SWORD: A Bounded Memory-Overhead Detector of OpenMP Data Races in Production Runs

Atzeni, Simone; Gopalakrishnan, Ganesh; Rakamarić, Zvonimir; Laguna, Ignacio; Lee, Gregory L.; Ahn, Dong H.

doi:10.1109/ipdps.2018.00094

Cited by 29 publications

(17 citation statements)

References 24 publications

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“…A large amount of research work has been devoted to detecting data races in OpenMP programs [2,3,5,6,14,16,18]. Based on the detection mechanisms, there are typically two types of race detection tools: static and dynamic.…”

Section: Related Workmentioning

confidence: 99%

“…That is, the two accesses may exhibit different execution results due to the lack of necessary synchronization to determine their happens-before order. To facilitate the detection of data races, a large amount of research effort has been devoted to automatic data race detection [2,3,5,6,14,18]. They typically employ a variety of static and dynamic program analysis techniques to figure out whether memory accesses issued by different threads may constitute data races.…”

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confidence: 99%

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Wang¹,

Lin

2021

Proceedings of the ACM International Conference on Supercomputing

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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.

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Section: Related Workmentioning

confidence: 99%

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confidence: 99%

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Wang¹,

Lin

2021

Proceedings of the ACM International Conference on Supercomputing

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“…Eraser [80] proposed lockset-based approach for race detection. Most of the earlier works have focused on pthread-based programs, while recent works such as ompVerify [8], Archer [4], ROMP [39], SWORD [5], DRACO [91], and PolyOMP [20] have targeted OpenMP programs.…”

Section: Related Workmentioning

confidence: 99%

“…SWORD [5] is a dynamic tool based on operational semantic rules and uses OpenMP tools framework OMPT [30]. SWORD uses locksets to implement the semantic rules by taking advantage of the events tracked by OMPT.…”

Section: :13mentioning

confidence: 99%

Llov

Bora

Das

Kukreja

et al. 2020

ACM Trans. Archit. Code Optim.

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In the era of Exascale computing, writing efficient parallel programs is indispensable, and, at the same time, writing sound parallel programs is very difficult. Specifying parallelism with frameworks such as OpenMP is relatively easy, but data races in these programs are an important source of bugs. In this article, we propose LLOV, a fast, lightweight, language agnostic, and static data race checker for OpenMP programs based on the LLVM compiler framework. We compare LLOV with other state-of-the-art data race checkers on a variety of well-established benchmarks. We show that the precision, accuracy, and the F1 score of LLOV is comparable to other checkers while being orders of magnitude faster. To the best of our knowledge, LLOV is the only tool among the state-of-the-art data race checkers that can verify a C/C++ or FORTRAN program to be data race free.

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“…Correctly understanding the semantics of OpenMP constructs becomes challenging even for experienced programmers, which makes OpenMP applications error-prone. There has been a large amount of prior work related to the correctness of OpenMP applications, including tools [3][4][5]7,11,16,21,24,26], benchmarks [1,9,12], and empirical studies [13,14,17]. Most of this past work focuses on concurrency bugs such as data races, deadlocks, and atomicity violations, since debugging concurrency bugs are notoriously difficult for programmers.…”

Section: Introductionmentioning

confidence: 99%

A Study of Memory Anomalies in OpenMP Applications

Protze

Hernández

et al. 2020

Lecture Notes in Computer Science

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Incorrect usage of OpenMP constructs may cause different kinds of defects in OpenMP applications. Most of the existing work focuses on concurrency bugs such as data races and deadlocks, since concurrency bugs are difficult to detect and debug. In this paper, we discuss an under-examined defect in OpenMP applications: memory anomalies. These occur when the application issues illegal memory accesses that may result in a non-deterministic result or even a program crash. Based on the latest OpenMP 5.0 specification, we analyze some OpenMP usage errors that may lead to memory anomalies. Then we illustrate three kinds of memory anomalies: use of uninitialized memory (UUM), use of stale data (USD), and use after free (UAF). While all three anomalies can occur in sequential programs, their manifestations in parallel OpenMP programs can be different, and debugging such anomalies in the context of parallel programs also imposes an additional complexity relative to sequential programs. To measure the effectiveness of memory anomaly detectors on OpenMP applications, we have evaluated three state-of-theart tools with a group of micro-benchmarks. These micro-benchmarks are either selected from the DRACC benchmark suite or constructed from our own experience. The evaluation result shows that none of these tools can currently handle all three kinds of memory anomalies.

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SWORD: A Bounded Memory-Overhead Detector of OpenMP Data Races in Production Runs

Cited by 29 publications

References 24 publications

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Llov

A Study of Memory Anomalies in OpenMP Applications

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