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Assessing gains from parallel computation on a supercomputer

Author

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  • Lilia Maliar

    () (Stanford University)

Abstract

We assess gains from parallel computation on Backlight supercomputer. The information transfers are expensive. We find that to make parallel computation efficient, a task per core must be sufficiently large, ranging from few seconds to one minute depending on the number of cores employed. For small problems, the shared memory programming (OpenMP) and a hybrid of shared and distributive memory programming (OpenMP&MPI) leads to a higher efficiency of parallelization than the distributive memory programming (MPI) alone.

Suggested Citation

  • Lilia Maliar, 2015. "Assessing gains from parallel computation on a supercomputer," Economics Bulletin, AccessEcon, vol. 35(1), pages 159-167.
  • Handle: RePEc:ebl:ecbull:eb-14-00773
    as

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    File URL: http://www.accessecon.com/Pubs/EB/2015/Volume35/EB-15-V35-I1-P18.pdf
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    References listed on IDEAS

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    1. Aldrich, Eric M. & Fernández-Villaverde, Jesús & Ronald Gallant, A. & Rubio-Ramírez, Juan F., 2011. "Tapping the supercomputer under your desk: Solving dynamic equilibrium models with graphics processors," Journal of Economic Dynamics and Control, Elsevier, vol. 35(3), pages 386-393, March.
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    4. Sergei Morozov & Sudhanshu Mathur, 2012. "Massively Parallel Computation Using Graphics Processors with Application to Optimal Experimentation in Dynamic Control," Computational Economics, Springer;Society for Computational Economics, vol. 40(2), pages 151-182, August.
    5. Amman, Hans M., 1986. "Are supercomputers useful for optimal control experiments?," Journal of Economic Dynamics and Control, Elsevier, vol. 10(1-2), pages 127-129, June.
    6. Nagurney, Anna & Zhang, Ding, 1998. "A massively parallel implementation of a discrete-time algorithm for the computation of dynamic elastic demand traffic problems modeled as projected dynamical systems," Journal of Economic Dynamics and Control, Elsevier, vol. 22(8-9), pages 1467-1485, August.
    7. Michael Creel & William Goffe, 2008. "Multi-core CPUs, Clusters, and Grid Computing: A Tutorial," Computational Economics, Springer;Society for Computational Economics, vol. 32(4), pages 353-382, November.
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    Cited by:

    1. John Gibson & James P Henson, 2016. "Getting the most from MATLAB: ditching canned routines and embracing coder," Economics Bulletin, AccessEcon, vol. 36(4), pages 2519-2525.

    More about this item

    Keywords

    Parallel Computation; Information transfers; Speedup; Supercomputers; OpenMP; MPI; Blacklight;

    JEL classification:

    • C0 - Mathematical and Quantitative Methods - - General
    • C6 - Mathematical and Quantitative Methods - - Mathematical Methods; Programming Models; Mathematical and Simulation Modeling

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