This is the program for the 2010 Joint Statistical Meetings in Vancouver, British Columbia.
Abstract Details
Activity Number:
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5
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Type:
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Invited
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Date/Time:
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Sunday, August 1, 2010 : 2:00 PM to 3:50 PM
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Sponsor:
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Section on Bayesian Statistical Science
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Abstract - #305999 |
Title:
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Embracing GPU Technology in Bayesian Computation: Massively Parallel Computing on Your Desktop
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Author(s):
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Marc Suchard*+
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Companies:
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University of California, Los Angeles
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Address:
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695 Charles E. Young Dr., South, Los Angeles, CA, 90095, United States
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Keywords:
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Bayesian ;
Parallel computing ;
Stochastic processes ;
Optimization ;
Bioinformatics ;
mixture models
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Abstract:
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Massive data present unique challenges to Bayesian statistics. I describe many-core computing algorithms that harness inexpensive graphics processing units (GPUs) to tackle this computation bottleneck. High-end GPUs contain hundreds of cores and are low-cost. I focus on efficient parallelization of integrating the complete data likelihood that plagues the statistical inference of many partially observed stochastic processes, including continuous-time Markov chain models in bioinformatics and Gaussian processes for spatial statistics. Novel algorithms demonstrate 100- to 200-fold speed-up on desktop computers. I conclude with a discussion of the future of many-core computing in statistics and touch upon recent experiences with statistical optimization problems and massively large and high-dimensional mixture models.
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Authors who are presenting talks have a * after their name.
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