[LCG] Add a unittest for the LazyCallGraph. I had a weak moment and resisted this for too long. Just with the basic testing here I was able to exercise the analysis in more detail and sift out both type signature bugs in the API and a bug in the DFS numbering. All of these are fixed here as well. The unittests will be much more important for the mutation support where it is necessary to craft minimal mutations and then inspect the state of the graph. There is just no way to do that with a standard FileCheck test. However, unittesting these kinds of analyses is really quite easy, especially as they're designed with the new pass manager where there is essentially no infrastructure required to rig up the core logic and exercise it at an API level. As a minor aside about the DFS numbering bug, the DFS numbering used in LCG is a bit unusual. Rather than numbering from 0, we number from 1, and use 0 as the sentinel "unvisited" state. Other implementations often use '-1' for this, but I find it easier to deal with 0 and it shouldn't make any real difference provided someone doesn't write silly bugs like forgetting to actually initialize the DFS numbering. Oops. ;] llvm-svn: 206954
2014-04-23 10:08:49 +02:00
std::sort(Nodes.begin(), Nodes.end());
[LCG] Re-order expectations to provide more useful output when debugging an issue. This way you see that the number of nodes was wrong before a crash due to accessing too many nodes. llvm-svn: 207094
2014-04-24 11:59:56 +02:00
EXPECT_EQ(3u, Nodes.size());
[LCG] Add a unittest for the LazyCallGraph. I had a weak moment and resisted this for too long. Just with the basic testing here I was able to exercise the analysis in more detail and sift out both type signature bugs in the API and a bug in the DFS numbering. All of these are fixed here as well. The unittests will be much more important for the mutation support where it is necessary to craft minimal mutations and then inspect the state of the graph. There is just no way to do that with a standard FileCheck test. However, unittesting these kinds of analyses is really quite easy, especially as they're designed with the new pass manager where there is essentially no infrastructure required to rig up the core logic and exercise it at an API level. As a minor aside about the DFS numbering bug, the DFS numbering used in LCG is a bit unusual. Rather than numbering from 0, we number from 1, and use 0 as the sentinel "unvisited" state. Other implementations often use '-1' for this, but I find it easier to deal with 0 and it shouldn't make any real difference provided someone doesn't write silly bugs like forgetting to actually initialize the DFS numbering. Oops. ;] llvm-svn: 206954
2014-04-23 10:08:49 +02:00
EXPECT_EQ("c1", Nodes[0]);
EXPECT_EQ("c2", Nodes[1]);
EXPECT_EQ("c3", Nodes[2]);
Nodes.clear();
[LCG] Add some basic methods for querying the parent/child relationships of SCCs in the SCC DAG. Exercise them in the big graph test case. These will be especially useful for establishing invariants in insertion logic. llvm-svn: 207749
2014-05-01 14:12:42 +02:00
EXPECT_TRUE(C.isParentOf(D));
EXPECT_FALSE(C.isChildOf(D));
EXPECT_TRUE(C.isAncestorOf(D));
EXPECT_FALSE(C.isDescendantOf(D));
[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
EXPECT_EQ(&C, &*std::next(CG.postorder_ref_scc_begin()));
[LCG] Add a unittest for the LazyCallGraph. I had a weak moment and resisted this for too long. Just with the basic testing here I was able to exercise the analysis in more detail and sift out both type signature bugs in the API and a bug in the DFS numbering. All of these are fixed here as well. The unittests will be much more important for the mutation support where it is necessary to craft minimal mutations and then inspect the state of the graph. There is just no way to do that with a standard FileCheck test. However, unittesting these kinds of analyses is really quite easy, especially as they're designed with the new pass manager where there is essentially no infrastructure required to rig up the core logic and exercise it at an API level. As a minor aside about the DFS numbering bug, the DFS numbering used in LCG is a bit unusual. Rather than numbering from 0, we number from 1, and use 0 as the sentinel "unvisited" state. Other implementations often use '-1' for this, but I find it easier to deal with 0 and it shouldn't make any real difference provided someone doesn't write silly bugs like forgetting to actually initialize the DFS numbering. Oops. ;] llvm-svn: 206954
2014-04-23 10:08:49 +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
LazyCallGraph::RefSCC &B = *J++;
ASSERT_EQ(1, B.size());
for (LazyCallGraph::Node &N : *B.begin())
Nodes.push_back(N.getFunction().getName());
[LCG] Add a unittest for the LazyCallGraph. I had a weak moment and resisted this for too long. Just with the basic testing here I was able to exercise the analysis in more detail and sift out both type signature bugs in the API and a bug in the DFS numbering. All of these are fixed here as well. The unittests will be much more important for the mutation support where it is necessary to craft minimal mutations and then inspect the state of the graph. There is just no way to do that with a standard FileCheck test. However, unittesting these kinds of analyses is really quite easy, especially as they're designed with the new pass manager where there is essentially no infrastructure required to rig up the core logic and exercise it at an API level. As a minor aside about the DFS numbering bug, the DFS numbering used in LCG is a bit unusual. Rather than numbering from 0, we number from 1, and use 0 as the sentinel "unvisited" state. Other implementations often use '-1' for this, but I find it easier to deal with 0 and it shouldn't make any real difference provided someone doesn't write silly bugs like forgetting to actually initialize the DFS numbering. Oops. ;] llvm-svn: 206954
2014-04-23 10:08:49 +02:00
std::sort(Nodes.begin(), Nodes.end());
[LCG] Re-order expectations to provide more useful output when debugging an issue. This way you see that the number of nodes was wrong before a crash due to accessing too many nodes. llvm-svn: 207094
2014-04-24 11:59:56 +02:00
EXPECT_EQ(3u, Nodes.size());
[LCG] Add a unittest for the LazyCallGraph. I had a weak moment and resisted this for too long. Just with the basic testing here I was able to exercise the analysis in more detail and sift out both type signature bugs in the API and a bug in the DFS numbering. All of these are fixed here as well. The unittests will be much more important for the mutation support where it is necessary to craft minimal mutations and then inspect the state of the graph. There is just no way to do that with a standard FileCheck test. However, unittesting these kinds of analyses is really quite easy, especially as they're designed with the new pass manager where there is essentially no infrastructure required to rig up the core logic and exercise it at an API level. As a minor aside about the DFS numbering bug, the DFS numbering used in LCG is a bit unusual. Rather than numbering from 0, we number from 1, and use 0 as the sentinel "unvisited" state. Other implementations often use '-1' for this, but I find it easier to deal with 0 and it shouldn't make any real difference provided someone doesn't write silly bugs like forgetting to actually initialize the DFS numbering. Oops. ;] llvm-svn: 206954
2014-04-23 10:08:49 +02:00
EXPECT_EQ("b1", Nodes[0]);
EXPECT_EQ("b2", Nodes[1]);
EXPECT_EQ("b3", Nodes[2]);
Nodes.clear();
[LCG] Add some basic methods for querying the parent/child relationships of SCCs in the SCC DAG. Exercise them in the big graph test case. These will be especially useful for establishing invariants in insertion logic. llvm-svn: 207749
2014-05-01 14:12:42 +02:00
EXPECT_TRUE(B.isParentOf(D));
EXPECT_FALSE(B.isChildOf(D));
EXPECT_TRUE(B.isAncestorOf(D));
EXPECT_FALSE(B.isDescendantOf(D));
EXPECT_FALSE(B.isAncestorOf(C));
EXPECT_FALSE(C.isAncestorOf(B));
[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
EXPECT_EQ(&B, &*std::next(CG.postorder_ref_scc_begin(), 2));
[LCG] Add a unittest for the LazyCallGraph. I had a weak moment and resisted this for too long. Just with the basic testing here I was able to exercise the analysis in more detail and sift out both type signature bugs in the API and a bug in the DFS numbering. All of these are fixed here as well. The unittests will be much more important for the mutation support where it is necessary to craft minimal mutations and then inspect the state of the graph. There is just no way to do that with a standard FileCheck test. However, unittesting these kinds of analyses is really quite easy, especially as they're designed with the new pass manager where there is essentially no infrastructure required to rig up the core logic and exercise it at an API level. As a minor aside about the DFS numbering bug, the DFS numbering used in LCG is a bit unusual. Rather than numbering from 0, we number from 1, and use 0 as the sentinel "unvisited" state. Other implementations often use '-1' for this, but I find it easier to deal with 0 and it shouldn't make any real difference provided someone doesn't write silly bugs like forgetting to actually initialize the DFS numbering. Oops. ;] llvm-svn: 206954
2014-04-23 10:08:49 +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
LazyCallGraph::RefSCC &A = *J++;
ASSERT_EQ(1, A.size());
for (LazyCallGraph::Node &N : *A.begin())
Nodes.push_back(N.getFunction().getName());
[LCG] Add a unittest for the LazyCallGraph. I had a weak moment and resisted this for too long. Just with the basic testing here I was able to exercise the analysis in more detail and sift out both type signature bugs in the API and a bug in the DFS numbering. All of these are fixed here as well. The unittests will be much more important for the mutation support where it is necessary to craft minimal mutations and then inspect the state of the graph. There is just no way to do that with a standard FileCheck test. However, unittesting these kinds of analyses is really quite easy, especially as they're designed with the new pass manager where there is essentially no infrastructure required to rig up the core logic and exercise it at an API level. As a minor aside about the DFS numbering bug, the DFS numbering used in LCG is a bit unusual. Rather than numbering from 0, we number from 1, and use 0 as the sentinel "unvisited" state. Other implementations often use '-1' for this, but I find it easier to deal with 0 and it shouldn't make any real difference provided someone doesn't write silly bugs like forgetting to actually initialize the DFS numbering. Oops. ;] llvm-svn: 206954
2014-04-23 10:08:49 +02:00
std::sort(Nodes.begin(), Nodes.end());
[LCG] Re-order expectations to provide more useful output when debugging an issue. This way you see that the number of nodes was wrong before a crash due to accessing too many nodes. llvm-svn: 207094
2014-04-24 11:59:56 +02:00
EXPECT_EQ(3u, Nodes.size());
[LCG] Add a unittest for the LazyCallGraph. I had a weak moment and resisted this for too long. Just with the basic testing here I was able to exercise the analysis in more detail and sift out both type signature bugs in the API and a bug in the DFS numbering. All of these are fixed here as well. The unittests will be much more important for the mutation support where it is necessary to craft minimal mutations and then inspect the state of the graph. There is just no way to do that with a standard FileCheck test. However, unittesting these kinds of analyses is really quite easy, especially as they're designed with the new pass manager where there is essentially no infrastructure required to rig up the core logic and exercise it at an API level. As a minor aside about the DFS numbering bug, the DFS numbering used in LCG is a bit unusual. Rather than numbering from 0, we number from 1, and use 0 as the sentinel "unvisited" state. Other implementations often use '-1' for this, but I find it easier to deal with 0 and it shouldn't make any real difference provided someone doesn't write silly bugs like forgetting to actually initialize the DFS numbering. Oops. ;] llvm-svn: 206954
2014-04-23 10:08:49 +02:00
EXPECT_EQ("a1", Nodes[0]);
EXPECT_EQ("a2", Nodes[1]);
EXPECT_EQ("a3", Nodes[2]);
Nodes.clear();
[LCG] Add some basic methods for querying the parent/child relationships of SCCs in the SCC DAG. Exercise them in the big graph test case. These will be especially useful for establishing invariants in insertion logic. llvm-svn: 207749
2014-05-01 14:12:42 +02:00
EXPECT_TRUE(A.isParentOf(B));
EXPECT_TRUE(A.isParentOf(C));
EXPECT_FALSE(A.isParentOf(D));
EXPECT_TRUE(A.isAncestorOf(B));
EXPECT_TRUE(A.isAncestorOf(C));
EXPECT_TRUE(A.isAncestorOf(D));
[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
EXPECT_EQ(&A, &*std::next(CG.postorder_ref_scc_begin(), 3));
[LCG] Add a unittest for the LazyCallGraph. I had a weak moment and resisted this for too long. Just with the basic testing here I was able to exercise the analysis in more detail and sift out both type signature bugs in the API and a bug in the DFS numbering. All of these are fixed here as well. The unittests will be much more important for the mutation support where it is necessary to craft minimal mutations and then inspect the state of the graph. There is just no way to do that with a standard FileCheck test. However, unittesting these kinds of analyses is really quite easy, especially as they're designed with the new pass manager where there is essentially no infrastructure required to rig up the core logic and exercise it at an API level. As a minor aside about the DFS numbering bug, the DFS numbering used in LCG is a bit unusual. Rather than numbering from 0, we number from 1, and use 0 as the sentinel "unvisited" state. Other implementations often use '-1' for this, but I find it easier to deal with 0 and it shouldn't make any real difference provided someone doesn't write silly bugs like forgetting to actually initialize the DFS numbering. Oops. ;] llvm-svn: 206954
2014-04-23 10:08:49 +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
EXPECT_EQ(CG.postorder_ref_scc_end(), J);
[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
EXPECT_EQ(J, std::next(CG.postorder_ref_scc_begin(), 4));
[LCG] Add a unittest for the LazyCallGraph. I had a weak moment and resisted this for too long. Just with the basic testing here I was able to exercise the analysis in more detail and sift out both type signature bugs in the API and a bug in the DFS numbering. All of these are fixed here as well. The unittests will be much more important for the mutation support where it is necessary to craft minimal mutations and then inspect the state of the graph. There is just no way to do that with a standard FileCheck test. However, unittesting these kinds of analyses is really quite easy, especially as they're designed with the new pass manager where there is essentially no infrastructure required to rig up the core logic and exercise it at an API level. As a minor aside about the DFS numbering bug, the DFS numbering used in LCG is a bit unusual. Rather than numbering from 0, we number from 1, and use 0 as the sentinel "unvisited" state. Other implementations often use '-1' for this, but I find it easier to deal with 0 and it shouldn't make any real difference provided someone doesn't write silly bugs like forgetting to actually initialize the DFS numbering. Oops. ;] llvm-svn: 206954
2014-04-23 10:08:49 +02:00
}
[LCG] Implement Tarjan's algorithm correctly this time. We have to walk up the stack finishing the exploration of each entries children before we're finished in addition to accounting for their low-links. Added a unittest that really hammers home the need for this with interlocking cycles that would each appear distinct otherwise and crash or compute the wrong result. As part of this, nuke a stale fixme and bring the rest of the implementation still more closely in line with the original algorithm. llvm-svn: 206966
2014-04-23 12:31:17 +02:00
static Function &lookupFunction(Module &M, StringRef Name) {
for (Function &F : M)
if (F.getName() == Name)
return F;
report_fatal_error("Couldn't find function!");
}
[LCG] Add the most basic of edge insertion to the lazy call graph. This just handles the pre-DFS case. Also add some test cases for this case to make sure it works. llvm-svn: 207411
2014-04-28 13:10:23 +02:00
TEST(LazyCallGraphTest, BasicGraphMutation) {
Remove every uses of getGlobalContext() in LLVM (but the C API) At the same time, fixes InstructionsTest::CastInst unittest: yes you can leave the IR in an invalid state and exit when you don't destroy the context (like the global one), no longer now. This is the first part of http://reviews.llvm.org/D19094 From: Mehdi Amini <mehdi.amini@apple.com> llvm-svn: 266379
2016-04-14 23:59:01 +02:00
LLVMContext Context;
std::unique_ptr<Module> M = parseAssembly(Context, "define void @a() {\n"
"entry:\n"
" call void @b()\n"
" call void @c()\n"
" ret void\n"
"}\n"
"define void @b() {\n"
"entry:\n"
" ret void\n"
"}\n"
"define void @c() {\n"
"entry:\n"
" ret void\n"
"}\n");
[LCG] Add the most basic of edge insertion to the lazy call graph. This just handles the pre-DFS case. Also add some test cases for this case to make sure it works. llvm-svn: 207411
2014-04-28 13:10:23 +02:00
LazyCallGraph CG(*M);
LazyCallGraph::Node &A = CG.get(lookupFunction(*M, "a"));
LazyCallGraph::Node &B = CG.get(lookupFunction(*M, "b"));
