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European Conference on Parallel Processing

Euro-Par 2019: Euro-Par 2019: Parallel Processing Workshops pp 320–332Cite as

Optimization of Data-Parallel Applications on Heterogeneous HPC Platforms for Dynamic Energy Through Workload Distribution

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Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 11997))

Abstract

Energy is one of the most important objectives for optimization on modern heterogeneous high performance computing (HPC) platforms. The tight integration of multicore CPUs with accelerators in these platforms present several challenges to optimization of multithreaded data-parallel applications for dynamic energy.

In this work, we formulate the optimization problem of data-parallel applications on heterogeneous HPC platforms for dynamic energy through workload distribution. We propose a solution method to solve the problem. It consists of a data-partitioning algorithm that employs load imbalancing technique to determine the workload distribution minimizing the dynamic energy consumption of the parallel execution of an application. The inputs to the algorithm are discrete dynamic energy profiles of individual computing devices.

We experimentally analyse the proposed algorithm using two multithreaded data-parallel applications, matrix multiplication and 2D fast Fourier transform. The load-imbalanced solutions provided by the algorithm achieve significant dynamic energy reductions (on the average 130% and 44%) compared to the load-balanced ones for the applications.

Supported by Science Foundation Ireland (SFI) under Grant Number 14/IA/2474.

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Author information

Authors and Affiliations

  1. School of Computer Science, University College Dublin, Belfield, Dublin 4, Ireland

    Hamidreza Khaleghzadeh, Muhammad Fahad, Ravi Reddy Manumachu & Alexey Lastovetsky

Authors
  1. Hamidreza Khaleghzadeh
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  2. Muhammad Fahad
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  3. Ravi Reddy Manumachu
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  4. Alexey Lastovetsky
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Corresponding author

Correspondence to Hamidreza Khaleghzadeh .

Editor information

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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Khaleghzadeh, H., Fahad, M., Manumachu, R.R., Lastovetsky, A. (2020). Optimization of Data-Parallel Applications on Heterogeneous HPC Platforms for Dynamic Energy Through Workload Distribution. 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_25

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  • DOI: https://doi.org/10.1007/978-3-030-48340-1_25

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-48339-5

  • Online ISBN: 978-3-030-48340-1

  • eBook Packages: Computer ScienceComputer Science (R0)

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