Empirical Comparison of Race Detection Tools for OpenMP Programs

Ha, Ok-Kyoon; Kim, Young-Joo; Kang, Mun-Hye; Jun, Yong-Kee

doi:10.1007/978-3-642-10549-4_13

Cited by 3 publications

(1 citation statement)

References 9 publications

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“…The tool checks the data dependency of accesses to shared variables (whenever an OpenMP directive is detected) by using sequentially traced information, and reports the accesses as races if their dependency satisfies an anti (write-after-read), flow (read-after-write), and output data dependency (writeafter-write). Oracle R Developer Studio Thread Analyzer [34] is based on similar techniques and yields comparable results as Thread Checker [18].…”

Section: Related Workmentioning

confidence: 99%

Hunting Superfluous Locks with Model Checking

Nguyên

Serwe

Mateescu

et al. 2019

From Software Engineering to Formal Methods and Tools, and Back

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Parallelization of existing sequential programs to increase their performance and exploit recent multi and many-core architectures is a challenging but inevitable effort. One increasingly popular parallelization approach is based on OpenMP, which enables the designer to annotate a sequential program with constructs specifying the parallel execution of code blocks. These constructs are then interpreted by the OpenMP compiler and runtime, which assigns blocks to threads running on a parallel architecture. Although this scheme is very flexible and not (very) intrusive, it does not prevent the occurrence of synchronization errors (e.g., deadlocks) or data races on shared variables. In this paper, we propose an iterative method to assist the OpenMP parallelization by using formal methods and verification. In each iteration, potential data races are identified by applying to the OpenMP program a lockset analysis, which computes the set of shared variables that potentially need to be protected by locks. To avoid the insertion of superfluous locks, an abstract, action-based formal model of the OpenMP program is extracted and analyzed using the ACTL on-the-fly model checker of the CADP formal verification toolbox. We describe the method, compare it with existing work, and illustrate its practical use.

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Section: Related Workmentioning

confidence: 99%