Parallelizing a Convergent Approximate Inference Method

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Parallelizing a Convergent Approximate Inference Method
Probabilistic inference in graphical models is a prevalent task in statistics and artificial intelligence. The ability to perform this inference task efficiently is critical in large scale applications. The ever-evolving parallel computing technology suggests that dramatic speedup might be achieved by appropriately mapping the current inference algorithms to a parallel framework. Parallel exact inference methods still suffer from worst-case exponential complexity. Approximate inference methods have been parallelized and good speedup achieved. In this report, we focus on a variant of the Belief Propagation algorithm. This variant has better convergence properties and is provably convergent under certain condition. We show that this method is amenable to coarse-grained parallelization and propose techniques to parallelize it optimally without sacrificing convergence. Experiments on a shared memory system demonstrate that near-ideal speedup is achieved with reasonable scalability. Thi...
Ming Su, Elizabeth Thompson
Added 24 Aug 2011
Updated 24 Aug 2011
Type Journal
Year 2011
Where AI
Authors Ming Su, Elizabeth Thompson
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