论文信息 - Parallel module network learning on distributed memory multiprocessors

Parallel module network learning on distributed memory multiprocessors

As an extension of the Bayesian network, the module network is used in situations where there are many variables but only a small set of data available. However, using this network is still time-consuming. In this paper, the authors proposed a parallel implementation of the module network, a less time-consuming, learning algorithm based on the message-passing model. In order to solve the load-imbalance problem introduced by either result caching or intrinsic computation, a grouping strategy was proposed, which groups computations by modules and then distributes them cyclically. The algorithm was tested 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.

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