Massively parallel computation using graphics processors with application to optimal experimentation in dynamic control
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.
|Date of creation:||10 Aug 2009|
|Date of revision:||04 Apr 2011|
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- Lars E.O. Svensson, 1995.
"Optimal Inflation Targets, `Conservative' Central Banks, and Linear Inflation Contracts,"
NBER Working Papers
5251, National Bureau of Economic Research, Inc.
- 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.
- Svensson, L.E.O., 1995. "Optimal Inflation Targets, 'Conservative' Central Banks, and Linear Inflation Contracts," Papers 595, Stockholm - International Economic Studies.
- Svensson, Lars E O, 1995. "Optimal Inflation Targets, 'Conservative' Central Banks, and Linear Inflation Contracts," CEPR Discussion Papers 1249, C.E.P.R. Discussion Papers.
- Aldrich, Eric M. & Fernández-Villaverde, Jesús & Ronald Gallant, A. & Rubio-Ramírez, Juan F., 2011.
"Tapping the supercomputer under your desk: Solving dynamic equilibrium models with graphics processors,"
Journal of Economic Dynamics and Control,
Elsevier, vol. 35(3), pages 386-393, March.
- Eric M. Aldrich & Jesús Fernández-Villaverde & A. Ronald Gallant & Juan F. Rubio-Ramírez, 2010. "Tapping the Supercomputer Under Your Desk: Solving Dynamic Equilibrium Models with Graphics Processors," NBER Working Papers 15909, National Bureau of Economic Research, Inc.
- Eric M. Aldrich & Jesus Fernandez-Villaverde & A. Ronald Gallant & Juan F. Rubio-Ramirez, 2010. "Tapping the Supercomputer Under Your Desk: Solving Dynamic Equilibrium Models with Graphics Processors," Working Papers 10-89, Duke University, Department of Economics.
- 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.
- 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.
- Volker W. Wieland, 1996.
"Learning by doing and the value of optimal experimentation,"
Finance and Economics Discussion Series
96-5, Board of Governors of the Federal Reserve System (U.S.).
- 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.
- 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.
- Christopher A. Sims & Daniel F. Waggoner & Tao Zha, 2006.
"Methods for inference in large multiple-equation Markov-switching models,"
FRB Atlanta Working Paper
2006-22, Federal Reserve Bank of Atlanta.
- Sims, Christopher A. & Waggoner, Daniel F. & Zha, Tao, 2008. "Methods for inference in large multiple-equation Markov-switching models," Journal of Econometrics, Elsevier, vol. 146(2), pages 255-274, October.
- Prescott, Edward C, 1972. "The Multi-Period Control Problem Under Uncertainty," Econometrica, Econometric Society, vol. 40(6), pages 1043-1058, November.
- Michael Creel, 2005. "User-Friendly Parallel Computations with Econometric Examples," Computational Economics, Springer;Society for Computational Economics, vol. 26(2), pages 107-128, October.
- Christopher Ferrall, 2003. "Solving Finite Mixture Models in Parallel," Computational Economics 0303003, EconWPA.
- Swann, Christopher A, 2002. "Maximum Likelihood Estimation Using Parallel Computing: An Introduction to MPI," Computational Economics, Springer;Society for Computational Economics, vol. 19(2), pages 145-178, April.
- 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.
- Michael Creel & William Goffe, 2008.
"Multi-core CPUs, Clusters, and Grid Computing: A Tutorial,"
Springer;Society for Computational Economics, vol. 32(4), pages 353-382, November.
- William L. Goffe & Michael Creel, 2005. "Multi-core CPUs, Clusters and Grid Computing: a Tutorial," Computing in Economics and Finance 2005 438, Society for Computational Economics.
- 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.
- Yock Y. Chong & David F. Hendry, 1986. "Econometric Evaluation of Linear Macro-Economic Models," Review of Economic Studies, Oxford University Press, vol. 53(4), pages 671-690.
- Kendrick, David, 1978. "Non-convexities from probing in adaptive control problems," Economics Letters, Elsevier, vol. 1(4), pages 347-351.
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