Efficiency Analysis of Parallel Batch Pattern NN Training Algorithm on General-Purpose Supercomputer

机译：通用超级计算机并行批处理模式神经网络训练算法的效率分析

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The theoretic and algorithmic description of the parallel batch pattern back propagation (BP) training algorithm of multilayer perceptron is presented in this paper. The efficiency research of the developed parallel algorithm is fulfilled at progressive increasing of the dimension of parallelized problem on general-purpose parallel computer NEC TX-7.

机译：本文介绍了多层感知器并行批处理模式反向传播训练算法的理论和算法描述。在通用并行计算机NEC TX-7上，并行化问题的规模逐步增加，从而完成了对并行算法的效率研究。

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《Distributed computing, artificial intelligence, Bioinformatics, soft computing, and ambient assisted living 》|2009年|223-226|共4页
会议地点 Salamanca(ES);Salamanca(ES)
作者
Volodymyr Turchenko; Lucio Grandinetti;
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作者单位

Center of Excellence of High Performance Computing, University of Calabria Via P. Bucci 22B, 87036, Rende (CS), Italy;

Center of Excellence of High Performance Computing, University of Calabria Via P. Bucci 22B, 87036, Rende (CS), Italy;

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原文格式 PDF
正文语种 eng
中图分类人工智能理论 ;
关键词
batch pattern training; neural network; parallelization efficiency;

机译：批处理模式培训；神经网络;并行化效率;

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专利

1. Improvement of parallelization efficiency of batch pattern BP training algorithm using Open MPI [J] . Volodymyr Turchenko, Lucio Grandinetti, George Bosilca, Procedia Computer Science . 2010 ,第1期

机译：使用Open MPI提高批处理模式BP训练算法的并行化效率
2. ALGORITHMS FOR PARALLELIZING A MATHEMATICAL MODEL OF FOREST FIRES ON SUPERCOMPUTERS AND THEORETICAL ESTIMATES FOR THE EFFICIENCY OF PARALLEL PROGRAMS [J] . N. V. Baranovskiy Cybernetics and Systems Analysis . 2015 ,第3期

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3. MODYLAS: A Highly Parallelized General-Purpose Molecular Dynamics Simulation Program for Large-Scale Systems with Long-Range Forces Calculated by Fast Multipole Method (FMM) and Highly Scalable Fine-Grained New Parallel Processing Algorithms [J] . Yoshimichi Andoh, Noriyuki Yoshii, Kazushi Fujimoto Journal of chemical theory and computation: JCTC . 2013 ,第7期

机译：MODYLAS：具有并行力的大型多用途通用分子动力学仿真程序，该程序由快速多极方法（FMM）和高度可扩展的细粒度新并行处理算法计算而得
4. Efficiency Analysis of Parallel Batch Pattern NN Training Algorithm on General-Purpose Supercomputer [C] . Volodymyr Turchenko, Lucio Grandinetti International Work-Conference on Artificial Neural Networks . 2009

机译：通用超级计算机平行批量模式NN训练算法效率分析
5. Vector and parallel algorithms for chemical process simulation on supercomputers. [D] . Mallya, Jayarama U. 1996

机译：用于超级计算机上化学过程模拟的矢量和并行算法。
6. Analysis of Parallel Algorithms on SMP Node and Cluster of Workstations Using Parallel Programming Models with New Tile-based Method for Large Biological Datasets [O] . D. D. Shrimankar, S. R. Sathe 2016

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7. Improvement of parallelization efficiency of batch pattern BP training algorithm using Open MPI [O] . Turchenko Volodymyr, Grandinetti Lucio, Bosilca George, 2010

机译：使用Open MPI提高批处理模式BP训练算法的并行化效率
8. Some computational challenges of developing efficient parallel algorithms for data-dependent computations in thermal-hydraulics supercomputer applications [R] . Woodruff, S B 1992

机译：在热工水力学超级计算机应用中开发用于数据相关计算的高效并行算法的一些计算挑战

Efficiency Analysis of Parallel Batch Pattern NN Training Algorithm on General-Purpose Supercomputer

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