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ONE View: A Fully Automatic Method for Aggregating Key Performance Metrics and Providing Users with a Synthetic View of HPC Applications

In: Tools for High Performance Computing 2018 / 2019

Author

Listed:
  • William Jalby

    (Exascale Computing Research and Université de Versailles St-Quentin-en-Yvelines)

  • Cédric Valensi

    (Exascale Computing Research and Université de Versailles St-Quentin-en-Yvelines)

  • Mathieu Tribalat

    (Exascale Computing Research and Université de Versailles St-Quentin-en-Yvelines)

  • Kevin Camus

    (Exascale Computing Research and Université de Versailles St-Quentin-en-Yvelines)

  • Youenn Lebras

    (Exascale Computing Research and Université de Versailles St-Quentin-en-Yvelines)

  • Emmanuel Oseret

    (Exascale Computing Research and Université de Versailles St-Quentin-en-Yvelines)

  • Salah Ibnamar

    (Exascale Computing Research and Université de Versailles St-Quentin-en-Yvelines)

Abstract

One of the major issues in the performance analysis of HPC codes is the difficulty to fully and accurately characterize the behavior of an application. In particular, it is essential to precisely pinpoint bottlenecks and their true causes. Additionally, providing an estimation of the possible gain obtained after fixing a particular bottleneck would surely allow for a more thorough choice of which optimizations to apply or avoid. In this paper, we present ONE View, a MAQAO module harnessing different techniques (sampling/tracing, static/dynamic analyses) to provide a comprehensive human-friendly view of performance issues and also guide the user’s optimization efforts on the most promising performance bottlenecks.

Suggested Citation

  • William Jalby & Cédric Valensi & Mathieu Tribalat & Kevin Camus & Youenn Lebras & Emmanuel Oseret & Salah Ibnamar, 2021. "ONE View: A Fully Automatic Method for Aggregating Key Performance Metrics and Providing Users with a Synthetic View of HPC Applications," Springer Books, in: Hartmut Mix & Christoph Niethammer & Huan Zhou & Wolfgang E. Nagel & Michael M. Resch (ed.), Tools for High Performance Computing 2018 / 2019, pages 219-235, Springer.
    • Handle: RePEc:spr:sprchp:978-3-030-66057-4_12
    DOI: 10.1007/978-3-030-66057-4_12
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