Symbolic verification of message passing interface programs

Yu, Hengbiao; Chen, Zhenbang; Fu, Xianjin; Wang, Ji; Su, Zhendong; Sun, Jun; Huang, Chun; Dong, Wei

doi:10.1145/3377811.3380419

Cited by 15 publications

(7 citation statements)

References 58 publications

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“…In [23], the authors present a benchmark to evaluate the ability of general-purpose model checkers to find deadlocks in MPI programs. [13] is used to automatically extract models from the considered MPI programs. Mutation techniques [24], [25] are used to generate several thousands of models by injecting artificial errors into the 10 considered MPI applications, and the resulting models are translated in the formalism of the tested model checkers (PAT, FDR, Spin, PRISM, and NuSMV).…”

Section: Related Workmentioning

confidence: 99%

“…Many tools have been proposed in the literature to detect errors in MPI programs, leveraging diverse approaches such as static analysis [2]- [4], dynamic analysis [5]- [9], or symbolic analysis [10]- [13]. Some of these approaches are better suited to detect some classes of errors, and authors may further specialize their tool to a subset of the potential errors.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

The MPI Bugs Initiative: a Framework for MPI Verification Tools Evaluation

Laurent

Saillard

Quinson

2021

2021 IEEE/ACM 5th International Workshop on Software Correctness for HPC Applications (Correctness)

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Ensuring the correctness of MPI programs becomes as challenging and important as achieving the best performance. Many tools have been proposed in the literature to detect incorrect usages of MPI in a given program. However, the limited set of code samples each tool provides and the lack of metadata stating the intent of each test make it difficult to assess the strengths and limitations of these tools. In this paper, we present the MPI BUGS INITIATIVE, a complete collection of MPI codes to assess the status of MPI verification tools. We introduce a classification of MPI errors and provide correct and incorrect codes covering many MPI features and our categorization of errors. The resulting suite comprises 1,668 codes, each coming with a well-formatted header that clarifies the intent of each code and specifies how to execute and evaluate it. We evaluated the completeness of the MPI BUGS INITIATIVE against eight stateof-the-art MPI verification tools.

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Section: Related Workmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

The MPI Bugs Initiative: a Framework for MPI Verification Tools Evaluation

Laurent

Saillard

Quinson

2021

2021 IEEE/ACM 5th International Workshop on Software Correctness for HPC Applications (Correctness)

View full text Add to dashboard Cite

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“…It is limited to communications on MPI_COMM_WORLD, doesn't detect missing wait and doesn't check collectives and persistent operations. CIVL [10] and MPI-SV [11], [12] both combine symbolic execution with model checking but unlike MPI-SV, CIVL does not support nonblocking operations. c) Hybrid Approaches: Hybrid approaches combine a static analysis with a dynamic one.…”

Section: Related Workmentioning

confidence: 99%

PARCOACH Extension for Static MPI Nonblocking and Persistent Communication Validation

Nguyen

Saillard

Jaeger

et al. 2020

2020 IEEE/ACM 4th International Workshop on Software Correctness for HPC Applications (Correctness)

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The Message Passing Interface (MPI) is a parallel programming model used to exchange data between working units in different nodes of a supercomputer. While MPI blocking operations return when the communication is complete, nonblocking and persistent operations return before the communication is complete, enabling a developer to hide communication latency. However the usage of these latter comes with additional rules the user has to abide to. This is error prone, which makes verification tools valuable for MPI program writers. PARCOACH is a framework that detects MPI collective errors using a static/dynamic analysis. The static phase studies the control-and data-flow of a program to detect potential errors while the dynamic phase uses compile-time information to verify the potential errors. In this paper we present an extension of PARCOACH static analysis to detect misuse of MPI nonblocking and persistent communications. Our new analysis adds the detection of four new error classes related to these types of communications.

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“…Research projects from the academy and software industry are adapting the software testing for the context of message‐passing parallel programs 2‐7 . Nondeterminism in message‐passing parallel programs is a challenge for testing activity, since the execution of these programs with the same test dataset can generate different outputs.…”

Section: Introductionmentioning

confidence: 99%

Structural testing for communication events into loops of message‐passing parallel programs

Diaz

Souza

2021

Concurrency and Computation

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There is a growing demand for correct parallel programs, mainly due to nowadays availability of parallel architectures. Structural testing allows identifying defects by analyzing the internal structures of a program. However, communication and synchronization in parallel programs bring new challenges to the testing activity, such as nondeterminism. Message‐passing parallel programs require structural testing criteria to support test models and tools capable of covering synchronization events with dynamic behaviors. Testing such primitives inside loops in message‐passing programs is still challenging with nontrivial solutions for criteria, models, and tools. This article proposes new structural testing criteria to guide the selection of test cases, improving the quality of message‐passing programs by revealing nondeterminism‐related defects present in loops. We present a new test model to support our criteria for structural testing of MPI‐applications and implement the proposed criteria in the tool ValiMPI. The analysis of nondeterminism‐related defects, paths inside loops, loop iterations, and nested loops allow us to establish a structured testing model. We validate the testing criteria through experimental studies using ValiMPI. Our results show that our criteria reveal unknown defects from communication and synchronization events in different loop iterations, increasing the quality of message‐passing parallel programs.

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Symbolic verification of message passing interface programs

Cited by 15 publications

References 58 publications

The MPI Bugs Initiative: a Framework for MPI Verification Tools Evaluation

The MPI Bugs Initiative: a Framework for MPI Verification Tools Evaluation

PARCOACH Extension for Static MPI Nonblocking and Persistent Communication Validation

Structural testing for communication events into loops of message‐passing parallel programs

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