Uncertainty in Artificial Intelligence
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Distributed Parallel Inference on Large Factor Graphs
Joseph Gonzalez, Yucheng Low, Carlos Guestrin, David O'Hallaron
Abstract:
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.
Keywords: null
Pages: 203-212
PS Link:
PDF Link: /papers/09/p203-gonzalez.pdf
BibTex:
@INPROCEEDINGS{Gonzalez09,
AUTHOR = "Joseph Gonzalez and Yucheng Low and Carlos Guestrin and David O'Hallaron",
TITLE = "Distributed Parallel Inference on Large Factor Graphs",
BOOKTITLE = "Proceedings of the Twenty-Fifth Conference Annual Conference on Uncertainty in Artificial Intelligence (UAI-09)",
PUBLISHER = "AUAI Press",
ADDRESS = "Corvallis, Oregon",
YEAR = "2009",
PAGES = "203--212"
}


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