Parallel computation in the Stan probabilistic programming languageČEŠNOVAR, ROK (Avtor) Bulić, Patricio (Mentor) Štrumbelj, Erik (Komentor) graphics processing unitsOpenCLautomatic differentiationBayesian inferenceThe field of Bayesian statistics has experienced a major boom in recent years with tools based on Hamiltonian Monte Carlo. These tools are computationally demanding, as they have to calculate a large number of gradients, most often with a large amount of data and a large number of parameters. Among the more popular tools for Bayesian inference, Stan is considered the most tailored to statisticians, as it has a very expressive and flexible language. Prior to the 2.19 release, Stan did not have support for parallel computing on graphics processing units (GPU). In this work, we present how we developed GPU support for Stan. We have achieved this by upgrading the Stan Math library so that it allows gradients to be calculated on the GPU using OpenCL. The implementation we made is extendable and easy to maintain, which is crucial when implementing support for a mature tool like Stan. In addition to the Math library, we modified the Stan-to-C++ transpiler to efficiently use the new support in the Stan Math library, and added a scheduling algorithm that ensures the fastest device is selected. With the upgrades in the CmdStan and by creating CmdStanR, we brought the presented work closer to a wider range of users. The work shows that both Stan Math functions as well as Stan models run significantly faster using a GPU, with speedups up to 60. With these new features Stan is now on par with the other currently widely used tools for Bayesian inference, where users have to focus more on the computational aspects of their implementation.20222022-05-10 12:00:01Doktorsko delo/naloga136539VisID: 34938COBISS_ID: 108349187sl