MLP并行批处理训练算法的计算代价模型研究

V. Turchenko, L. Grandinetti
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引用次数: 3

摘要

本文提出了一种多层感知器并行批量反向传播训练算法及其计算代价模型。采用批量同步并行的方法建立了并行算法的计算代价模型。得到了计算成本模型的具体参数。利用所建立的计算代价模型对算法的并行化效率进行了理论预测。在两台高性能并行计算机上,比较了不同并行化方案下的预测和实际并行化效率。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Investigation of computational cost model of MLP parallel batch training algorithm
The development of a parallel batch back propagation training algorithm of a multilayer perceptron and its computational cost model are presented in this paper. The computational cost model of the parallel algorithm is developed using Bulk Synchronous Parallelism approach. The concrete parameters of the computational cost model are obtained. The developed computational cost model is used for theoretical prediction of a parallelization efficiency of the algorithm. The predicted and real parallelization efficiencies are compared for different parallelization scenarios on two parallel high performance computers.
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