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Assessing gains from parallel computation on supercomputers

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  • Lilia Maliar

    ()
    (Universidad de Alicante)

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    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).

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    File URL: http://www.ivie.es/downloads/docs/wpasad/wpasad-2013-10.pdf
    File Function: Fisrt version / Primera version, 2013
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    Bibliographic Info

    Paper provided by Instituto Valenciano de Investigaciones Económicas, S.A. (Ivie) in its series Working Papers. Serie AD with number 2013-10.

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    Length: 15 pages
    Date of creation: Dec 2013
    Date of revision:
    Publication status: Published by Ivie
    Handle: RePEc:ivi:wpasad:2013-10

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    Related research

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

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    References

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    1. 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.
    2. William L. Goffe & Michael Creel, 2005. "Multi-core CPUs, Clusters and Grid Computing: a Tutorial," Computing in Economics and Finance 2005 438, Society for Computational Economics.
    3. Villemot, Sébastien, 2012. "Accelerating the resolution of sovereign debt models using an endogenous grid method," Dynare Working Papers 17, CEPREMAP.
    4. Amman, Hans M., 1990. "Implementing stochastic control software on supercomputing machines," Journal of Economic Dynamics and Control, Elsevier, vol. 14(2), pages 265-279, May.
    5. Mathur, Sudhanshu & Morozov, Sergei, 2009. "Massively Parallel Computation Using Graphics Processors with Application to Optimal Experimentation in Dynamic Control," MPRA Paper 16721, University Library of Munich, Germany.
    6. Eric M. Aldrich & Jesús Fernández-Villaverde & A. Ronald Gallant & Juan F. Rubio-Ramírez, 2010. "Tapping the Supercomputer Under Your Desk: Solving Dynamic Equilibrium Models with Graphics Processors," NBER Working Papers 15909, National Bureau of Economic Research, Inc.
    7. Michael Creel, 2005. "User-Friendly Parallel Computations with Econometric Examples," Computational Economics, Society for Computational Economics, vol. 26(2), pages 107-128, October.
    8. Yongyang Cai & Kenneth L. Judd & Greg Thain & Stephen J. Wright, 2013. "Solving Dynamic Programming Problems on a Computational Grid," NBER Working Papers 18714, National Bureau of Economic Research, Inc.
    9. Michael Creel, 2008. "Using Parallelization to Solve a Macroeconomic Model: A Parallel Parameterized Expectations Algorithm," Computational Economics, Society for Computational Economics, vol. 32(4), pages 343-352, November.
    10. Sergei Morozov & Sudhanshu Mathur, 2012. "Massively Parallel Computation Using Graphics Processors with Application to Optimal Experimentation in Dynamic Control," Computational Economics, Society for Computational Economics, vol. 40(2), pages 151-182, August.
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