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LRnLA Algorithm ConeFold with Non-local Vectorization for LBM Implementation

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Supercomputing (RuSCDays 2018)

Part of the book series: Communications in Computer and Information Science ((CCIS,volume 965))

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Abstract

We have achieved a \({\sim }0.3\) GLUps performance on a 4 core CPU for the D3Q19 Lattice Boltzmann method by taking an advanced time-space decomposition approach. The LRnLA algorithm ConeFold was used with a new non-local mirrored vectorization. The roofline model was used for the performance estimation and parameter choice. There are many expansion possibilities, so the developed kernel may become a foundation for more complex LBM variations.

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Acknowledgement

The work is partially supported by the Russian Science Foundation (project #18-71-10004).

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Authors and Affiliations

  1. Keldysh Institute of Applied Mathematics RAS, Moscow, Russia

    Anastasia Perepelkina & Vadim Levchenko

Authors
  1. Anastasia Perepelkina
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  2. Vadim Levchenko
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Corresponding author

Correspondence to Anastasia Perepelkina .

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Editors and Affiliations

  1. Research Computing Center (RCC), Moscow State University, Moscow, Russia

    Vladimir Voevodin

  2. RCC, Moscow State University, Moscow, Russia

    Sergey Sobolev

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Perepelkina, A., Levchenko, V. (2019). LRnLA Algorithm ConeFold with Non-local Vectorization for LBM Implementation. In: Voevodin, V., Sobolev, S. (eds) Supercomputing. RuSCDays 2018. Communications in Computer and Information Science, vol 965. Springer, Cham. https://doi.org/10.1007/978-3-030-05807-4_9

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  • DOI: https://doi.org/10.1007/978-3-030-05807-4_9

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-05806-7

  • Online ISBN: 978-3-030-05807-4

  • eBook Packages: Computer ScienceComputer Science (R0)

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