Dependence-aware, unbounded sound predictive race detection

Genç, Kaan; Roemer, Jake; Yu-fan, XU; Bond, Michael D.

doi:10.1145/3360605

Cited by 12 publications

(28 citation statements)

References 88 publications

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“…Likewise, an algorithm is complete if the algorithm reports YES whenever the input trace has a data race. Our convention for this nomenclature ensures that no false positives are reported by a sound algorithm [Sergey 2019] and is consistent with prior work on data race prediction [Genç et al 2019;Kini et al 2017;Pavlogiannis 2019;Roemer et al 2018;Smaragdakis et al 2012]. Soundness is often a desirable property for dynamic race predictors for widespread adoption [Gorogiannis et al 2019].…”

Section: Optimal Prediction Of Synchronization-preserving Racesmentioning

confidence: 80%

“…This style of formalizing alternative executions based on semantics of concurrent objects was popularized by [Herlihy and Wing 1990] and by prior race detection works [Said et al 2011;Şerbănuţă et al 2012]. Our definition of correct reordering has been derived from [Smaragdakis et al 2012], which has subsequently also been used in the literature [Genç et al 2019;Kini et al 2017;Mathur et al 2018Mathur et al , 2020aPavlogiannis 2019;Roemer et al 2018].…”

Section: Optimal Prediction Of Synchronization-preserving Racesmentioning

confidence: 99%

“…We call such alternate executions sync(hronization)preserving executions. Other, more powerful race prediction techniques [Genç et al 2019;Huang et al 2014;Huang and Rajagopalan 2016;Pavlogiannis 2019;Roemer et al 2018;Smaragdakis et al 2012] sacrifice this principle and consider alternate executions that are not sync-preserving. Naturally, this typically results in performance degradation, as the problem is in general NPhard [Mathur et al 2020a], and considerable efforts are made towards improving the scalability of such techniques Roemer et al 2020].…”

Section: Introductionmentioning

confidence: 99%

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Optimal prediction of synchronization-preserving races

Mathur

Pavlogiannis

Viswanathan

2021

Proc. ACM Program. Lang.

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Concurrent programs are notoriously hard to write correctly, as scheduling nondeterminism introduces subtle errors that are both hard to detect and to reproduce. The most common concurrency errors are (data) races, which occur when memory-conflicting actions are executed concurrently. Consequently, considerable effort has been made towards developing efficient techniques for race detection. The most common approach is dynamic race prediction: given an observed, race-free trace σ of a concurrent program, the task is to decide whether events of σ can be correctly reordered to a trace σ * that witnesses a race hidden in σ. In this work we introduce the notion of sync(hronization)-preserving races. A sync-preserving race occurs in σ when there is a witness σ * in which synchronization operations (e.g., acquisition and release of locks) appear in the same order as in σ. This is a broad definition that strictly subsumes the famous notion of happens-before races. Our main results are as follows. First, we develop a sound and complete algorithm for predicting sync-preserving races. For moderate values of parameters like the number of threads, the algorithm runs in Õ( N ) time and space, where N is the length of the trace σ. Second, we show that the problem has a Ω( N /log 2 N ) space lower bound, and thus our algorithm is essentially time and space optimal. Third, we show that predicting races with even just a single reversal of two sync operations is NP-complete and even W1-hard when parameterized by the number of threads. Thus, sync-preservation characterizes exactly the tractability boundary of race prediction, and our algorithm is nearly optimal for the tractable side. Our experiments show that our algorithm is fast in practice, while sync-preservation characterizes races often missed by state-of-the-art methods.

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Section: Optimal Prediction Of Synchronization-preserving Racesmentioning

confidence: 80%

Section: Optimal Prediction Of Synchronization-preserving Racesmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Optimal prediction of synchronization-preserving races

Mathur

Pavlogiannis

Viswanathan

2021

Proc. ACM Program. Lang.

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“…Vector clocks have been very useful in designing linear time algorithms for dynamic analysis of multi-threaded systems [14,23,24,27,43,50,52]. The broad principle behind these algorithms, is to assign vector timestamps to events as the trace is generated/observed so that the ordering between these assigned timestamps captures causal ordering.…”

Section: Challenges In Designing a Vector Clock Algorithmmentioning

confidence: 99%

Atomicity Checking in Linear Time using Vector Clocks

Mathur

Viswanathan

2020

Proceedings of the Twenty-Fifth International Conference on Architectural Support for Programming Languages and Operating Syste

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Multi-threaded programs are challenging to write. Developers often need to reason about a prohibitively large number of thread interleavings to reason about the behavior of software. A non-interference property like atomicity can reduce this interleaving space by ensuring that any execution is equivalent to an execution where all atomic blocks are executed serially. We consider the well studied notion of conflict serializability for dynamically checking atomicity. Existing algorithms detect violations of conflict serializability by detecting cycles in a graph of transactions observed in a given execution. The number of edges in such a graph can grow quadratically with the length of the trace making the analysis not scalable. In this paper, we present AeroDrome, a novel single pass linear time algorithm that uses vector clocks to detect violations of conflict serializability in an online setting. Experiments show that AeroDrome scales to traces with a large number of events with significant speedup. CCS Concepts. • Software and its engineering → Dynamic analysis; Software testing and debugging.

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“…Other approaches include statistical techniques (Bond et al 2010;Marino et al 2009) and static race-detectors (Naik et al 2006;Pratikakis et al 2011;Voung et al 2007). Recently, (Genç et al 2019) applied a combination of static and dynamic techniques to allow for correct reorderings in which the observation of some read events is allowed to di er between the input and witness trace, as long as the read does not a ect the control-ow of the respective thread. Such static information can be directly incorporated in the techniques we have developed in this paper, and is le for interesting follow-up work.…”

Section: Related Workmentioning

confidence: 99%

Fast, sound, and effectively complete dynamic race prediction

Pavlogiannis

2019

Proc. ACM Program. Lang.

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Writing concurrent programs is highly error-prone due to the nondeterminism in interprocess communication.e most reliable indicators of errors in concurrency are data races, which are accesses to a shared resource that can be executed concurrently. We study the problem of predicting data races in lock-based concurrent programs. e input consists of a concurrent trace t, and the task is to determine all pairs of events of t that constitute a data race. e problem lies at the heart of concurrent veri cation and has been extensively studied for over three decades. However, existing polynomial-time sound techniques are highly incomplete and can miss simple races.In this work we develop M2: a new polynomial-time algorithm for this problem, which has no false positives. In addition, our algorithm is complete for input traces that consist of two processes, i.e., it provably detects all races in the trace. We also develop su cient criteria for detecting completeness dynamically in cases of more than two processes. We make an experimental evaluation of our algorithm on a challenging set of benchmarks taken from recent literature on the topic. Our algorithm soundly reports hundreds of real races, many of which are missed by existing methods. In addition, using our dynamic completeness criteria, M2 concludes that it has detected all races in the benchmark set, hence the reports are both sound and complete. Finally, its running times are comparable, and o en smaller than the theoretically fastest, yet highly incomplete, existing methods. To our knowledge, M2 is the rst sound algorithm that achieves such a level of performance on both running time and completeness of the reported races. CCS Concepts: •So ware and its engineering → So ware veri cation and validation; • eory of computation → eory and algorithms for application domains; Program analysis; Additional

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Dependence-aware, unbounded sound predictive race detection

Cited by 12 publications

References 88 publications

Optimal prediction of synchronization-preserving races

Optimal prediction of synchronization-preserving races

Atomicity Checking in Linear Time using Vector Clocks

Fast, sound, and effectively complete dynamic race prediction

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