GEM: Graphical Explorer of MPI Programs

Humphrey, Alan; Derrick, Christopher; Gopalakrishnan, Ganesh; Tibbitts, Beth R.

doi:10.1109/icppw.2010.33

Cited by 10 publications

(6 citation statements)

References 7 publications

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“…For detecting runtime errors including mismatching, data race, and deadlocks, many testing tools have been used such as MUST [10], [11], MEMCHECKER [5], Intel Message Checker [12], STAT [13], Nasty-MPI [14], and MARMOT [15]. In addition, the testing tools that have been used for detecting mismatching and deadlocks include Umpire [16], GEM [17], and MPI-CHECK [18]. For race condition and deadlocks, MPI detections MAD [19], [20], and PDT [21] have been used.…”

Section: Related Workmentioning

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: Related Workmentioning

confidence: 99%

ACC_TEST: Hybrid Testing Techniques for MPI-Based Programs

et al. 2020

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“…The testing tools MEMCHECKER [19], MUST [20], STAT [21], Nasty-MPI [22], and Intel Message Checker [23] were used to detect MPI runtime errors, including deadlocks, data race, and mismatching. For detecting deadlocks and mismatching, MPI-CHECK [24], GEM [25], and Umpire [26] were used. The tools PDT [27], MAD [28], and [29] were used for detecting MPI deadlocks and race conditions.…”

Section: Dynamic Testing Techniquesmentioning

confidence: 99%

Parallel Hybrid Testing Techniques for the Dual-Programming Models-Based Programs

et al. 2020

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The importance of high-performance computing is increasing, and Exascale systems will be feasible in a few years. These systems can be achieved by enhancing the hardware’s ability as well as the parallelism in the application by integrating more than one programming model. One of the dual-programming model combinations is Message Passing Interface (MPI) + OpenACC, which has several features including increased system parallelism, support for different platforms with more performance, better productivity, and less programming effort. Several testing tools target parallel applications built by using programming models, but more effort is needed, especially for high-level Graphics Processing Unit (GPU)-related programming models. Owing to the integration of different programming models, errors will be more frequent and unpredictable. Testing techniques are required to detect these errors, especially runtime errors resulting from the integration of MPI and OpenACC; studying their behavior is also important, especially some OpenACC runtime errors that cannot be detected by any compiler. In this paper, we enhance the capabilities of ACC_TEST to test the programs built by using the dual-programming models MPI + OpenACC and detect their related errors. Our tool integrated both static and dynamic testing techniques to create ACC_TEST and allowed us to benefit from the advantages of both techniques reducing overheads, enhancing system execution time, and covering a wide range of errors. Finally, ACC_TEST is a parallel testing tool that creates testing threads based on the number of application threads for detecting runtime errors.

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“…Dyvise [43] supports the detection of memory problems through the visualization of the Java heap. GEM [44] (shown in Figure 4) is a graphical explorer of MPI programs. Gzoltar [45] has shown evidence of effectiveness through an experiment.…”

Section: ) Behaviormentioning

confidence: 99%

VISON: An Ontology-Based Approach for Software Visualization Tool Discoverability

Merino

Kozlova

Nierstrasz

et al. 2019

2019 Working Conference on Software Visualization (VISSOFT)

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Although many tools have been presented in the research literature of software visualization, there is little evidence of their adoption. To choose a suitable visualization tool, practitioners need to analyze various characteristics of tools such as their supported software concerns and level of maturity. Indeed, some tools can be prototypes for which the lifespan is expected to be short, whereas others can be fairly mature products that are maintained for a longer time. Although such characteristics are often described in papers, we conjecture that practitioners willing to adopt software visualizations require additional support to discover suitable visualization tools. In this paper, we elaborate on our efforts to provide such support. To this end, we systematically analyzed research papers in the literature of software visualization and curated a catalog of 70 available tools that employ various visualization techniques to support the analysis of multiple software concerns. We further encapsulate these characteristics in an ontology. VISON, our software visualization ontology, captures these semantics as concepts and relationships. We report on early results of usage scenarios that demonstrate how the ontology can support (i) developers to find suitable tools for particular development concerns, and (ii) researchers who propose new software visualization tools to identify a baseline tool for a controlled experiment. arXiv:1908.04090v1 [cs.SE] 12 Aug 2019Step 2. Consider reusing existing ontologies. To the best of our knowledge, this is the first ontology of software visualizations.Step 3. Enumerate important terms in the ontology. We include the characteristics of software visualization and their evaluations, as well as the classifications presented in previous studies [6], [7].

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GEM: Graphical Explorer of MPI Programs

Cited by 10 publications

References 7 publications

ACC_TEST: Hybrid Testing Techniques for MPI-Based Programs

ACC_TEST: Hybrid Testing Techniques for MPI-Based Programs

Parallel Hybrid Testing Techniques for the Dual-Programming Models-Based Programs

VISON: An Ontology-Based Approach for Software Visualization Tool Discoverability

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