EXPECT_EQ(2, std::distance(A.begin(), A.end()));
EXPECT_EQ(0, std::distance(B.begin(), B.end()));
[LCG] Build an edge abstraction for the LazyCallGraph and use it to differentiate between indirect references to functions an direct calls. This doesn't do a whole lot yet other than change the print out produced by the analysis, but it lays the groundwork for a very major change I'm working on next: teaching the call graph to actually be a call graph, modeling *both* the indirect reference graph and the call graph simultaneously. More details on that in the next patch though. The rest of this is essentially a bunch of over-engineering that won't be interesting until the next patch. But this also isolates essentially all of the churn necessary to introduce the edge abstraction from the very important behavior change necessary in order to separately model the two graphs. So it should make review of the subsequent patch a bit easier at the cost of making this patch seem poorly motivated. ;] Differential Revision: http://reviews.llvm.org/D16038 llvm-svn: 259463
2016-02-02 04:57:13 +01:00
CG.insertEdge(B, lookupFunction(*M, "c"), LazyCallGraph::Edge::Call);
[LCG] Add the most basic of edge insertion to the lazy call graph. This just handles the pre-DFS case. Also add some test cases for this case to make sure it works. llvm-svn: 207411
2014-04-28 13:10:23 +02:00
EXPECT_EQ(1, std::distance(B.begin(), B.end()));
[LCG] Build an edge abstraction for the LazyCallGraph and use it to differentiate between indirect references to functions an direct calls. This doesn't do a whole lot yet other than change the print out produced by the analysis, but it lays the groundwork for a very major change I'm working on next: teaching the call graph to actually be a call graph, modeling *both* the indirect reference graph and the call graph simultaneously. More details on that in the next patch though. The rest of this is essentially a bunch of over-engineering that won't be interesting until the next patch. But this also isolates essentially all of the churn necessary to introduce the edge abstraction from the very important behavior change necessary in order to separately model the two graphs. So it should make review of the subsequent patch a bit easier at the cost of making this patch seem poorly motivated. ;] Differential Revision: http://reviews.llvm.org/D16038 llvm-svn: 259463
2016-02-02 04:57:13 +01:00
LazyCallGraph::Node &C = B.begin()->getNode(CG);
[LCG] Add the most basic of edge insertion to the lazy call graph. This just handles the pre-DFS case. Also add some test cases for this case to make sure it works. llvm-svn: 207411
2014-04-28 13:10:23 +02:00
EXPECT_EQ(0, std::distance(C.begin(), C.end()));
[LCG] Build an edge abstraction for the LazyCallGraph and use it to differentiate between indirect references to functions an direct calls. This doesn't do a whole lot yet other than change the print out produced by the analysis, but it lays the groundwork for a very major change I'm working on next: teaching the call graph to actually be a call graph, modeling *both* the indirect reference graph and the call graph simultaneously. More details on that in the next patch though. The rest of this is essentially a bunch of over-engineering that won't be interesting until the next patch. But this also isolates essentially all of the churn necessary to introduce the edge abstraction from the very important behavior change necessary in order to separately model the two graphs. So it should make review of the subsequent patch a bit easier at the cost of making this patch seem poorly motivated. ;] Differential Revision: http://reviews.llvm.org/D16038 llvm-svn: 259463
2016-02-02 04:57:13 +01:00
CG.insertEdge(C, B.getFunction(), LazyCallGraph::Edge::Call);
[LCG] Add the most basic of edge insertion to the lazy call graph. This just handles the pre-DFS case. Also add some test cases for this case to make sure it works. llvm-svn: 207411
2014-04-28 13:10:23 +02:00
EXPECT_EQ(1, std::distance(C.begin(), C.end()));
[LCG] Build an edge abstraction for the LazyCallGraph and use it to differentiate between indirect references to functions an direct calls. This doesn't do a whole lot yet other than change the print out produced by the analysis, but it lays the groundwork for a very major change I'm working on next: teaching the call graph to actually be a call graph, modeling *both* the indirect reference graph and the call graph simultaneously. More details on that in the next patch though. The rest of this is essentially a bunch of over-engineering that won't be interesting until the next patch. But this also isolates essentially all of the churn necessary to introduce the edge abstraction from the very important behavior change necessary in order to separately model the two graphs. So it should make review of the subsequent patch a bit easier at the cost of making this patch seem poorly motivated. ;] Differential Revision: http://reviews.llvm.org/D16038 llvm-svn: 259463
2016-02-02 04:57:13 +01:00
EXPECT_EQ(&B, C.begin()->getNode());
[LCG] Add the most basic of edge insertion to the lazy call graph. This just handles the pre-DFS case. Also add some test cases for this case to make sure it works. llvm-svn: 207411
2014-04-28 13:10:23 +02:00
[LCG] Build an edge abstraction for the LazyCallGraph and use it to differentiate between indirect references to functions an direct calls. This doesn't do a whole lot yet other than change the print out produced by the analysis, but it lays the groundwork for a very major change I'm working on next: teaching the call graph to actually be a call graph, modeling *both* the indirect reference graph and the call graph simultaneously. More details on that in the next patch though. The rest of this is essentially a bunch of over-engineering that won't be interesting until the next patch. But this also isolates essentially all of the churn necessary to introduce the edge abstraction from the very important behavior change necessary in order to separately model the two graphs. So it should make review of the subsequent patch a bit easier at the cost of making this patch seem poorly motivated. ;] Differential Revision: http://reviews.llvm.org/D16038 llvm-svn: 259463
2016-02-02 04:57:13 +01:00
CG.insertEdge(C, C.getFunction(), LazyCallGraph::Edge::Call);
[LCG] Add the most basic of edge insertion to the lazy call graph. This just handles the pre-DFS case. Also add some test cases for this case to make sure it works. llvm-svn: 207411
2014-04-28 13:10:23 +02:00
EXPECT_EQ(2, std::distance(C.begin(), C.end()));
[LCG] Build an edge abstraction for the LazyCallGraph and use it to differentiate between indirect references to functions an direct calls. This doesn't do a whole lot yet other than change the print out produced by the analysis, but it lays the groundwork for a very major change I'm working on next: teaching the call graph to actually be a call graph, modeling *both* the indirect reference graph and the call graph simultaneously. More details on that in the next patch though. The rest of this is essentially a bunch of over-engineering that won't be interesting until the next patch. But this also isolates essentially all of the churn necessary to introduce the edge abstraction from the very important behavior change necessary in order to separately model the two graphs. So it should make review of the subsequent patch a bit easier at the cost of making this patch seem poorly motivated. ;] Differential Revision: http://reviews.llvm.org/D16038 llvm-svn: 259463
2016-02-02 04:57:13 +01:00
EXPECT_EQ(&B, C.begin()->getNode());
EXPECT_EQ(&C, std::next(C.begin())->getNode());
[LCG] Actually test the *basic* edge removal bits (IE, the non-SCC bits), and discover that it's totally broken. Yay tests. Boo bug. Fix the basic edge removal so that it works by nulling out the removed edges rather than actually removing them. This leaves the indices valid in the map from callee to index, and preserves some of the locality for iterating over edges. The iterator is made bidirectional to reflect that it now has to skip over null entries, and the skipping logic is layered onto it. As future work, I would like to track essentially the "load factor" of the edge list, and when it falls below a threshold do a compaction. An alternative I considered (and continue to consider) is storing the callees in a doubly linked list where each element of the list is in a set (which is essentially the classical linked-hash-table datastructure). The problem with that approach is that either you need to heap allocate the linked list nodes and use pointers to them, or use a bucket hash table (with even *more* linked list pointer overhead!), etc. It's pretty easy to get 5x overhead for values that are just pointers. So far, I think punching holes in the vector, and periodic compaction is likely to be much more efficient overall in the space/time tradeoff. llvm-svn: 207619
2014-04-30 09:45:27 +02:00
CG.removeEdge(C, B.getFunction());
EXPECT_EQ(1, std::distance(C.begin(), C.end()));
[LCG] Build an edge abstraction for the LazyCallGraph and use it to differentiate between indirect references to functions an direct calls. This doesn't do a whole lot yet other than change the print out produced by the analysis, but it lays the groundwork for a very major change I'm working on next: teaching the call graph to actually be a call graph, modeling *both* the indirect reference graph and the call graph simultaneously. More details on that in the next patch though. The rest of this is essentially a bunch of over-engineering that won't be interesting until the next patch. But this also isolates essentially all of the churn necessary to introduce the edge abstraction from the very important behavior change necessary in order to separately model the two graphs. So it should make review of the subsequent patch a bit easier at the cost of making this patch seem poorly motivated. ;] Differential Revision: http://reviews.llvm.org/D16038 llvm-svn: 259463
2016-02-02 04:57:13 +01:00
EXPECT_EQ(&C, C.begin()->getNode());
[LCG] Actually test the *basic* edge removal bits (IE, the non-SCC bits), and discover that it's totally broken. Yay tests. Boo bug. Fix the basic edge removal so that it works by nulling out the removed edges rather than actually removing them. This leaves the indices valid in the map from callee to index, and preserves some of the locality for iterating over edges. The iterator is made bidirectional to reflect that it now has to skip over null entries, and the skipping logic is layered onto it. As future work, I would like to track essentially the "load factor" of the edge list, and when it falls below a threshold do a compaction. An alternative I considered (and continue to consider) is storing the callees in a doubly linked list where each element of the list is in a set (which is essentially the classical linked-hash-table datastructure). The problem with that approach is that either you need to heap allocate the linked list nodes and use pointers to them, or use a bucket hash table (with even *more* linked list pointer overhead!), etc. It's pretty easy to get 5x overhead for values that are just pointers. So far, I think punching holes in the vector, and periodic compaction is likely to be much more efficient overall in the space/time tradeoff. llvm-svn: 207619
2014-04-30 09:45:27 +02:00
CG.removeEdge(C, C.getFunction());
EXPECT_EQ(0, std::distance(C.begin(), C.end()));
CG.removeEdge(B, C.getFunction());
EXPECT_EQ(0, std::distance(B.begin(), B.end()));
[LCG] Add the most basic of edge insertion to the lazy call graph. This just handles the pre-DFS case. Also add some test cases for this case to make sure it works. llvm-svn: 207411
2014-04-28 13:10:23 +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
TEST(LazyCallGraphTest, InnerSCCFormation) {
Remove every uses of getGlobalContext() in LLVM (but the C API) At the same time, fixes InstructionsTest::CastInst unittest: yes you can leave the IR in an invalid state and exit when you don't destroy the context (like the global one), no longer now. This is the first part of http://reviews.llvm.org/D19094 From: Mehdi Amini <mehdi.amini@apple.com> llvm-svn: 266379
2016-04-14 23:59:01 +02:00
LLVMContext Context;
std::unique_ptr<Module> M = parseAssembly(Context, DiamondOfTriangles);
[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
LazyCallGraph CG(*M);
// Now mutate the graph to connect every node into a single RefSCC to ensure
// that our inner SCC formation handles the rest.
CG.insertEdge(lookupFunction(*M, "d1"), lookupFunction(*M, "a1"),
LazyCallGraph::Edge::Ref);
// Build vectors and sort them for the rest of the assertions to make them
// independent of order.
std::vector<std::string> Nodes;
// We should build a single RefSCC for the entire graph.
auto I = CG.postorder_ref_scc_begin();
LazyCallGraph::RefSCC &RC = *I++;
EXPECT_EQ(CG.postorder_ref_scc_end(), I);
// Now walk the four SCCs which should be in post-order.
auto J = RC.begin();
LazyCallGraph::SCC &D = *J++;
for (LazyCallGraph::Node &N : D)
Nodes.push_back(N.getFunction().getName());
std::sort(Nodes.begin(), Nodes.end());
EXPECT_EQ(3u, Nodes.size());
EXPECT_EQ("d1", Nodes[0]);
EXPECT_EQ("d2", Nodes[1]);
EXPECT_EQ("d3", Nodes[2]);
Nodes.clear();
LazyCallGraph::SCC &B = *J++;
for (LazyCallGraph::Node &N : B)
Nodes.push_back(N.getFunction().getName());
std::sort(Nodes.begin(), Nodes.end());
EXPECT_EQ(3u, Nodes.size());
EXPECT_EQ("b1", Nodes[0]);
EXPECT_EQ("b2", Nodes[1]);
EXPECT_EQ("b3", Nodes[2]);
Nodes.clear();
LazyCallGraph::SCC &C = *J++;
for (LazyCallGraph::Node &N : C)
Nodes.push_back(N.getFunction().getName());
std::sort(Nodes.begin(), Nodes.end());
EXPECT_EQ(3u, Nodes.size());
EXPECT_EQ("c1", Nodes[0]);
EXPECT_EQ("c2", Nodes[1]);
EXPECT_EQ("c3", Nodes[2]);
Nodes.clear();
LazyCallGraph::SCC &A = *J++;
for (LazyCallGraph::Node &N : A)
Nodes.push_back(N.getFunction().getName());
std::sort(Nodes.begin(), Nodes.end());
EXPECT_EQ(3u, Nodes.size());
EXPECT_EQ("a1", Nodes[0]);
EXPECT_EQ("a2", Nodes[1]);
EXPECT_EQ("a3", Nodes[2]);
Nodes.clear();
EXPECT_EQ(RC.end(), J);
}
[LCG] Implement Tarjan's algorithm correctly this time. We have to walk up the stack finishing the exploration of each entries children before we're finished in addition to accounting for their low-links. Added a unittest that really hammers home the need for this with interlocking cycles that would each appear distinct otherwise and crash or compute the wrong result. As part of this, nuke a stale fixme and bring the rest of the implementation still more closely in line with the original algorithm. llvm-svn: 206966
2014-04-23 12:31:17 +02:00
TEST(LazyCallGraphTest, MultiArmSCC) {
Remove every uses of getGlobalContext() in LLVM (but the C API) At the same time, fixes InstructionsTest::CastInst unittest: yes you can leave the IR in an invalid state and exit when you don't destroy the context (like the global one), no longer now. This is the first part of http://reviews.llvm.org/D19094 From: Mehdi Amini <mehdi.amini@apple.com> llvm-svn: 266379
2016-04-14 23:59:01 +02:00
LLVMContext Context;
[LCG] Implement Tarjan's algorithm correctly this time. We have to walk up the stack finishing the exploration of each entries children before we're finished in addition to accounting for their low-links. Added a unittest that really hammers home the need for this with interlocking cycles that would each appear distinct otherwise and crash or compute the wrong result. As part of this, nuke a stale fixme and bring the rest of the implementation still more closely in line with the original algorithm. llvm-svn: 206966
2014-04-23 12:31:17 +02:00
// Two interlocking cycles. The really useful thing about this SCC is that it
// will require Tarjan's DFS to backtrack and finish processing all of the
[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
// children of each node in the SCC. Since this involves call edges, both
// Tarjan implementations will have to successfully navigate the structure.
Remove every uses of getGlobalContext() in LLVM (but the C API) At the same time, fixes InstructionsTest::CastInst unittest: yes you can leave the IR in an invalid state and exit when you don't destroy the context (like the global one), no longer now. This is the first part of http://reviews.llvm.org/D19094 From: Mehdi Amini <mehdi.amini@apple.com> llvm-svn: 266379
2016-04-14 23:59:01 +02:00
std::unique_ptr<Module> M = parseAssembly(Context, "define void @f1() {\n"
"entry:\n"
" call void @f2()\n"
" call void @f4()\n"
" ret void\n"
"}\n"
"define void @f2() {\n"
"entry:\n"
" call void @f3()\n"
" ret void\n"
"}\n"
"define void @f3() {\n"
"entry:\n"
" call void @f1()\n"
" ret void\n"
"}\n"
"define void @f4() {\n"
"entry:\n"
" call void @f5()\n"
" ret void\n"
"}\n"
"define void @f5() {\n"
"entry:\n"
" call void @f1()\n"
" ret void\n"
"}\n");
[LCG] Implement Tarjan's algorithm correctly this time. We have to walk up the stack finishing the exploration of each entries children before we're finished in addition to accounting for their low-links. Added a unittest that really hammers home the need for this with interlocking cycles that would each appear distinct otherwise and crash or compute the wrong result. As part of this, nuke a stale fixme and bring the rest of the implementation still more closely in line with the original algorithm. llvm-svn: 206966
2014-04-23 12:31:17 +02:00
LazyCallGraph CG(*M);
// Force the graph to be fully expanded.
