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    Benchmarking solvers for the one dimensional cubic nonlinear klein gordon equation on a single core

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    Bench19_paper_15.pdf
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    Type
    Conference Paper
    Authors
    Muite, B. K.
    Aseeri, Samar
    KAUST Department
    Extreme Computing Research Center
    Computer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division
    Date
    2020-06-09
    Online Publication Date
    2020-06-09
    Print Publication Date
    2020
    Permanent link to this record
    http://hdl.handle.net/10754/664492
    
    Metadata
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    Abstract
    To determine the best method for solving a numerical problem modeled by a partial differential equation, one should consider the discretization of the problem, the computational hardware used and the implementation of the software solution. In solving a scientific computing problem, the level of accuracy can also be important, with some numerical methods being efficient for low accuracy simulations, but others more efficient for high accuracy simulations. Very few high performance benchmarking efforts allow the computational scientist to easily measure such tradeoffs in order to obtain an accurate enough numerical solution at a low computational cost. These tradeoffs are examined in the numerical solution of the one dimensional Klein Gordon equation on single cores of an ARM CPU, an AMD x86-64 CPU, two Intel x86-64 CPUs and a NEC SX-ACE vector processor. The work focuses on comparing the speed and accuracy of several high order finite difference spatial discretizations using a conjugate gradient linear solver and a fast Fourier transform based spatial discretization. In addition implementations using second and fourth order timestepping are also included in the comparison. The work uses accuracy-efficiency frontiers to compare the effectiveness of five hardware platforms
    Citation
    Muite, B. K., & Aseeri, S. (2020). Benchmarking Solvers for the One Dimensional Cubic Nonlinear Klein Gordon Equation on a Single Core. Lecture Notes in Computer Science, 172–184. doi:10.1007/978-3-030-49556-5_18
    Sponsors
    BKM was partially supported by HPC Europa 3 (INFRAIA-2016-1-730897). Compute time on Isamabard was partially supported by ESPRC grant EP/P020224/1.. Acknowledgements. We thank Holger Berger, José Gracia, John Linford and Simon McIntosh-Smith for helpful conversations. We thank Höchstleistungsrechenzentrum Stuttgart (HLRS), the KAUST Supercomputing Laboratory, the University of Tartu High Performance Computing Center and the GW4 Isamabard project for access to supercomputing resources used in development and testing.
    Publisher
    Springer Nature
    Conference/Event name
    2nd International Symposium on Benchmarking, Measuring, and Optimization, Bench 2019
    ISBN
    9783030495558
    DOI
    10.1007/978-3-030-49556-5_18
    Additional Links
    http://link.springer.com/10.1007/978-3-030-49556-5_18
    ae974a485f413a2113503eed53cd6c53
    10.1007/978-3-030-49556-5_18
    Scopus Count
    Collections
    Conference Papers; Extreme Computing Research Center; Computer, Electrical and Mathematical Science and Engineering (CEMSE) Division

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