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

ABSTRACT: This article describes advances in statistical computation for large-scale data analysis in structured Bayesian mixture models via graphics processing unit (GPU) programming. The developments are partly motivated by computational challenges arising in fitting models of increasing heterogeneity to increasingly large datasets. An example context concerns common biological studies using high-throughput technologies generating many, very large datasets and requiring increasingly high-dimensional mixture models with large numbers of mixture components. We outline important strategies and processes for GPU computation in Bayesian simulation and optimization approaches, give examples of the benefits of GPU implementations in terms of processing speed and scale-up in ability to analyze large datas

SUBMITTER: Suchard MA 

PROVIDER: S-EPMC2945379 | biostudies-literature | 2010 Jun

REPOSITORIES: biostudies-literature