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Parallelization and Auto-scheduling of Data Access Queries in ML Workloads

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Euro-Par 2021: Parallel Processing Workshops (Euro-Par 2021)

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 13098))

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Abstract

We propose an auto-scheduling mechanism to execute counting queries in machine learning applications. Our approach improves the runtime efficiency of query streams by selecting, in the on-line manner, the optimal execution strategy for each query. We also discuss how to scale up counting queries in multi-threaded applications.

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Acknowledgments

This research was supported by the National Science Centre (Poland) under grant no. UMO-2017/26/D/ST6/00687.

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Authors and Affiliations

  1. Czestochowa University of Technology, Dabrowskiego 69, 42-201, Czestochowa, Poland

    Pawel Bratek & Lukasz Szustak

  2. University at Buffalo, Buffalo, NY, 14260, USA

    Jaroslaw Zola

Authors
  1. Pawel Bratek
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  2. Lukasz Szustak
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  3. Jaroslaw Zola
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Corresponding author

Correspondence to Pawel Bratek .

Editor information

Editors and Affiliations

  1. University of Lisbon, Lisbon, Portugal

    Ricardo Chaves

  2. Department of Computer Engineering, CiTIUS, University of Santiago de Compostela, Santiago de Compostela, La Coruña, Spain

    Dora B. Heras

  3. University of Lisbon, Lisbon, Portugal

    Aleksandar Ilic

  4. Koç University, Istanbul, Turkey

    Didem Unat

  5. Barcelona Supercomputing Center, Barcelona, Spain

    Rosa M. Badia

  6. University of Stirling, Stirling, UK

    Andrea Bracciali

  7. Louisiana State University, Baton Rouge, USA

    Patrick Diehl

  8. Mathematics and Computer Science, Argonne National Laboratory, Lemont, IL, USA

    Anshu Dubey

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

    Oh Sangyoon

  10. Tennessee Technological University, Cookeville, TN, USA

    Stephen L. Scott

  11. University of Pisa, Pisa, Italy

    Laura Ricci

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Bratek, P., Szustak, L., Zola, J. (2022). Parallelization and Auto-scheduling of Data Access Queries in ML Workloads. In: Chaves, R., et al. Euro-Par 2021: Parallel Processing Workshops. Euro-Par 2021. Lecture Notes in Computer Science, vol 13098. Springer, Cham. https://doi.org/10.1007/978-3-031-06156-1_43

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  • DOI: https://doi.org/10.1007/978-3-031-06156-1_43

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-031-06155-4

  • Online ISBN: 978-3-031-06156-1

  • eBook Packages: Computer ScienceComputer Science (R0)

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