I/O Performance Evaluation of Large-Scale Deep Learning on an HPC System

Minho Bae, Minjoong Jeong, Sangho Yeo, Sangyoon Oh, Oh-Kyoung Kwon
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引用次数: 2

Abstract

Recently, deep learning has become important in diverse fields. Because the process requires a huge amount of computing resources, many researchers have proposed methods to utilize large-scale clusters to reduce the training time. Despite many proposals concerning the training process for large-scale clusters, there remain areas to be developed. In this study, we benchmark the performance of Intel-Caffe, which is a generalpurpose distributed deep learning framework on the Nurion supercomputer of the Korea Institute of Science and Technology Information. We particularly focus on identifying the file I/O factors that affect the performance of Intel-Caffe, as well as a performance evaluation in a container-based environment. Finally, to the best of our knowledge, we present the first benchmark results for distributed deep learning in the container-based environment for a large-scale cluster.
高性能计算系统上大规模深度学习的I/O性能评估
最近,深度学习在各个领域变得越来越重要。由于这个过程需要大量的计算资源,许多研究人员提出了利用大规模集群来减少训练时间的方法。尽管有许多关于大规模集群培训过程的建议,但仍有有待发展的领域。在本研究中,我们在韩国科学技术信息研究院的Nurion超级计算机上对通用分布式深度学习框架Intel-Caffe的性能进行了基准测试。我们特别关注识别影响Intel-Caffe性能的文件I/O因素,以及基于容器环境中的性能评估。最后,据我们所知,我们在基于容器的环境中为大规模集群提供了分布式深度学习的第一个基准测试结果。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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