Does it matter?

Abstract

Data races are a primary source of concurrency bugs in parallel programs. Yet, debugging data races is not easy, even with a large amount of data race detection tools. In particular, there still exists a manually-intensive and time-consuming investigation process after data races are reported by existing race detection tools. To address this issue, we present OMPSanitizer in this paper. OMPSanitizer employs a novel and semantic-aware impact analysis mechanism to assess the potential impact of detected data races so that developers can focus on data races with a high probability to produce a harmful impact. This way, OMPSanitizer can remove the heavy debugging burden of data races from developers and simultaneously enhance the debugging efficiency. We have implemented OMPSanitizer based on the widely-used dynamic binary instrumentation infrastructure, Intel Pin. Our evaluation results on a broad range of OpenMP programs from the DataRaceBench benchmark suite and an ECP Proxy application demonstrate that OMPSanitizer can precisely report the impact of data races detected by existing race detectors, e.g., Helgrind and ThreadSanitizer. We believe OMPSanitizer will provide a new perspective on automating the debugging support for data races in OpenMP programs. CCS CONCEPTS• Software and its engineering → Software testing and debugging; Parallel programming languages; Multithreading.

Temporal-Logic-Based Testing Tool Architecture for Dual-Programming Model Systems

“…Notable examples include Intel Inspector [22], ThreadSanitizer (TSan) [23], and Helgrind [24], all considered to be dynamic analyzing tools. Extensive work has been carried out on the OpenMP programming model to detect data races using static tools such as ompVerify [25], DRACO [26], PolyOMP [27,28], OMPRacer [29], and LLOV [30], as well as dynamic race detection techniques such as SWORD [31], ROMP [32], and OMPSanitizer [33].…”