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LRnLA Algorithm ConeFold with Non-local Vectorization for LBM Implementation

  • Anastasia PerepelkinaEmail author
  • Vadim Levchenko
Conference paper
Part of the Communications in Computer and Information Science book series (CCIS, volume 965)

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.

Keywords

Lattice Boltzmann method LRnLA algorithms Parallel computation 

Notes

Acknowledgement

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

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Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  1. 1.Keldysh Institute of Applied Mathematics RASMoscowRussia

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