  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.
