Massively Parallel Computation Using Graphics Processors with Application to Optimal Experimentation in Dynamic Control
AbstractThe 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.
Download InfoIf you experience problems downloading a file, check if you have the proper application to view it first. In case of further problems read the IDEAS help page. Note that these files are not on the IDEAS site. Please be patient as the files may be large.
Bibliographic InfoPaper provided by University Library of Munich, Germany in its series MPRA Paper with number 16721.
Date of creation: 09 Jul 2009
Date of revision:
Graphics Processing Units; CUDA programming; Dynamic programming; Learning; Experimentation;
Find related papers by 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
This paper has been announced in the following NEP Reports:
Please report citation or reference errors to , or , if you are the registered author of the cited work, log in to your RePEc Author Service profile, click on "citations" and make appropriate adjustments.:
- 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.
- Prescott, Edward C, 1972. "The Multi-Period Control Problem Under Uncertainty," Econometrica, Econometric Society, vol. 40(6), pages 1043-58, November.
- 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.
- Volker 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.).
- 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.
- Coleman, Wilbur John, II, 1993.
"Solving Nonlinear Dynamic Models on Parallel Computers,"
Journal of Business & Economic Statistics,
American Statistical Association, vol. 11(3), pages 325-30, July.
- Wilbur John Coleman II, 1992. "Solving nonlinear dynamic models on parallel computers," Discussion Paper / Institute for Empirical Macroeconomics 66, Federal Reserve Bank of Minneapolis.
- Michael Creel, 2005. "User-Friendly Parallel Computations with Econometric Examples," Computational Economics, Society for Computational Economics, vol. 26(2), pages 107-128, October.
- 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.
- Jurgen A. Doornik & David F. Hendry & Neil Shephard, .
"Computationally-intensive Econometrics using a Distributed Matrix-programming Language,"
2001-W22, Economics Group, Nuffield College, University of Oxford.
- David Hendry & Neil Shephard & Jurgen Doornik, 2001. "Computationally-intensive Econometrics using a Distributed Matrix-programming Language," Economics Series Working Papers 2001-W22, University of Oxford, Department of Economics.
- Kendrick, David, 1978. "Non-convexities from probing in adaptive control problems," Economics Letters, Elsevier, vol. 1(4), pages 347-351.
- 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).
- 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,"
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.
- Chong, Yock Y & Hendry, David F, 1986. "Econometric Evaluation of Linear Macro-Economic Models," Review of Economic Studies, Wiley Blackwell, vol. 53(4), pages 671-90, August.
- 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, Society for Computational Economics, vol. 19(2), pages 145-78, April.
- Roberto Casarin & Stefano Grassi & Francesco Ravazzolo & Herman K. van Dijk, 2013.
"Parallel Sequential Monte Carlo for Efficient Density Combination: The Deco Matlab Toolbox,"
CREATES Research Papers
2013-09, School of Economics and Management, University of Aarhus.
- Roberto Casarin & Stefano Grassi & Francesco Ravazzolo & Herman K. van Dijk, 2013. "Parallel Sequential Monte Carlo for Efficient Density Combination: The DeCo Matlab Toolbox," Working Papers 2013:08, Department of Economics, University of Venice "Ca' Foscari".
- 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.
- repec:dgr:uvatin:2013055 is not listed on IDEAS
For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: (Ekkehart Schlicht).
If references are entirely missing, you can add them using this form.