Mathur, Sudhanshu and Morozov, Sergei (2009): Massively Parallel Computation Using Graphics Processors with Application to Optimal Experimentation in Dynamic Control.

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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 nongraphics 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 tradeoff 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 massivelyparallel 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 singlecore processors and thus establish a better reference point on the computational speed vs. coding complexity tradeoff 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.
Item Type:  MPRA Paper 

Original Title:  Massively Parallel Computation Using Graphics Processors with Application to Optimal Experimentation in Dynamic Control 
Language:  English 
Keywords:  Graphics Processing Units, CUDA programming, Dynamic programming, Learning, Experimentation 
Subjects:  C  Mathematical and Quantitative Methods > C6  Mathematical Methods; Programming Models; Mathematical and Simulation Modeling > C63  Computational Techniques; Simulation Modeling C  Mathematical and Quantitative Methods > C8  Data Collection and Data Estimation Methodology; Computer Programs > C80  General 
Item ID:  16721 
Depositing User:  Sergei Morozov 
Date Deposited:  10. Aug 2009 10:43 
Last Modified:  23. Feb 2013 15:32 
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URI:  http://mpra.ub.unimuenchen.de/id/eprint/16721 