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Understanding GPU Programming for Statistical Computation: Studies in Massively Parallel Massive Mixtures

Marc A. Suchard, Quanli Wang, Cliburn Chan, Jacob Frelinger, Andrew Cron, Mike West
Departments of Biomathematics, Human Genetics and Biostatistics, University of California, Los Angeles, CA 90095
Journal of Computational & Graphical Statistics (JCGS), 19, 419-438, 2010

@article{suchard2010understanding,

   title={Understanding GPU programming for statistical computation: Studies in massively parallel massive mixtures},

   author={Suchard, M.A. and Wang, Q. and Chan, C. and Frelinger, J. and Cron, A. and West, M.},

   journal={Journal of Computational and Graphical Statistics},

   volume={19},

   number={2},

   pages={419–438},

   issn={1061-8600},

   year={2010},

   publisher={ASA}

}

We describe advances in statistical computation for large-scale data analysis in structured Bayesian mixture models via GPU (graphics processing unit) programming. The developments are partly motivated by computational challenges arising in increasingly prevalent biological studies using high-throughput flow cytometry methods, generating many, very large data sets and requiring increasingly high-dimensional mixture models with large numbers of mixture components. The paper describes the strategies and process for GPU computation in Bayesian simulation and optimization approaches, examples of the benefits of GPU implementations in terms of processing speed and scale-up in ability to analyze large data sets, while providing a detailed, tutorial-style exposition that will benefit readers interested in developing GPU-based approaches in other statistical models.
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