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I/O Performance Evaluation of Large-Scale Deep Learning on an HPC System
  • Bae, Minho ;
  • Jeong, Minjoong ;
  • Yeo, Sangho ;
  • Oh, Sangyoon ;
  • Kwon, Oh Kyoung
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Publication Year
2019-07-01
Journal
2019 International Conference on High Performance Computing and Simulation, HPCS 2019
Publisher
Institute of Electrical and Electronics Engineers Inc.
Citation
2019 International Conference on High Performance Computing and Simulation, HPCS 2019, pp.436-439
Keyword
componentdistributed deep learningHPCIntel-Caffelarge mini-batchlarge-scale cluster
Mesh Keyword
Computing resourceDiverse fieldsLarge-scale clustersLearning frameworksScience and TechnologyTraining processTraining time
All Science Classification Codes (ASJC)
Computer Science ApplicationsHardware and ArchitectureModeling and SimulationComputer Networks and Communications
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.
Language
eng
URI
https://aurora.ajou.ac.kr/handle/2018.oak/36432
https://www.scopus.com/inward/record.uri?partnerID=HzOxMe3b&scp=85092018797&origin=inward
DOI
https://doi.org/10.1109/hpcs48598.2019.9188225
Journal URL
http://ieeexplore.ieee.org/xpl/mostRecentIssue.jsp?punumber=9183768
Type
Conference
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Oh, Sangyoon오상윤
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