Fast Parallel Exact Inference on Bayesian Networks: Poster
This work addresses efficiency issues for users of Bayesian networks in machine learning, but it appears incremental as it builds on existing parallelism techniques.
The authors tackled the problem of slow exact inference on Bayesian networks by proposing Fast-BNI, a solution that uses hybrid parallelism on multi-core CPUs, achieving significant speed improvements, though specific numbers are not provided.
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.