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

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  • Mathur, Sudhanshu
  • Morozov, Sergei

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

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Cited by:
  1. Matt Dziubinski & Stefano Grassi, 2014. "Heterogeneous Computing in Economics: A Simplified Approach," Computational Economics, Society for Computational Economics, Society for Computational Economics, vol. 43(4), pages 485-495, April.
  2. repec:dgr:uvatin:2013055 is not listed on IDEAS
  3. 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.
  4. Lilia Maliar, 2013. "Assessing gains from parallel computation on supercomputers," Working Papers. Serie AD, Instituto Valenciano de Investigaciones Económicas, S.A. (Ivie) 2013-10, Instituto Valenciano de Investigaciones Económicas, S.A. (Ivie).

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