Exact Inference on Manycore Processors using Pointer Jumping

Abstract

Exact inference is a key problem in exploring probabilistic graphical models. Most parallel algorithms for exact inference explore data and structural parallelism. These algorithms result in limited performance if the input model offers limited data and structural parallelism. In this paper, we study a pointer jumping based method on manycore systems for exact inference in junction trees. We adapt the technique for both evidence collection and evidence distribution so as to efficiently process junction trees with multiple evidence cliques. We also study the impact of junction tree topology on evidence collection. We implement the proposed method on state-of-the-art manycore systems. Experimental results show that, for junction trees with limited data and structural parallelism, pointer jumping is well suited to accelerate exact inference on manycore systems.