On the Utility of Graphics Cards to Perform Massively Parallel Simulation of Advanced Monte Carlo Methods

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 la...

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Vydané v:Journal of computational and graphical statistics Ročník 19; číslo 4; s. 769 - 789
Hlavní autori: Lee, Anthony, Yau, Christopher, Giles, Michael B., Doucet, Arnaud, Holmes, Christopher C.
Médium: Journal Article
Jazyk:English
Vydavateľské údaje: United States Taylor & Francis 01.12.2010
JCGS Management Committee of the American Statistical Association, Institute of Mathematical Statistics, and Interface Foundation of North America
Taylor & Francis Ltd
Predmet:
Algorithms
Architecture
Arithmetic
Computer memory
General purpose computation on graphics processing units
Many-core architecture
Markov analysis
Markov chains
Microprocessors
Monte Carlo methods
Monte Carlo simulation
Outliers and Influential Cases
Parallel computing
Parallel processing
Population-based Markov chain Monte Carlo
Sampling distributions
Sampling methods
Simulation
Stochastic models
Stochastic simulation
Studies
ISSN:1061-8600, 1537-2715
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Popis
Shrnutí: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.
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ISSN:1061-8600
1537-2715
DOI:10.1198/jcgs.2010.10039