We investigate data parallelism for belief propagation in acyclic factor graphs on multicore/manycore processors. Belief propagation is a key problem in exploring factor graphs, a probabilistic graphical model that has found applications in many domains. In this paper, we identify basic computations called node level primitives for propagating the belief in a factor graph. Algorithms for these primitives are developed using data parallel techniques. We propose a complete belief propagation

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... ithm using the primitives to perform exact inference in factor graphs. We implement the proposed algorithms on state-of-the-art multi-socket multi-core systems with additional NUMA-aware optimizations. Our proposed algorithms exhibit good scalability using a representative set of factor graphs. On a four-socket Intel Westmere-EX based system with 40 cores, we achieve 39.5× speedup for the primitives and 39.2× for the complete algorithm using factor graphs with large potential tables.