分布式反向传播实现训练的性能评估

S. Babii
{"title":"分布式反向传播实现训练的性能评估","authors":"S. Babii","doi":"10.1109/SACI.2007.375524","DOIUrl":null,"url":null,"abstract":"This paper presents the results of some experiments in parallelizing the training phase of a feed-forward, artificial neural network. More specifically, we develop and analyze a parallelization strategy of the widely used neural net learning algorithm called back-propagation. We describe an approach for parallelizing the back- propagation algorithm. We implemented these algorithms on several LANs, permitting us to evaluate and analyze their performances based on the results of actual runs. We were interested on the qualitative aspect of the analysis, in order to achieve a fair understanding of the factors determining the behavior of this parallel algorithms. We were interested in discovering and dealing with some of the specific circumstances that have to be considered when a parallelized neural net learning algorithm is to be implemented on a set of workstations in a LAN. Part of our purpose is to investigate whether it is possible to exploit the computational resources of such a set of workstations.","PeriodicalId":138224,"journal":{"name":"2007 4th International Symposium on Applied Computational Intelligence and Informatics","volume":"500 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2007-06-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"3","resultStr":"{\"title\":\"Performance Evaluation for Training a Distributed BackPropagation Implementation\",\"authors\":\"S. Babii\",\"doi\":\"10.1109/SACI.2007.375524\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"This paper presents the results of some experiments in parallelizing the training phase of a feed-forward, artificial neural network. More specifically, we develop and analyze a parallelization strategy of the widely used neural net learning algorithm called back-propagation. We describe an approach for parallelizing the back- propagation algorithm. We implemented these algorithms on several LANs, permitting us to evaluate and analyze their performances based on the results of actual runs. We were interested on the qualitative aspect of the analysis, in order to achieve a fair understanding of the factors determining the behavior of this parallel algorithms. We were interested in discovering and dealing with some of the specific circumstances that have to be considered when a parallelized neural net learning algorithm is to be implemented on a set of workstations in a LAN. Part of our purpose is to investigate whether it is possible to exploit the computational resources of such a set of workstations.\",\"PeriodicalId\":138224,\"journal\":{\"name\":\"2007 4th International Symposium on Applied Computational Intelligence and Informatics\",\"volume\":\"500 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2007-06-25\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"3\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2007 4th International Symposium on Applied Computational Intelligence and Informatics\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/SACI.2007.375524\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2007 4th International Symposium on Applied Computational Intelligence and Informatics","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/SACI.2007.375524","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 3

摘要

本文介绍了前馈人工神经网络训练阶段并行化的一些实验结果。更具体地说,我们开发和分析了广泛使用的神经网络学习算法的并行化策略,称为反向传播。我们描述了一种并行化反向传播算法的方法。我们在几个局域网上实现了这些算法,允许我们根据实际运行的结果评估和分析它们的性能。我们对分析的定性方面很感兴趣,以便公平地理解决定这种并行算法行为的因素。当并行神经网络学习算法要在局域网上的一组工作站上实现时,我们对发现和处理一些必须考虑的特定情况很感兴趣。我们的部分目的是调查是否有可能利用这样一组工作站的计算资源。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Performance Evaluation for Training a Distributed BackPropagation Implementation
This paper presents the results of some experiments in parallelizing the training phase of a feed-forward, artificial neural network. More specifically, we develop and analyze a parallelization strategy of the widely used neural net learning algorithm called back-propagation. We describe an approach for parallelizing the back- propagation algorithm. We implemented these algorithms on several LANs, permitting us to evaluate and analyze their performances based on the results of actual runs. We were interested on the qualitative aspect of the analysis, in order to achieve a fair understanding of the factors determining the behavior of this parallel algorithms. We were interested in discovering and dealing with some of the specific circumstances that have to be considered when a parallelized neural net learning algorithm is to be implemented on a set of workstations in a LAN. Part of our purpose is to investigate whether it is possible to exploit the computational resources of such a set of workstations.
求助全文
通过发布文献求助,成功后即可免费获取论文全文。 去求助
来源期刊
自引率
0.00%
发文量
0
×
引用
GB/T 7714-2015
复制
MLA
复制
APA
复制
导出至
BibTeX EndNote RefMan NoteFirst NoteExpress
×
提示
您的信息不完整,为了账户安全,请先补充。
现在去补充
×
提示
您因"违规操作"
具体请查看互助需知
我知道了
×
提示
确定
请完成安全验证×
copy
已复制链接
快去分享给好友吧!
我知道了
右上角分享
点击右上角分享
0
联系我们:info@booksci.cn Book学术提供免费学术资源搜索服务,方便国内外学者检索中英文文献。致力于提供最便捷和优质的服务体验。 Copyright © 2023 布克学术 All rights reserved.
京ICP备2023020795号-1
ghs 京公网安备 11010802042870号
Book学术文献互助
Book学术文献互助群
群 号:604180095
Book学术官方微信