Augur: a Modeling Language for Data-Parallel Probabilistic Inference

It is time-consuming and error-prone to implement inference procedures for each new probabilistic model. Probabilistic programming addresses this problem by allowing a user to specify the model and having a compiler automatically generate an inference procedure for it. For this approach to be practical, it is important to generate inference code that has reasonable performance. In this paper, we present a probabilistic programming language and compiler for Bayesian networks designed to make effective use of data-parallel architectures such as GPUs. We show that the compiler can generate dataparallel inference code scalable to thousands of GPU cores by making use of the conditional independence relationships in the Bayesian network.