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Numerical Modeling of Hydrodynamic Turbulence with Self-gravity on Intel Xeon Phi KNL

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Parallel Computational Technologies (PCT 2019)

Part of the book series: Communications in Computer and Information Science ((CCIS,volume 1063))

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Abstract

In this paper, we present the results of numerical simulations of hydrodynamic turbulence with self-gravity, employing the latest Intel Xeon Phi accelerators with KNL architecture. A new vectorized numerical method with a high order of accuracy on a local stencil is described in details. We outline the main features of the program implementation of the method for massively parallel architectures and study the code parallel implementation. We achieved a performance of 173 gigaFLOPS and an acceleration factor of 48 using a single Intel Xeon Phi KNL. Using 16 accelerators, we were able to achieve a scalability of 97%.

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Acknowledgments

The research was supported by the Russian Science Foundation (project 18-11-00044).

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Authors and Affiliations

  1. Institute of Computational Mathematics and Mathematical Geophysics SB RAS, Novosibirsk, Russia

    Igor Kulikov, Igor Chernykh, Evgeny Berendeev, Victor Protasov, Alexander Serenko, Vladimir Prigarin, Ivan Ulyanichev & Dmitry Karavaev

  2. Novosibirsk State Technical University, Novosibirsk, Russia

    Victor Protasov, Vladimir Prigarin & Ivan Ulyanichev

  3. University of Vienna, Vienna, Austria

    Eduard Vorobyov

  4. Institute of Astronomy RAS, Moskva, Russia

    Alexander Tutukov

Authors
  1. Igor Kulikov
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  2. Igor Chernykh
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  3. Evgeny Berendeev
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  4. Victor Protasov
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  5. Alexander Serenko
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  6. Vladimir Prigarin
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  7. Ivan Ulyanichev
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  9. Eduard Vorobyov
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  10. Alexander Tutukov
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Corresponding author

Correspondence to Igor Kulikov .

Editor information

Editors and Affiliations

  1. South Ural State University, Chelyabinsk, Russia

    Leonid Sokolinsky

  2. South Ural State University, Chelyabinsk, Russia

    Mikhail Zymbler

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Kulikov, I. et al. (2019). Numerical Modeling of Hydrodynamic Turbulence with Self-gravity on Intel Xeon Phi KNL. In: Sokolinsky, L., Zymbler, M. (eds) Parallel Computational Technologies. PCT 2019. Communications in Computer and Information Science, vol 1063. Springer, Cham. https://doi.org/10.1007/978-3-030-28163-2_22

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  • DOI: https://doi.org/10.1007/978-3-030-28163-2_22

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-28162-5

  • Online ISBN: 978-3-030-28163-2

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