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a59e5882a0
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 git-svn-id: https://llvm.org/svn/llvm-project/llvm/trunk@261040 91177308-0d34-0410-b5e6-96231b3b80d8 |
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| .. | ||
| AssumptionCache | ||
| BasicAA | ||
| BlockFrequencyInfo | ||
| BranchProbabilityInfo | ||
| CallGraph | ||
| CFLAliasAnalysis | ||
| CostModel | ||
| Delinearization | ||
| DemandedBits | ||
| DependenceAnalysis | ||
| DivergenceAnalysis | ||
| Dominators | ||
| GlobalsModRef | ||
| LazyCallGraph | ||
| Lint | ||
| LoopAccessAnalysis | ||
| LoopInfo | ||
| MemoryDependenceAnalysis | ||
| PostDominators | ||
| RegionInfo | ||
| ScalarEvolution | ||
| ScopedNoAliasAA | ||
| TypeBasedAliasAnalysis | ||
| ValueTracking | ||
| alias-analysis-uses.ll | ||