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On Defragmentation Algorithms for GPU-Native Octree-Based AMR Grids

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Parallel Computing Technologies (PaCT 2021)

Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 12942))

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Abstract

The GPU-native CFD framework with dynamical adaptive mesh refinement (AMR) requires periodical execution of memory compaction operations to relieve memory expenses. The present paper addresses several different parallel GPU memory defragmentation algorithms for octree-based AMR grids. These algorithms are tested on benchmark CFD problems typical for AMR transformation. The results show that the memory defragmentation algorithm based on the prefix scan procedure is not only 1–2 order faster compared to algorithm based on space filling curve (z-curve) but also surprisingly and dramatically impacts on CFD solver performance by reducing total GPU runtime for some problems up to 37%.

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Acknowledgments

This work was supported by Moscow Center of Fundamental and Applied Mathematics, Agreement with the Ministry of Science and Higher Education of the Russian Federation, No. 075-15-2019-1623.

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Authors and Affiliations

  1. Keldysh Institute of Applied Mathematics, Moscow, 125047, Russia

    Pavel Pavlukhin & Igor Menshov

  2. Research and Development Institute “Kvant”, Moscow, 125438, Russia

    Pavel Pavlukhin

Authors
  1. Pavel Pavlukhin
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  2. Igor Menshov
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Corresponding author

Correspondence to Pavel Pavlukhin .

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Editors and Affiliations

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

    Victor Malyshkin

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Pavlukhin, P., Menshov, I. (2021). On Defragmentation Algorithms for GPU-Native Octree-Based AMR Grids. In: Malyshkin, V. (eds) Parallel Computing Technologies. PaCT 2021. Lecture Notes in Computer Science(), vol 12942. Springer, Cham. https://doi.org/10.1007/978-3-030-86359-3_18

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  • DOI: https://doi.org/10.1007/978-3-030-86359-3_18

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-86358-6

  • Online ISBN: 978-3-030-86359-3

  • eBook Packages: Computer ScienceComputer Science (R0)

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