Parallelizing the fuzzy ARTMAP algorithm on a Beowulf cluster

Fuzzy ARTMAP neural networks have been proven to be good classifiers on a variety of classification problems. However, the time that it takes fuzzy ARTMAP to converge to a solution increases rapidly as the number of patterns used for training increases. In this paper, we propose a coarse grain parallelization technique, based on a pipeline approach, to speed-up fuzzy ARTMAP's training process. In particular, we first parallelized fuzzy ARTMAP, without the match-tracking mechanism, and then we parallelized fuzzy ARTMAP with the match-tracking mechanism. Results run on a Beowulf cluster with a well known large database (Forrest Covertype database from the UCI repository) show linear speedup with respect to the number of processors used in the pipeline.