2019 IEEE International Conference on Autonomic Computing (ICAC)最新文献

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The Elastic Node: An Experimentation Platform for Hardware Accelerator Research in the Internet of Things 弹性节点:物联网硬件加速器研究的实验平台
2019 IEEE International Conference on Autonomic Computing (ICAC) Pub Date : 2019-06-01 DOI: 10.1109/ICAC.2019.00020
Gregor Schiele, Alwyn Burger, Christopher Cichiwskyj
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引用次数: 15
Speeding up Deep Learning with Transient Servers 利用瞬态服务器加速深度学习
2019 IEEE International Conference on Autonomic Computing (ICAC) Pub Date : 2019-02-28 DOI: 10.1109/ICAC.2019.00024
Shijian Li, R. Walls, Lijie Xu, Tian Guo
{"title":"Speeding up Deep Learning with Transient Servers","authors":"Shijian Li, R. Walls, Lijie Xu, Tian Guo","doi":"10.1109/ICAC.2019.00024","DOIUrl":"https://doi.org/10.1109/ICAC.2019.00024","url":null,"abstract":"Distributed training frameworks, like TensorFlow, have been proposed as a means to reduce the training time of deep learning models by using a cluster of GPU servers. While such speedups are often desirable-e.g., for rapidly evaluating new model designs-they often come with significantly higher monetary costs due to sublinear scalability. In this paper, we investigate the feasibility of using training clusters composed of cheaper transient GPU servers to get the benefits of distributed training without the high costs. We conduct the first large-scale empirical analysis, launching more than a thousand GPU servers of various capacities, aimed at understanding the characteristics of transient GPU servers and their impact on distributed training performance. Our study demonstrates the potential of transient servers with a speedup of 7.7X with more than 62.9% monetary savings for some cluster configurations. We also identify a number of important challenges and opportunities for redesigning distributed training frameworks to be transient-aware. For example, the dynamic cost and availability characteristics of transient servers suggest the need for frameworks to dynamically change cluster configurations to best take advantage of current conditions.","PeriodicalId":442645,"journal":{"name":"2019 IEEE International Conference on Autonomic Computing (ICAC)","volume":"111 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-02-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116824373","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 12
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