16426

GPU-accelerated Gibbs Sampling

Alexander Terenin, Shawfeng Dong, David Draper
Applied Mathematics and Statistics, University of California, Santa Cruz
arXiv:1608.04329 [stat.CO], (15 Aug 2016)
@article{terenin2016gpuaccelerated,

   title={GPU-accelerated Gibbs Sampling},

   author={Terenin, Alexander and Dong, Shawfeng and Draper, David},

   year={2016},

   month={aug},

   archivePrefix={"arXiv"},

   primaryClass={stat.CO}

}

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Gibbs sampling is a widely used Markov Chain Monte Carlo (MCMC) method for numerically approximating integrals of interest in Bayesian statistics and other mathematical sciences. Many implementations of MCMC methods do not extend easily to parallel computing environments, as their inherently sequential nature incurs a large synchronization cost. In this paper, we show how to do Gibbs sampling in a fully data-parallel manner on a graphics processing unit (GPU) for a large class of exchangeable models that admit latent variable representations. We demonstrate the scheme on a horseshoe probit regression model, and find that our implementation scales effectively to thousands of predictors and millions of data points simultaneously.
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