An Implement of Parallel Module Network Learning Algorithm on Distributed Memory Multiprocessors*

As an extension of Bayesian Network, Module Network is good for dealing with problems where there are many variables but only a small set of data available. However it’s still time-consuming to learn a Module Network. In this paper, we propose a parallel implementation of the Module Network learning algorithm based on the message passing model. In order to solve the load-imbalance problem introduced by either result caching or intrinsic computation, we propose a grouping strategy, which groups computations by modules and then distributed them cyclically. We tested our algorithm on eight 4-way Intel Xeon multiprocessors. Speedups of 29.26 on 32 processors have been observed. The result shows that our algorithm is effective.