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

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Author Info

  • Eric M. Aldrich
  • Jesus Fernandez-Villaverde
  • A. Ronald Gallant
  • Juan F. Rubio-Ramirez

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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Bibliographic Info

Paper provided by Duke University, Department of Economics in its series Working Papers with number 10-89.

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Length: 17
Date of creation: 2010
Date of revision:
Handle: RePEc:duk:dukeec:10-89

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Postal: Department of Economics Duke University 213 Social Sciences Building Box 90097 Durham, NC 27708-0097
Phone: (919) 660-1800
Fax: (919) 684-8974
Web page: http://econ.duke.edu/

Related research

Keywords: CUDA; Dynamic Programming; Parallelization; Growth Model; Business Cycles;

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References

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  1. 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.
  2. Judd, Kenneth L., 1992. "Projection methods for solving aggregate growth models," Journal of Economic Theory, Elsevier, vol. 58(2), pages 410-452, December.
  3. Tauchen, George, 1986. "Finite state markov-chain approximations to univariate and vector autoregressions," Economics Letters, Elsevier, vol. 20(2), pages 177-181.
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Citations

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
Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
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Cited by:
  1. Theodosios Dimopoulos & Stefano Sacchetto, . "Technological Heterogeneity and Corporate Investment," GSIA Working Papers 2012-E48, Carnegie Mellon University, Tepper School of Business.
  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. 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.
  4. Andrew Blake, 2012. "DSGE Modeling on an iPhone/iPad Using SpaceTime," Computational Economics, Society for Computational Economics, vol. 40(4), pages 313-332, December.
  5. Kyle Klein & Julian Neira, 2014. "Nelder-Mead Simplex Optimization Routine for Large-Scale Problems: A Distributed Memory Implementation," Computational Economics, Society for Computational Economics, vol. 43(4), pages 447-461, April.
  6. Nalan Basturk, 2014. "On the Rise of Bayesian Econometrics after Cowles Foundation Monographs 10, 14," Tinbergen Institute Discussion Papers 14-085/III, Tinbergen Institute.
  7. Volker Tjaden, 2013. "Foreign Customer Accumulation and Export Dynamics," Bonn Econ Discussion Papers bgse06_2013, University of Bonn, Germany.
  8. repec:dgr:uvatin:2013055 is not listed on IDEAS
  9. 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, revised 25 Jul 2014.
  10. Lilia Maliar, 2013. "Assessing gains from parallel computation on supercomputers," Working Papers. Serie AD 2013-10, Instituto Valenciano de Investigaciones Económicas, S.A. (Ivie).
  11. Posch, Olaf & Trimborn, Timo, 2013. "Numerical solution of dynamic equilibrium models under Poisson uncertainty," Journal of Economic Dynamics and Control, Elsevier, vol. 37(12), pages 2602-2622.
  12. Matt Dziubinski & Stefano Grassi, 2014. "Heterogeneous Computing in Economics: A Simplified Approach," Computational Economics, Society for Computational Economics, vol. 43(4), pages 485-495, April.
  13. 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.
  14. Eric Aldrich, 2012. "Trading Volume in General Equilibrium with Complete Markets," 2012 Meeting Papers 36, Society for Economic Dynamics.

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