As computer clusters become more common and the size of the problems encountered in the field of AI grows, there is an increasing demand for efficient parallel inference algorithms. We consider the problem of parallel inference on large factor graphs in the distributed memory setting of computer clusters. We develop a new efficient parallel inference algorithm, DBRSplash, which incorporates over-segmented graph partitioning, belief residual scheduling, and uniform work Splash operations. We empirically evaluate the DBRSplash algorithm on a 120 processor cluster and demonstrate linear to super-linear performance gains on large factor graph models.
@article{arxiv.1205.2645,
title = {Distributed Parallel Inference on Large Factor Graphs},
author = {Joseph E. Gonzalez and Yucheng Low and Carlos E. Guestrin and David O'Hallaron},
journal= {arXiv preprint arXiv:1205.2645},
year = {2012}
}
Comments
Appears in Proceedings of the Twenty-Fifth Conference on Uncertainty in Artificial Intelligence (UAI2009)