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Tapping the supercomputer under your desk: Solving dynamic equilibrium models with graphics processors

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Author Info

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

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)Â 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.sciencedirect.com/science/article/B6V85-517BB1F-1/2/07bba84b275cbac9fbcc21bb42114def
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Bibliographic Info

Article provided by Elsevier in its journal Journal of Economic Dynamics and Control.

Volume (Year): 35 (2011)
Issue (Month): 3 (March)
Pages: 386-393

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Handle: RePEc:eee:dyncon:v:35:y:2011:i:3:p:386-393

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Web page: http://www.elsevier.com/locate/jedc

Related research

Keywords: CUDA Dynamic programming Parallelization Growth model Business cycles;

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References

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  1. Aruoba, S. Boragan & Fernandez-Villaverde, Jesus & Rubio-Ramirez, Juan F., 2006. "Comparing solution methods for dynamic equilibrium economies," Journal of Economic Dynamics and Control, Elsevier, vol. 30(12), pages 2477-2508, December.
  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. 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.
  2. Theodosios Dimopoulos & Stefano Sacchetto, . "Technological Heterogeneity and Corporate Investment," GSIA Working Papers 2012-E48, Carnegie Mellon University, Tepper School of Business.
  3. 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.
  4. Volker Tjaden, 2013. "Foreign Customer Accumulation and Export Dynamics," Bonn Econ Discussion Papers bgse06_2013, University of Bonn, Germany.
  5. 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.
  6. 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.
  7. Roberto Casarin & Stefano Grassi & Francesco Ravazzolo & Herman K. van Dijk, 2013. "Parallel Sequential Monte Carlo for Efficient Density Combination: The Deco Matlab Toolbox," CREATES Research Papers 2013-09, School of Economics and Management, University of Aarhus.
  8. 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.
  9. 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.
  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.
  11. Lilia Maliar, 2013. "Assessing gains from parallel computation on supercomputers," Working Papers. Serie AD 2013-10, Instituto Valenciano de Investigaciones Económicas, S.A. (Ivie).
  12. Eric Aldrich, 2012. "Trading Volume in General Equilibrium with Complete Markets," 2012 Meeting Papers 36, Society for Economic Dynamics.
  13. Andrew Blake, 2012. "DSGE Modeling on an iPhone/iPad Using SpaceTime," Computational Economics, Society for Computational Economics, vol. 40(4), pages 313-332, December.

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