Bayesian Networks for Data Mining
Data Mining and Knowledge Discovery
A parallel Lauritzen-Spiegelhalter algorithm for probabilistic inference
Proceedings of the 1994 ACM/IEEE conference on Supercomputing
Artificial Intelligence: A Modern Approach
Artificial Intelligence: A Modern Approach
Parallel exact inference on the cell broadband engine processor
Proceedings of the 2008 ACM/IEEE conference on Supercomputing
Exploiting unbalanced thread scheduling for energy and performance on a CMP of SMT processors
IPDPS'06 Proceedings of the 20th international conference on Parallel and distributed processing
Logarithmic time parallel Bayesian inference
UAI'98 Proceedings of the Fourteenth conference on Uncertainty in artificial intelligence
JSSPP'10 Proceedings of the 15th international conference on Job scheduling strategies for parallel processing
Parallel approaches to machine learning-A comprehensive survey
Journal of Parallel and Distributed Computing
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In this paper, we design and implement an efficient technique for parallel evidence propagation on state-of-the-art multicore processor systems. Evidence propagation is a major step in exact inference, a key problem in exploring probabilistic graphical models. We propose a rerooting algorithm to minimize the critical path in evidence propagation. The rerooted junction tree is used to construct a directed acyclic graph (DAG) where each node represents a computation task for evidence propagation. We develop a collaborative scheduler to dynamically allocate the tasks to the cores of the processors. In addition, we integrate a task partitioning module in the scheduler to partition large tasks so as to achieve load balance across the cores. We implemented the proposed method using Pthreads on both AMD and Intel quadcore processors. For a representative set of junction trees, our method achieved almost linear speedup. The execution time of our method was around twice as fast as the OpenMP based implementation on both the platforms.