Task Scheduling for Energy Consumption Constrained Parallel Applications on Heterogeneous Computing Systems

2020 ◽  
Vol 31 (5) ◽  
pp. 1165-1182 ◽  
Author(s):  
Zhe Quan ◽  
Zhi-Jie Wang ◽  
Ting Ye ◽  
Song Guo
Keyword(s):  
Energy Consumption ◽  
Task Scheduling ◽  
Heterogeneous Computing ◽  
Parallel Applications ◽  
Computing Systems ◽  
Heterogeneous Computing Systems

Minimizing Energy of Heterogeneous Computing Systems by Task Scheduling Approach,

2020 ◽  
Vol 29 (12) ◽  
pp. 2050194
Author(s):  
Junke Li ◽  
Junwei Li ◽  
Mingjiang Li ◽  
Guanyu Wang ◽  
Jincheng Zhou ◽  
...  
Keyword(s):  
Energy Consumption ◽  
Task Scheduling ◽  
Knapsack Problem ◽  
Heterogeneous Computing ◽  
Environmental Problem ◽  
Time Slot ◽  
Performance Information ◽  
Computing Systems ◽  
Single Task ◽  
Heterogeneous Computing Systems

As an important component of computer system, GPU has been used more widely in the system under the support of general computing. In addition to focusing on its performance, the issues of its energy consumption and environmental problem have gradually attracted the concerns of researchers, computer architects, and developers. Current researches only consider single-task scheduling for saving energy, lacking the focus on energy saving from scheduling the overall tasks. In view of the shortcomings of current researches, we propose a METS (Minimizing Execution Time Slot) approach to reduce energy by rationally allocating the tasks across GPUs. It first collects the number of tasks and the corresponding estimated performance information. Next, it decides whether to turn the problem into a 0–1 knapsack problem or to use FIFO method based on the number of tasks. Then, we conduct our experiment on typical platform to verify our proposed approach. The experimental results show that METS can save on average 8.43% of energy when compared with the existing approaches. This shows that the proposed METS method is effective, reasonable and feasible.

Task Scheduling in Heterogeneous Computing Systems Based on Machine Learning Approach

2020 ◽  
Vol 34 (12) ◽  
pp. 2051012
Author(s):  
Hui Xie ◽  
Li Wei ◽  
Dong Liu ◽  
Luda Wang
Keyword(s):  
Task Scheduling ◽  
Heterogeneous Computing ◽  
Scheduling Algorithm ◽  
Computing System ◽  
Scheduling Problem ◽  
Computing Systems ◽  
Task Scheduling Algorithm ◽  
Machine Learning Approach ◽  
Heterogeneous Computing Systems ◽  
Regression Theory

Task scheduling problem of heterogeneous computing system (HCS), which with increasing popularity, nowadays has become a research hotspot in this domain. The task scheduling problem of HCS, which can be described essentially as assigning tasks to the proper processor for executing, has been shown to be NP-complete. However, the existing scheduling algorithm suffers from an inherent limitation of lacking global view. Here, we reported a novel task scheduling algorithm based on Multi-Logistic Regression theory (called MLRS) in heterogeneous computing environment. First, we collected the best scheduling plans as the historical training set, and then a scheduling model was established by which we could predict the following schedule action. Through the analysis of experimental results, it is interpreted that the proposed algorithm has better optimization effect and robustness.

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