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Assessing gains from parallel computation on supercomputers

  • Lilia Maliar

    ()

    (Universidad de Alicante)

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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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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  1. 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.
  2. Villemot, Sébastien, 2012. "Accelerating the resolution of sovereign debt models using an endogenous grid method," Dynare Working Papers 17, CEPREMAP.
  3. 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.
  4. Eric M. Aldrich & Jesús Fernández-Villaverde & Ronald Gallant & Juan F. Rubio-Ramírez, 2010. "Tapping the Supercomputer Under Your Desk: Solving Dynamic Equilibrium Models with Graphics Processors," PIER Working Paper Archive 10-014, Penn Institute for Economic Research, Department of Economics, University of Pennsylvania.
  5. 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.
  6. Michael Creel, 2005. "User-Friendly Parallel Computations with Econometric Examples," Computational Economics, Society for Computational Economics, vol. 26(2), pages 107-128, October.
  7. Michael Creel & William Goffe, 2008. "Multi-core CPUs, Clusters, and Grid Computing: A Tutorial," Computational Economics, Society for Computational Economics, vol. 32(4), pages 353-382, November.
  8. 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.
  9. 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.
  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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