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


  • Morozov, Sergei
  • Mathur, Sudhanshu


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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  • 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.
  • Handle: RePEc:pra:mprapa:30298

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    References listed on IDEAS

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    Cited by:

    1. Casarin, Roberto & Grassi, Stefano & Ravazzolo, Francesco & van Dijk, Herman K., 2015. "Parallel Sequential Monte Carlo for Efficient Density Combination: The DeCo MATLAB Toolbox," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 68(i03).

    More about this item


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

    JEL classification:

    • C63 - Mathematical and Quantitative Methods - - Mathematical Methods; Programming Models; Mathematical and Simulation Modeling - - - Computational Techniques
    • C80 - Mathematical and Quantitative Methods - - Data Collection and Data Estimation Methodology; Computer Programs - - - General


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