[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
auto I = CG.postorder_ref_scc_begin();
LazyCallGraph::RefSCC &RC = *I++;
EXPECT_EQ(CG.postorder_ref_scc_end(), I);
LazyCallGraph::Node &N1 = *CG.lookup(lookupFunction(*M, "f1"));
LazyCallGraph::Node &N2 = *CG.lookup(lookupFunction(*M, "f2"));
LazyCallGraph::Node &N3 = *CG.lookup(lookupFunction(*M, "f3"));
LazyCallGraph::Node &N4 = *CG.lookup(lookupFunction(*M, "f4"));
LazyCallGraph::Node &N5 = *CG.lookup(lookupFunction(*M, "f4"));
EXPECT_EQ(&RC, CG.lookupRefSCC(N1));
EXPECT_EQ(&RC, CG.lookupRefSCC(N2));
EXPECT_EQ(&RC, CG.lookupRefSCC(N3));
EXPECT_EQ(&RC, CG.lookupRefSCC(N4));
EXPECT_EQ(&RC, CG.lookupRefSCC(N5));
ASSERT_EQ(1, RC.size());
LazyCallGraph::SCC &C = *RC.begin();
EXPECT_EQ(&C, CG.lookupSCC(N1));
EXPECT_EQ(&C, CG.lookupSCC(N2));
EXPECT_EQ(&C, CG.lookupSCC(N3));
EXPECT_EQ(&C, CG.lookupSCC(N4));
EXPECT_EQ(&C, CG.lookupSCC(N5));
[LCG] Implement Tarjan's algorithm correctly this time. We have to walk up the stack finishing the exploration of each entries children before we're finished in addition to accounting for their low-links. Added a unittest that really hammers home the need for this with interlocking cycles that would each appear distinct otherwise and crash or compute the wrong result. As part of this, nuke a stale fixme and bring the rest of the implementation still more closely in line with the original algorithm. llvm-svn: 206966
2014-04-23 12:31:17 +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
TEST(LazyCallGraphTest, OutgoingEdgeMutation) {
Remove every uses of getGlobalContext() in LLVM (but the C API) At the same time, fixes InstructionsTest::CastInst unittest: yes you can leave the IR in an invalid state and exit when you don't destroy the context (like the global one), no longer now. This is the first part of http://reviews.llvm.org/D19094 From: Mehdi Amini <mehdi.amini@apple.com> llvm-svn: 266379
2016-04-14 23:59:01 +02:00
LLVMContext Context;
std::unique_ptr<Module> M = parseAssembly(Context, "define void @a() {\n"
"entry:\n"
" call void @b()\n"
" call void @c()\n"
" ret void\n"
"}\n"
"define void @b() {\n"
"entry:\n"
" call void @d()\n"
" ret void\n"
"}\n"
"define void @c() {\n"
"entry:\n"
" call void @d()\n"
" ret void\n"
"}\n"
"define void @d() {\n"
"entry:\n"
" ret void\n"
"}\n");
[LCG] Reorder the tests to be a bit more logical: inter-SCC mutation before intra-SCC mutation, insertion before removal. No functionality changed. llvm-svn: 207934
2014-05-04 11:38:23 +02:00
LazyCallGraph CG(*M);
// Force the graph to be fully expanded.
[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
for (LazyCallGraph::RefSCC &RC : CG.postorder_ref_sccs())
[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
dbgs() << "Formed RefSCC: " << RC << "\n";
[LCG] Reorder the tests to be a bit more logical: inter-SCC mutation before intra-SCC mutation, insertion before removal. No functionality changed. llvm-svn: 207934
2014-05-04 11:38:23 +02:00
LazyCallGraph::Node &A = *CG.lookup(lookupFunction(*M, "a"));
LazyCallGraph::Node &B = *CG.lookup(lookupFunction(*M, "b"));
LazyCallGraph::Node &C = *CG.lookup(lookupFunction(*M, "c"));
LazyCallGraph::Node &D = *CG.lookup(lookupFunction(*M, "d"));
LazyCallGraph::SCC &AC = *CG.lookupSCC(A);
LazyCallGraph::SCC &BC = *CG.lookupSCC(B);
LazyCallGraph::SCC &CC = *CG.lookupSCC(C);
LazyCallGraph::SCC &DC = *CG.lookupSCC(D);
[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
LazyCallGraph::RefSCC &ARC = *CG.lookupRefSCC(A);
LazyCallGraph::RefSCC &BRC = *CG.lookupRefSCC(B);
LazyCallGraph::RefSCC &CRC = *CG.lookupRefSCC(C);
LazyCallGraph::RefSCC &DRC = *CG.lookupRefSCC(D);
EXPECT_TRUE(ARC.isParentOf(BRC));
[LCG] Start using SCC relationship predicates in the unittest. This mostly gives us nice unittesting of the predicates themselves. I'll start using them further in subsequent commits to help test the actual operations performed on the graph. llvm-svn: 287698
2016-11-22 21:35:32 +01:00
EXPECT_TRUE(AC.isParentOf(BC));
[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
EXPECT_TRUE(ARC.isParentOf(CRC));
[LCG] Start using SCC relationship predicates in the unittest. This mostly gives us nice unittesting of the predicates themselves. I'll start using them further in subsequent commits to help test the actual operations performed on the graph. llvm-svn: 287698
2016-11-22 21:35:32 +01:00
EXPECT_TRUE(AC.isParentOf(CC));
[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
EXPECT_FALSE(ARC.isParentOf(DRC));
[LCG] Start using SCC relationship predicates in the unittest. This mostly gives us nice unittesting of the predicates themselves. I'll start using them further in subsequent commits to help test the actual operations performed on the graph. llvm-svn: 287698
2016-11-22 21:35:32 +01:00
EXPECT_FALSE(AC.isParentOf(DC));
[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
EXPECT_TRUE(ARC.isAncestorOf(DRC));
[LCG] Start using SCC relationship predicates in the unittest. This mostly gives us nice unittesting of the predicates themselves. I'll start using them further in subsequent commits to help test the actual operations performed on the graph. llvm-svn: 287698
2016-11-22 21:35:32 +01:00
EXPECT_TRUE(AC.isAncestorOf(DC));
[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
EXPECT_FALSE(DRC.isChildOf(ARC));
[LCG] Start using SCC relationship predicates in the unittest. This mostly gives us nice unittesting of the predicates themselves. I'll start using them further in subsequent commits to help test the actual operations performed on the graph. llvm-svn: 287698
2016-11-22 21:35:32 +01:00
EXPECT_FALSE(DC.isChildOf(AC));
[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
EXPECT_TRUE(DRC.isDescendantOf(ARC));
[LCG] Start using SCC relationship predicates in the unittest. This mostly gives us nice unittesting of the predicates themselves. I'll start using them further in subsequent commits to help test the actual operations performed on the graph. llvm-svn: 287698
2016-11-22 21:35:32 +01:00
EXPECT_TRUE(DC.isDescendantOf(AC));
[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
EXPECT_TRUE(DRC.isChildOf(BRC));
[LCG] Start using SCC relationship predicates in the unittest. This mostly gives us nice unittesting of the predicates themselves. I'll start using them further in subsequent commits to help test the actual operations performed on the graph. llvm-svn: 287698
2016-11-22 21:35:32 +01:00
EXPECT_TRUE(DC.isChildOf(BC));
[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
EXPECT_TRUE(DRC.isChildOf(CRC));
[LCG] Start using SCC relationship predicates in the unittest. This mostly gives us nice unittesting of the predicates themselves. I'll start using them further in subsequent commits to help test the actual operations performed on the graph. llvm-svn: 287698
2016-11-22 21:35:32 +01:00
EXPECT_TRUE(DC.isChildOf(CC));
[LCG] Reorder the tests to be a bit more logical: inter-SCC mutation before intra-SCC mutation, insertion before removal. No functionality changed. llvm-svn: 207934
2014-05-04 11:38:23 +02:00
EXPECT_EQ(2, std::distance(A.begin(), A.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
ARC.insertOutgoingEdge(A, D, LazyCallGraph::Edge::Call);
[LCG] Reorder the tests to be a bit more logical: inter-SCC mutation before intra-SCC mutation, insertion before removal. No functionality changed. llvm-svn: 207934
2014-05-04 11:38:23 +02:00
EXPECT_EQ(3, std::distance(A.begin(), A.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 LazyCallGraph::Edge &NewE = A[D];
EXPECT_TRUE(NewE);
EXPECT_TRUE(NewE.isCall());
EXPECT_EQ(&D, NewE.getNode());
// Only the parent and child tests sholud have changed. The rest of the graph
// remains the same.
EXPECT_TRUE(ARC.isParentOf(DRC));
[LCG] Start using SCC relationship predicates in the unittest. This mostly gives us nice unittesting of the predicates themselves. I'll start using them further in subsequent commits to help test the actual operations performed on the graph. llvm-svn: 287698
2016-11-22 21:35:32 +01:00
EXPECT_TRUE(AC.isParentOf(DC));
[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
EXPECT_TRUE(ARC.isAncestorOf(DRC));
[LCG] Start using SCC relationship predicates in the unittest. This mostly gives us nice unittesting of the predicates themselves. I'll start using them further in subsequent commits to help test the actual operations performed on the graph. llvm-svn: 287698
2016-11-22 21:35:32 +01:00
EXPECT_TRUE(AC.isAncestorOf(DC));
[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
EXPECT_TRUE(DRC.isChildOf(ARC));
[LCG] Start using SCC relationship predicates in the unittest. This mostly gives us nice unittesting of the predicates themselves. I'll start using them further in subsequent commits to help test the actual operations performed on the graph. llvm-svn: 287698
2016-11-22 21:35:32 +01:00
EXPECT_TRUE(DC.isChildOf(AC));
[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
EXPECT_TRUE(DRC.isDescendantOf(ARC));
[LCG] Start using SCC relationship predicates in the unittest. This mostly gives us nice unittesting of the predicates themselves. I'll start using them further in subsequent commits to help test the actual operations performed on the graph. llvm-svn: 287698
2016-11-22 21:35:32 +01:00
EXPECT_TRUE(DC.isDescendantOf(AC));
[LCG] Reorder the tests to be a bit more logical: inter-SCC mutation before intra-SCC mutation, insertion before removal. No functionality changed. llvm-svn: 207934
2014-05-04 11:38:23 +02:00
EXPECT_EQ(&AC, CG.lookupSCC(A));
EXPECT_EQ(&BC, CG.lookupSCC(B));
EXPECT_EQ(&CC, CG.lookupSCC(C));
EXPECT_EQ(&DC, CG.lookupSCC(D));
[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
EXPECT_EQ(&ARC, CG.lookupRefSCC(A));
EXPECT_EQ(&BRC, CG.lookupRefSCC(B));
EXPECT_EQ(&CRC, CG.lookupRefSCC(C));
EXPECT_EQ(&DRC, CG.lookupRefSCC(D));
ARC.switchOutgoingEdgeToRef(A, D);
EXPECT_FALSE(NewE.isCall());
[LCG] Start using SCC relationship predicates in the unittest. This mostly gives us nice unittesting of the predicates themselves. I'll start using them further in subsequent commits to help test the actual operations performed on the graph. llvm-svn: 287698
2016-11-22 21:35:32 +01:00
// Verify the reference graph remains the same but the SCC graph is updated.
[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
EXPECT_TRUE(ARC.isParentOf(DRC));
[LCG] Start using SCC relationship predicates in the unittest. This mostly gives us nice unittesting of the predicates themselves. I'll start using them further in subsequent commits to help test the actual operations performed on the graph. llvm-svn: 287698
2016-11-22 21:35:32 +01:00
EXPECT_FALSE(AC.isParentOf(DC));
[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
EXPECT_TRUE(ARC.isAncestorOf(DRC));
[LCG] Start using SCC relationship predicates in the unittest. This mostly gives us nice unittesting of the predicates themselves. I'll start using them further in subsequent commits to help test the actual operations performed on the graph. llvm-svn: 287698
2016-11-22 21:35:32 +01:00
EXPECT_TRUE(AC.isAncestorOf(DC));
[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
EXPECT_TRUE(DRC.isChildOf(ARC));
[LCG] Start using SCC relationship predicates in the unittest. This mostly gives us nice unittesting of the predicates themselves. I'll start using them further in subsequent commits to help test the actual operations performed on the graph. llvm-svn: 287698
2016-11-22 21:35:32 +01:00
EXPECT_FALSE(DC.isChildOf(AC));
[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
EXPECT_TRUE(DRC.isDescendantOf(ARC));
[LCG] Start using SCC relationship predicates in the unittest. This mostly gives us nice unittesting of the predicates themselves. I'll start using them further in subsequent commits to help test the actual operations performed on the graph. llvm-svn: 287698
2016-11-22 21:35:32 +01:00
EXPECT_TRUE(DC.isDescendantOf(AC));
[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
EXPECT_EQ(&AC, CG.lookupSCC(A));
EXPECT_EQ(&BC, CG.lookupSCC(B));
EXPECT_EQ(&CC, CG.lookupSCC(C));
EXPECT_EQ(&DC, CG.lookupSCC(D));
EXPECT_EQ(&ARC, CG.lookupRefSCC(A));
EXPECT_EQ(&BRC, CG.lookupRefSCC(B));
EXPECT_EQ(&CRC, CG.lookupRefSCC(C));
EXPECT_EQ(&DRC, CG.lookupRefSCC(D));
ARC.switchOutgoingEdgeToCall(A, D);
EXPECT_TRUE(NewE.isCall());
[LCG] Start using SCC relationship predicates in the unittest. This mostly gives us nice unittesting of the predicates themselves. I'll start using them further in subsequent commits to help test the actual operations performed on the graph. llvm-svn: 287698
2016-11-22 21:35:32 +01:00
// Verify the reference graph remains the same but the SCC graph is updated.
[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
EXPECT_TRUE(ARC.isParentOf(DRC));
[LCG] Start using SCC relationship predicates in the unittest. This mostly gives us nice unittesting of the predicates themselves. I'll start using them further in subsequent commits to help test the actual operations performed on the graph. llvm-svn: 287698
2016-11-22 21:35:32 +01:00
EXPECT_TRUE(AC.isParentOf(DC));
[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
EXPECT_TRUE(ARC.isAncestorOf(DRC));
[LCG] Start using SCC relationship predicates in the unittest. This mostly gives us nice unittesting of the predicates themselves. I'll start using them further in subsequent commits to help test the actual operations performed on the graph. llvm-svn: 287698
2016-11-22 21:35:32 +01:00
EXPECT_TRUE(AC.isAncestorOf(DC));
[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
EXPECT_TRUE(DRC.isChildOf(ARC));
[LCG] Start using SCC relationship predicates in the unittest. This mostly gives us nice unittesting of the predicates themselves. I'll start using them further in subsequent commits to help test the actual operations performed on the graph. llvm-svn: 287698
2016-11-22 21:35:32 +01:00
EXPECT_TRUE(DC.isChildOf(AC));
[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
EXPECT_TRUE(DRC.isDescendantOf(ARC));
[LCG] Start using SCC relationship predicates in the unittest. This mostly gives us nice unittesting of the predicates themselves. I'll start using them further in subsequent commits to help test the actual operations performed on the graph. llvm-svn: 287698
2016-11-22 21:35:32 +01:00
EXPECT_TRUE(DC.isDescendantOf(AC));
[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
EXPECT_EQ(&AC, CG.lookupSCC(A));
EXPECT_EQ(&BC, CG.lookupSCC(B));
EXPECT_EQ(&CC, CG.lookupSCC(C));
EXPECT_EQ(&DC, CG.lookupSCC(D));
EXPECT_EQ(&ARC, CG.lookupRefSCC(A));
EXPECT_EQ(&BRC, CG.lookupRefSCC(B));
EXPECT_EQ(&CRC, CG.lookupRefSCC(C));
EXPECT_EQ(&DRC, CG.lookupRefSCC(D));
ARC.removeOutgoingEdge(A, D);
EXPECT_EQ(2, std::distance(A.begin(), A.end()));
// Now the parent and child tests fail again but the rest remains the same.
