Long viewed as a strong statistical inference technique, Bayesian networks have emerged as an important class of applications for high-performance computing. We have applied an architecture-conscious approach to parallelizing the Lauritzen-Spiegelhalter Junction Tree algorithm for exact inferencing of Bayesian networks. In optimizing the Junction Tree algorithm, we have implemented both in-clique and topological parallelism strategies to best leverage the fine-grained synchronization and massive-scale multithreading of the Cray XMT architecture. Two topological techniques were developed to parallelize the evidence propagation process through the Bayesian network. The first technique involves performing intelligent scheduling of junction tree nodes based on the tree's topology and the relative sizes of nodes. The second technique involves decomposing the junction tree into a finer state graph representation to offer many more opportunities for parallelism. We evaluate these optimizations on five different Bayesian networks and report our findings and observations. From this development and evaluation, we demonstrate the application of massive-scale multithreading for load balancing and use of implicit parallelism-based compiler optimizations for designing scalable inferencing algorithms.
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