  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.
