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Application-specific feature selection and clustering approach with HPC system profiling data

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Abstract

Exascale computing, the next-generation computing environment, is expected to be applied to scientific and engineering applications. Accordingly, high-performance computing (HPC) technology is also being developed to improve the performance and high-speed parallelism of many-core processors. Previous researches on improving HPC performance have developed in the form of improving the overall system performance by analyzing the state of the system occurring in the range of the knowledge of expert. However, performance events occurring in a processor in a many-core environment have a large number of indicators, and it is difficult to analyze the correlation between them. In this paper, we propose an application-specific feature selection and clustering approach with HPC system profiling data. The proposed approach performs PCA-based feature selections for efficient performance analysis methods. In addition, the application-specific characteristics from profiling data can be analyzed by unsupervised learning. In our experiments, we evaluated highly parallel supercomputers with NAS parallel benchmark and were able to cluster applications efficiently.

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Acknowledgements

This work was supported by Korea Institute of Science and Technology Information (KISTI) Grant (No. K-20-L02-C08-S01).

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Authors and Affiliations

  1. Department of Computer Science and Engineering, Chung-Ang University, Seoul, Republic of Korea

    Mincheol Shin

  2. Division of Supercomputing, Korea Institute of Science and Technology Information, Daejeon, Republic of Korea

    Geunchul Park & Chan Yeol Park

  3. Department of Computer and Software Engineering, Wonkwang University, Iksan, Republic of Korea

    Jongmin Lee

  4. School of Computer Science and Engineering, Chung-Ang University, Seoul, Republic of Korea

    Mucheol Kim

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  1. Mincheol Shin
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  2. Geunchul Park
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  3. Chan Yeol Park
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  4. Jongmin Lee
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  5. Mucheol Kim
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Correspondence to Jongmin Lee or Mucheol Kim.

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Shin, M., Park, G., Park, C.Y. et al. Application-specific feature selection and clustering approach with HPC system profiling data. J Supercomput 77, 6817–6831 (2021). https://doi.org/10.1007/s11227-020-03533-2

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