Fast Parallel Exact Inference on Bayesian Networks: Poster

December 08, 2022 Β· Declared Dead Β· πŸ› ACM SIGPLAN Symposium on Principles & Practice of Parallel Programming

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Authors Jiantong Jiang, Zeyi Wen, Atif Mansoor, Ajmal Mian arXiv ID 2212.04241 Category cs.DC: Distributed Computing Cross-listed cs.AI Citations 5 Venue ACM SIGPLAN Symposium on Principles & Practice of Parallel Programming Repository https://github.com/jjiantong/FastBN Last Checked 1 month ago
Abstract
Bayesian networks (BNs) are attractive, because they are graphical and interpretable machine learning models. However, exact inference on BNs is time-consuming, especially for complex problems. To improve the efficiency, we propose a fast BN exact inference solution named Fast-BNI on multi-core CPUs. Fast-BNI enhances the efficiency of exact inference through hybrid parallelism that tightly integrates coarse- and fine-grained parallelism. We also propose techniques to further simplify the bottleneck operations of BN exact inference. Fast-BNI source code is freely available at https://github.com/jjiantong/FastBN.
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