An Efficient Algorithm for Computing MHP Information for Concurrent Java Programs

Naumovich, Gleb; Avrunin, George S.; Clarke, Lori A.

doi:10.1007/3-540-48166-4_21

Cited by 56 publications

(75 citation statements)

References 14 publications

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“…Flavers [2] is a finite-state property checker that employs conservative state and interleaving reductions, e.g. a may-happen-in-parallel dataflow analysis ( [18]) that conservatively prunes interleavings. Even with techniques like these and others like partial-order and symmetry reduction that mitigate the effect of combinatorial explosion in interleavings, model checking has only been shown to scale to relatively small code bases.…”

Section: Related Workmentioning

confidence: 99%

Dataflow analysis for concurrent programs using datarace detection

Chugh

Voung

Jhala

et al. 2008

Proceedings of the 29th ACM SIGPLAN Conference on Programming Language Design and Implementation

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Dataflow analyses for concurrent programs differ from their singlethreaded counterparts in that they must account for shared memory locations being overwritten by concurrent threads. Existing dataflow analysis techniques for concurrent programs typically fall at either end of a spectrum: at one end, the analysis conservatively kills facts about all data that might possibly be shared by multiple threads; at the other end, a precise thread-interleaving analysis determines which data may be shared, and thus which dataflow facts must be invalidated. The former approach can suffer from imprecision, whereas the latter does not scale.We present RADAR, a framework that automatically converts a dataflow analysis for sequential programs into one that is correct for concurrent programs. RADAR uses a race detection engine to kill the dataflow facts, generated and propagated by the sequential analysis, that become invalid due to concurrent writes. Our approach of factoring all reasoning about concurrency into a race detection engine yields two benefits. First, to obtain analyses for code using new concurrency constructs, one need only design a suitable race detection engine for the constructs. Second, it gives analysis designers an easy way to tune the scalability and precision of the overall analysis by only modifying the race detection engine. We describe the RADAR framework and its implementation using a preexisting race detection engine. We show how RADAR was used to generate a concurrent version of a null-pointer dereference analysis, and we analyze the result of running the generated concurrent analysis on several benchmarks.

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Section: Related Workmentioning

confidence: 99%

Dataflow analysis for concurrent programs using datarace detection

Chugh

Voung

Jhala

et al. 2008

Proceedings of the 29th ACM SIGPLAN Conference on Programming Language Design and Implementation

View full text Add to dashboard Cite

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“…Category 5. Naumovich et al [16] designed and evaluated a static data-flow analysis that computes the may-happen-in-parallel information for program statements in different threads. Such analyses provide similar information as our may-happen-before analysis.…”

Section: Related Workmentioning

confidence: 99%

Approximating happens-before order: interplay between static analysis and state space traversal

Parízek

Jančík

2014

Proceedings of the 2014 International SPIN Symposium on Model Checking of Software

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Techniques and tools for verification of multi-threaded programs must cope with the huge number of possible thread interleavings. Tools based on systematic exploration of a program state space employ partial order reduction to avoid redundant thread interleavings. The key idea is to make non-deterministic thread scheduling choices only at statements that read or modify the global state shared by multiple threads. We focus on the approach to partial order reduction used in tools such as Java Pathfinder (JPF), which construct the program state space on-the-fly, and therefore can use only information available in the current program state and execution history to identify statements that may be globally-relevant.In our previous work, we developed a field access analysis that provides information about fields that may be accessed during program execution, and used it in JPF for more precise identification of globally-relevant statements. We build upon that and propose a may-happen-before analysis that computes a sound approximation of the happens-before ordering. Partial order reduction techniques can use the happens-before ordering to detect pairs of globallyrelevant field access statements that cannot be interleaved arbitrarily (due to thread synchronization), and based on that avoid making unnecessary thread scheduling choices. The may-happen-before analysis combines static analysis with knowledge of information available from the dynamic program state. Results of experiments with several Java programs show that usage of the may-happenbefore analysis further improves the performance of JPF.

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“…Recently, Krinke [19] proposed a context-sensitive algorithm for slicing concurrent programs. However, these algorithms rely on symbolic execution and/or may-happen-in-parallel(MHP) [1,25] information to prune interference dependences. However, in the absence of synchronization operations, the use of MHP algorithms will not provide large reductions in the dependences as it is harder to detect instruction execution ordering.…”

Section: Related Workmentioning

confidence: 99%

Pruning Interference and Ready Dependence for Slicing Concurrent Java Programs

Ranganath

Hatcliff

2004

Lecture Notes in Computer Science

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Abstract. In this paper, we show how previous work on escape analysis can be adapted and extended to yield a static analysis that is efficient yet effective for reducing the number of interference dependence edges considered while slicing concurrent Java programs. The key idea is to statically detect situations where run-time heap objects are reachable from a single thread and use it to prune spurious interference dependence edges. We also show how this analysis can be extended to reduce the number of ready dependence edges -dependences that capture indefinite delay due to Java synchronization constructs. The analysis we describe has been implemented in the Bandera[9] slicer which is being applied to reduce the size of software models for modelchecking. Using this implementation, we give experimental results that demonstrate the effectiveness of our approach. We believe leveraging escape information in the manner we describe is a crucial element in scaling slicing techniques to larger concurrent object-oriented programs.

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An Efficient Algorithm for Computing MHP Information for Concurrent Java Programs

Cited by 56 publications

References 14 publications

Dataflow analysis for concurrent programs using datarace detection

Dataflow analysis for concurrent programs using datarace detection

Approximating happens-before order: interplay between static analysis and state space traversal

Pruning Interference and Ready Dependence for Slicing Concurrent Java Programs

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