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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 propagationdoi:10.1109/sbac-pad.2011.34 dblp:conf/sbac-pad/MaXP11 fatcat:ziclr2joovhn7jeczbpbyomumi