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Massively Parallel Advanced Monte Carlo Methods on Many-Core Processors

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If you have a question about this talk, please contact Rachel Fogg .

A recent trend in desktop computer architecture is the move from traditional, single-core processors to multi-core processors and further to many-core or massively multi-core processors. Therefore, statistical methods that can take advantage of many-core architectures can make the best use of the latest technology. A particularly promising avenue in this regard is the implementation of statistical algorithms for execution on graphics processing units (GPUs) since they are dedicated, low cost, low maintenance, energy-efficient devices that are becoming increasingly easy to program. I present an introduction to this architecture and a case study on the suitability of using GPUs for three population-based Monte Carlo algorithms – population-based MCMC , sequential Monte Carlo samplers and the particle filter – with speedups ranging from 35 to 500 fold over conventional single-threaded computation.

This talk is part of the Probabilistic Systems, Information, and Inference Group Seminars series.

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