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On the utility of graphics cards to perform massively parallel simulation of advanced Monte Carlo methods
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Lee, Anthony, Yau, Christopher, Giles, Michael B., Doucet, Arnaud and Holmes, Christopher (2010) On the utility of graphics cards to perform massively parallel simulation of advanced Monte Carlo methods. Journal of Computational and Graphical Statistics, Vol.19 (No.4). pp. 769-789. doi:10.1198/jcgs.2010.10039 ISSN 1061-8600.
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Official URL: http://dx.doi.org/10.1198/jcgs.2010.10039
Abstract
We present a case study on the utility of graphics cards to perform massively parallel simulation of advanced Monte Carlo methods. Graphics cards, containing multiple Graphics Processing Units (GPUs), are self-contained parallel computational devices that can be housed in conventional desktop and laptop computers and can be thought of as prototypes of the next generation of many-core processors. For certain classes of population-based Monte Carlo algorithms they offer massively parallel simulation, with the added advantage over conventional distributed multicore processors that they are cheap, easily accessible, easy to maintain, easy to code, dedicated local devices with low power consumption. On a canonical set of stochastic simulation examples including population-based Markov chain Monte Carlo methods and Sequential Monte Carlo methods, we find speedups from 35- to 500-fold over conventional single-threaded computer code. Our findings suggest that GPUs have the potential to facilitate the growth of statistical modeling into complex data-rich domains through the availability of cheap and accessible many-core computation. We believe the speedup we observe should motivate wider use of parallelizable simulation methods and greater methodological attention to their design. This article has supplementary material online.
Item Type: | Journal Article | ||||
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Divisions: | Faculty of Science, Engineering and Medicine > Science > Statistics | ||||
Journal or Publication Title: | Journal of Computational and Graphical Statistics | ||||
Publisher: | American Statistical Association | ||||
ISSN: | 1061-8600 | ||||
Official Date: | 1 December 2010 | ||||
Dates: |
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Volume: | Vol.19 | ||||
Number: | No.4 | ||||
Page Range: | pp. 769-789 | ||||
DOI: | 10.1198/jcgs.2010.10039 | ||||
Status: | Peer Reviewed | ||||
Publication Status: | Published | ||||
Access rights to Published version: | Restricted or Subscription Access |
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