User QoS-aware deep learning task dynamic scheduling on GPU clusters

Computer Engineering & Science ›› 2021, Vol. 43 ›› Issue (08): 1331-1340.

Previous Articles Next Articles

User QoS-aware deep learning task dynamic scheduling on GPU clusters

LUO Lei,CHEN Zhao-yun,WANG Li-xuan

（College of Computer Science and Technology,National University of Defense Technology,Changsha 410073,China）

Received:2020-06-09 Revised:2020-09-23 Accepted:2021-08-25 Online:2021-08-25 Published:2021-08-24

Abstract

Abstract:

A QoS (Quality of Service)-aware deep learning task dynamic scheduling method on GPU clusters is proposed. The offline evaluation module is used to perform offline evaluation of deep learning tasks and build a computational performance prediction model. Based on the performance prediction model, combined with the expected QoS of the task, the online scheduling module carries out the scheduling of task placement and task execution sequence. Experiments on a distributed GPU cluster demonstrate that the proposed method can achieve higher QoS-guarantee percentage and cluster resource utilization than other baseline schedulers.

Key words: deep learning, GPU cluster, task scheduling；QoS

LUO Lei, CHEN Zhao-yun, WANG Li-xuan. User QoS-aware deep learning task dynamic scheduling on GPU clusters[J]. Computer Engineering & Science, 2021, 43(08): 1331-1340.

[1]	CHEN Xinran, LIU Ning, YAN Zhongmin, LIU Lei, CUI Lizhen. An attention-guided dual-granularity cross-modal medical representation learning framework [J]. Computer Engineering & Science, 2025, 47(01): 150-159.
[2]	LUO Jing, YE Zhi-sheng, YANG Ze-hua, FU Tian-hao, WEI Xiong, WANG Xiao-lin, LUO Ying-wei, . Constructing and analyzing deep learning task dataset for R&D GPU clusters [J]. Computer Engineering & Science, 2024, 46(12): 2128-2137.
[3]	JING Chao, BI Yu-shen. OASIS: An interference-aware online scheduling algorithm for deep learning jobs [J]. Computer Engineering & Science, 2024, 46(12): 2138-2148.
[4]	CHEN Lei, LIANG Zheng-you, SUN Yu, CAI Jun-min. Mobile monocular depth estimation based on multi-scale feature fusion [J]. Computer Engineering & Science, 2024, 46(09): 1616-1524.
[5]	LIU Qiang, LI Mu-chun, WU Xiao-jie, WANG Yu-heng. S-JSMA: A fast JSMA adversarial example generation method with low disturbance redundancy [J]. Computer Engineering & Science, 2024, 46(08): 1395-1402.
[6]	HU Zhao-hua, WANG Chang-fu, . A small object detection algorithm of remote sensing image based on improved Faster R-CNN [J]. Computer Engineering & Science, 2024, 46(06): 1063-1071.
[7]	YI Xiao, MA Sheng, XIAO Nong. Running optimization of deep learning accelerators under different pruning strategies [J]. Computer Engineering & Science, 2023, 45(07): 1141-1148.
[8]	KANG Yu-han, SHI Yang, CHEN Zhao-yun, WEN Mei. A deep learning programming framework for FT-Matrix DSP+MatrixZone heterogeneous systems [J]. Computer Engineering & Science, 2023, 45(07): 1149-1158.
[9]	LIU Hao-han, SUN Cheng, HE Huai-qing, HUI Kang-hua. Metal surface defect detection based on improved YOLOv3 [J]. Computer Engineering & Science, 2023, 45(07): 1226-1235.
[10]	CHE Sheng-bing, ZHANG Guang-lin . Webshell detection based on deep learning [J]. Computer Engineering & Science, 2022, 44(06): 994-1002.
[11]	WANG Dong, YANG Ke, XUAN Jia-xing, HAN Yu-tong, ZHAO Li-hua, WANG Xu-ren. Realization of malicious code family classification based on semi-supervised generative adversarial network [J]. Computer Engineering & Science, 2022, 44(05): 826-833.
[12]	LI Li-rong, WANG Zi-yan, ZHANG Kai, YANG Di-chun, XIONG Wei, GONG Peng-cheng, . A train bottom parts detection algorithm based on OSE-dResnet neural networks [J]. Computer Engineering & Science, 2022, 44(04): 692-698.
[13]	LI Meng-yao, ZHOU Ya-tong, WEI Chuang, LI Min. Research on human joint point positioning algorithm under the scene of self-service shelves [J]. Computer Engineering & Science, 2021, 43(03): 494-502.
[14]	XU Lei，XU Ying，JIANG Rong-lin，ZHANG Dan-dan. Implementation and optimization of three-dimensional UPML-FDTD algorithm on GPU cluster [J]. J4, 2013, 35(11): 160-167.

User QoS-aware deep learning task dynamic scheduling on GPU clusters

PDF

Knowledge

Abstract

Cite this article

share this article

Related Articles 14

Recommended Articles 0

Metrics

Comments