Discrete Applied Mathematics
A parallel Lauritzen-Spiegelhalter algorithm for probabilistic inference
Proceedings of the 1994 ACM/IEEE conference on Supercomputing
Scalable Parallel Implementation of Bayesian Network to Junction Tree Conversion for Exact Inference
SBAC-PAD '06 Proceedings of the 18th International Symposium on Computer Architecture and High Performance Computing
MapReduce: simplified data processing on large clusters
OSDI'04 Proceedings of the 6th conference on Symposium on Opearting Systems Design & Implementation - Volume 6
Parallel exact inference on the cell broadband engine processor
Proceedings of the 2008 ACM/IEEE conference on Supercomputing
Regional category parsing in undirected graphical models
Pattern Recognition Letters
Distributed parallel inference on large factor graphs
UAI '09 Proceedings of the Twenty-Fifth Conference on Uncertainty in Artificial Intelligence
Logarithmic time parallel Bayesian inference
UAI'98 Proceedings of the Fourteenth conference on Uncertainty in artificial intelligence
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The ability to efficiently perform probabilistic inference task is critical to large scale applications in statistics and artificial intelligence. Dramatic speedup might be achieved by appropriately mapping the current inference algorithms to the parallel framework. Parallel exact inference methods still suffer from exponential complexity in the worst case. Approximate inference methods have been parallelized and good speedup is achieved. In this paper, we focus on a variant of Belief Propagation algorithm. This variant has better convergent property and is provably convergent under certain conditions. We show that this method is amenable to coarse-grained parallelization and propose techniques to optimally parallelize it without sacrificing convergence. Experiments on a shared memory systems demonstrate that near-ideal speedup is achieved with reasonable scalability.