首页> 外文会议>ICANN 2010;International conference on artificial neural networks >Application of BSP-Based Computational Cost Model to Predict Parallelization Efficiency of MLP Training Algorithm
【24h】

Application of BSP-Based Computational Cost Model to Predict Parallelization Efficiency of MLP Training Algorithm

机译:基于BSP的计算成本模型在预测MLP训练算法并行化效率中的应用

获取原文

摘要

The development of a computational cost model of parallel batch pattern back propagation training algorithm of a multilayer perceptron is presented in this paper. The model is developed using Bulk Synchronous Parallelism approach. The concrete parameters of the computational cost model are obtained. The developed model is used for the 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 systems.
机译:提出了一种多层感知器并行批处理模式反向传播训练算法的计算成本模型。该模型是使用批量同步并行方法开发的。获得了计算成本模型的具体参数。所开发的模型用于算法的并行化效率的理论预测。比较了两个并行高性能系统上不同并行化方案的预测效率和实际并行化效率。

著录项

相似文献

  • 外文文献
  • 中文文献
  • 专利
获取原文

客服邮箱:kefu@zhangqiaokeyan.com

京公网安备:11010802029741号 ICP备案号:京ICP备15016152号-6 六维联合信息科技 (北京) 有限公司©版权所有
  • 客服微信

  • 服务号