EXPECT_FALSE(ARC.isParentOf(DRC));
[LCG] Start using SCC relationship predicates in the unittest. This mostly gives us nice unittesting of the predicates themselves. I'll start using them further in subsequent commits to help test the actual operations performed on the graph. llvm-svn: 287698
2016-11-22 21:35:32 +01:00
EXPECT_FALSE(AC.isParentOf(DC));
[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
EXPECT_TRUE(ARC.isAncestorOf(DRC));
[LCG] Start using SCC relationship predicates in the unittest. This mostly gives us nice unittesting of the predicates themselves. I'll start using them further in subsequent commits to help test the actual operations performed on the graph. llvm-svn: 287698
2016-11-22 21:35:32 +01:00
EXPECT_TRUE(AC.isAncestorOf(DC));
[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
EXPECT_FALSE(DRC.isChildOf(ARC));
[LCG] Start using SCC relationship predicates in the unittest. This mostly gives us nice unittesting of the predicates themselves. I'll start using them further in subsequent commits to help test the actual operations performed on the graph. llvm-svn: 287698
2016-11-22 21:35:32 +01:00
EXPECT_FALSE(DC.isChildOf(AC));
[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
EXPECT_TRUE(DRC.isDescendantOf(ARC));
[LCG] Start using SCC relationship predicates in the unittest. This mostly gives us nice unittesting of the predicates themselves. I'll start using them further in subsequent commits to help test the actual operations performed on the graph. llvm-svn: 287698
2016-11-22 21:35:32 +01:00
EXPECT_TRUE(DC.isDescendantOf(AC));
[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
EXPECT_EQ(&AC, CG.lookupSCC(A));
EXPECT_EQ(&BC, CG.lookupSCC(B));
EXPECT_EQ(&CC, CG.lookupSCC(C));
EXPECT_EQ(&DC, CG.lookupSCC(D));
EXPECT_EQ(&ARC, CG.lookupRefSCC(A));
EXPECT_EQ(&BRC, CG.lookupRefSCC(B));
EXPECT_EQ(&CRC, CG.lookupRefSCC(C));
EXPECT_EQ(&DRC, CG.lookupRefSCC(D));
[LCG] Reorder the tests to be a bit more logical: inter-SCC mutation before intra-SCC mutation, insertion before removal. No functionality changed. llvm-svn: 207934
2014-05-04 11:38:23 +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
TEST(LazyCallGraphTest, IncomingEdgeInsertion) {
Remove every uses of getGlobalContext() in LLVM (but the C API) At the same time, fixes InstructionsTest::CastInst unittest: yes you can leave the IR in an invalid state and exit when you don't destroy the context (like the global one), no longer now. This is the first part of http://reviews.llvm.org/D19094 From: Mehdi Amini <mehdi.amini@apple.com> llvm-svn: 266379
2016-04-14 23:59:01 +02:00
LLVMContext Context;
[LCG] Add the last (and most complex) of the edge insertion mutation operations on the call graph. This one forms a cycle, and while not as complex as removing an internal edge from an SCC, it involves a reasonable amount of work to find all of the nodes newly connected in a cycle. Also somewhat alarming is the worst case complexity here: it might have to walk roughly the entire SCC inverse DAG to insert a single edge. This is carefully documented in the API (I hope). llvm-svn: 207935
2014-05-04 11:38:32 +02:00
// We want to ensure we can add edges even across complex diamond graphs, so
// we use the diamond of triangles graph defined above. The ascii diagram is
// repeated here for easy reference.
//
// d1 |
// / \ |
// d3--d2 |
// / \ |
// b1 c1 |
// / \ / \ |
// b3--b2 c3--c2 |
// \ / |
// a1 |
// / \ |
// a3--a2 |
//
Remove every uses of getGlobalContext() in LLVM (but the C API) At the same time, fixes InstructionsTest::CastInst unittest: yes you can leave the IR in an invalid state and exit when you don't destroy the context (like the global one), no longer now. This is the first part of http://reviews.llvm.org/D19094 From: Mehdi Amini <mehdi.amini@apple.com> llvm-svn: 266379
2016-04-14 23:59:01 +02:00
std::unique_ptr<Module> M = parseAssembly(Context, DiamondOfTriangles);
[LCG] Add the last (and most complex) of the edge insertion mutation operations on the call graph. This one forms a cycle, and while not as complex as removing an internal edge from an SCC, it involves a reasonable amount of work to find all of the nodes newly connected in a cycle. Also somewhat alarming is the worst case complexity here: it might have to walk roughly the entire SCC inverse DAG to insert a single edge. This is carefully documented in the API (I hope). llvm-svn: 207935
2014-05-04 11:38:32 +02:00
LazyCallGraph CG(*M);
// Force the graph to be fully expanded.
[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
for (LazyCallGraph::RefSCC &RC : CG.postorder_ref_sccs())
[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
dbgs() << "Formed RefSCC: " << RC << "\n";
[LCG] Add the last (and most complex) of the edge insertion mutation operations on the call graph. This one forms a cycle, and while not as complex as removing an internal edge from an SCC, it involves a reasonable amount of work to find all of the nodes newly connected in a cycle. Also somewhat alarming is the worst case complexity here: it might have to walk roughly the entire SCC inverse DAG to insert a single edge. This is carefully documented in the API (I hope). llvm-svn: 207935
2014-05-04 11:38:32 +02:00
LazyCallGraph::Node &A1 = *CG.lookup(lookupFunction(*M, "a1"));
LazyCallGraph::Node &A2 = *CG.lookup(lookupFunction(*M, "a2"));
LazyCallGraph::Node &A3 = *CG.lookup(lookupFunction(*M, "a3"));
LazyCallGraph::Node &B1 = *CG.lookup(lookupFunction(*M, "b1"));
LazyCallGraph::Node &B2 = *CG.lookup(lookupFunction(*M, "b2"));
LazyCallGraph::Node &B3 = *CG.lookup(lookupFunction(*M, "b3"));
LazyCallGraph::Node &C1 = *CG.lookup(lookupFunction(*M, "c1"));
LazyCallGraph::Node &C2 = *CG.lookup(lookupFunction(*M, "c2"));
LazyCallGraph::Node &C3 = *CG.lookup(lookupFunction(*M, "c3"));
LazyCallGraph::Node &D1 = *CG.lookup(lookupFunction(*M, "d1"));
LazyCallGraph::Node &D2 = *CG.lookup(lookupFunction(*M, "d2"));
LazyCallGraph::Node &D3 = *CG.lookup(lookupFunction(*M, "d3"));
[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
LazyCallGraph::RefSCC &ARC = *CG.lookupRefSCC(A1);
LazyCallGraph::RefSCC &BRC = *CG.lookupRefSCC(B1);
LazyCallGraph::RefSCC &CRC = *CG.lookupRefSCC(C1);
LazyCallGraph::RefSCC &DRC = *CG.lookupRefSCC(D1);
ASSERT_EQ(&ARC, CG.lookupRefSCC(A2));
ASSERT_EQ(&ARC, CG.lookupRefSCC(A3));
ASSERT_EQ(&BRC, CG.lookupRefSCC(B2));
ASSERT_EQ(&BRC, CG.lookupRefSCC(B3));
ASSERT_EQ(&CRC, CG.lookupRefSCC(C2));
ASSERT_EQ(&CRC, CG.lookupRefSCC(C3));
ASSERT_EQ(&DRC, CG.lookupRefSCC(D2));
ASSERT_EQ(&DRC, CG.lookupRefSCC(D3));
[LCG] Add the last (and most complex) of the edge insertion mutation operations on the call graph. This one forms a cycle, and while not as complex as removing an internal edge from an SCC, it involves a reasonable amount of work to find all of the nodes newly connected in a cycle. Also somewhat alarming is the worst case complexity here: it might have to walk roughly the entire SCC inverse DAG to insert a single edge. This is carefully documented in the API (I hope). llvm-svn: 207935
2014-05-04 11:38:32 +02:00
ASSERT_EQ(1, std::distance(D2.begin(), D2.end()));
// Add an edge to make the graph:
//
// d1 |
// / \ |
// d3--d2---. |
// / \ | |
// b1 c1 | |
// / \ / \ / |
// b3--b2 c3--c2 |
// \ / |
// a1 |
// / \ |
// a3--a2 |
[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
auto MergedRCs = CRC.insertIncomingRefEdge(D2, C2);
[LCG] Add the last (and most complex) of the edge insertion mutation operations on the call graph. This one forms a cycle, and while not as complex as removing an internal edge from an SCC, it involves a reasonable amount of work to find all of the nodes newly connected in a cycle. Also somewhat alarming is the worst case complexity here: it might have to walk roughly the entire SCC inverse DAG to insert a single edge. This is carefully documented in the API (I hope). llvm-svn: 207935
2014-05-04 11:38:32 +02:00
// Make sure we connected the 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
for (LazyCallGraph::Edge E : D2) {
if (E.getNode() == &D3)
continue;
EXPECT_EQ(&C2, E.getNode());
}
// And marked the D ref-SCC as no longer valid.
EXPECT_EQ(1u, MergedRCs.size());
EXPECT_EQ(&DRC, MergedRCs[0]);
[LCG] Add the last (and most complex) of the edge insertion mutation operations on the call graph. This one forms a cycle, and while not as complex as removing an internal edge from an SCC, it involves a reasonable amount of work to find all of the nodes newly connected in a cycle. Also somewhat alarming is the worst case complexity here: it might have to walk roughly the entire SCC inverse DAG to insert a single edge. This is carefully documented in the API (I hope). llvm-svn: 207935
2014-05-04 11:38:32 +02:00
// Make sure we have the correct nodes in the SCC sets.
[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
EXPECT_EQ(&ARC, CG.lookupRefSCC(A1));
EXPECT_EQ(&ARC, CG.lookupRefSCC(A2));
EXPECT_EQ(&ARC, CG.lookupRefSCC(A3));
EXPECT_EQ(&BRC, CG.lookupRefSCC(B1));
EXPECT_EQ(&BRC, CG.lookupRefSCC(B2));
EXPECT_EQ(&BRC, CG.lookupRefSCC(B3));
EXPECT_EQ(&CRC, CG.lookupRefSCC(C1));
EXPECT_EQ(&CRC, CG.lookupRefSCC(C2));
EXPECT_EQ(&CRC, CG.lookupRefSCC(C3));
EXPECT_EQ(&CRC, CG.lookupRefSCC(D1));
EXPECT_EQ(&CRC, CG.lookupRefSCC(D2));
EXPECT_EQ(&CRC, CG.lookupRefSCC(D3));
[LCG] Add the last (and most complex) of the edge insertion mutation operations on the call graph. This one forms a cycle, and while not as complex as removing an internal edge from an SCC, it involves a reasonable amount of work to find all of the nodes newly connected in a cycle. Also somewhat alarming is the worst case complexity here: it might have to walk roughly the entire SCC inverse DAG to insert a single edge. This is carefully documented in the API (I hope). llvm-svn: 207935
2014-05-04 11:38:32 +02:00
// And that ancestry tests have been updated.
[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
EXPECT_TRUE(ARC.isParentOf(CRC));
EXPECT_TRUE(BRC.isParentOf(CRC));
[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
// And verify the post-order walk reflects the updated structure.
auto I = CG.postorder_ref_scc_begin(), E = CG.postorder_ref_scc_end();
ASSERT_NE(I, E);
EXPECT_EQ(&CRC, &*I) << "Actual RefSCC: " << *I;
ASSERT_NE(++I, E);
EXPECT_EQ(&BRC, &*I) << "Actual RefSCC: " << *I;
ASSERT_NE(++I, E);
EXPECT_EQ(&ARC, &*I) << "Actual RefSCC: " << *I;
EXPECT_EQ(++I, E);
[LCG] Add the last (and most complex) of the edge insertion mutation operations on the call graph. This one forms a cycle, and while not as complex as removing an internal edge from an SCC, it involves a reasonable amount of work to find all of the nodes newly connected in a cycle. Also somewhat alarming is the worst case complexity here: it might have to walk roughly the entire SCC inverse DAG to insert a single edge. This is carefully documented in the API (I hope). llvm-svn: 207935
2014-05-04 11:38:32 +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
TEST(LazyCallGraphTest, IncomingEdgeInsertionMidTraversal) {
Remove every uses of getGlobalContext() in LLVM (but the C API) At the same time, fixes InstructionsTest::CastInst unittest: yes you can leave the IR in an invalid state and exit when you don't destroy the context (like the global one), no longer now. This is the first part of http://reviews.llvm.org/D19094 From: Mehdi Amini <mehdi.amini@apple.com> llvm-svn: 266379
2016-04-14 23:59:01 +02:00
LLVMContext Context;
[LCG] Add the last (and most complex) of the edge insertion mutation operations on the call graph. This one forms a cycle, and while not as complex as removing an internal edge from an SCC, it involves a reasonable amount of work to find all of the nodes newly connected in a cycle. Also somewhat alarming is the worst case complexity here: it might have to walk roughly the entire SCC inverse DAG to insert a single edge. This is carefully documented in the API (I hope). llvm-svn: 207935
2014-05-04 11:38:32 +02:00
// This is the same fundamental test as the previous, but we perform it
[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
// having only partially walked the RefSCCs of the graph.
Remove every uses of getGlobalContext() in LLVM (but the C API) At the same time, fixes InstructionsTest::CastInst unittest: yes you can leave the IR in an invalid state and exit when you don't destroy the context (like the global one), no longer now. This is the first part of http://reviews.llvm.org/D19094 From: Mehdi Amini <mehdi.amini@apple.com> llvm-svn: 266379
2016-04-14 23:59:01 +02:00
std::unique_ptr<Module> M = parseAssembly(Context, DiamondOfTriangles);
[LCG] Add the last (and most complex) of the edge insertion mutation operations on the call graph. This one forms a cycle, and while not as complex as removing an internal edge from an SCC, it involves a reasonable amount of work to find all of the nodes newly connected in a cycle. Also somewhat alarming is the worst case complexity here: it might have to walk roughly the entire SCC inverse DAG to insert a single edge. This is carefully documented in the API (I hope). llvm-svn: 207935
2014-05-04 11:38:32 +02:00
LazyCallGraph CG(*M);
[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
// Walk the RefSCCs until we find the one containing 'c1'.
auto I = CG.postorder_ref_scc_begin(), E = CG.postorder_ref_scc_end();
ASSERT_NE(I, E);
LazyCallGraph::RefSCC &DRC = *I;
ASSERT_NE(&DRC, nullptr);
++I;
ASSERT_NE(I, E);
LazyCallGraph::RefSCC &CRC = *I;
ASSERT_NE(&CRC, nullptr);
[LCG] Add the last (and most complex) of the edge insertion mutation operations on the call graph. This one forms a cycle, and while not as complex as removing an internal edge from an SCC, it involves a reasonable amount of work to find all of the nodes newly connected in a cycle. Also somewhat alarming is the worst case complexity here: it might have to walk roughly the entire SCC inverse DAG to insert a single edge. This is carefully documented in the API (I hope). llvm-svn: 207935
2014-05-04 11:38:32 +02:00
ASSERT_EQ(nullptr, CG.lookup(lookupFunction(*M, "a1")));
ASSERT_EQ(nullptr, CG.lookup(lookupFunction(*M, "a2")));
ASSERT_EQ(nullptr, CG.lookup(lookupFunction(*M, "a3")));
ASSERT_EQ(nullptr, CG.lookup(lookupFunction(*M, "b1")));
ASSERT_EQ(nullptr, CG.lookup(lookupFunction(*M, "b2")));
ASSERT_EQ(nullptr, CG.lookup(lookupFunction(*M, "b3")));
LazyCallGraph::Node &C1 = *CG.lookup(lookupFunction(*M, "c1"));
LazyCallGraph::Node &C2 = *CG.lookup(lookupFunction(*M, "c2"));
LazyCallGraph::Node &C3 = *CG.lookup(lookupFunction(*M, "c3"));
LazyCallGraph::Node &D1 = *CG.lookup(lookupFunction(*M, "d1"));
LazyCallGraph::Node &D2 = *CG.lookup(lookupFunction(*M, "d2"));
LazyCallGraph::Node &D3 = *CG.lookup(lookupFunction(*M, "d3"));
[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
ASSERT_EQ(&CRC, CG.lookupRefSCC(C1));
ASSERT_EQ(&CRC, CG.lookupRefSCC(C2));
ASSERT_EQ(&CRC, CG.lookupRefSCC(C3));
ASSERT_EQ(&DRC, CG.lookupRefSCC(D1));
ASSERT_EQ(&DRC, CG.lookupRefSCC(D2));
ASSERT_EQ(&DRC, CG.lookupRefSCC(D3));
[LCG] Add the last (and most complex) of the edge insertion mutation operations on the call graph. This one forms a cycle, and while not as complex as removing an internal edge from an SCC, it involves a reasonable amount of work to find all of the nodes newly connected in a cycle. Also somewhat alarming is the worst case complexity here: it might have to walk roughly the entire SCC inverse DAG to insert a single edge. This is carefully documented in the API (I hope). llvm-svn: 207935
2014-05-04 11:38:32 +02:00
ASSERT_EQ(1, std::distance(D2.begin(), D2.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
auto MergedRCs = CRC.insertIncomingRefEdge(D2, C2);
// Make sure we connected the nodes.
for (LazyCallGraph::Edge E : D2) {
if (E.getNode() == &D3)
continue;
EXPECT_EQ(&C2, E.getNode());
}
// And marked the D ref-SCC as no longer valid.
