Open Access

ARTICLE

Resource Scheduling Strategy for Performance Optimization Based on Heterogeneous CPU-GPU Platform

Juan Fang^1,*, Kuan Zhou¹, Mengyuan Zhang¹, Wei Xiang^2,3

1 Faculty of Information Technology, Beijing University of Technology, Beijing, 100124, China
2 La Trobe University, Melbourne, VIC, 3086, Australia
3 James Cook University, Cains, QLD, 4878, Australia

* Corresponding Author: Juan Fang. Email:

Computers, Materials & Continua 2022, 73(1), 1621-1635. https://doi.org/10.32604/cmc.2022.027147

Received 11 January 2022; Accepted 14 April 2022; Issue published 18 May 2022

Abstract

In recent years, with the development of processor architecture, heterogeneous processors including Center processing unit (CPU) and Graphics processing unit (GPU) have become the mainstream. However, due to the differences of heterogeneous core, the heterogeneous system is now facing many problems that need to be solved. In order to solve these problems, this paper try to focus on the utilization and efficiency of heterogeneous core and design some reasonable resource scheduling strategies. To improve the performance of the system, this paper proposes a combination strategy for a single task and a multi-task scheduling strategy for multiple tasks. The combination strategy consists of two sub-strategies, the first strategy improves the execution efficiency of tasks on the GPU by changing the thread organization structure. The second focuses on the working state of the efficient core and develops more reasonable workload balancing schemes to improve resource utilization of heterogeneous systems. The multi-task scheduling strategy obtains the execution efficiency of heterogeneous cores and global task information through the processing of task samples. Based on this information, an improved ant colony algorithm is used to quickly obtain a reasonable task allocation scheme, which fully utilizes the characteristics of heterogeneous cores. The experimental results show that the combination strategy reduces task execution time by 29.13% on average. In the case of processing multiple tasks, the multi-task scheduling strategy reduces the execution time by up to 23.38% based on the combined strategy. Both strategies can make better use of the resources of heterogeneous systems and significantly reduce the execution time of tasks on heterogeneous systems.

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Keywords

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