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Tapping the Supercomputer Under Your Desk: Solving Dynamic Equilibrium Models with Graphics Processors

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  • Eric M. Aldrich
  • Jesús Fernández-Villaverde
  • A. Ronald Gallant
  • Juan F. Rubio-Ramírez

Abstract

This paper shows how to build algorithms that use graphics processing units (GPUs) installed in most modern computers to solve dynamic equilibrium models in economics. In particular, we rely on the compute unified device architecture (CUDA) of NVIDIA GPUs. We illustrate the power of the approach by solving a simple real business cycle model with value function iteration. We document improvements in speed of around 200 times and suggest that even further gains are likely.

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File URL: http://www.nber.org/papers/w15909.pdf
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Bibliographic Info

Paper provided by National Bureau of Economic Research, Inc in its series NBER Working Papers with number 15909.

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Date of creation: Apr 2010
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Handle: RePEc:nbr:nberwo:15909

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  1. Judd, Kenneth L., 1992. "Projection methods for solving aggregate growth models," Journal of Economic Theory, Elsevier, vol. 58(2), pages 410-452, December.
  2. Tauchen, George, 1986. "Finite state markov-chain approximations to univariate and vector autoregressions," Economics Letters, Elsevier, vol. 20(2), pages 177-181.
  3. S. B. Aruoba & Jesús Fernández-Villaverde & Juan F. Rubio-Ramirez, 2005. "Comparing Solution Methods for Dynamic Equilibrium Economies," Levine's Bibliography 122247000000000855, UCLA Department of Economics.
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Blog mentions

As found by EconAcademics.org, the blog aggregator for Economics research:
  1. Tapping the Supercomputer Under Your Desk: Solving Dynamic Equilibrium Models with Graphics Processors
    by Christian Zimmermann in NEP-DGE blog on 2010-04-18 16:57:12
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Cited by:
  1. Olaf Posch & Timo Trimborn, 2011. "Numerical Solution of Dynamic Equilibrium Models under Poisson Uncertainty," DEGIT Conference Papers c016_044, DEGIT, Dynamics, Economic Growth, and International Trade.
  2. 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.
  3. Andrew Blake, 2012. "DSGE Modeling on an iPhone/iPad Using SpaceTime," Computational Economics, Society for Computational Economics, vol. 40(4), pages 313-332, December.
  4. Roberto Casarin & Stefano Grassi & Francesco Ravazzolo & Herman K. van Dijk, 2013. "Parallel Sequential Monte Carlo for Efficient Density Combination: The Deco Matlab Toolbox," Tinbergen Institute Discussion Papers 13-055/III, Tinbergen Institute.
  5. Eric Aldrich, 2012. "Trading Volume in General Equilibrium with Complete Markets," 2012 Meeting Papers 36, Society for Economic Dynamics.
  6. Lilia Maliar, 2013. "Assessing gains from parallel computation on supercomputers," Working Papers. Serie AD 2013-10, Instituto Valenciano de Investigaciones Económicas, S.A. (Ivie).
  7. Theodosios Dimopoulos & Stefano Sacchetto, . "Technological Heterogeneity and Corporate Investment," GSIA Working Papers 2012-E48, Carnegie Mellon University, Tepper School of Business.
  8. Morozov, Sergei & Mathur, Sudhanshu, 2009. "Massively parallel computation using graphics processors with application to optimal experimentation in dynamic control," MPRA Paper 30298, University Library of Munich, Germany, revised 04 Apr 2011.
  9. repec:dgr:uvatin:2013055 is not listed on IDEAS
  10. Volker Tjaden, 2013. "Foreign Customer Accumulation and Export Dynamics," Bonn Econ Discussion Papers bgse06_2013, University of Bonn, Germany.
  11. Grey Gordon, 2011. "Computing Dynamic Heterogeneous-Agent Economies: Tracking the Distribution," PIER Working Paper Archive 11-018, Penn Institute for Economic Research, Department of Economics, University of Pennsylvania.

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