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Comparing the efficiency of group learning of multilayer perceptron on a parallel computer and computational cluster

Turchenko V.O.

The development of a parallel method for batch pattern training of a multilayer perceptron with the back propagation training algorithm and the research of its efficiency on general-purpose parallel computer and computational clusters are presented in this paper. The model of a multilayer perceptron and the usual sequential batch pattern training method are theoretically described. An algorithmic description of the parallel version of the batch pattern training method is presented. The efficiency of parallelization of the developed method is investigated on the progressive increasing the dimension of the parallelized problem. The results of the experimental researches show that (i) the computational cluster with Infiniband interconnection shows better values of parallelization efficiency in comparison with general-purpose parallel computer with ccNuma architecture due to lower communication overhead and (ii) the parallelization efficiency of the method is high enough for its appropriate usage on general-purpose parallel computers and clusters available within modern computational grids.


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