EXPECT_EQ(1u, MergedRCs.size());
EXPECT_EQ(&DRC, MergedRCs[0]);
// Make sure we have the correct nodes in the RefSCCs.
EXPECT_EQ(&CRC, CG.lookupRefSCC(C1));
EXPECT_EQ(&CRC, CG.lookupRefSCC(C2));
EXPECT_EQ(&CRC, CG.lookupRefSCC(C3));
EXPECT_EQ(&CRC, CG.lookupRefSCC(D1));
EXPECT_EQ(&CRC, CG.lookupRefSCC(D2));
EXPECT_EQ(&CRC, CG.lookupRefSCC(D3));
[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
// Verify that the post-order walk reflects the updated but still incomplete
// structure.
auto J = CG.postorder_ref_scc_begin();
EXPECT_NE(J, E);
EXPECT_EQ(&CRC, &*J) << "Actual RefSCC: " << *J;
EXPECT_EQ(I, J);
// Check that we can form the last two RefSCCs now, and even that we can do
// it with alternating iterators.
++J;
EXPECT_NE(J, E);
LazyCallGraph::RefSCC &BRC = *J;
[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
EXPECT_NE(&BRC, nullptr);
EXPECT_EQ(&BRC, CG.lookupRefSCC(*CG.lookup(lookupFunction(*M, "b1"))));
EXPECT_EQ(&BRC, CG.lookupRefSCC(*CG.lookup(lookupFunction(*M, "b2"))));
EXPECT_EQ(&BRC, CG.lookupRefSCC(*CG.lookup(lookupFunction(*M, "b3"))));
EXPECT_TRUE(BRC.isParentOf(CRC));
++I;
[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
EXPECT_EQ(J, I);
EXPECT_EQ(&BRC, &*I) << "Actual RefSCC: " << *I;
// Increment I this time to form the new RefSCC, flopping back to the first
// iterator.
++I;
[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
EXPECT_NE(I, E);
LazyCallGraph::RefSCC &ARC = *I;
EXPECT_NE(&ARC, nullptr);
EXPECT_EQ(&ARC, CG.lookupRefSCC(*CG.lookup(lookupFunction(*M, "a1"))));
EXPECT_EQ(&ARC, CG.lookupRefSCC(*CG.lookup(lookupFunction(*M, "a2"))));
EXPECT_EQ(&ARC, CG.lookupRefSCC(*CG.lookup(lookupFunction(*M, "a3"))));
EXPECT_TRUE(ARC.isParentOf(CRC));
[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
++J;
EXPECT_EQ(I, J);
EXPECT_EQ(&ARC, &*J) << "Actual RefSCC: " << *J;
[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
++I;
EXPECT_EQ(E, I);
[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
++J;
EXPECT_EQ(E, J);
}
TEST(LazyCallGraphTest, IncomingEdgeInsertionRefGraph) {
LLVMContext Context;
// Another variation of the above test but with all the edges switched to
// references rather than calls.
std::unique_ptr<Module> M =
parseAssembly(Context, DiamondOfTrianglesRefGraph);
LazyCallGraph CG(*M);
// Force the graph to be fully expanded.
for (LazyCallGraph::RefSCC &RC : CG.postorder_ref_sccs())
dbgs() << "Formed RefSCC: " << RC << "\n";
LazyCallGraph::Node &A1 = *CG.lookup(lookupFunction(*M, "a1"));
LazyCallGraph::Node &A2 = *CG.lookup(lookupFunction(*M, "a2"));
LazyCallGraph::Node &A3 = *CG.lookup(lookupFunction(*M, "a3"));
LazyCallGraph::Node &B1 = *CG.lookup(lookupFunction(*M, "b1"));
LazyCallGraph::Node &B2 = *CG.lookup(lookupFunction(*M, "b2"));
LazyCallGraph::Node &B3 = *CG.lookup(lookupFunction(*M, "b3"));
LazyCallGraph::Node &C1 = *CG.lookup(lookupFunction(*M, "c1"));
LazyCallGraph::Node &C2 = *CG.lookup(lookupFunction(*M, "c2"));
LazyCallGraph::Node &C3 = *CG.lookup(lookupFunction(*M, "c3"));
LazyCallGraph::Node &D1 = *CG.lookup(lookupFunction(*M, "d1"));
LazyCallGraph::Node &D2 = *CG.lookup(lookupFunction(*M, "d2"));
LazyCallGraph::Node &D3 = *CG.lookup(lookupFunction(*M, "d3"));
LazyCallGraph::RefSCC &ARC = *CG.lookupRefSCC(A1);
LazyCallGraph::RefSCC &BRC = *CG.lookupRefSCC(B1);
LazyCallGraph::RefSCC &CRC = *CG.lookupRefSCC(C1);
LazyCallGraph::RefSCC &DRC = *CG.lookupRefSCC(D1);
ASSERT_EQ(&ARC, CG.lookupRefSCC(A2));
ASSERT_EQ(&ARC, CG.lookupRefSCC(A3));
ASSERT_EQ(&BRC, CG.lookupRefSCC(B2));
ASSERT_EQ(&BRC, CG.lookupRefSCC(B3));
ASSERT_EQ(&CRC, CG.lookupRefSCC(C2));
ASSERT_EQ(&CRC, CG.lookupRefSCC(C3));
ASSERT_EQ(&DRC, CG.lookupRefSCC(D2));
ASSERT_EQ(&DRC, CG.lookupRefSCC(D3));
ASSERT_EQ(1, std::distance(D2.begin(), D2.end()));
// Add an edge to make the graph:
//
// d1 |
// / \ |
// d3--d2---. |
// / \ | |
// b1 c1 | |
// / \ / \ / |
// b3--b2 c3--c2 |
// \ / |
// a1 |
// / \ |
// a3--a2 |
auto MergedRCs = CRC.insertIncomingRefEdge(D2, C2);
// Make sure we connected the nodes.
for (LazyCallGraph::Edge E : D2) {
if (E.getNode() == &D3)
continue;
EXPECT_EQ(&C2, E.getNode());
}
// And marked the D ref-SCC as no longer valid.
EXPECT_EQ(1u, MergedRCs.size());
EXPECT_EQ(&DRC, MergedRCs[0]);
// Make sure we have the correct nodes in the SCC sets.
EXPECT_EQ(&ARC, CG.lookupRefSCC(A1));
EXPECT_EQ(&ARC, CG.lookupRefSCC(A2));
EXPECT_EQ(&ARC, CG.lookupRefSCC(A3));
EXPECT_EQ(&BRC, CG.lookupRefSCC(B1));
EXPECT_EQ(&BRC, CG.lookupRefSCC(B2));
EXPECT_EQ(&BRC, CG.lookupRefSCC(B3));
EXPECT_EQ(&CRC, CG.lookupRefSCC(C1));
EXPECT_EQ(&CRC, CG.lookupRefSCC(C2));
EXPECT_EQ(&CRC, CG.lookupRefSCC(C3));
EXPECT_EQ(&CRC, CG.lookupRefSCC(D1));
EXPECT_EQ(&CRC, CG.lookupRefSCC(D2));
EXPECT_EQ(&CRC, CG.lookupRefSCC(D3));
// And that ancestry tests have been updated.
EXPECT_TRUE(ARC.isParentOf(CRC));
EXPECT_TRUE(BRC.isParentOf(CRC));
// And verify the post-order walk reflects the updated structure.
auto I = CG.postorder_ref_scc_begin(), E = CG.postorder_ref_scc_end();
ASSERT_NE(I, E);
EXPECT_EQ(&CRC, &*I) << "Actual RefSCC: " << *I;
ASSERT_NE(++I, E);
EXPECT_EQ(&BRC, &*I) << "Actual RefSCC: " << *I;
ASSERT_NE(++I, E);
EXPECT_EQ(&ARC, &*I) << "Actual RefSCC: " << *I;
EXPECT_EQ(++I, E);
}
TEST(LazyCallGraphTest, IncomingEdgeInsertionLargeCallCycle) {
LLVMContext Context;
std::unique_ptr<Module> M = parseAssembly(Context, "define void @a() {\n"
"entry:\n"
" call void @b()\n"
" ret void\n"
"}\n"
"define void @b() {\n"
"entry:\n"
" call void @c()\n"
" ret void\n"
"}\n"
"define void @c() {\n"
"entry:\n"
" call void @d()\n"
" ret void\n"
"}\n"
"define void @d() {\n"
"entry:\n"
" ret void\n"
"}\n");
LazyCallGraph CG(*M);
// Force the graph to be fully expanded.
for (LazyCallGraph::RefSCC &RC : CG.postorder_ref_sccs())
dbgs() << "Formed RefSCC: " << RC << "\n";
LazyCallGraph::Node &A = *CG.lookup(lookupFunction(*M, "a"));
LazyCallGraph::Node &B = *CG.lookup(lookupFunction(*M, "b"));
LazyCallGraph::Node &C = *CG.lookup(lookupFunction(*M, "c"));
LazyCallGraph::Node &D = *CG.lookup(lookupFunction(*M, "d"));
LazyCallGraph::SCC &AC = *CG.lookupSCC(A);
LazyCallGraph::SCC &BC = *CG.lookupSCC(B);
LazyCallGraph::SCC &CC = *CG.lookupSCC(C);
LazyCallGraph::SCC &DC = *CG.lookupSCC(D);
LazyCallGraph::RefSCC &ARC = *CG.lookupRefSCC(A);
LazyCallGraph::RefSCC &BRC = *CG.lookupRefSCC(B);
LazyCallGraph::RefSCC &CRC = *CG.lookupRefSCC(C);
LazyCallGraph::RefSCC &DRC = *CG.lookupRefSCC(D);
// Connect the top to the bottom forming a large RefSCC made up mostly of calls.
auto MergedRCs = ARC.insertIncomingRefEdge(D, A);
// Make sure we connected the nodes.
EXPECT_NE(D.begin(), D.end());
EXPECT_EQ(&A, D.begin()->getNode());
// Check that we have the dead RCs, but ignore the order.
EXPECT_EQ(3u, MergedRCs.size());
EXPECT_NE(find(MergedRCs, &BRC), MergedRCs.end());
EXPECT_NE(find(MergedRCs, &CRC), MergedRCs.end());
EXPECT_NE(find(MergedRCs, &DRC), MergedRCs.end());
// Make sure the nodes point to the right place now.
EXPECT_EQ(&ARC, CG.lookupRefSCC(A));
EXPECT_EQ(&ARC, CG.lookupRefSCC(B));
EXPECT_EQ(&ARC, CG.lookupRefSCC(C));
EXPECT_EQ(&ARC, CG.lookupRefSCC(D));
// Check that the SCCs are in postorder.
EXPECT_EQ(4, ARC.size());
EXPECT_EQ(&DC, &ARC[0]);
EXPECT_EQ(&CC, &ARC[1]);
EXPECT_EQ(&BC, &ARC[2]);
EXPECT_EQ(&AC, &ARC[3]);
// And verify the post-order walk reflects the updated structure.
auto I = CG.postorder_ref_scc_begin(), E = CG.postorder_ref_scc_end();
ASSERT_NE(I, E);
EXPECT_EQ(&ARC, &*I) << "Actual RefSCC: " << *I;
EXPECT_EQ(++I, E);
}
TEST(LazyCallGraphTest, IncomingEdgeInsertionLargeRefCycle) {
LLVMContext Context;
std::unique_ptr<Module> M =
parseAssembly(Context, "define void @a() {\n"
"entry:\n"
" %p = alloca void ()*\n"
" store void ()* @b, void ()** %p\n"
" ret void\n"
"}\n"
"define void @b() {\n"
"entry:\n"
" %p = alloca void ()*\n"
" store void ()* @c, void ()** %p\n"
" ret void\n"
"}\n"
"define void @c() {\n"
"entry:\n"
" %p = alloca void ()*\n"
" store void ()* @d, void ()** %p\n"
" ret void\n"
"}\n"
"define void @d() {\n"
"entry:\n"
" ret void\n"
"}\n");
LazyCallGraph CG(*M);
// Force the graph to be fully expanded.
for (LazyCallGraph::RefSCC &RC : CG.postorder_ref_sccs())
dbgs() << "Formed RefSCC: " << RC << "\n";
LazyCallGraph::Node &A = *CG.lookup(lookupFunction(*M, "a"));
LazyCallGraph::Node &B = *CG.lookup(lookupFunction(*M, "b"));
LazyCallGraph::Node &C = *CG.lookup(lookupFunction(*M, "c"));
LazyCallGraph::Node &D = *CG.lookup(lookupFunction(*M, "d"));
LazyCallGraph::RefSCC &ARC = *CG.lookupRefSCC(A);
LazyCallGraph::RefSCC &BRC = *CG.lookupRefSCC(B);
LazyCallGraph::RefSCC &CRC = *CG.lookupRefSCC(C);
LazyCallGraph::RefSCC &DRC = *CG.lookupRefSCC(D);
// Connect the top to the bottom forming a large RefSCC made up just of
// references.
auto MergedRCs = ARC.insertIncomingRefEdge(D, A);
// Make sure we connected the nodes.
EXPECT_NE(D.begin(), D.end());
EXPECT_EQ(&A, D.begin()->getNode());
// Check that we have the dead RCs, but ignore the order.
EXPECT_EQ(3u, MergedRCs.size());
EXPECT_NE(find(MergedRCs, &BRC), MergedRCs.end());
EXPECT_NE(find(MergedRCs, &CRC), MergedRCs.end());
EXPECT_NE(find(MergedRCs, &DRC), MergedRCs.end());
// Make sure the nodes point to the right place now.
EXPECT_EQ(&ARC, CG.lookupRefSCC(A));
EXPECT_EQ(&ARC, CG.lookupRefSCC(B));
EXPECT_EQ(&ARC, CG.lookupRefSCC(C));
EXPECT_EQ(&ARC, CG.lookupRefSCC(D));
// And verify the post-order walk reflects the updated structure.
auto I = CG.postorder_ref_scc_begin(), End = CG.postorder_ref_scc_end();
ASSERT_NE(I, End);
EXPECT_EQ(&ARC, &*I) << "Actual RefSCC: " << *I;
EXPECT_EQ(++I, End);
[LCG] Add the last (and most complex) of the edge insertion mutation operations on the call graph. This one forms a cycle, and while not as complex as removing an internal edge from an SCC, it involves a reasonable amount of work to find all of the nodes newly connected in a cycle. Also somewhat alarming is the worst case complexity here: it might have to walk roughly the entire SCC inverse DAG to insert a single edge. This is carefully documented in the API (I hope). llvm-svn: 207935
2014-05-04 11:38:32 +02:00
}
[LCG] Add the necessary functionality to the LazyCallGraph to support inlining. The basic inlining operation makes the following changes to the call graph: 1) Add edges that were previously transitive edges. This is always trivial and this patch gives the LCG helper methods to make this more convenient. 2) Remove the inlined edge. We had existing support for this, but it contained bugs that needed to be fixed. Testing in the same pattern as the inliner exposes these bugs very nicely. 3) Delete a function when it becomes dead because it is internal and all calls have been inlined. The LCG had no support at all for this operation, so this adds that support. Two unittests have been added that exercise this specific mutation pattern to the call graph. They were extremely effective in uncovering bugs. Sadly, a large fraction of the code here is just to implement those unit tests, but I think they're paying for themselves. =] This was split out of a patch that actually uses the routines to implement inlining in the new pass manager in order to isolate (with unit tests) the logic that was entirely within the LCG. Many thanks for the careful review from folks! There will be a few minor follow-up patches based on the comments in the review as well. Differential Revision: https://reviews.llvm.org/D24225 llvm-svn: 283982
2016-10-12 09:59:56 +02:00
TEST(LazyCallGraphTest, InlineAndDeleteFunction) {
LLVMContext Context;
// We want to ensure we can delete nodes from relatively complex graphs and
// so use the diamond of triangles graph defined above.
