Augur: a Modeling Language for Data-Parallel Probabilistic Inference
Abstract
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. Our language is fully integrated within the Scala programming language and benefits from tools such as IDE support, type-checking, and code completion. We show that the compiler can generate data-parallel inference code scalable to thousands of GPU cores by making use of the conditional independence relationships in the Bayesian network.
Cite
@article{arxiv.1312.3613,
title = {Augur: a Modeling Language for Data-Parallel Probabilistic Inference},
author = {Jean-Baptiste Tristan and Daniel Huang and Joseph Tassarotti and Adam Pocock and Stephen J. Green and Guy L. Steele},
journal= {arXiv preprint arXiv:1312.3613},
year = {2014}
}