high performance computing on graphics processing units: hgpu.org

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.