Automatic Loop Invariant Generation for Data Dependence Analysis
Parallelization of programs relies on sound and precise analysis of data dependences in the code, specifically, when dealing with loops. State-of-art tools are based on dynamic profiling and static analysis. They tend to over- and, occasionally, to under-approximate dependences. The former misses pa...
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| Vydáno v: | 2022 IEEE/ACM 10th International Conference on Formal Methods in Software Engineering (FormaliSE) s. 34 - 45 |
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| Hlavní autoři: | , , |
| Médium: | Konferenční příspěvek |
| Jazyk: | angličtina |
| Vydáno: |
ACM
01.05.2022
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| Témata: | |
| ISSN: | 2575-5099 |
| On-line přístup: | Získat plný text |
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| Shrnutí: | Parallelization of programs relies on sound and precise analysis of data dependences in the code, specifically, when dealing with loops. State-of-art tools are based on dynamic profiling and static analysis. They tend to over- and, occasionally, to under-approximate dependences. The former misses parallelization opportunities, the latter can change the behavior of the parallelized program. In this paper we present a sound and highly precise approach to generate data dependences based on deductive verification. The central technique is to infer a specific form of loop invariant tailored to express dependences. To achieve full automation, we adapt predicate abstraction in a suitable manner. To retain as much precision as possible, we generalized logic-based symbolic execution to compute abstract dependence predicates. We implemented our approach for Java on top of a deductive verification tool. The evaluation shows that our approach can generate highly precise data dependences for representative code taken from HPC applications. |
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| ISSN: | 2575-5099 |
| DOI: | 10.1145/3524482.3527649 |