Augur: a Modeling Language for Data-Parallel Probabilistic Inference by Jean-Baptiste Tristan,


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.

A very good paper but the authors should highlight the caveat in the introduction:

We claim that many MCMC inference algorithms are highly data-parallel (Hillis & Steele, 1986; Blelloch, 1996) if we take advantage of the conditional independence relationships of the input model (e.g. the assumption of i.i.d. data makes the likelihood independent across data points).

(Where i.i.d. = Independent and identically distributed random variables.)

That assumption does allow for parallel processing, but users should be cautious about accepting assumptions about data.

The algorithms will still work, even if your assumptions about the data are incorrect.

But the answer you get may not be as useful as you would like.

I first saw this in a tweet by Stefano Bertolo.

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