2019/07/01 by Rok Češnovar, Steve Bronder, Češnovar, Rok +11
Computer Science · Mathematics · Physics and Astronomy · #Computation (stat.CO) #Distributed #FOS: Computer and information sciences #Gaussian Processes and Bayesian Inference #Markov Chains and Monte Carlo Methods #Mathematical Software (cs.MS) #Parallel #Scientific Research and Discoveries #and Cluster Computing (cs.DC)
paper · pdf · doi:10.48550/arxiv.1907.01063
openalex publication_date 2019/07/01 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
This paper details an extensible OpenCL framework that allows Stan to utilize heterogeneous compute devices. It includes GPU-optimized routines for the Cholesky decomposition, its derivative, other matrix algebra primitives and some commonly used likelihoods, with more additions planned for the near future. Stan users can now benefit from large speedups offered by GPUs with little effort and without changes to their existing Stan code. We demonstrate the practical utility of our work with two examples - logistic regression and Gaussian Process regression.