Recursive tree traversal dependence analysis
While there has been much work done on analyzing and transforming regular programs that operate over linear arrays and dense matrices, comparatively little has been done to try to carry these optimizations over to programs that operate over heap-based data structures using pointers. Previous work has shown that point blocking, a technique similar to loop tiling in regular programs, can help increase the temporal locality of repeated tree traversals. Point blocking, however, has only been shown to work on tree traversals where each traversal is fully independent and would allow parallelization, greatly limiting the types of applications that this transformation could be applied to. The purpose of this study is to develop a new framework for analyzing recursive methods that perform traversals over trees, called tree dependence analysis. This analysis translates dependence analysis techniques for regular programs to the irregular space, identifying the structure of dependences within a recursive method that traverses trees. In this study, a dependence test that exploits the dependence structure of such programs is developed, and is shown to be able to prove the legality of several locality— and parallelism-enhancing transformations, including point blocking. In addition, the analysis is extended with a novel path-dependent, conditional analysis to refine the dependence test and prove the legality of transformations for a wider range of algorithms. These analyses are then used to show that several common algorithms that manipulate trees recursively are amenable to several locality— and parallelism-enhancing transformations. This work shows that classical dependence analysis techniques, which have largely been confined to nested loops over array data structures, can be extended and translated to work for complex, recursive programs that operate over pointer-based data structures.
Kulkarni, Purdue University.
Computer Engineering|Computer science
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