Econometrics on GPUs

A graphical processing unit (GPU) is a hardware device normally used to manipulate computer memory for the display of images. GPU computing is the practice of using a GPU device for scientific or general purpose computations that are not necessarily related to the display of images. Many problems in econometrics have a structure that allows for successful use of GPU computing. We explore two examples. The first is simple: repeated evaluation of a likelihood function at different parameter values. The second is a more complicated estimator that involves simulation and nonparametric fitting. We find speedups from 1.5 up to 55.4 times, compared to computations done on a single CPU core. These speedups can be obtained with very little expense, energy consumption, and time dedicated to system maintenance, compared to equivalent performance solutions using CPUs. Code for the examples is provided.

[1]  Cliburn Chan,et al.  Understanding GPU Programming for Statistical Computation: Studies in Massively Parallel Massive Mixtures , 2010, Journal of computational and graphical statistics : a joint publication of American Statistical Association, Institute of Mathematical Statistics, Interface Foundation of North America.

[2]  P. Donnelly,et al.  Inferring coalescence times from DNA sequence data. , 1997, Genetics.

[3]  Jesús Fernández-Villaverde,et al.  Tapping the Supercomputer Under Your Desk: Solving Dynamic Equilibrium Models With Graphics Processors , 2010 .

[4]  Tong Li.  Indirect inference in structural econometric models. , 2010 .

[5]  Qi Li,et al.  Nonparametric Econometrics: Theory and Practice , 2006 .

[6]  Shiliang Li.  THREE ESSAYS ON ECONOMETRICS: ASYMMETRIC EXPONENTIAL POWER DISTRIBUTION, ECONOMETRIC COMPUTATION, AND MULTIFACTOR MODEL , 2011 .

[7]  Michael Creel,et al.  User-Friendly Parallel Computations with Econometric Examples , 2005 .

[8]  Sergei Morozov,et al.  Massively Parallel Computation Using Graphics Processors with Application to Optimal Experimentation in Dynamic Control , 2011, Computational Economics.

[9]  John Geweke,et al.  Massively Parallel Sequential Monte Carlo for Bayesian Inference , 2011 .

[10]  Denis Nekipelov,et al.  Numerical Performance of MCMC Algorithms for Classical Estimation , 2010 .

[11]  Mark M. Tanaka,et al.  Sequential Monte Carlo without likelihoods , 2007, Proceedings of the National Academy of Sciences.

[12]  D. Balding,et al.  Approximate Bayesian computation in population genetics. , 2002, Genetics.

[13]  V. Chernozhukov,et al.  An MCMC Approach to Classical Estimation , 2002, 2301.07782.

[14]  Dennis Kristensen,et al.  INDIRECT LIKELIHOOD INFERENCE , 2013 .

[15]  Vincent Garcia,et al.  Suivi d'objets d'intérêt dans une séquence d'images : des points saillants aux mesures statistiques , 2008 .

[16]  Paul Marjoram,et al.  Markov chain Monte Carlo without likelihoods , 2003, Proceedings of the National Academy of Sciences of the United States of America.

[17]  장훈,et al.  [서평]「Computer Organization and Design, The Hardware/Software Interface」 , 1997 .

[18]  Michel Barlaud,et al.  Fast k nearest neighbor search using GPU , 2008, 2008 IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops.

[19]  Erika Cule,et al.  ABC-SysBio—approximate Bayesian computation in Python with GPU support , 2010, Bioinform..