Exact inference is a key problem in exploring probabilistic graphical models, where the computational complexity varies dramatically as the parameters of the graphical models changes. To achieve scalability over hundreds of threads remains a fundamental challenge. In this paper, we design an efficient scheduler hosted by the CPU to allocate cliques in junction trees to the GPGPU at run time. The scheduler can merge multiple small cliques or split large cliques dynamically so as to maximize the

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... tilization of the GPGPU resources. We propose a conflict free potential table organization and an optimal data layout for coalescing memory access. In addition, we develop a double buffering based asynchronous data transfer between the CPU and GPGPU to overlap the clique processing on the GPGPU with the data transfer and scheduling. Our implementation of the proposed method on GPGPU platforms achieved 30× speedup compared with state-of-the-art multicore processors, and it sustains 70% of the theoretical upper bound of the GPGPU throughput. *