Toward Integration of Data Race Detection in DSM Systems

Itzkovitz, Ayal; Schuster, Assaf; Zeev-Ben-Mordehai, Oren

doi:10.1006/jpdc.1999.1574

“…Then, happensbefore analysis is applied only to those variables. In [34], the DJIT algorithm is presented, which is a variation of happensbefore analysis. MultiRace [35] combines DJIT with locksets to reduce false positives.…”

Section: Related Workmentioning

confidence: 99%

Efficient Data-Race Detection with Dynamic Symbolic Execution

Ibing

¹

2016

Annals of Computer Science and Information Systems

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Abstract-This paper presents data race detection using dynamic symbolic execution and hybrid lockset / happens-before analysis. Symbolic execution is used to explore the execution tree of multi-threaded software for FIFO scheduling on a single CPU core. Compared to exploring the joint scheduling and execution tree, the combinatorial explosion is drastically reduced. An SMT solver is used to control a debugger's machine interface for adaptive dynamic instrumentation to drive program execution into desired paths. Data races are detected in concrete execution with available static binary instrumentation using hybrid analysis. State interpolation using unsatisfiable cores is employed for path pruning, to avoid exploration of paths that do not contribute to increasing branch coverage. An implementation in Eclipse CDT is described and evaluated with data race test cases from the Juliet C/C++ test suite for program analyzers.

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“…As is common for happens-before race detectors, FastTrack guarantees detection only of the first race on each variable [3,8,15,16,32]. Detecting the first race on each variable is sufficient for common uses of race detection, e.g., treating data races as exceptions in production [12,21] and trying to fix or annotate all races when debugging [1].…”

Section: Conventional Detectionmentioning

confidence: 99%

“…Many researchers have studied how to detect data races dynamically via software techniques [2,8,9,12,15,16,32,34,35,39,50] and hardware techniques [11,24,30,33,51]. Despite much progress, dynamic data race detection remains expensive on commodity hardware, especially when analyzing unmanaged code.…”

Section: Related Workmentioning

confidence: 99%

Parallelizing data race detection

Wester

¹

,

Devecsery

²

,

Chen

³

et al. 2013

Proceedings of the Eighteenth International Conference on Architectural Support for Programming Languages and Operating Systems

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Detecting data races in multithreaded programs is a crucial part of debugging such programs, but traditional data race detectors are too slow to use routinely. This paper shows how to speed up race detection by spreading the work across multiple cores. Our strategy relies on uniparallelism, which executes time intervals of a program (called epochs) in parallel to provide scalability, but executes all threads from a single epoch on a single core to eliminate locking overhead. We use several techniques to make parallelization effective: dividing race detection into three phases, predicting a subset of the analysis state, eliminating sequential work via transitive reduction, and reducing the work needed to maintain multiple versions of analysis via factorization. We demonstrate our strategy by parallelizing a happens-before detector and a lockset-based detector. We find that uniparallelism can significantly speed up data race detection. With 4× the number of cores as the original application, our strategy speeds up the median execution time by 4.4× for a happens-before detector and 3.3× for a lockset race detector. Even on the same number of cores as the conventional detectors, the ability for uniparallelism to elide analysis locks allows it to reduce the median overhead by 13% for a happens-before detector and 8% for a lockset detector.

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“…This section shows how to parallelize a happens-before race detection algorithm. We examine how to parallelize FastTrack [15], a state-of-the-art precise detection algorithm, as a representative example of happensbefore detectors (several other detection algorithms are closely related [3,6,11,16,32]). Section 4 describes how we parallelize the other approach to race detection by examining a locking-discipline detector.…”

Section: Parallel Happens-beforementioning

confidence: 99%

Parallelizing data race detection

Wester

¹

,

Devecsery

²

,

Chen

³

et al. 2013

SIGPLAN Not.

2

0

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Detecting data races in multithreaded programs is a crucial part of debugging such programs, but traditional data race detectors are too slow to use routinely. This paper shows how to speed up race detection by spreading the work across multiple cores. Our strategy relies on uniparallelism, which executes time intervals of a program (called epochs) in parallel to provide scalability, but executes all threads from a single epoch on a single core to eliminate locking overhead. We use several techniques to make parallelization effective: dividing race detection into three phases, predicting a subset of the analysis state, eliminating sequential work via transitive reduction, and reducing the work needed to maintain multiple versions of analysis via factorization. We demonstrate our strategy by parallelizing a happens-before detector and a lockset-based detector. We find that uniparallelism can significantly speed up data race detection. With 4× the number of cores as the original application, our strategy speeds up the median execution time by 4.4× for a happens-before detector and 3.3× for a lockset race detector. Even on the same number of cores as the conventional detectors, the ability for uniparallelism to elide analysis locks allows it to reduce the median overhead by 13% for a happens-before detector and 8% for a lockset detector.

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Toward Integration of Data Race Detection in DSM Systems

Cited by 56 publications

References 16 publications

Efficient Data-Race Detection with Dynamic Symbolic Execution

Efficient Data-Race Detection with Dynamic Symbolic Execution

Parallelizing data race detection

Parallelizing data race detection

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