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Massively Parallel Computation Using Graphics Processors with Application to Optimal Experimentation in Dynamic Control

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Author Info
Mathur , Sudhanshu
Morozov, Sergei

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Abstract

The rapid increase in the performance of graphics hardware, coupled with recent improvements in its programmability has lead to its adoption in many non-graphics applications, including 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 linear autoregression with 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 policy function need not be continuous. This complication makes the problem a suitable target for massively-parallel computation using graphics processors. 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 and thus establish a better reference point on the computational speed vs. coding complexity trade-off frontier. While further gains and wider applicability may lie behind steep learning barrier, we argue that the future of many computations belong to parallel algorithms anyway.

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Paper provided by University Library of Munich, Germany in its series MPRA Paper with number 16721.

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Date of creation: 09 Jul 2009
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Handle: RePEc:pra:mprapa:16721

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

Find related papers by JEL classification:
C63 - Mathematical and Quantitative Methods - - Mathematical Methods and Programming - - - Computational Techniques
C80 - Mathematical and Quantitative Methods - - Data Collection and Data Estimation Methodology; Computer Programs - - - General

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  3. 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. [Downloadable!] (restricted)
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  8. Prescott, Edward C, 1972. "The Multi-Period Control Problem Under Uncertainty," Econometrica, Econometric Society, vol. 40(6), pages 1043-58, November. [Downloadable!] (restricted)
  9. Wilbur John Coleman II, 1992. "Solving nonlinear dynamic models on parallel computers," Discussion Paper / Institute for Empirical Macroeconomics 66, Federal Reserve Bank of Minneapolis. [Downloadable!]
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  10. Beck, Gunter W. & Wieland, Volker, 2002. "Learning and control in a changing economic environment," Journal of Economic Dynamics and Control, Elsevier, vol. 26(9-10), pages 1359-1377, August. [Downloadable!] (restricted)
  11. 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. [Downloadable!] (restricted)
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  13. Michael Creel, 2005. "User-Friendly Parallel Computations with Econometric Examples," UFAE and IAE Working Papers 637.05, Unitat de Fonaments de l'Anàlisi Econòmica (UAB) and Institut d'Anàlisi Econòmica (CSIC). [Downloadable!]
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