Econometrics on GPUs
Michael Creel (),
Sonik Mandal and
No 669, Working Papers from Barcelona Graduate School of Economics
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.
Keywords: parallel computing; graphical processing unit; GPU; econometrics; simulation-based methods; Bayesian estimation (search for similar items in EconPapers)
JEL-codes: C13 C14 C15 C33 (search for similar items in EconPapers)
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Working Paper: Econometrics on GPUs (2012)
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Persistent link: http://EconPapers.repec.org/RePEc:bge:wpaper:669
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