//
// The ascii diagram is repeated here for easy reference.
//
// d1 |
// / \ |
// d3--d2 |
// / \ |
// b1 c1 |
// / \ / \ |
// b3--b2 c3--c2 |
// \ / |
// a1 |
// / \ |
// a3--a2 |
//
std::unique_ptr<Module> M = parseAssembly(Context, DiamondOfTriangles);
LazyCallGraph CG(*M);
// Force the graph to be fully expanded.
for (LazyCallGraph::RefSCC &RC : CG.postorder_ref_sccs())
dbgs() << "Formed RefSCC: " << RC << "\n";
LazyCallGraph::Node &A1 = *CG.lookup(lookupFunction(*M, "a1"));
LazyCallGraph::Node &A2 = *CG.lookup(lookupFunction(*M, "a2"));
LazyCallGraph::Node &A3 = *CG.lookup(lookupFunction(*M, "a3"));
LazyCallGraph::Node &B1 = *CG.lookup(lookupFunction(*M, "b1"));
LazyCallGraph::Node &B2 = *CG.lookup(lookupFunction(*M, "b2"));
LazyCallGraph::Node &B3 = *CG.lookup(lookupFunction(*M, "b3"));
LazyCallGraph::Node &C1 = *CG.lookup(lookupFunction(*M, "c1"));
LazyCallGraph::Node &C2 = *CG.lookup(lookupFunction(*M, "c2"));
LazyCallGraph::Node &C3 = *CG.lookup(lookupFunction(*M, "c3"));
LazyCallGraph::Node &D1 = *CG.lookup(lookupFunction(*M, "d1"));
LazyCallGraph::Node &D2 = *CG.lookup(lookupFunction(*M, "d2"));
LazyCallGraph::Node &D3 = *CG.lookup(lookupFunction(*M, "d3"));
LazyCallGraph::RefSCC &ARC = *CG.lookupRefSCC(A1);
LazyCallGraph::RefSCC &BRC = *CG.lookupRefSCC(B1);
LazyCallGraph::RefSCC &CRC = *CG.lookupRefSCC(C1);
LazyCallGraph::RefSCC &DRC = *CG.lookupRefSCC(D1);
ASSERT_EQ(&ARC, CG.lookupRefSCC(A2));
ASSERT_EQ(&ARC, CG.lookupRefSCC(A3));
ASSERT_EQ(&BRC, CG.lookupRefSCC(B2));
ASSERT_EQ(&BRC, CG.lookupRefSCC(B3));
ASSERT_EQ(&CRC, CG.lookupRefSCC(C2));
ASSERT_EQ(&CRC, CG.lookupRefSCC(C3));
ASSERT_EQ(&DRC, CG.lookupRefSCC(D2));
ASSERT_EQ(&DRC, CG.lookupRefSCC(D3));
ASSERT_EQ(1, std::distance(D2.begin(), D2.end()));
// Delete d2 from the graph, as if it had been inlined.
//
// d1 |
// / / |
// d3--. |
// / \ |
// b1 c1 |
// / \ / \ |
// b3--b2 c3--c2 |
// \ / |
// a1 |
// / \ |
// a3--a2 |
Function &D2F = D2.getFunction();
CallInst *C1Call = nullptr, *D1Call = nullptr;
for (User *U : D2F.users()) {
CallInst *CI = dyn_cast<CallInst>(U);
ASSERT_TRUE(CI) << "Expected a call: " << *U;
if (CI->getParent()->getParent() == &C1.getFunction()) {
ASSERT_EQ(nullptr, C1Call) << "Found too many C1 calls: " << *CI;
C1Call = CI;
} else if (CI->getParent()->getParent() == &D1.getFunction()) {
ASSERT_EQ(nullptr, D1Call) << "Found too many D1 calls: " << *CI;
D1Call = CI;
} else {
FAIL() << "Found an unexpected call instruction: " << *CI;
}
}
ASSERT_NE(C1Call, nullptr);
ASSERT_NE(D1Call, nullptr);
ASSERT_EQ(&D2F, C1Call->getCalledFunction());
ASSERT_EQ(&D2F, D1Call->getCalledFunction());
C1Call->setCalledFunction(&D3.getFunction());
D1Call->setCalledFunction(&D3.getFunction());
ASSERT_EQ(0u, D2F.getNumUses());
// Insert new edges first.
CRC.insertTrivialCallEdge(C1, D3);
DRC.insertTrivialCallEdge(D1, D3);
// Then remove the old ones.
LazyCallGraph::SCC &DC = *CG.lookupSCC(D2);
auto NewCs = DRC.switchInternalEdgeToRef(D1, D2);
EXPECT_EQ(&DC, CG.lookupSCC(D2));
EXPECT_EQ(NewCs.end(), std::next(NewCs.begin()));
LazyCallGraph::SCC &NewDC = *NewCs.begin();
EXPECT_EQ(&NewDC, CG.lookupSCC(D1));
EXPECT_EQ(&NewDC, CG.lookupSCC(D3));
auto NewRCs = DRC.removeInternalRefEdge(D1, D2);
EXPECT_EQ(&DRC, CG.lookupRefSCC(D2));
EXPECT_EQ(NewRCs.end(), std::next(NewRCs.begin()));
LazyCallGraph::RefSCC &NewDRC = **NewRCs.begin();
EXPECT_EQ(&NewDRC, CG.lookupRefSCC(D1));
EXPECT_EQ(&NewDRC, CG.lookupRefSCC(D3));
EXPECT_FALSE(NewDRC.isParentOf(DRC));
EXPECT_TRUE(CRC.isParentOf(DRC));
EXPECT_TRUE(CRC.isParentOf(NewDRC));
EXPECT_TRUE(DRC.isParentOf(NewDRC));
CRC.removeOutgoingEdge(C1, D2);
EXPECT_FALSE(CRC.isParentOf(DRC));
EXPECT_TRUE(CRC.isParentOf(NewDRC));
EXPECT_TRUE(DRC.isParentOf(NewDRC));
// Now that we've updated the call graph, D2 is dead, so remove it.
CG.removeDeadFunction(D2F);
// Check that the graph still looks the same.
EXPECT_EQ(&ARC, CG.lookupRefSCC(A1));
EXPECT_EQ(&ARC, CG.lookupRefSCC(A2));
EXPECT_EQ(&ARC, CG.lookupRefSCC(A3));
EXPECT_EQ(&BRC, CG.lookupRefSCC(B1));
EXPECT_EQ(&BRC, CG.lookupRefSCC(B2));
EXPECT_EQ(&BRC, CG.lookupRefSCC(B3));
EXPECT_EQ(&CRC, CG.lookupRefSCC(C1));
EXPECT_EQ(&CRC, CG.lookupRefSCC(C2));
EXPECT_EQ(&CRC, CG.lookupRefSCC(C3));
EXPECT_EQ(&NewDRC, CG.lookupRefSCC(D1));
EXPECT_EQ(&NewDRC, CG.lookupRefSCC(D3));
EXPECT_TRUE(CRC.isParentOf(NewDRC));
// Verify the post-order walk hasn't changed.
auto I = CG.postorder_ref_scc_begin(), E = CG.postorder_ref_scc_end();
ASSERT_NE(I, E);
EXPECT_EQ(&NewDRC, &*I) << "Actual RefSCC: " << *I;
ASSERT_NE(++I, E);
EXPECT_EQ(&CRC, &*I) << "Actual RefSCC: " << *I;
ASSERT_NE(++I, E);
EXPECT_EQ(&BRC, &*I) << "Actual RefSCC: " << *I;
ASSERT_NE(++I, E);
EXPECT_EQ(&ARC, &*I) << "Actual RefSCC: " << *I;
EXPECT_EQ(++I, E);
}
TEST(LazyCallGraphTest, InlineAndDeleteFunctionMidTraversal) {
LLVMContext Context;
// This is the same fundamental test as the previous, but we perform it
// having only partially walked the RefSCCs of the graph.
//
// The ascii diagram is repeated here for easy reference.
//
// d1 |
// / \ |
// d3--d2 |
// / \ |
// b1 c1 |
// / \ / \ |
// b3--b2 c3--c2 |
// \ / |
// a1 |
// / \ |
// a3--a2 |
//
std::unique_ptr<Module> M = parseAssembly(Context, DiamondOfTriangles);
LazyCallGraph CG(*M);
// Walk the RefSCCs until we find the one containing 'c1'.
auto I = CG.postorder_ref_scc_begin(), E = CG.postorder_ref_scc_end();
ASSERT_NE(I, E);
LazyCallGraph::RefSCC &DRC = *I;
ASSERT_NE(&DRC, nullptr);
++I;
ASSERT_NE(I, E);
LazyCallGraph::RefSCC &CRC = *I;
ASSERT_NE(&CRC, nullptr);
ASSERT_EQ(nullptr, CG.lookup(lookupFunction(*M, "a1")));
ASSERT_EQ(nullptr, CG.lookup(lookupFunction(*M, "a2")));
ASSERT_EQ(nullptr, CG.lookup(lookupFunction(*M, "a3")));
ASSERT_EQ(nullptr, CG.lookup(lookupFunction(*M, "b1")));
ASSERT_EQ(nullptr, CG.lookup(lookupFunction(*M, "b2")));
ASSERT_EQ(nullptr, CG.lookup(lookupFunction(*M, "b3")));
LazyCallGraph::Node &C1 = *CG.lookup(lookupFunction(*M, "c1"));
LazyCallGraph::Node &C2 = *CG.lookup(lookupFunction(*M, "c2"));
LazyCallGraph::Node &C3 = *CG.lookup(lookupFunction(*M, "c3"));
LazyCallGraph::Node &D1 = *CG.lookup(lookupFunction(*M, "d1"));
LazyCallGraph::Node &D2 = *CG.lookup(lookupFunction(*M, "d2"));
LazyCallGraph::Node &D3 = *CG.lookup(lookupFunction(*M, "d3"));
ASSERT_EQ(&CRC, CG.lookupRefSCC(C1));
ASSERT_EQ(&CRC, CG.lookupRefSCC(C2));
ASSERT_EQ(&CRC, CG.lookupRefSCC(C3));
ASSERT_EQ(&DRC, CG.lookupRefSCC(D1));
ASSERT_EQ(&DRC, CG.lookupRefSCC(D2));
ASSERT_EQ(&DRC, CG.lookupRefSCC(D3));
ASSERT_EQ(1, std::distance(D2.begin(), D2.end()));
// Delete d2 from the graph, as if it had been inlined.
//
// d1 |
// / / |
// d3--. |
// / \ |
// b1 c1 |
// / \ / \ |
// b3--b2 c3--c2 |
// \ / |
// a1 |
// / \ |
// a3--a2 |
Function &D2F = D2.getFunction();
CallInst *C1Call = nullptr, *D1Call = nullptr;
for (User *U : D2F.users()) {
CallInst *CI = dyn_cast<CallInst>(U);
ASSERT_TRUE(CI) << "Expected a call: " << *U;
if (CI->getParent()->getParent() == &C1.getFunction()) {
ASSERT_EQ(nullptr, C1Call) << "Found too many C1 calls: " << *CI;
C1Call = CI;
} else if (CI->getParent()->getParent() == &D1.getFunction()) {
ASSERT_EQ(nullptr, D1Call) << "Found too many D1 calls: " << *CI;
D1Call = CI;
} else {
FAIL() << "Found an unexpected call instruction: " << *CI;
}
}
ASSERT_NE(C1Call, nullptr);
ASSERT_NE(D1Call, nullptr);
ASSERT_EQ(&D2F, C1Call->getCalledFunction());
ASSERT_EQ(&D2F, D1Call->getCalledFunction());
C1Call->setCalledFunction(&D3.getFunction());
D1Call->setCalledFunction(&D3.getFunction());
ASSERT_EQ(0u, D2F.getNumUses());
// Insert new edges first.
CRC.insertTrivialCallEdge(C1, D3);
DRC.insertTrivialCallEdge(D1, D3);
// Then remove the old ones.
LazyCallGraph::SCC &DC = *CG.lookupSCC(D2);
auto NewCs = DRC.switchInternalEdgeToRef(D1, D2);
EXPECT_EQ(&DC, CG.lookupSCC(D2));
EXPECT_EQ(NewCs.end(), std::next(NewCs.begin()));
LazyCallGraph::SCC &NewDC = *NewCs.begin();
EXPECT_EQ(&NewDC, CG.lookupSCC(D1));
EXPECT_EQ(&NewDC, CG.lookupSCC(D3));
auto NewRCs = DRC.removeInternalRefEdge(D1, D2);
EXPECT_EQ(&DRC, CG.lookupRefSCC(D2));
EXPECT_EQ(NewRCs.end(), std::next(NewRCs.begin()));
LazyCallGraph::RefSCC &NewDRC = **NewRCs.begin();
EXPECT_EQ(&NewDRC, CG.lookupRefSCC(D1));
EXPECT_EQ(&NewDRC, CG.lookupRefSCC(D3));
EXPECT_FALSE(NewDRC.isParentOf(DRC));
EXPECT_TRUE(CRC.isParentOf(DRC));
EXPECT_TRUE(CRC.isParentOf(NewDRC));
EXPECT_TRUE(DRC.isParentOf(NewDRC));
CRC.removeOutgoingEdge(C1, D2);
EXPECT_FALSE(CRC.isParentOf(DRC));
EXPECT_TRUE(CRC.isParentOf(NewDRC));
EXPECT_TRUE(DRC.isParentOf(NewDRC));
// Now that we've updated the call graph, D2 is dead, so remove it.
CG.removeDeadFunction(D2F);
// Check that the graph still looks the same.
EXPECT_EQ(&CRC, CG.lookupRefSCC(C1));
EXPECT_EQ(&CRC, CG.lookupRefSCC(C2));
EXPECT_EQ(&CRC, CG.lookupRefSCC(C3));
EXPECT_EQ(&NewDRC, CG.lookupRefSCC(D1));
EXPECT_EQ(&NewDRC, CG.lookupRefSCC(D3));
EXPECT_TRUE(CRC.isParentOf(NewDRC));
// Verify that the post-order walk reflects the updated but still incomplete
// structure.
auto J = CG.postorder_ref_scc_begin();
EXPECT_NE(J, E);
EXPECT_EQ(&NewDRC, &*J) << "Actual RefSCC: " << *J;
++J;
EXPECT_NE(J, E);
EXPECT_EQ(&CRC, &*J) << "Actual RefSCC: " << *J;
EXPECT_EQ(I, J);
// Check that we can form the last two RefSCCs now, and even that we can do
// it with alternating iterators.
++J;
EXPECT_NE(J, E);
LazyCallGraph::RefSCC &BRC = *J;
EXPECT_NE(&BRC, nullptr);
EXPECT_EQ(&BRC, CG.lookupRefSCC(*CG.lookup(lookupFunction(*M, "b1"))));
EXPECT_EQ(&BRC, CG.lookupRefSCC(*CG.lookup(lookupFunction(*M, "b2"))));
EXPECT_EQ(&BRC, CG.lookupRefSCC(*CG.lookup(lookupFunction(*M, "b3"))));
EXPECT_TRUE(BRC.isParentOf(NewDRC));
++I;
EXPECT_EQ(J, I);
EXPECT_EQ(&BRC, &*I) << "Actual RefSCC: " << *I;
// Increment I this time to form the new RefSCC, flopping back to the first
// iterator.
