GBRAD : A General Framework to Evaluate Design Strategies for Hybrid Race Detection

Research output: Chapters, Conference Papers, Creative and Literary Works (RGC: 12, 32, 41, 45)32_Refereed conference paper (with ISBN/ISSN)Not applicablepeer-review

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Detail(s)

Original languageEnglish
Title of host publicationProceedings : 2018 IEEE 42nd Annual Computer Software and Applications Conference : COMPSAC 2018
PublisherIEEE
Pages126-131
Volume1
ISBN (Electronic)9781538626665
ISBN (Print)9781538626672
StatePublished - Jul 2018

Publication series

NameProceedings - International Computer Software and Applications Conference
ISSN (Print)0730-3157

Conference

Title42nd IEEE International Conference on Computer, Software & Applications, COMPSAC 2018
LocationNational Center of Sciences
PlaceJapan
CityTokyo
Period23 - 27 July 2018

Abstract

Data race detection is a method in testing multithreaded programs to ensure their reliability against concurrency errors. In this paper, we present the GBRAD framework to support the initialization of various hybrid race detection techniques, which also support the evaluations of these strategies, at two decision points based on two major design factors of hybrid race detectors. In the GBRAD framework, one decision point consists of six skipping strategies and another decision point consists of eight reduction strategies. By combining those strategies, 48 hybrid detection techniques are initialized. We report a controlled experiment on the PARSEC benchmark suite as well as four real-world applications to evaluate these 48 techniques and their strategies in terms of runtime slowdown, memory overhead, and race detection effectiveness. The experiment identified 9 previously unknown techniques that are comparable to the present state-of-the-art hybrid race detection technique.

Research Area(s)

  • Benchmark testing, Detectors, History, Instruction sets, Memory management, Switches, Synchronization, controlled experiment, data race, dynamic analysis, lockset discipline violation, multithreaded program

Citation Format(s)

GBRAD : A General Framework to Evaluate Design Strategies for Hybrid Race Detection. / Yang, Jialin; Chan, W. K.; Yu, Y. T.; Keung, Jacky.

Proceedings : 2018 IEEE 42nd Annual Computer Software and Applications Conference : COMPSAC 2018. Vol. 1 IEEE, 2018. p. 126-131 8377648 (Proceedings - International Computer Software and Applications Conference).

Research output: Chapters, Conference Papers, Creative and Literary Works (RGC: 12, 32, 41, 45)32_Refereed conference paper (with ISBN/ISSN)Not applicablepeer-review