Parallel Hybrid Testing Tool for Applications Developed by Using MPI + OpenACC Dual-Programming Model

Alghamdi, Ahmed Mohammed; Eassa, Fathy

doi:10.25046/aj040227

Cited by 5 publications

(3 citation statements)

References 22 publications

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“…As a part of our previous work [1]- [3], we proposed and created a parallel hybrid testing tool named ACC_TEST that targeted programs built in a heterogeneous architecture and covering different errors. In addition, we aim to develop hybrid-testing techniques for detecting errors in the dual-programming models MPI + OpenACC at the ends of our project.…”

Section: Introductionmentioning

confidence: 99%

ACC_TEST: Hybrid Testing Techniques for MPI-Based Programs

et al. 2020

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Recently, MPI has become widely used in many scientific applications, including different non-computer science fields, for parallelizing their applications. An MPI programming model is used for supporting parallelism in several programming languages, including C, C++, and Fortran. MPI also supports integration with some programming models and has several implementations from different vendors, including open-source and commercial implementations. However, testing parallel programs is a difficult task, especially when using programming models with different behaviours and types of error based on the programming model type. In addition, the increased use of these programming models by non-computer science specialists can cause several errors due to lack of experience in programming, which needs to be considered when using any testing tools. We noticed that dynamic testing techniques have been used for testing the majority of MPI programs. The dynamic testing techniques detect errors by analyzing the source code during runtime, which will cause overheads, and this will affect the program's performance, especially when targeting massive parallel applications generating thousands or millions of threads. In this paper, we enhance ACC_TEST to have the ability to test MPI-based programs and detect runtime errors occurring with different types of MPI communications. We decided to use hybrid-testing techniques by combining both static and dynamic testing techniques to gain the benefit of each and reduce the cost. INDEX TERMS MPI, MPI testing tool, hybrid testing techniques, parallel programming, ACC_TEST.

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Section: Introductionmentioning

confidence: 99%

ACC_TEST: Hybrid Testing Techniques for MPI-Based Programs

et al. 2020

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“…However, OpenACC has not been targeted clearly in any testing tool or previous study, even though OpenACC has many benefits and features that could lead to increasingly achieving parallel systems with less effort. As a part of our previous work [4]- [7], we proposed and created a parallel hybrid-testing tool named ACC_TEST that targeted programs built in a heterogeneous architecture and covering different errors. In addition, we aim to achieve hybrid-testing techniques for detecting errors in the dual-programming models MPI + OpenACC at the end of our project.…”

Section: Introductionmentioning

confidence: 99%

ACC_TEST: Hybrid Testing Approach for OpenACC-Based Programs

et al. 2020

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In recent years, OpenACC has been used in many supercomputers and attracted many non-computer science specialists for parallelizing their programs in different scientific fields, including weather forecasting and simulations. OpenACC is a high-level programming model that supports parallelism and is easy to learn to use by adding high-level directives without considering too many low-level details. Testing parallel programs is a difficult task, made even harder if using programming models, especially if they have been badly programmed. If so, it will be challenging to detect their runtime errors as well as their causes, whether the error is from the user source code or from the programming model directives. Even when these errors are detected and the source code modified, we cannot guarantee that the errors have been corrected or are still hidden. There are many tools and studies that have investigated several programming models for identifying and detecting related errors. However, OpenACC has not been targeted clearly in any testing tool or previous studies, even though OpenACC has many benefits and features that could lead to increasing use in achieving parallel systems with less effort. In this paper, we enhance ACC_TEST with the ability to test OpenACC-based programs and detect runtime errors by using hybrid-testing techniques that enhance error coverage occurring in OpenACC as well as overheads and testing time.INDEX TERMS OpenACC, OpenACC testing tool, hybrid-testing techniques, parallel programming, ACC_TEST.

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“…In this paper, our contribution is introducing new static techniques for detecting OpenACC errors, and for the first time classifying errors that can occur in OpenACC software programs. We briefly mention some OpenACC run-time errors in our previous study, which was published in [1], but in this paper, we broadly cover OpenACC run-time errors and explain their causes with examples. Part of our study focuses on OpenACC errors that cannot be detected by compilers.…”

Section: Introductionmentioning

confidence: 99%

OpenACC Errors Classification and Static Detection Techniques

Alghamdi

Eassa

2019

IEEE Access

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With the continued increase of usage of High-Performance Computing (HPC) in scientific fields, the need for programming models in a heterogeneous architecture with less programming effort has become important in scientific applications. OpenACC is a high-level parallel programming model used with FORTRAN, C, and C++ programming languages to accelerate the programmers' code with fewer changes and less effort, which reduces programmer workloads and makes it easier to use and learn. Also, OpenACC has been increasingly used in many top supercomputers around the world, and three of the top five HPC applications in Intersect360 Research are currently using OpenACC. However, when programmers use OpenACC to parallelize their code without correctly understanding OpenACC directives and their usage or following OpenACC instructions, they can cause run-time errors that vary from causing wrong results, performance issues, and other undefined behaviors. In addition, building parallel systems by using a higher level programming model increase the possibility to introduce errors, and the parallel applications thus have non-determined behavior, which makes testing and detecting their run-time errors a challenging task. Although there are many testing tools that detect run-time errors, this is still inadequate for detecting errors that occur in applications implemented in high-level parallel programming models, especially OpenACC related applications. As a result, OpenACC errors that cannot be detected by compilers should be identified, and their causes should be explained. In this paper, our contribution is introducing new static techniques for detecting OpenACC errors, as well as for the first time classifying errors that can occur in OpenACC software programs. Finally, to the best of our knowledge, there is no published work to date that identifies or classifies OpenACC-related errors, nor is there a testing tool designed to test OpenACC applications and detect their run-time errors.INDEX TERMS OpenACC, OpenACC run-time errors, OpenACC error classifications, OpenACC testing tool, static approach for OpenACC application.

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Parallel Hybrid Testing Tool for Applications Developed by Using MPI + OpenACC Dual-Programming Model

Cited by 5 publications

References 22 publications

ACC_TEST: Hybrid Testing Techniques for MPI-Based Programs

ACC_TEST: Hybrid Testing Techniques for MPI-Based Programs

ACC_TEST: Hybrid Testing Approach for OpenACC-Based Programs

OpenACC Errors Classification and Static Detection Techniques

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