++I;
EXPECT_NE(I, E);
LazyCallGraph::RefSCC &ARC = *I;
EXPECT_NE(&ARC, nullptr);
EXPECT_EQ(&ARC, CG.lookupRefSCC(*CG.lookup(lookupFunction(*M, "a1"))));
EXPECT_EQ(&ARC, CG.lookupRefSCC(*CG.lookup(lookupFunction(*M, "a2"))));
EXPECT_EQ(&ARC, CG.lookupRefSCC(*CG.lookup(lookupFunction(*M, "a3"))));
EXPECT_TRUE(ARC.isParentOf(BRC));
EXPECT_TRUE(ARC.isParentOf(CRC));
++J;
EXPECT_EQ(I, J);
EXPECT_EQ(&ARC, &*J) << "Actual RefSCC: " << *J;
++I;
EXPECT_EQ(E, I);
++J;
EXPECT_EQ(E, J);
}
[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(LazyCallGraphTest, InternalEdgeMutation) {
Remove every uses of getGlobalContext() in LLVM (but the C API) At the same time, fixes InstructionsTest::CastInst unittest: yes you can leave the IR in an invalid state and exit when you don't destroy the context (like the global one), no longer now. This is the first part of http://reviews.llvm.org/D19094 From: Mehdi Amini <mehdi.amini@apple.com> llvm-svn: 266379
2016-04-14 23:59:01 +02:00
LLVMContext Context;
std::unique_ptr<Module> M = parseAssembly(Context, "define void @a() {\n"
"entry:\n"
" call void @b()\n"
" ret void\n"
"}\n"
"define void @b() {\n"
"entry:\n"
" call void @c()\n"
" ret void\n"
"}\n"
"define void @c() {\n"
"entry:\n"
" call void @a()\n"
" ret void\n"
"}\n");
[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
LazyCallGraph CG(*M);
// Force the graph to be fully expanded.
[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
auto I = CG.postorder_ref_scc_begin();
LazyCallGraph::RefSCC &RC = *I++;
EXPECT_EQ(CG.postorder_ref_scc_end(), I);
[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
[LCG] Switch the SCC lookup to be in terms of call graph nodes rather than functions. So far, this access pattern is *much* more common. It seems likely that any user of this interface is going to have nodes at the point that they are querying the SCCs. No functionality changed. llvm-svn: 207045
2014-04-24 01:12:06 +02:00
LazyCallGraph::Node &A = *CG.lookup(lookupFunction(*M, "a"));
LazyCallGraph::Node &B = *CG.lookup(lookupFunction(*M, "b"));
[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
LazyCallGraph::Node &C = *CG.lookup(lookupFunction(*M, "c"));
EXPECT_EQ(&RC, CG.lookupRefSCC(A));
EXPECT_EQ(&RC, CG.lookupRefSCC(B));
EXPECT_EQ(&RC, CG.lookupRefSCC(C));
EXPECT_EQ(1, RC.size());
EXPECT_EQ(&*RC.begin(), CG.lookupSCC(A));
EXPECT_EQ(&*RC.begin(), CG.lookupSCC(B));
EXPECT_EQ(&*RC.begin(), CG.lookupSCC(C));
// Insert an edge from 'a' to 'c'. Nothing changes about the graph.
RC.insertInternalRefEdge(A, C);
EXPECT_EQ(2, std::distance(A.begin(), A.end()));
EXPECT_EQ(&RC, CG.lookupRefSCC(A));
EXPECT_EQ(&RC, CG.lookupRefSCC(B));
EXPECT_EQ(&RC, CG.lookupRefSCC(C));
EXPECT_EQ(1, RC.size());
EXPECT_EQ(&*RC.begin(), CG.lookupSCC(A));
EXPECT_EQ(&*RC.begin(), CG.lookupSCC(B));
EXPECT_EQ(&*RC.begin(), CG.lookupSCC(C));
// Switch the call edge from 'b' to 'c' to a ref edge. This will break the
// call cycle and cause us to form more SCCs. The RefSCC will remain the same
// though.
RC.switchInternalEdgeToRef(B, C);
EXPECT_EQ(&RC, CG.lookupRefSCC(A));
EXPECT_EQ(&RC, CG.lookupRefSCC(B));
EXPECT_EQ(&RC, CG.lookupRefSCC(C));
auto J = RC.begin();
// The SCCs must be in *post-order* which means successors before
// predecessors. At this point we have call edges from C to A and from A to
// B. The only valid postorder is B, A, C.
EXPECT_EQ(&*J++, CG.lookupSCC(B));
EXPECT_EQ(&*J++, CG.lookupSCC(A));
EXPECT_EQ(&*J++, CG.lookupSCC(C));
EXPECT_EQ(RC.end(), J);
// Test turning the ref edge from A to C into a call edge. This will form an
// SCC out of A and C. Since we previously had a call edge from C to A, the
// C SCC should be preserved and have A merged into it while the A SCC should
// be invalidated.
[LCG] Switch the SCC lookup to be in terms of call graph nodes rather than functions. So far, this access pattern is *much* more common. It seems likely that any user of this interface is going to have nodes at the point that they are querying the SCCs. No functionality changed. llvm-svn: 207045
2014-04-24 01:12:06 +02:00
LazyCallGraph::SCC &AC = *CG.lookupSCC(A);
[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
LazyCallGraph::SCC &CC = *CG.lookupSCC(C);
auto InvalidatedSCCs = RC.switchInternalEdgeToCall(A, C);
ASSERT_EQ(1u, InvalidatedSCCs.size());
EXPECT_EQ(&AC, InvalidatedSCCs[0]);
EXPECT_EQ(2, CC.size());
EXPECT_EQ(&CC, CG.lookupSCC(A));
EXPECT_EQ(&CC, CG.lookupSCC(C));
J = RC.begin();
EXPECT_EQ(&*J++, CG.lookupSCC(B));
EXPECT_EQ(&*J++, CG.lookupSCC(C));
EXPECT_EQ(RC.end(), J);
[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
}
[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(LazyCallGraphTest, InternalEdgeRemoval) {
Remove every uses of getGlobalContext() in LLVM (but the C API) At the same time, fixes InstructionsTest::CastInst unittest: yes you can leave the IR in an invalid state and exit when you don't destroy the context (like the global one), no longer now. This is the first part of http://reviews.llvm.org/D19094 From: Mehdi Amini <mehdi.amini@apple.com> llvm-svn: 266379
2016-04-14 23:59:01 +02:00
LLVMContext Context;
[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 nice fully connected (including self-edges) RefSCC.
std::unique_ptr<Module> M = parseAssembly(
Remove every uses of getGlobalContext() in LLVM (but the C API) At the same time, fixes InstructionsTest::CastInst unittest: yes you can leave the IR in an invalid state and exit when you don't destroy the context (like the global one), no longer now. This is the first part of http://reviews.llvm.org/D19094 From: Mehdi Amini <mehdi.amini@apple.com> llvm-svn: 266379
2016-04-14 23:59:01 +02:00
Context, "define void @a(i8** %ptr) {\n"
"entry:\n"
" store i8* bitcast (void(i8**)* @a to i8*), i8** %ptr\n"
" store i8* bitcast (void(i8**)* @b to i8*), i8** %ptr\n"
" store i8* bitcast (void(i8**)* @c to i8*), i8** %ptr\n"
" ret void\n"
"}\n"
"define void @b(i8** %ptr) {\n"
"entry:\n"
" store i8* bitcast (void(i8**)* @a to i8*), i8** %ptr\n"
" store i8* bitcast (void(i8**)* @b to i8*), i8** %ptr\n"
" store i8* bitcast (void(i8**)* @c to i8*), i8** %ptr\n"
" ret void\n"
"}\n"
"define void @c(i8** %ptr) {\n"
"entry:\n"
" store i8* bitcast (void(i8**)* @a to i8*), i8** %ptr\n"
" store i8* bitcast (void(i8**)* @b to i8*), i8** %ptr\n"
" store i8* bitcast (void(i8**)* @c to i8*), i8** %ptr\n"
" ret void\n"
"}\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
LazyCallGraph CG(*M);
[LCG] Add the really, *really* boring edge insertion case: adding an edge entirely within an existing SCC. Shockingly, making the connected component more connected is ... a total snooze fest. =] Anyways, its wired up, and I even added a test case to make sure it pretty much sorta works. =D llvm-svn: 207631
2014-04-30 12:48:36 +02:00
// Force the graph to be fully expanded.
[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
auto I = CG.postorder_ref_scc_begin(), E = CG.postorder_ref_scc_end();
LazyCallGraph::RefSCC &RC = *I;
EXPECT_EQ(E, std::next(I));
[LCG] Add the really, *really* boring edge insertion case: adding an edge entirely within an existing SCC. Shockingly, making the connected component more connected is ... a total snooze fest. =] Anyways, its wired up, and I even added a test case to make sure it pretty much sorta works. =D llvm-svn: 207631
2014-04-30 12:48:36 +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
LazyCallGraph::Node &A = *CG.lookup(lookupFunction(*M, "a"));
LazyCallGraph::Node &B = *CG.lookup(lookupFunction(*M, "b"));
LazyCallGraph::Node &C = *CG.lookup(lookupFunction(*M, "c"));
EXPECT_EQ(&RC, CG.lookupRefSCC(A));
EXPECT_EQ(&RC, CG.lookupRefSCC(B));
EXPECT_EQ(&RC, CG.lookupRefSCC(C));
// Remove the edge from b -> a, which should leave the 3 functions still in
// a single connected component because of a -> b -> c -> a.
SmallVector<LazyCallGraph::RefSCC *, 1> NewRCs =
RC.removeInternalRefEdge(B, A);
EXPECT_EQ(0u, NewRCs.size());
EXPECT_EQ(&RC, CG.lookupRefSCC(A));
EXPECT_EQ(&RC, CG.lookupRefSCC(B));
EXPECT_EQ(&RC, CG.lookupRefSCC(C));
[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
auto J = CG.postorder_ref_scc_begin();
EXPECT_EQ(I, J);
EXPECT_EQ(&RC, &*J);
EXPECT_EQ(E, std::next(J));
[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
// Remove the edge from c -> a, which should leave 'a' in the original RefSCC
// and form a new RefSCC for 'b' and 'c'.
NewRCs = RC.removeInternalRefEdge(C, A);
EXPECT_EQ(1u, NewRCs.size());
EXPECT_EQ(&RC, CG.lookupRefSCC(A));
EXPECT_EQ(1, std::distance(RC.begin(), RC.end()));
[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
LazyCallGraph::RefSCC &RC2 = *CG.lookupRefSCC(B);
EXPECT_EQ(&RC2, CG.lookupRefSCC(C));
EXPECT_EQ(&RC2, NewRCs[0]);
J = CG.postorder_ref_scc_begin();
EXPECT_NE(I, J);
EXPECT_EQ(&RC2, &*J);
++J;
EXPECT_EQ(I, J);
EXPECT_EQ(&RC, &*J);
++I;
EXPECT_EQ(E, I);
++J;
EXPECT_EQ(E, J);
[LCG] Add the really, *really* boring edge insertion case: adding an edge entirely within an existing SCC. Shockingly, making the connected component more connected is ... a total snooze fest. =] Anyways, its wired up, and I even added a test case to make sure it pretty much sorta works. =D llvm-svn: 207631
2014-04-30 12:48:36 +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
TEST(LazyCallGraphTest, InternalCallEdgeToRef) {
Remove every uses of getGlobalContext() in LLVM (but the C API) At the same time, fixes InstructionsTest::CastInst unittest: yes you can leave the IR in an invalid state and exit when you don't destroy the context (like the global one), no longer now. This is the first part of http://reviews.llvm.org/D19094 From: Mehdi Amini <mehdi.amini@apple.com> llvm-svn: 266379
2016-04-14 23:59:01 +02:00
LLVMContext Context;
[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 nice fully connected (including self-edges) SCC (and RefSCC)
Remove every uses of getGlobalContext() in LLVM (but the C API) At the same time, fixes InstructionsTest::CastInst unittest: yes you can leave the IR in an invalid state and exit when you don't destroy the context (like the global one), no longer now. This is the first part of http://reviews.llvm.org/D19094 From: Mehdi Amini <mehdi.amini@apple.com> llvm-svn: 266379
2016-04-14 23:59:01 +02:00
std::unique_ptr<Module> M = parseAssembly(Context, "define void @a() {\n"
"entry:\n"
" call void @a()\n"
" call void @b()\n"
" call void @c()\n"
" ret void\n"
"}\n"
"define void @b() {\n"
"entry:\n"
" call void @a()\n"
" call void @b()\n"
" call void @c()\n"
" ret void\n"
"}\n"
"define void @c() {\n"
"entry:\n"
" call void @a()\n"
" call void @b()\n"
" call void @c()\n"
" ret void\n"
"}\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
LazyCallGraph CG(*M);
[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
// Force the graph to be fully expanded.
[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
auto I = CG.postorder_ref_scc_begin();
LazyCallGraph::RefSCC &RC = *I++;
EXPECT_EQ(CG.postorder_ref_scc_end(), I);
[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
[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
EXPECT_EQ(1, RC.size());
LazyCallGraph::SCC &CallC = *RC.begin();
[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
[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
LazyCallGraph::Node &A = *CG.lookup(lookupFunction(*M, "a"));
LazyCallGraph::Node &B = *CG.lookup(lookupFunction(*M, "b"));
LazyCallGraph::Node &C = *CG.lookup(lookupFunction(*M, "c"));
EXPECT_EQ(&CallC, CG.lookupSCC(A));
EXPECT_EQ(&CallC, CG.lookupSCC(B));
EXPECT_EQ(&CallC, CG.lookupSCC(C));
// Remove the call edge from b -> a to a ref edge, which should leave the
// 3 functions still in a single connected component because of a -> b ->
// c -> a.
RC.switchInternalEdgeToRef(B, A);
EXPECT_EQ(1, RC.size());
EXPECT_EQ(&CallC, CG.lookupSCC(A));
EXPECT_EQ(&CallC, CG.lookupSCC(B));
EXPECT_EQ(&CallC, CG.lookupSCC(C));
[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
// Remove the edge from c -> a, which should leave 'a' in the original SCC
// and form a new SCC for 'b' and '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
RC.switchInternalEdgeToRef(C, A);
EXPECT_EQ(2, RC.size());
EXPECT_EQ(&CallC, CG.lookupSCC(A));
LazyCallGraph::SCC &BCallC = *CG.lookupSCC(B);
EXPECT_NE(&BCallC, &CallC);
EXPECT_EQ(&BCallC, CG.lookupSCC(C));
auto J = RC.find(CallC);
EXPECT_EQ(&CallC, &*J);
--J;
EXPECT_EQ(&BCallC, &*J);
EXPECT_EQ(RC.begin(), J);
// Remove the edge from c -> b, which should leave 'b' in the original SCC
// and form a new SCC for 'c'. It shouldn't change 'a's SCC.
RC.switchInternalEdgeToRef(C, B);
EXPECT_EQ(3, RC.size());
EXPECT_EQ(&CallC, CG.lookupSCC(A));
EXPECT_EQ(&BCallC, CG.lookupSCC(B));
LazyCallGraph::SCC &CCallC = *CG.lookupSCC(C);
EXPECT_NE(&CCallC, &CallC);
EXPECT_NE(&CCallC, &BCallC);
J = RC.find(CallC);
EXPECT_EQ(&CallC, &*J);
--J;
EXPECT_EQ(&BCallC, &*J);
--J;
EXPECT_EQ(&CCallC, &*J);
EXPECT_EQ(RC.begin(), J);
}
TEST(LazyCallGraphTest, InternalRefEdgeToCall) {
Remove every uses of getGlobalContext() in LLVM (but the C API) At the same time, fixes InstructionsTest::CastInst unittest: yes you can leave the IR in an invalid state and exit when you don't destroy the context (like the global one), no longer now. This is the first part of http://reviews.llvm.org/D19094 From: Mehdi Amini <mehdi.amini@apple.com> llvm-svn: 266379
2016-04-14 23:59:01 +02:00
LLVMContext Context;
[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
// Basic tests for making a ref edge a call. This hits the basics of the
// process only.
