CLC number: TP314
On-line Access: 2024-08-27
Received: 2023-10-17
Revision Accepted: 2024-05-08
Crosschecked: 2015-06-05
Cited: 2
Clicked: 7113
Yong-xing Liu, Ken-li Li, Zhuo Tang, Ke-qin Li. Energy-aware scheduling with reconstruction and frequency equalization on heterogeneous systems[J]. Frontiers of Information Technology & Electronic Engineering, 2015, 16(7): 519-531.
@article{title="Energy-aware scheduling with reconstruction and frequency equalization on heterogeneous systems",
author="Yong-xing Liu, Ken-li Li, Zhuo Tang, Ke-qin Li",
journal="Frontiers of Information Technology & Electronic Engineering",
volume="16",
number="7",
pages="519-531",
year="2015",
publisher="Zhejiang University Press & Springer",
doi="10.1631/FITEE.1400399"
}
%0 Journal Article
%T Energy-aware scheduling with reconstruction and frequency equalization on heterogeneous systems
%A Yong-xing Liu
%A Ken-li Li
%A Zhuo Tang
%A Ke-qin Li
%J Frontiers of Information Technology & Electronic Engineering
%V 16
%N 7
%P 519-531
%@ 2095-9184
%D 2015
%I Zhejiang University Press & Springer
%DOI 10.1631/FITEE.1400399
TY - JOUR
T1 - Energy-aware scheduling with reconstruction and frequency equalization on heterogeneous systems
A1 - Yong-xing Liu
A1 - Ken-li Li
A1 - Zhuo Tang
A1 - Ke-qin Li
J0 - Frontiers of Information Technology & Electronic Engineering
VL - 16
IS - 7
SP - 519
EP - 531
%@ 2095-9184
Y1 - 2015
PB - Zhejiang University Press & Springer
ER -
DOI - 10.1631/FITEE.1400399
Abstract: With the increasing energy consumption of computing systems and the growing advocacy for green computing, energy efficiency has become one of the critical challenges in high-performance heterogeneous computing systems. Energy consumption can be reduced by not only hardware design but also software design. In this paper, we propose an energy-aware scheduling algorithm with equalized frequency, called EASEF, for parallel applications on heterogeneous computing systems. The EASEF approach aims to minimize the finish time and overall energy consumption. First, EASEF extracts the set of paths from an application. Then, it reconstructs the application based on the extracted set of paths to achieve a reasonable schedule. Finally, it adopts a progressive way to equalize the frequency of tasks to reduce the total energy consumption of systems. Randomly generated applications and two real-world applications are examined in our experiments. Experimental results show that the EASEF algorithm outperforms two existing algorithms in terms of makespan and energy consumption.
This paper proposes a scheduling method for the heterogeneous computing system to reduce the energy consumption, as called Heterogeneous Energy-Aware Scheduling (HEAS) algorithm, which consists of three stages: 1) generating the critical paths, 2) reconstructing the directed acyclic graph (DAG) and calculating task priority, and 3) scheduling the task with energy-awareness. Overall, the paper is well written and clearly organized.
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