2012/05/09 by Joseph E. Gonzalez, Yucheng Low, Gonzalez, Joseph E. +5
Computer Science · #Artificial Intelligence (cs.AI) #Distributed #FOS: Computer and information sciences #Parallel #and Cluster Computing (cs.DC) #cs.AI #cs.DC
paper · pdf · doi:10.48550/arxiv.1205.2645
Appears in Proceedings of the Twenty-Fifth Conference on Uncertainty in Artificial Intelligence (UAI2009)
arxiv created 2012/05/09 · arxiv updated 2012/05/14
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.