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    • 39-Issue 1
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    Compressed Neighbour Lists for SPH

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    v39i1pp531-542.pdf (1.522Mb)
    Date
    2020
    Author
    Band, Stefan
    Gissler, Christoph
    Teschner, Matthias
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    Abstract
    We propose a novel compression scheme to store neighbour lists for iterative solvers that employ Smoothed Particle Hydrodynamics (SPH). The compression scheme is inspired by Stream VByte, but uses a non‐linear mapping from data to data bytes, yielding memory savings of up to 87%. It is part of a novel variant of the Cell‐Linked‐List (CLL) concept that is inspired by compact hashing with an improved processing of the cell‐particle relations. We show that the resulting neighbour search outperforms compact hashing in terms of speed and memory consumption. Divergence‐Free SPH (DFSPH) scenarios with up to 1.3 billion SPH particles can be processed on a 24‐core PC using 172 GB of memory. Scenes with more than 7 billion SPH particles can be processed in a Message Passing Interface (MPI) environment with 112 cores and 880 GB of RAM. The neighbour search is also useful for interactive applications. A DFSPH simulation step for up to 0.2 million particles can be computed in less than 40 ms on a 12‐core PC.
    BibTeX
    @article {10.1111:cgf.13890,
    journal = {Computer Graphics Forum},
    title = {{Compressed Neighbour Lists for SPH}},
    author = {Band, Stefan and Gissler, Christoph and Teschner, Matthias},
    year = {2020},
    publisher = {© 2020 Eurographics ‐ The European Association for Computer Graphics and John Wiley & Sons Ltd},
    ISSN = {1467-8659},
    DOI = {10.1111/cgf.13890}
    }
    URI
    https://doi.org/10.1111/cgf.13890
    https://diglib.eg.org:443/handle/10.1111/cgf13890
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    Eurographics Association copyright © 2013 - 2023 
    Send Feedback | Contact - Imprint | Data Privacy Policy | Disable Google Analytics
    Theme by @mire NV
    System hosted at  Graz University of Technology.
    TUGFhA