Sound and precise analysis of parallel programs through schedule specialization

Wu, Junjie; Tang, Yang; Hu, Gang; Cui, Heming; Yang, Junfeng

doi:10.1145/2345156.2254090

Cited by 7 publications

(8 citation statements)

References 29 publications

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“…Cui et al [4,5,34] propose schedule specialization to guard against bugs caused due to nondeterminism. Huang and Zhang [11] design a tool PECAN to generate a feasible schedule for access anomalies in programs that use locks in a nested way.…”

Section: Related Workmentioning

confidence: 99%

Trace driven dynamic deadlock detection and reproduction

Samak

Ramanathan

2014

Proceedings of the 19th ACM SIGPLAN Symposium on Principles and Practice of Parallel Programming

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Dynamic analysis techniques have been proposed to detect potential deadlocks. Analyzing and comprehending each potential deadlock to determine whether the deadlock is feasible in a real execution requires significant programmer effort. Moreover, empirical evidence shows that existing analyses are quite imprecise. This imprecision of the analyses further void the manual effort invested in reasoning about non-existent defects.In this paper, we address the problems of imprecision of existing analyses and the subsequent manual effort necessary to reason about deadlocks. We propose a novel approach for deadlock detection by designing a dynamic analysis that intelligently leverages execution traces. To reduce the manual effort, we replay the program by making the execution follow a schedule derived based on the observed trace. For a real deadlock, its feasibility is automatically verified if the replay causes the execution to deadlock.We have implemented our approach as part of WOLF and have analyzed many large (upto 160KLoC) Java programs. Our experimental results show that we are able to identify 74% of the reported defects as true (or false) positives automatically leaving very few defects for manual analysis. The overhead of our approach is negligible making it a compelling tool for practical adoption.

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Section: Related Workmentioning

confidence: 99%

Trace driven dynamic deadlock detection and reproduction

Samak

Ramanathan

2014

Proceedings of the 19th ACM SIGPLAN Symposium on Principles and Practice of Parallel Programming

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“…· i) to O(|Σ| · i), where k is the length of execution and i is the number of possible inputs. This approach is called schedule specialization and it has been shown to have real benefits [39]. For example, consider the following code:…”

Section: System Overviewmentioning

confidence: 99%

“…We refer to Wu et al [39] and Yang et al [41] for more detailed arguments in favor of schedule specialization. Assumptions.…”

Section: System Overviewmentioning

confidence: 99%

“…The idea to analyze a multithreaded program by first reducing it to an equivalent sequential program is shared by the technique of schedule specialization [39] that we summarized in §1.1. Unfortunately, all of the above reductions are incomplete in practice, in the sense that they do not analyze all possible schedules.…”

Section: Related Workmentioning

confidence: 99%

“…By constraining execution to a small set of input-covering schedules, our approach can make these promising reductions complete. Notably, the approach to schedule specialization taken by Wu et al [39] can be directly applied to our proposed system by producing a specialized program for each schedule in Σ.…”

Section: Related Workmentioning

confidence: 99%

See 2 more Smart Citations

Input-covering schedules for multithreaded programs

Bergan

Ceze

Grossman

2013

Proceedings of the 2013 ACM SIGPLAN International Conference on Object Oriented Programming Systems Languages &Amp; Application

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We propose constraining multithreaded execution to small sets of input-covering schedules, which we define as follows: given a program P, we say that a set of schedules Σ covers all inputs of program P if, when given any input, P's execution can be constrained to some schedule in Σ and still produce a semantically valid result.Our approach is to first compute a small Σ for a given program P, and then, at runtime, constrain P's execution to always follow some schedule in Σ, and never deviate. We have designed an algorithm that uses symbolic execution to systematically enumerate a set of input-covering schedules, Σ. To deal with programs that run for an unbounded length of time, we partition execution into bounded epochs, find input-covering schedules for each epoch in isolation, and then piece the schedules together at runtime. We have implemented this algorithm along with a constrained execution runtime for pthreads programs, and we report results.Our approach has the following advantage: because all possible runtime schedules are known a priori, we can seek to validate the program by thoroughly verifying each schedule in Σ, in isolation, without needing to reason about the huge space of thread interleavings that arises due to conventional nondeterministic execution.

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Boosted decision trees for behaviour mining of concurrent programmes

Avros

Dudka

Křena

et al. 2017

Concurrency and Computation

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SummaryTesting of concurrent programmes is difficult since the scheduling nondeterminism requires one to test a huge number of different thread interleavings. Moreover, repeated test executions that are performed in the same environment will typically examine similar interleavings only. One possible way how to deal with this problem is to use the noise injection approach, which influences the scheduling by injecting various kinds of noise (delays, context switches, etc) into the common thread behaviour. However, for noise injection to be efficient, one has to choose suitable noise injection heuristics from among the many existing ones as well as to suitably choose values of their various parameters, which is not easy. In this paper, we propose a novel way how to deal with the problem of choosing suitable noise injection heuristics and suitable values of their parameters (as well as suitable values of parameters of the programmes being tested themselves). Here, by suitable, we mean such settings that maximize chances of meeting a given testing goal (such as, eg, maximizing coverage of rare behaviours and thus maximizing chances to find rarely occurring concurrency‐related bugs). Our approach is, in particular, based on using data mining in the context of noise‐based testing to get more insight about the importance of the different heuristics in a particular testing context as well as to improve fully automated noise‐based testing (in combination with both random as well as genetically optimized noise setting).

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Sound and precise analysis of parallel programs through schedule specialization

Cited by 7 publications

References 29 publications

Trace driven dynamic deadlock detection and reproduction

Trace driven dynamic deadlock detection and reproduction

Input-covering schedules for multithreaded programs

Boosted decision trees for behaviour mining of concurrent programmes

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