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arXiv · 1312.3613

Augur: a Modeling Language for Data-Parallel Probabilistic Inference

Jean-Baptiste Tristan·Daniel Huang·Joseph Tassarotti·Adam Pocock·Stephen J. Green·Guy L. Steele Jr
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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.

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BibTeXRIS

Jean-Baptiste Tristan, Daniel Huang, Joseph Tassarotti, Adam Pocock, Stephen J. Green, Guy L. Steele Jr. 2013-12-12. Augur: a Modeling Language for Data-Parallel Probabilistic Inference. https://arxiv.org/abs/1312.3613

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