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    Compact data structure and scalable algorithms for the sparse grid technique

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    Type
    Conference Paper
    Authors
    Murarasu, Alin
    Weidendorfer, Josef
    Buse, Gerrit
    Butnaru, Daniel
    Pflüger, Dirk
    KAUST Grant Number
    UKC0020
    Date
    2011
    Permanent link to this record
    http://hdl.handle.net/10754/597803
    
    Metadata
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    Abstract
    The sparse grid discretization technique enables a compressed representation of higher-dimensional functions. In its original form, it relies heavily on recursion and complex data structures, thus being far from well-suited for GPUs. In this paper, we describe optimizations that enable us to implement compression and decompression, the crucial sparse grid algorithms for our application, on Nvidia GPUs. The main idea consists of a bijective mapping between the set of points in a multi-dimensional sparse grid and a set of consecutive natural numbers. The resulting data structure consumes a minimum amount of memory. For a 10-dimensional sparse grid with approximately 127 million points, it consumes up to 30 times less memory than trees or hash tables which are typically used. Compared to a sequential CPU implementation, the speedups achieved on GPU are up to 17 for compression and up to 70 for decompression, respectively. We show that the optimizations are also applicable to multicore CPUs. Copyright © 2011 ACM.
    Citation
    Murarasu A, Weidendorfer J, Buse G, Butnaru D, Pflüger D (2011) Compact data structure and scalable algorithms for the sparse grid technique. Proceedings of the 16th ACM symposium on Principles and practice of parallel programming - PPoPP ’11. Available: http://dx.doi.org/10.1145/1941553.1941559.
    Sponsors
    This publication is based on work supported by Award No. UKC0020,made by King Abdullah University of Science and Technology(KAUST).
    Publisher
    Association for Computing Machinery (ACM)
    Journal
    Proceedings of the 16th ACM symposium on Principles and practice of parallel programming - PPoPP '11
    DOI
    10.1145/1941553.1941559
    10.1145/2038037.1941559
    ae974a485f413a2113503eed53cd6c53
    10.1145/1941553.1941559
    Scopus Count
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