Faster-BNI: Fast Parallel Exact Inference on Bayesian Networks

Bayesian networks (BNs) have recently attracted more attention, because they are interpretable machine learning models and enable a direct representation of causal relations between variables. However, exact inference on BNs is time-consuming, especially for complex problems, which hinders the wides...

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Vydáno v:IEEE transactions on parallel and distributed systems Ročník 35; číslo 8; s. 1444 - 1455
Hlavní autoři: Jiang, Jiantong, Wen, Zeyi, Mansoor, Atif, Mian, Ajmal
Médium: Journal Article
Jazyk:angličtina
Vydáno: New York IEEE 01.08.2024
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Témata:
Algorithms
Bayesian analysis
Bayesian networks
exact inference
Inference
Inference algorithms
junction tree algorithm
Junctions
Machine learning
Message passing
Optimization
Parallel processing
Particle separators
Random variables
ISSN:1045-9219, 1558-2183
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Shrnutí:Bayesian networks (BNs) have recently attracted more attention, because they are interpretable machine learning models and enable a direct representation of causal relations between variables. However, exact inference on BNs is time-consuming, especially for complex problems, which hinders the widespread adoption of BNs. To improve the efficiency, we propose a fast BN exact inference named Faster-BNI on multi-core CPUs. Faster-BNI enhances the efficiency of a well-known BN exact inference algorithm, namely the junction tree algorithm, through hybrid parallelism that tightly integrates coarse- and fine-grained parallelism. Moreover, we identify that the bottleneck of BN exact inference methods lies in recursively updating the potential tables of the network. To reduce the table update cost, Faster-BNI employs novel optimizations, including the reduction of potential tables and re-organizing the potential table storage, to avoid unnecessary memory consumption and simplify potential table operations. Comprehensive experiments on real-world BNs show that the sequential version of Faster-BNI outperforms existing sequential implementation by 9 to 22 times, and the parallel version of Faster-BNI achieves up to 11 times faster inference than its parallel counterparts.
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ISSN:1045-9219
1558-2183
DOI:10.1109/TPDS.2024.3414177