Low Power Scheduling Approach for Heterogeneous System Based on Heuristic and Greedy Method

Junke Li; Bing Guo; Kai Liu; Jincheng Zhou

doi:10.1155/2022/9598933

Low Power Scheduling Approach for Heterogeneous System Based on Heuristic and Greedy Method

Comput Intell Neurosci. 2022 Jun 26:2022:9598933. doi: 10.1155/2022/9598933. eCollection 2022.

Authors

Junke Li^{1

2

3}, Bing Guo⁴, Kai Liu^{1

2

5}, Jincheng Zhou^{2

6}

Affiliations

¹ School of Information Engineering, Suqian University, Suqian, Jiangsu 223800, China.
² School of Computer and Information, Qiannan Normal University for Nationalities, Duyun, Guizhou 558000, China.
³ Jiangsu Province Engineering Research Center of Smart Poultry Farming and Intelligent Equipment, Suqian, Jiangsu 223800, China.
⁴ College of Computer Science, Sichuan University, Chengdu, Sichuan 610065, China.
⁵ Key Laboratory of Machine Learning and Unstructured Data Processing of Qiannan, Duyun 558000, China.
⁶ Key Laboratory of Complex Systems and Intelligent Optimization of Guizhou, Duyun 558000, China.

Abstract

Big data, cloud computing, and artificial intelligence technologies supported by heterogeneous systems are constantly changing our life and cognition of the world. At the same time, its energy consumption affects the operation cost and system reliability, and this attracts the attention of architecture designers and researchers. In order to solve the problem of energy in heterogeneous system environment, inspired by the results of 0-1 programming, a scheduling method of heuristic and greedy energy saving (HGES) approach is proposed to allocate tasks reasonably to achieve the purpose of energy saving. Firstly, all tasks are assigned to each GPU in the system, and then the tasks are divided into high-value tasks and low-value tasks by the calculated average time value and variance value of all tasks. By using the greedy method, the high-value tasks are assigned first, and then the low-value tasks are allocated. In order to verify the effectiveness and rationality of HGES, different tasks with different inputs and different comparison methods are designed and tested. The experimental results on different platforms show that the HGES has better energy saving than that of existing method and can get result faster than that of the 0-1 programming.