BlockRace : A Big Data Approach to Dynamic Block-based Data Race Detection for Multithreaded Programs

Research output: Chapters, Conference Papers, Creative and Literary Works (RGC: 12, 32, 41, 45)32_Refereed conference paper (with ISBN/ISSN)peer-review

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Detail(s)

Original languageEnglish
Title of host publicationProceedings - 2020 IEEE/ACM 1st International Conference on Automation of Software Test (AST 2020)
PublisherAssociation for Computing Machinery
Pages71-80
ISBN (Print)978-1-4503-7957-1
Publication statusPublished - Jul 2020

Publication series

NameProceedings - IEEE/ACM 1st International Conference on Automation of Software Test, AST

Conference

Title2020 IEEE/ACM 1st International Conference on Automation of Software Test, AST 2020
PlaceKorea, Republic of
CitySeoul, Virtual
Period15 - 16 July 2020

Abstract

The advent of multicore systems and distributed frameworks enables distributed strategies to address challenges in large-scale divisible problems by decomposing them rnto small ones, processing the corresponding sub-solutions and aggregating these sub-solutions into the final result. However, dynamic online detection of data races in execution traces of multithreaded programs is challenging to be parallelized due to their inherent historic event sensitivity and incremental inference of happens-before transitive closure To examine the extent of such detection to be transformed into parallel versions, in this paper, we present BlockRace, a novel dynamic block-based data race detection technique, which precisely detects data races in such traces and checks pairs of events blocks in parallel using its novel strategy. We evaluate BlockRace on 18 programs, and the results show that BlockRace achieves 1.96x to 5.5x speedups compared to its sequential counterparts. To the best of our knowledge BlockRace is the first technique to detect races in block parrs where these block pairs can be run m parallel on Big Data frameworks.

Research Area(s)

  • data race detection, parallelization, concurrency bug, multithreaded, program

Citation Format(s)

BlockRace : A Big Data Approach to Dynamic Block-based Data Race Detection for Multithreaded Programs. / Mei, Xiupei; Wei, Zhengyuan; Zhang, Hao; Chan, W. K.

Proceedings - 2020 IEEE/ACM 1st International Conference on Automation of Software Test (AST 2020). Association for Computing Machinery, 2020. p. 71-80 (Proceedings - IEEE/ACM 1st International Conference on Automation of Software Test, AST ).

Research output: Chapters, Conference Papers, Creative and Literary Works (RGC: 12, 32, 41, 45)32_Refereed conference paper (with ISBN/ISSN)peer-review