Dynamic race detection for C++11

Lidbury, Christopher; Donaldson, Alastair F.

doi:10.1145/3009837.3009857

Cited by 19 publications

(29 citation statements)

References 40 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…As a sanity check, both c11_rar.cat and c11_simp_2.cat were compared against c11_lidbury.cat,which formalises Lidbury and Donaldson [19], revealing the exact same sets of counterexamples.…”

Section: E Mechanisation In Memalloymentioning

confidence: 99%

“…However precise, axiomatic definitions are unsuitable for program verification (in particular, those involving invariant-based reasoning), which requires one to consider the step-wise execution of a program. There has therefore Conference'17, July 2017, Washington, DC, USA 2019. been a substantial effort to develop an operational semantics: for weak memory models in general [12,13,15] and for C11 specifically [19,20].…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Verifying C11 programs operationally

Doherty

Dongol

Wehrheim

et al. 2019

Proceedings of the 24th Symposium on Principles and Practice of Parallel Programming

View full text Add to dashboard Cite

This paper develops an operational semantics for a releaseacquire fragment of the C11 memory model with relaxed accesses. We show that the semantics is both sound and complete with respect to the axiomatic model. The semantics relies on a per-thread notion of observability, which allows one to reason about a weak memory C11 program in program order. On top of this, we develop a proof calculus for invariant-based reasoning, which we use to verify the release-acquire version of Peterson's mutual exclusion algorithm.

show abstract

“…As a sanity check, both c11_rar.cat and c11_simp_2.cat were compared against c11_lidbury.cat,which formalises Lidbury and Donaldson [19], revealing the exact same sets of counterexamples.…”

Section: E Mechanisation In Memalloymentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Verifying C11 programs operationally

Doherty

Dongol

Wehrheim

et al. 2019

Proceedings of the 24th Symposium on Principles and Practice of Parallel Programming

View full text Add to dashboard Cite

show abstract

“…Such static information can be directly incorporated in the techniques we have developed in this paper, and is le for interesting follow-up work. Dynamic race detection has also been studied under structured parallelism (Raman et al 2012) and relaxed memory models (Kim et al 2009;Lidbury and Donaldson 2017).…”

Section: Related Workmentioning

confidence: 99%

Fast, sound, and effectively complete dynamic race prediction

Pavlogiannis

2019

Proc. ACM Program. Lang.

View full text Add to dashboard Cite

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

show abstract

“…For clarity, many optimisations to the algorithm, incorporated in our implementation (see §7.1) are omitted from the presentation of Figure 6. Appendix A in the extended version of the paper presents the optimised algorithm [27]. As an example, the VVC does not need to be used until there are two active release sequences.…”

Section: Algorithmmentioning

confidence: 99%

“…They are defined for each atomic instruction in our simple language, as well as for a few internal instructions that do not appear in source programs. Details of the non-atomic instructions appear in Appendix B [27].…”

Section: Operational Model Formalisedmentioning

confidence: 99%

Dynamic race detection for C++11

Lidbury

Donaldson

2017

SIGPLAN Not.

Self Cite

View full text Add to dashboard Cite

The intricate rules for memory ordering and synchronisation associated with the C/C++11 memory model mean that data races can be difficult to eliminate from concurrent programs. Dynamic data race analysis can pinpoint races in large and complex applications, but the state-of-the-art ThreadSanitizer (tsan) tool for C/C++ considers only sequentially consistent program executions, and does not correctly model synchronisation between C/C++11 atomic operations. We present a scalable dynamic data race analysis for C/C++11 that correctly captures C/C++11 synchronisation, and uses instrumentation to support exploration of a class of non sequentially consistent executions. We concisely define the memory model fragment captured by our instrumentation via a restricted axiomatic semantics, and show that the axiomatic semantics permits exactly those executions explored by our instrumentation. We have implemented our analysis in tsan, and evaluate its effectiveness on benchmark programs, enabling a comparison with the CDSChecker tool, and on two large and highly concurrent applications: the Firefox and Chromium web browsers. Our results show that our method can detect races that are beyond the scope of the original tsan tool, and that the overhead associated with applying our enhanced instrumentation to large applications is tolerable.

show abstract

Dynamic race detection for C++11

Cited by 19 publications

References 40 publications

Verifying C11 programs operationally

Verifying C11 programs operationally

Fast, sound, and effectively complete dynamic race prediction

Dynamic race detection for C++11

Contact Info

Product

Resources

About