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arXiv · 2009.02775

A Thread-Local Semantics and Efficient Static Analyses for Race Free Programs

Suvam Mukherjee·Oded Padon·Sharon Shoham·Deepak D'Souza·Noam Rinetzky
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Abstract

Data race free (DRF) programs constitute an important class of concurrent programs. In this paper we provide a framework for designing and proving the correctness of data flow analyses that target this class of programs. These analyses are in the same spirit as the "sync-CFG" analysis proposed in earlier literature. To achieve this, we first propose a novel concrete semantics for DRF programs, called L-DRF, that is thread-local in nature---each thread operates on its own copy of the data state. We show that abstractions of our semantics allow us to reduce the analysis of DRF programs to a sequential analysis. This aids in rapidly porting existing sequential analyses to sound and scalable analyses for DRF programs. Next, we parameterize L-DRF with a partitioning of the program variables into "regions" which are accessed atomically. Abstractions of the region-parameterized semantics yield more precise analyses for "region-race" free concurrent programs. We instantiate these abstractions to devise efficient relational analyses for race free programs, which we have implemented in a prototype tool called RATCOP. On the benchmarks, RATCOP was able to prove up to 65% of the assertions, in comparison to 25% proved by our baseline. Moreover, in a comparative study with a recent concurrent static analyzer, RATCOP was up to 5 orders of magnitude faster.

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Suvam Mukherjee, Oded Padon, Sharon Shoham, Deepak D'Souza, Noam Rinetzky. 2020-09-06. A Thread-Local Semantics and Efficient Static Analyses for Race Free Programs. https://arxiv.org/abs/2009.02775

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