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Energy Performance Evaluation of Quasi-Monte Carlo Algorithms on Hybrid HPC

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Large-Scale Scientific Computing (LSSC 2015)

Part of the book series: Lecture Notes in Computer Science ((LNISA,volume 9374))

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Abstract

The increasing demands of scientific applications and the increasing capacity of modern computing systems lead to the need of evaluating energy consumption and, consequently, to the development of energy efficient algorithms. In this paper we study the energy performance of a class of quasi-Monte Carlo algorithms on hybrid HPC systems. These algorithms are applied to solve quantum kinetic integral equations using Sobol and Halton sequences. The energy performance results are compared on a CPU-based computer platform and computer platforms with accelerators like GPU cards and Intel Xeon Phi coprocessors with respect to several metrics. Directions for future work are also given.

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Acknowledgments

This work was supported by the National Science Fund of Bulgaria under Grant DFNI-I02/8.

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Authors and Affiliations

  1. IICT-BAS, Acad. G. Bonchev Street, Bl. 25A, 1113, Sofia, Bulgaria

    E. Atanassov, T. Gurov & A. Karaivanova

Authors
  1. E. Atanassov
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  2. T. Gurov
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  3. A. Karaivanova
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Corresponding author

Correspondence to T. Gurov .

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Editors and Affiliations

  1. Bulgarian Academy of Sciences, Sofia, Bulgaria

    Ivan Lirkov

  2. Inst. of Inform. & Communication, Bulgarian Academy of Sciences, Sofia, Bulgaria

    Svetozar D. Margenov

  3. Informatics and Mathematical Modell, Technical University of Denmark, Kongens Lyngby, Denmark

    Jerzy Waśniewski

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Atanassov, E., Gurov, T., Karaivanova, A. (2015). Energy Performance Evaluation of Quasi-Monte Carlo Algorithms on Hybrid HPC. In: Lirkov, I., Margenov, S., Waśniewski, J. (eds) Large-Scale Scientific Computing. LSSC 2015. Lecture Notes in Computer Science(), vol 9374. Springer, Cham. https://doi.org/10.1007/978-3-319-26520-9_18

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  • DOI: https://doi.org/10.1007/978-3-319-26520-9_18

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-26519-3

  • Online ISBN: 978-3-319-26520-9

  • eBook Packages: Computer ScienceComputer Science (R0)

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