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Abstract Details
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Activity Number:
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119
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Type:
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Topic Contributed
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Date/Time:
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Monday, August 1, 2011 : 8:30 AM to 10:20 AM
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Sponsor:
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Section on Bayesian Statistical Science
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| Abstract - #302338 |
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Title:
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Exploiting Scala's Parallel Collections and Actors for Parallel Bayesian Computations
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Author(s):
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David B. Dahl*+
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Companies:
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Texas A & M University
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Address:
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Department of Statistics, College Station, TX, 77840,
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Keywords:
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Bayesian nonparametrics ;
Dirichlet process ;
multithreaded programming ;
parellel computing ;
random partition models ;
SMP
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Abstract:
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As microprocessor clock speeds stagnate and multicore processors proliferate, strategies for parallel computing are becoming increasing important for advances in Bayesian computation. This talk provides an overview of parallelize within a single Markov chain Monte Carlo run on a single computer using Scala's new parallel collections library and Scala's actors. Scala is a programming language with excellent support for both object-oriented and functional programming. Scala's support for concurrency allows the statistician to naturally exploit conditional independence with little programming or computational overhead. The talk shows how to adapt traditional MCMC code to exploit parallelize and demonstrates how to access this code from with R. Wall and CPU times are compared. The ideas demonstrated in the talk are illustrated in several Bayesian models.
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