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Massively parallel computation using graphics processors with application to optimal experimentation in dynamic control

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  • Morozov, Sergei
  • Mathur, Sudhanshu
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    Abstract

    The rapid growth in the performance of graphics hardware, coupled with recent improvements in its programmability has lead to its adoption in many non-graphics applications, including a wide variety of scientific computing fields. At the same time, a number of important dynamic optimal policy problems in economics are athirst of computing power to help overcome dual curses of complexity and dimensionality. We investigate if computational economics may benefit from new tools on a case study of imperfect information dynamic programming problem with learning and experimentation trade-off, that is, a choice between controlling the policy target and learning system parameters. Specifically, we use a model of active learning and control of a linear autoregression with the unknown slope that appeared in a variety of macroeconomic policy and other contexts. The endogeneity of posterior beliefs makes the problem difficult in that the value function need not be convex and the policy function need not be continuous. This complication makes the problem a suitable target for massively-parallel computation using graphics processors (GPUs). Our findings are cautiously optimistic in that new tools let us easily achieve a factor of 15 performance gain relative to an implementation targeting single-core processors. Further gains up to a factor of 26 are also achievable but lie behind a learning and experimentation barrier of their own. Drawing upon experience with CUDA programming architecture and GPUs provides general lessons on how to best exploit future trends in parallel computation in economics.

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    File URL: http://mpra.ub.uni-muenchen.de/30298/
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    Bibliographic Info

    Paper provided by University Library of Munich, Germany in its series MPRA Paper with number 30298.

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    Date of creation: 10 Aug 2009
    Date of revision: 04 Apr 2011
    Handle: RePEc:pra:mprapa:30298

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    Related research

    Keywords: Graphics Processing Units; CUDA programming; Dynamic programming; Learning; Experimentation;

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    1. Nagurney, Anna & Takayama, Takashi & Zhang, Ding, 1995. "Massively parallel computation of spatial price equilibrium problems as dynamical systems," Journal of Economic Dynamics and Control, Elsevier, vol. 19(1-2), pages 3-37.
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    3. Svensson, Lars E O, 1997. "Optimal Inflation Targets, "Conservative" Central Banks, and Linear Inflation Contracts," American Economic Review, American Economic Association, vol. 87(1), pages 98-114, March.
    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. Wieland, Volker, 2000. "Learning by doing and the value of optimal experimentation," Journal of Economic Dynamics and Control, Elsevier, vol. 24(4), pages 501-534, April.
    6. 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.
    7. Goffe, William L. & Ferrier, Gary D. & Rogers, John, 1994. "Global optimization of statistical functions with simulated annealing," Journal of Econometrics, Elsevier, vol. 60(1-2), pages 65-99.
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    9. Chong, Yock Y & Hendry, David F, 1986. "Econometric Evaluation of Linear Macro-Economic Models," Review of Economic Studies, Wiley Blackwell, vol. 53(4), pages 671-90, August.
    10. Brezzi, Monica & Lai, Tze Leung, 2002. "Optimal learning and experimentation in bandit problems," Journal of Economic Dynamics and Control, Elsevier, vol. 27(1), pages 87-108, November.
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    12. Prescott, Edward C, 1972. "The Multi-Period Control Problem Under Uncertainty," Econometrica, Econometric Society, vol. 40(6), pages 1043-58, November.
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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," CREATES Research Papers 2013-09, School of Economics and Management, University of Aarhus.
    2. repec:dgr:uvatin:2013055 is not listed on IDEAS

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