CHESS: Joint Energy and Makespan Optimization for Dynamic CNN Task Scheduling on the Heterogeneous System

Zimo Ma; Yifeng Li; Di Liu; Ziheng Zhang; Kuangyu Zheng

doi:10.1145/3587716.3587744

CHESS: Joint Energy and Makespan Optimization for Dynamic CNN Task Scheduling on the Heterogeneous System

Zimo Ma
, Yifeng Li
, Di Liu
, Ziheng Zhang
, Kuangyu Zheng^*

^*此作品的通讯作者

电子信息工程学院

Beihang University

科研成果: 书/报告/会议事项章节 › 会议稿件 › 同行评审

摘要

In this paper, we investigate both the energy consumption and running time of different CNN tasks on GPUs or CPUs, and analyze their characterization for different CNN models under different application and system configuration factors. We find that this joint energy consumption and makespan optimization problem can be formulated as an integer linear programming problem. Then we propose CHESS (CNN-task Heterogeneous Efficient Scheduling System) with a two-stage heuristic scheduling algorithm, to better allocate computing resources for the upcoming tasks, and to schedule them dynamically on the heterogeneous cluster. Experiments show that our CHESS can save up to 15.9% energy and decrease up to 32.7% makespan over existing approaches.

源语言	英语
主期刊名	ICMLC 2023 - Proceedings of the 2023 15th International Conference on Machine Learning and Computing
出版商	Association for Computing Machinery
页	172-176
页数	5
ISBN（电子版）	9781450398411
DOI	https://doi.org/10.1145/3587716.3587744
出版状态	已出版 - 17 2月 2023
活动	15th International Conference on Machine Learning and Computing, ICMLC 2023 - Hybrid, Zhuhai, 中国期限: 17 2月 2023 → 20 2月 2023

出版系列

姓名	ACM International Conference Proceeding Series

会议

会议	15th International Conference on Machine Learning and Computing, ICMLC 2023
国家/地区	中国
市	Hybrid, Zhuhai
时期	17/02/23 → 20/02/23

联合国可持续发展目标

此成果有助于实现下列可持续发展目标：

可持续发展目标 7 经济适用的清洁能源

访问文件

10.1145/3587716.3587744

其它文件与链接

链接到 Scopus 的出版物

引用此

Ma, Z., Li, Y., Liu, D., Zhang, Z., & Zheng, K. (2023). CHESS: Joint Energy and Makespan Optimization for Dynamic CNN Task Scheduling on the Heterogeneous System. 在 ICMLC 2023 - Proceedings of the 2023 15th International Conference on Machine Learning and Computing (页码 172-176). (ACM International Conference Proceeding Series). Association for Computing Machinery . https://doi.org/10.1145/3587716.3587744

@inproceedings{3e0e9c57336e4cd4bf784a186e14fe81,

title = "CHESS: Joint Energy and Makespan Optimization for Dynamic CNN Task Scheduling on the Heterogeneous System",

abstract = "In this paper, we investigate both the energy consumption and running time of different CNN tasks on GPUs or CPUs, and analyze their characterization for different CNN models under different application and system configuration factors. We find that this joint energy consumption and makespan optimization problem can be formulated as an integer linear programming problem. Then we propose CHESS (CNN-task Heterogeneous Efficient Scheduling System) with a two-stage heuristic scheduling algorithm, to better allocate computing resources for the upcoming tasks, and to schedule them dynamically on the heterogeneous cluster. Experiments show that our CHESS can save up to 15.9\% energy and decrease up to 32.7\% makespan over existing approaches.",

keywords = "CNN, characteristic analysis, dynamic task scheduling, efficient computing, heterogeneous system",

author = "Zimo Ma and Yifeng Li and Di Liu and Ziheng Zhang and Kuangyu Zheng",

note = "Publisher Copyright: {\textcopyright} 2023 ACM.; 15th International Conference on Machine Learning and Computing, ICMLC 2023 ; Conference date: 17-02-2023 Through 20-02-2023",

