Performance Analysis of Google Colaboratory as a Tool for Accelerating Deep Learning Applications
Carneiro, Tiago1; Medeiros Da Nobrega, Raul Victor2; Nepomuceno, Thiago3; Bian, Gui-Bin4; De Albuquerque, Victor Hugo C.5; Reboucas Filho, Pedro Pedrosa1
发表期刊IEEE ACCESS
ISSN2169-3536
2018
卷号6页码:61677-61685
通讯作者Bian, Gui-Bin(guibin.bian@ia.ac.cn)
摘要Google Colaboratory (also known as Colab) is a cloud service based on Jupyter Notebooks for disseminating machine learning education and research. It provides a runtime fully configured for deep learning and free-of-charge access to a robust GPU. This paper presents a detailed analysis of Colaboratory regarding hardware resources, performance, and limitations. This analysis is performed through the use of Colaboratory for accelerating deep learning for computer vision and other GPU-centric applications. The chosen test-cases are a parallel tree-based combinatorial search and two computer vision applications: object detection/classification and object localization/segmentation. The hardware under the accelerated runtime is compared with a mainstream workstation and a robust Linux server equipped with 20 physical cores. Results show that the performance reached using this cloud service is equivalent to the performance of the dedicated testbeds, given similar resources. Thus, this service can be effectively exploited to accelerate not only deep learning but also other classes of GPU-centric applications. For instance, it is faster to train a CNN on Colaboratory's accelerated runtime than using 20 physical cores of a Linux server. The performance of the GPU made available by Colaboratory may be enough for several profiles of researchers and students. However, these free-of-charge hardware resources are far from enough to solve demanding real-world problems and are not scalable. The most significant limitation found is the lack of CPU cores. Finally, several strengths and limitations of this cloud service are discussed, which might be useful for helping potential users.
关键词Deep learning Colab convolutional neural networks Google colaboratory GPU computing Deep learning Colab convolutional neural networks Google colaboratory GPU computing
DOI10.1109/ACCESS.2018.2874767
收录类别SCI
语种英语
资助项目Youth Innovation Promotion Association of the Chinese Academy of Sciences[218165]
项目资助者Youth Innovation Promotion Association of the Chinese Academy of Sciences
WOS研究方向Computer Science ; Engineering ; Telecommunications
WOS类目Computer Science, Information Systems ; Engineering, Electrical & Electronic ; Telecommunications
WOS记录号WOS:000450369200001
出版者IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
引用统计
文献类型期刊论文
条目标识符http://ir.ia.ac.cn/handle/173211/22606
专题复杂系统管理与控制国家重点实验室_先进机器人
通讯作者Bian, Gui-Bin
作者单位1.Inst Fed Educ Ciencia & Tecnol Ceara, BR-60040531 Fortaleza, CE, Brazil
2.Inst Fed Educ Ciencia & Tecnol Ceara, Comp Sci, BR-60040531 Fortaleza, CE, Brazil
3.Fraunhofer Arbeitsgrp Supply Chain Serv SCS, D-90411 Nurnberg, Germany
4.Chinese Acad Sci, Inst Automat, State Key Lab Management & Control Complex Syst, Beijing 100190, Peoples R China
5.Univ Fortaleza, Programa Posgrad Informt Aplicada, BR-60811905 Fortaleza, CE, Brazil
推荐引用方式
GB/T 7714
Carneiro, Tiago,Medeiros Da Nobrega, Raul Victor,Nepomuceno, Thiago,et al. Performance Analysis of Google Colaboratory as a Tool for Accelerating Deep Learning Applications[J]. IEEE ACCESS,2018,6:61677-61685.
APA Carneiro, Tiago,Medeiros Da Nobrega, Raul Victor,Nepomuceno, Thiago,Bian, Gui-Bin,De Albuquerque, Victor Hugo C.,&Reboucas Filho, Pedro Pedrosa.(2018).Performance Analysis of Google Colaboratory as a Tool for Accelerating Deep Learning Applications.IEEE ACCESS,6,61677-61685.
MLA Carneiro, Tiago,et al."Performance Analysis of Google Colaboratory as a Tool for Accelerating Deep Learning Applications".IEEE ACCESS 6(2018):61677-61685.
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