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Sparse Grid Regression for Performance Prediction Using High-Dimensional Run Time Data

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Euro-Par 2019: Parallel Processing Workshops (Euro-Par 2019)

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Abstract

We employ sparse grid regression to predict the run time in three types of numerical simulation: molecular dynamics (MD), weather and climate simulation. The impact of algorithmic, OpenMP/MPI and hardware-aware optimization parameters on performance is studied. We show that normalization of run time data via algorithmic complexity arguments significantly improves prediction accuracy. Mean relative prediction errors are in the range of few percent; in MD, a five-dimensional parameter space exploration results in mean relative prediction errors of ca. 15% using ca. 178 run time samples.

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Notes

  1. 1.

    https://redmine.dkrz.de/projects/icon-benchmark/wiki/Instructions_on_download_execution_and_analysis_ICON_Benchmark_v160.

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Acknowledgements

Financial support by the Federal Ministry of Education and Research, Germany, grant number 01IH16008B (project TaLPas), and by the European project ESiWACE is acknowledged. ESiWACE has received funding from the European Union’s Horizon 2020 research and innovation programme under grant agreement No 675191. P. Neumann thanks K. Brusch, R. Brown and P. Harder for initial works on sparse grid-based performance evaluations.

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Authors and Affiliations

  1. Universität Hamburg, Bundesstr. 45a, 20146, Hamburg, Germany

    Philipp Neumann

  2. Deutsches Klimarechenzentrum, Bundesstr. 45a, 20146, Hamburg, Germany

    Philipp Neumann

  3. Helmut-Schmidt-Universität, Holstenhofweg 85, 22043, Hamburg, Germany

    Philipp Neumann

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  1. Philipp Neumann
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Correspondence to Philipp Neumann .

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Editors and Affiliations

  1. Gesellschaft für Wissenschaftliche Datenverarbeitung mbH, Göttingen, Germany

    Ulrich Schwardmann

  2. Gesellschaft für Wissenschaftliche Datenverarbeitung mbH, Göttingen, Germany

    Christian Boehme

  3. CiTIUS, Santiago de Compostela, Spain

    Dora B. Heras

  4. University of Rome "Tor Vergata", Rome, Italy

    Valeria Cardellini

  5. Inria Bordeaux Sud-Ouest, Talence, France

    Emmanuel Jeannot

  6. Engineering Sardegna, Cagliari, Italy

    Antonio Salis

  7. University of Turin, Torino, Italy

    Claudio Schifanella

  8. University College Dublin, Dublin, Ireland

    Ravi Reddy Manumachu

  9. DLR-AS, Göttingen, Germany

    Dieter Schwamborn

  10. University of Pisa, Pisa, Italy

    Laura Ricci

  11. Ajou University, Suwon, Korea (Republic of)

    Oh Sangyoon

  12. RRZE Friedrich-Alexander-Universität, Erlangen, Germany

    Thomas Gruber

  13. ICAR-CNR, Napoli, Italy

    Laura Antonelli

  14. Tennessee Technological University, Cookeville, TN, USA

    Stephen L. Scott

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Neumann, P. (2020). Sparse Grid Regression for Performance Prediction Using High-Dimensional Run Time Data. In: Schwardmann, U., et al. Euro-Par 2019: Parallel Processing Workshops. Euro-Par 2019. Lecture Notes in Computer Science(), vol 11997. Springer, Cham. https://doi.org/10.1007/978-3-030-48340-1_46

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  • DOI: https://doi.org/10.1007/978-3-030-48340-1_46

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