An Implementation of Parallel Bayesian Network Learning

Joseph S. Haddad, Timothy W. O'Neil, Anthony Deeter, and Zhong-Hui Duan

Volume 8, Issue 2 (August 2017), pp. 24–28

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  author={Joseph S. Haddad and Timothy W. O'Neil and Anthony Deeter and Zhong-Hui Duan},
  title={An Implementation of Parallel Bayesian Network Learning},
  journal={The Journal of Computational Science Education},
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Bayesian networks may be utilized to infer genetic relations among genes. This has proven useful in providing information about how gene interactions influence life. However, Bayesian network learning is slow due to the nature of the algorithm. K2, a search space reduction, helps speed up the learning process but may introduce bias. To eliminate this bias, multiple Bayesian networks must be computed. This paper evaluates and realizes parallelization of network generation and the reasoning behind the choices made. Methods are developed and tested to evaluate the results of the implemented accelerations. Generating networks across multiple cores results in a linear speed-up with negligible overhead. Distributing the generation of networks across multiple machines also introduces linear speed-up, but results in additional overhead.