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Assessing gains from parallel computation on supercomputers

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  • Lilia Maliar

    (Universidad de Alicante)

Abstract

We assess gains from parallel computation on Backlight supercomputer. We find that information transfers are expensive. 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) leads to a higher efficiency of parallelization than the distributive memory programming (MPI).

Suggested Citation

  • Lilia Maliar, 2013. "Assessing gains from parallel computation on supercomputers," Working Papers. Serie AD 2013-10, Instituto Valenciano de Investigaciones Económicas, S.A. (Ivie).
    • Handle: RePEc:ivi:wpasad:2013-10
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    File URL: http://www.ivie.es/downloads/docs/wpasad/wpasad-2013-10.pdf
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    References listed on IDEAS

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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.
    2. Judd, Kenneth L. & Maliar, Lilia & Maliar, Serguei & Valero, Rafael, 2014. "Smolyak method for solving dynamic economic models: Lagrange interpolation, anisotropic grid and adaptive domain," Journal of Economic Dynamics and Control, Elsevier, vol. 44(C), pages 92-123.

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    More about this item

    Keywords

    Parallel Computation; Information transfers; Speedup; Supercomputers; OpenMP; MPI; Blacklight;
    All these keywords.

    JEL classification:

    • C63 - Mathematical and Quantitative Methods - - Mathematical Methods; Programming Models; Mathematical and Simulation Modeling - - - Computational Techniques
    • C68 - Mathematical and Quantitative Methods - - Mathematical Methods; Programming Models; Mathematical and Simulation Modeling - - - Computable General Equilibrium Models

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