EAIS: Energy-aware adaptive scheduling for CNN inference on high-performance GPUs

doi:10.1016/j.future.2022.01.004

	EAIS: Energy-aware adaptive scheduling for CNN inference on high-performance GPUs
	Yao, Chunrong 1; Liu, Wantao 2; Tang, Weiqing 1,3; Hu, Songlin 2
	2022-05-01
发表期刊	FUTURE GENERATION COMPUTER SYSTEMS-THE INTERNATIONAL JOURNAL OF ESCIENCE
ISSN	0167-739X
卷号	130 页码:253-268
摘要	Recently, a large number of convolutional neural network (CNN) inference services have emerged on high-performance Graphic Processing Units (GPUs). However, GPUs are high power consumption units, and the energy consumption increases sharply along with the deployment of deep learning tasks. Although previous studies have considered the latency Service-Level-Objective (SLO) of inference services, they fail to directly take account of the energy consumption. Our investigation shows that coordinating batching and dynamic voltage frequency scaling (DVFS) settings can decrease the energy consumption of CNN inference. But it is affected by (i) larger configuration spaces; (ii) GPUs' underutilization while data are transferred between CPUs and GPUs; (iii) fluctuating workloads. In this paper, we propose EAIS, an energy-aware adaptive scheduling framework that is comprised of a performance model, an asynchronous execution strategy, and an energy-aware scheduler. The performance model provides valid information about the performance characteristics of CNN inference services to shrink the feasible configuration space. The asynchronous execution strategy overlaps data upload and GPU execution to improve the system processing capacity. The energy-aware scheduler adaptively coordinates batching and DVFS according to fluctuating workloads to minimize energy consumption while meeting latency SLO. Our experimental results on NVIDIA Tesla M40 and V100 GPUs show that, compared to the state-of-the-art methods, EAIS decreases the energy consumption by up to 28.02% and improves the system processing capacity by up to 7.22% while meeting latency SLO. Besides, EAIS has been proved to have good versatility under different latency SLO constraints. (C) 2022 Elsevier B.V. All rights reserved.
关键词	Energy-aware Convolutional neural network (CNN) inference High-performance GPUs Workload scheduling Service-Level-Objective (SLO)
DOI	10.1016/j.future.2022.01.004
收录类别	SCI
语种	英语
资助项目	National Key Research and Development Program of China[2017YFB1010000]
WOS研究方向	Computer Science
WOS类目	Computer Science, Theory & Methods
WOS记录号	WOS:000819692500020
出版者	ELSEVIER
引用统计	被引频次：10[WOS] [WOS记录] [WOS相关记录]
文献类型	期刊论文
条目标识符	http://119.78.100.204/handle/2XEOYT63/19519
专题	中国科学院计算技术研究所期刊论文_英文
通讯作者	Liu, Wantao
作者单位	1.Nanjing Univ Sci & Technol, Sch Comp Sci & Engn, Nanjing 210094, Peoples R China 2.Chinese Acad Sci, Inst Informat Engn, Beijing 100093, Peoples R China 3.Chinese Acad Sci, Inst Comp Technol, Beijing 100190, Peoples R China
推荐引用方式 GB/T 7714	Yao, Chunrong,Liu, Wantao,Tang, Weiqing,et al. EAIS: Energy-aware adaptive scheduling for CNN inference on high-performance GPUs[J]. FUTURE GENERATION COMPUTER SYSTEMS-THE INTERNATIONAL JOURNAL OF ESCIENCE,2022,130:253-268.
APA	Yao, Chunrong,Liu, Wantao,Tang, Weiqing,&Hu, Songlin.(2022).EAIS: Energy-aware adaptive scheduling for CNN inference on high-performance GPUs.FUTURE GENERATION COMPUTER SYSTEMS-THE INTERNATIONAL JOURNAL OF ESCIENCE,130,253-268.
MLA	Yao, Chunrong,et al."EAIS: Energy-aware adaptive scheduling for CNN inference on high-performance GPUs".FUTURE GENERATION COMPUTER SYSTEMS-THE INTERNATIONAL JOURNAL OF ESCIENCE 130(2022):253-268.