POSTER: Fast Parallel Exact Inference on Bayesian Networks

Jiantong Jiang, Zeyi Wen, Atif Mansoor, Ajmal Mian

Research output: Chapter in Book/Conference paperOther chapter contributionpeer-review

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.
Original languageEnglish
Title of host publicationPPoPP '23: Proceedings of the 28th ACM SIGPLAN Annual Symposium on Principles and Practice of Parallel Programming
PublisherAssociation for Computing Machinery (ACM)
Pages425-426
Number of pages2
ISBN (Electronic)979-8-4007-0015-6
DOIs
Publication statusPublished - 25 Feb 2023
Event28th ACM SIGPLAN Annual Symposium on Principles and Practice of Parallel Programming, PPoPP 2023 - Montreal, Canada
Duration: 25 Feb 20231 Mar 2023

Conference

Conference28th ACM SIGPLAN Annual Symposium on Principles and Practice of Parallel Programming, PPoPP 2023
Country/TerritoryCanada
CityMontreal
Period25/02/231/03/23

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