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2019 | OriginalPaper | Chapter

LRnLA Algorithm ConeFold with Non-local Vectorization for LBM Implementation

Authors : Anastasia Perepelkina, Vadim Levchenko

Published in: Supercomputing

Publisher: Springer International Publishing

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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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Metadata
Title
LRnLA Algorithm ConeFold with Non-local Vectorization for LBM Implementation
Authors
Anastasia Perepelkina
Vadim Levchenko
Copyright Year
2019
Book
Supercomputing

Print ISBN: 978-3-030-05806-7

Electronic ISBN: 978-3-030-05807-4

Copyright Year: 2019

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

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