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    Assembly of finite element methods on graphics processors

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    Type
    Article
    Authors
    Cecka, Cris
    Lew, Adrian J.
    Darve, E.
    Date
    2010-08-23
    Online Publication Date
    2010-08-23
    Print Publication Date
    2011-02-04
    Permanent link to this record
    http://hdl.handle.net/10754/597607
    
    Metadata
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    Abstract
    Recently, graphics processing units (GPUs) have had great success in accelerating many numerical computations. We present their application to computations on unstructured meshes such as those in finite element methods. Multiple approaches in assembling and solving sparse linear systems with NVIDIA GPUs and the Compute Unified Device Architecture (CUDA) are created and analyzed. Multiple strategies for efficient use of global, shared, and local memory, methods to achieve memory coalescing, and optimal choice of parameters are introduced. We find that with appropriate preprocessing and arrangement of support data, the GPU coprocessor using single-precision arithmetic achieves speedups of 30 or more in comparison to a well optimized double-precision single core implementation. We also find that the optimal assembly strategy depends on the order of polynomials used in the finite element discretization. © 2010 John Wiley & Sons, Ltd.
    Citation
    Cecka C, Lew AJ, Darve E (2010) Assembly of finite element methods on graphics processors. Int J Numer Meth Engng 85: 640–669. Available: http://dx.doi.org/10.1002/nme.2989.
    Sponsors
    This work was partially supported by a research grant from the Academic Excellence Alliance program between King Abdullah University of Science and Technology (KAUST) and the Stanford University.
    Publisher
    Wiley
    Journal
    International Journal for Numerical Methods in Engineering
    DOI
    10.1002/nme.2989
    ae974a485f413a2113503eed53cd6c53
    10.1002/nme.2989
    Scopus Count
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