Parallel exact inference on the Cell Broadband Engine processor

Abstract

We present the design and implementation of a parallel exact inference algorithm on the Cell Broadband Engine (Cell BE) processor, a heterogeneous multicore architecture. Exact inference is a key problem in exploring probabilistic graphical models, where the computation complexity increases dramatically with the network structure and clique size. In this paper, we exploit parallelism in exact inference at multiple levels. We propose a rerooting method to minimize the critical path for exact inference, and an efficient scheduler to dynamically allocate SPEs. In addition, we explore potential table representation and layout to optimize DMA transfer between local store and main memory. We implemented the proposed method and conducted experiments on the Cell BE processor in the IBM QS20 Blade. We achieved speedup up to 10 × on the Cell, compared to state-of-the-art processors. The methodology proposed in this paper can be used for online scheduling of directed acyclic graph (DAG) structured computations.