year = "2023",

month = feb,

day = "17",

doi = "10.1145/3587716.3587744",

language = "英语",

series = "ACM International Conference Proceeding Series",

publisher = "Association for Computing Machinery ",

pages = "172--176",

booktitle = "ICMLC 2023 - Proceedings of the 2023 15th International Conference on Machine Learning and Computing",

address = "美国",

}

Ma, Z, Li, Y, Liu, D, Zhang, Z & Zheng, K 2023, CHESS: Joint Energy and Makespan Optimization for Dynamic CNN Task Scheduling on the Heterogeneous System. 在 ICMLC 2023 - Proceedings of the 2023 15th International Conference on Machine Learning and Computing. ACM International Conference Proceeding Series, Association for Computing Machinery , 页码 172-176, 15th International Conference on Machine Learning and Computing, ICMLC 2023, Hybrid, Zhuhai, 中国, 17/02/23. https://doi.org/10.1145/3587716.3587744

CHESS: Joint Energy and Makespan Optimization for Dynamic CNN Task Scheduling on the Heterogeneous System. / Ma, Zimo; Li, Yifeng; Liu, Di 等.
ICMLC 2023 - Proceedings of the 2023 15th International Conference on Machine Learning and Computing. Association for Computing Machinery , 2023. 页码 172-176 (ACM International Conference Proceeding Series).

科研成果: 书/报告/会议事项章节 › 会议稿件 › 同行评审

TY - GEN

T1 - CHESS

T2 - 15th International Conference on Machine Learning and Computing, ICMLC 2023

AU - Ma, Zimo

AU - Li, Yifeng

AU - Liu, Di

AU - Zhang, Ziheng

AU - Zheng, Kuangyu

PY - 2023/2/17

Y1 - 2023/2/17

N2 - In this paper, we investigate both the energy consumption and running time of different CNN tasks on GPUs or CPUs, and analyze their characterization for different CNN models under different application and system configuration factors. We find that this joint energy consumption and makespan optimization problem can be formulated as an integer linear programming problem. Then we propose CHESS (CNN-task Heterogeneous Efficient Scheduling System) with a two-stage heuristic scheduling algorithm, to better allocate computing resources for the upcoming tasks, and to schedule them dynamically on the heterogeneous cluster. Experiments show that our CHESS can save up to 15.9% energy and decrease up to 32.7% makespan over existing approaches.

AB - In this paper, we investigate both the energy consumption and running time of different CNN tasks on GPUs or CPUs, and analyze their characterization for different CNN models under different application and system configuration factors. We find that this joint energy consumption and makespan optimization problem can be formulated as an integer linear programming problem. Then we propose CHESS (CNN-task Heterogeneous Efficient Scheduling System) with a two-stage heuristic scheduling algorithm, to better allocate computing resources for the upcoming tasks, and to schedule them dynamically on the heterogeneous cluster. Experiments show that our CHESS can save up to 15.9% energy and decrease up to 32.7% makespan over existing approaches.

KW - CNN

KW - characteristic analysis

KW - dynamic task scheduling

KW - efficient computing

KW - heterogeneous system

UR - https://www.scopus.com/pages/publications/85173860840

U2 - 10.1145/3587716.3587744

DO - 10.1145/3587716.3587744

M3 - 会议稿件

AN - SCOPUS:85173860840

T3 - ACM International Conference Proceeding Series

SP - 172

EP - 176

BT - ICMLC 2023 - Proceedings of the 2023 15th International Conference on Machine Learning and Computing

PB - Association for Computing Machinery

Y2 - 17 February 2023 through 20 February 2023

ER -

Ma Z, Li Y, Liu D, Zhang Z, Zheng K. CHESS: Joint Energy and Makespan Optimization for Dynamic CNN Task Scheduling on the Heterogeneous System. 在 ICMLC 2023 - Proceedings of the 2023 15th International Conference on Machine Learning and Computing. Association for Computing Machinery . 2023. 页码 172-176. (ACM International Conference Proceeding Series). doi: 10.1145/3587716.3587744

CHESS: Joint Energy and Makespan Optimization for Dynamic CNN Task Scheduling on the Heterogeneous System

摘要

出版系列

会议

联合国可持续发展目标

访问文件

其它文件与链接

指纹

引用此