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Abstract Details
Activity Number:
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207
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Type:
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Invited
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Date/Time:
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Monday, August 1, 2011 : 2:00 PM to 3:50 PM
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Sponsor:
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JCGS-Journal of Computational and Graphical Statistics
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Abstract - #300451 |
Title:
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On the Utility of Graphics Cards to Perform Massively Parallel Simulation of Advanced Monte Carlo Methods
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Author(s):
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Anthony Lee*+ and Chris Holmes and Arnaud Doucet and Chris Yau and Mike Giles
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Companies:
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University of Oxford and University of Oxford and University of British Columbia and University of Oxford and University of Oxford
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Address:
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Department of Statistics, Oxford, OX1 3TG, UK
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Keywords:
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GPU ;
stochastic simulation ;
Monte Carlo ;
MCMC ;
Particle Filter
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Abstract:
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For certain types of scientific algorithms, desktop graphics cards using graphical processing units (GPUs) offer the performance of cluster-based computing at a fraction of the cost. Moreover, GPUs are dedicated, low maintenance, energy-efficient devices that are becoming increasingly easy to program. In this talk we overview the class of statistical algorithms amenable to GPU computation. We then present a case study using advanced Monte Carlo algorithms including population-based MCMC, sequential Monte Carlo samplers and the particle filter. We demonstrate that GPUs can lead to substantial speedups ranging from 35 to 500 fold over conventional CPU single-threaded computation. This suggests that GPUs and other multi-core devices are likely to change the landscape of high performance statistical computing in the near future.
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