Remove every uses of getGlobalContext() in LLVM (but the C API) At the same time, fixes InstructionsTest::CastInst unittest: yes you can leave the IR in an invalid state and exit when you don't destroy the context (like the global one), no longer now. This is the first part of http://reviews.llvm.org/D19094 From: Mehdi Amini <mehdi.amini@apple.com> llvm-svn: 266379
2016-04-14 23:59:01 +02:00
std::unique_ptr<Module> M =
parseAssembly(Context, "define void @a() {\n"
"entry:\n"
" call void @b()\n"
" call void @c()\n"
" store void()* @d, void()** undef\n"
" ret void\n"
"}\n"
"define void @b() {\n"
"entry:\n"
" store void()* @c, void()** undef\n"
" call void @d()\n"
" ret void\n"
"}\n"
"define void @c() {\n"
"entry:\n"
" store void()* @b, void()** undef\n"
" call void @d()\n"
" ret void\n"
"}\n"
"define void @d() {\n"
"entry:\n"
" store void()* @a, void()** undef\n"
" ret void\n"
"}\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
LazyCallGraph CG(*M);
// Force the graph to be fully expanded.
auto I = CG.postorder_ref_scc_begin();
LazyCallGraph::RefSCC &RC = *I++;
EXPECT_EQ(CG.postorder_ref_scc_end(), I);
LazyCallGraph::Node &A = *CG.lookup(lookupFunction(*M, "a"));
LazyCallGraph::Node &B = *CG.lookup(lookupFunction(*M, "b"));
LazyCallGraph::Node &C = *CG.lookup(lookupFunction(*M, "c"));
LazyCallGraph::Node &D = *CG.lookup(lookupFunction(*M, "d"));
LazyCallGraph::SCC &AC = *CG.lookupSCC(A);
LazyCallGraph::SCC &BC = *CG.lookupSCC(B);
LazyCallGraph::SCC &CC = *CG.lookupSCC(C);
LazyCallGraph::SCC &DC = *CG.lookupSCC(D);
// Check the initial post-order. Note that B and C could be flipped here (and
// in our mutation) without changing the nature of this test.
ASSERT_EQ(4, RC.size());
EXPECT_EQ(&DC, &RC[0]);
EXPECT_EQ(&BC, &RC[1]);
EXPECT_EQ(&CC, &RC[2]);
EXPECT_EQ(&AC, &RC[3]);
// Switch the ref edge from A -> D to a call edge. This should have no
// effect as it is already in postorder and no new cycles are formed.
auto MergedCs = RC.switchInternalEdgeToCall(A, D);
EXPECT_EQ(0u, MergedCs.size());
ASSERT_EQ(4, RC.size());
EXPECT_EQ(&DC, &RC[0]);
EXPECT_EQ(&BC, &RC[1]);
EXPECT_EQ(&CC, &RC[2]);
EXPECT_EQ(&AC, &RC[3]);
// Switch B -> C to a call edge. This doesn't form any new cycles but does
// require reordering the SCCs.
MergedCs = RC.switchInternalEdgeToCall(B, C);
EXPECT_EQ(0u, MergedCs.size());
ASSERT_EQ(4, RC.size());
EXPECT_EQ(&DC, &RC[0]);
EXPECT_EQ(&CC, &RC[1]);
EXPECT_EQ(&BC, &RC[2]);
EXPECT_EQ(&AC, &RC[3]);
// Switch C -> B to a call edge. This forms a cycle and forces merging SCCs.
MergedCs = RC.switchInternalEdgeToCall(C, B);
ASSERT_EQ(1u, MergedCs.size());
EXPECT_EQ(&CC, MergedCs[0]);
ASSERT_EQ(3, RC.size());
EXPECT_EQ(&DC, &RC[0]);
EXPECT_EQ(&BC, &RC[1]);
EXPECT_EQ(&AC, &RC[2]);
EXPECT_EQ(2, BC.size());
EXPECT_EQ(&BC, CG.lookupSCC(B));
EXPECT_EQ(&BC, CG.lookupSCC(C));
}
TEST(LazyCallGraphTest, InternalRefEdgeToCallNoCycleInterleaved) {
Remove every uses of getGlobalContext() in LLVM (but the C API) At the same time, fixes InstructionsTest::CastInst unittest: yes you can leave the IR in an invalid state and exit when you don't destroy the context (like the global one), no longer now. This is the first part of http://reviews.llvm.org/D19094 From: Mehdi Amini <mehdi.amini@apple.com> llvm-svn: 266379
2016-04-14 23:59:01 +02:00
LLVMContext Context;
[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 for having a post-order prior to changing a ref edge to a call edge
// with SCCs connecting to the source and connecting to the target, but not
// connecting to both, interleaved between the source and target. This
// ensures we correctly partition the range rather than simply moving one or
// the other.
Remove every uses of getGlobalContext() in LLVM (but the C API) At the same time, fixes InstructionsTest::CastInst unittest: yes you can leave the IR in an invalid state and exit when you don't destroy the context (like the global one), no longer now. This is the first part of http://reviews.llvm.org/D19094 From: Mehdi Amini <mehdi.amini@apple.com> llvm-svn: 266379
2016-04-14 23:59:01 +02:00
std::unique_ptr<Module> M =
parseAssembly(Context, "define void @a() {\n"
"entry:\n"
" call void @b1()\n"
" call void @c1()\n"
" ret void\n"
"}\n"
"define void @b1() {\n"
"entry:\n"
" call void @c1()\n"
" call void @b2()\n"
" ret void\n"
"}\n"
"define void @c1() {\n"
"entry:\n"
" call void @b2()\n"
" call void @c2()\n"
" ret void\n"
"}\n"
"define void @b2() {\n"
"entry:\n"
" call void @c2()\n"
" call void @b3()\n"
" ret void\n"
"}\n"
"define void @c2() {\n"
"entry:\n"
" call void @b3()\n"
" call void @c3()\n"
" ret void\n"
"}\n"
"define void @b3() {\n"
"entry:\n"
" call void @c3()\n"
" call void @d()\n"
" ret void\n"
"}\n"
"define void @c3() {\n"
"entry:\n"
" store void()* @b1, void()** undef\n"
" call void @d()\n"
" ret void\n"
"}\n"
"define void @d() {\n"
"entry:\n"
" store void()* @a, void()** undef\n"
" ret void\n"
"}\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
LazyCallGraph CG(*M);
// Force the graph to be fully expanded.
auto I = CG.postorder_ref_scc_begin();
LazyCallGraph::RefSCC &RC = *I++;
EXPECT_EQ(CG.postorder_ref_scc_end(), I);
LazyCallGraph::Node &A = *CG.lookup(lookupFunction(*M, "a"));
LazyCallGraph::Node &B1 = *CG.lookup(lookupFunction(*M, "b1"));
LazyCallGraph::Node &B2 = *CG.lookup(lookupFunction(*M, "b2"));
LazyCallGraph::Node &B3 = *CG.lookup(lookupFunction(*M, "b3"));
LazyCallGraph::Node &C1 = *CG.lookup(lookupFunction(*M, "c1"));
LazyCallGraph::Node &C2 = *CG.lookup(lookupFunction(*M, "c2"));
LazyCallGraph::Node &C3 = *CG.lookup(lookupFunction(*M, "c3"));
LazyCallGraph::Node &D = *CG.lookup(lookupFunction(*M, "d"));
LazyCallGraph::SCC &AC = *CG.lookupSCC(A);
LazyCallGraph::SCC &B1C = *CG.lookupSCC(B1);
LazyCallGraph::SCC &B2C = *CG.lookupSCC(B2);
LazyCallGraph::SCC &B3C = *CG.lookupSCC(B3);
LazyCallGraph::SCC &C1C = *CG.lookupSCC(C1);
LazyCallGraph::SCC &C2C = *CG.lookupSCC(C2);
LazyCallGraph::SCC &C3C = *CG.lookupSCC(C3);
LazyCallGraph::SCC &DC = *CG.lookupSCC(D);
// Several call edges are initially present to force a particual post-order.
// Remove them now, leaving an interleaved post-order pattern.
RC.switchInternalEdgeToRef(B3, C3);
RC.switchInternalEdgeToRef(C2, B3);
RC.switchInternalEdgeToRef(B2, C2);
RC.switchInternalEdgeToRef(C1, B2);
RC.switchInternalEdgeToRef(B1, C1);
// Check the initial post-order. We ensure this order with the extra edges
// that are nuked above.
ASSERT_EQ(8, RC.size());
EXPECT_EQ(&DC, &RC[0]);
EXPECT_EQ(&C3C, &RC[1]);
EXPECT_EQ(&B3C, &RC[2]);
EXPECT_EQ(&C2C, &RC[3]);
EXPECT_EQ(&B2C, &RC[4]);
EXPECT_EQ(&C1C, &RC[5]);
EXPECT_EQ(&B1C, &RC[6]);
EXPECT_EQ(&AC, &RC[7]);
// Switch C3 -> B1 to a call edge. This doesn't form any new cycles but does
// require reordering the SCCs in the face of tricky internal node
// structures.
auto MergedCs = RC.switchInternalEdgeToCall(C3, B1);
EXPECT_EQ(0u, MergedCs.size());
ASSERT_EQ(8, RC.size());
EXPECT_EQ(&DC, &RC[0]);
EXPECT_EQ(&B3C, &RC[1]);
EXPECT_EQ(&B2C, &RC[2]);
EXPECT_EQ(&B1C, &RC[3]);
EXPECT_EQ(&C3C, &RC[4]);
EXPECT_EQ(&C2C, &RC[5]);
EXPECT_EQ(&C1C, &RC[6]);
EXPECT_EQ(&AC, &RC[7]);
}
TEST(LazyCallGraphTest, InternalRefEdgeToCallBothPartitionAndMerge) {
Remove every uses of getGlobalContext() in LLVM (but the C API) At the same time, fixes InstructionsTest::CastInst unittest: yes you can leave the IR in an invalid state and exit when you don't destroy the context (like the global one), no longer now. This is the first part of http://reviews.llvm.org/D19094 From: Mehdi Amini <mehdi.amini@apple.com> llvm-svn: 266379
2016-04-14 23:59:01 +02:00
LLVMContext Context;
[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 for having a postorder where between the source and target are all
// three kinds of other SCCs:
// 1) One connected to the target only that have to be shifted below the
// source.
// 2) One connected to the source only that have to be shifted below the
// target.
// 3) One connected to both source and target that has to remain and get
// merged away.
//
// To achieve this we construct a heavily connected graph to force
// a particular post-order. Then we remove the forcing edges and connect
// a cycle.
//
// Diagram for the graph we want on the left and the graph we use to force
// the ordering on the right. Edges ponit down or right.
//
// A | A |
// / \ | / \ |
// B E | B \ |
// |\ | | |\ | |
// | D | | C-D-E |
// | \| | | \| |
// C F | \ F |
// \ / | \ / |
// G | G |
//
// And we form a cycle by connecting F to B.
Remove every uses of getGlobalContext() in LLVM (but the C API) At the same time, fixes InstructionsTest::CastInst unittest: yes you can leave the IR in an invalid state and exit when you don't destroy the context (like the global one), no longer now. This is the first part of http://reviews.llvm.org/D19094 From: Mehdi Amini <mehdi.amini@apple.com> llvm-svn: 266379
2016-04-14 23:59:01 +02:00
std::unique_ptr<Module> M =
parseAssembly(Context, "define void @a() {\n"
"entry:\n"
" call void @b()\n"
" call void @e()\n"
" ret void\n"
"}\n"
"define void @b() {\n"
"entry:\n"
" call void @c()\n"
" call void @d()\n"
" ret void\n"
"}\n"
"define void @c() {\n"
"entry:\n"
" call void @d()\n"
" call void @g()\n"
" ret void\n"
"}\n"
"define void @d() {\n"
"entry:\n"
" call void @e()\n"
" call void @f()\n"
" ret void\n"
"}\n"
"define void @e() {\n"
"entry:\n"
" call void @f()\n"
" ret void\n"
"}\n"
"define void @f() {\n"
"entry:\n"
" store void()* @b, void()** undef\n"
" call void @g()\n"
" ret void\n"
"}\n"
"define void @g() {\n"
"entry:\n"
" store void()* @a, void()** undef\n"
" ret void\n"
"}\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
LazyCallGraph CG(*M);
// Force the graph to be fully expanded.
auto I = CG.postorder_ref_scc_begin();
LazyCallGraph::RefSCC &RC = *I++;
EXPECT_EQ(CG.postorder_ref_scc_end(), I);
LazyCallGraph::Node &A = *CG.lookup(lookupFunction(*M, "a"));
LazyCallGraph::Node &B = *CG.lookup(lookupFunction(*M, "b"));
LazyCallGraph::Node &C = *CG.lookup(lookupFunction(*M, "c"));
LazyCallGraph::Node &D = *CG.lookup(lookupFunction(*M, "d"));
LazyCallGraph::Node &E = *CG.lookup(lookupFunction(*M, "e"));
LazyCallGraph::Node &F = *CG.lookup(lookupFunction(*M, "f"));
LazyCallGraph::Node &G = *CG.lookup(lookupFunction(*M, "g"));
LazyCallGraph::SCC &AC = *CG.lookupSCC(A);
LazyCallGraph::SCC &BC = *CG.lookupSCC(B);
LazyCallGraph::SCC &CC = *CG.lookupSCC(C);
LazyCallGraph::SCC &DC = *CG.lookupSCC(D);
LazyCallGraph::SCC &EC = *CG.lookupSCC(E);
LazyCallGraph::SCC &FC = *CG.lookupSCC(F);
LazyCallGraph::SCC &GC = *CG.lookupSCC(G);
// Remove the extra edges that were used to force a particular post-order.
RC.switchInternalEdgeToRef(C, D);
RC.switchInternalEdgeToRef(D, E);
// Check the initial post-order. We ensure this order with the extra edges
// that are nuked above.
ASSERT_EQ(7, RC.size());
EXPECT_EQ(&GC, &RC[0]);
EXPECT_EQ(&FC, &RC[1]);
EXPECT_EQ(&EC, &RC[2]);
EXPECT_EQ(&DC, &RC[3]);
EXPECT_EQ(&CC, &RC[4]);
EXPECT_EQ(&BC, &RC[5]);
EXPECT_EQ(&AC, &RC[6]);
// Switch F -> B to a call edge. This merges B, D, and F into a single SCC,
// and has to place the C and E SCCs on either side of it:
// A A |
// / \ / \ |
// B E | E |
// |\ | \ / |
// | D | -> B |
// | \| / \ |
// C F C | |
// \ / \ / |
// G G |
auto MergedCs = RC.switchInternalEdgeToCall(F, B);
ASSERT_EQ(2u, MergedCs.size());
EXPECT_EQ(&FC, MergedCs[0]);
EXPECT_EQ(&DC, MergedCs[1]);
EXPECT_EQ(3, BC.size());
// And make sure the postorder was updated.
ASSERT_EQ(5, RC.size());
EXPECT_EQ(&GC, &RC[0]);
EXPECT_EQ(&CC, &RC[1]);
EXPECT_EQ(&BC, &RC[2]);
EXPECT_EQ(&EC, &RC[3]);
EXPECT_EQ(&AC, &RC[4]);
[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
}
[LCG] Add a unittest for the LazyCallGraph. I had a weak moment and resisted this for too long. Just with the basic testing here I was able to exercise the analysis in more detail and sift out both type signature bugs in the API and a bug in the DFS numbering. All of these are fixed here as well. The unittests will be much more important for the mutation support where it is necessary to craft minimal mutations and then inspect the state of the graph. There is just no way to do that with a standard FileCheck test. However, unittesting these kinds of analyses is really quite easy, especially as they're designed with the new pass manager where there is essentially no infrastructure required to rig up the core logic and exercise it at an API level. As a minor aside about the DFS numbering bug, the DFS numbering used in LCG is a bit unusual. Rather than numbering from 0, we number from 1, and use 0 as the sentinel "unvisited" state. Other implementations often use '-1' for this, but I find it easier to deal with 0 and it shouldn't make any real difference provided someone doesn't write silly bugs like forgetting to actually initialize the DFS numbering. Oops. ;] llvm-svn: 206954
2014-04-23 10:08:49 +02:00
}
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