Strategy of resource brokering for efficient parallelization of MLP training

A strategy of resource brokering for efficient parallelization of the parallel batch pattern back propagation training algorithm of a multilayer perceptron is presented in this paper. A BSP-based computational cost model of the parallel algorithm is used for the prediction of its execution time and parallelization efficiency. The strategy of resource brokering is based on Pareto optimality with the weighted sum approach for choosing optimal solutions for efficient parallelization of the algorithm. The results of experimental research show that the developed resource brokering strategy has good conformity with the desired scheduling policy of minimization of the execution time of the algorithm with maximization of the parallelization efficiency in the most economic way.