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Energy Efficient Job Scheduling with DVFS for CPU-GPU Heterogeneous Systems

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Energy Efficient Job Scheduling with DVFS for CPU-GPU Heterogeneous Systems

Content Provider	ACM Digital Library
Author	Chau, Vincent Chu, Xiaowen Liu, Hai Leung, Yiu-Wing
Abstract	The past few years have witnessed significant growth in the computational capabilities of GPUs. The race for computing performance makes the uses of many-core accelerators more necessary. However, GPUs consume a significant amount of energy as compared with CPUs. One way to reduce the energy consumption is to scale the speed and/or voltage of the processor. Typically, the faster the processor runs, the faster we finish jobs, but the more power is required by the processor. It is hence important to balance between performance and power consumption. In this paper, we consider the following scheduling problem. We have a set of jobs to be assigned to different processors. Each job may have different characteristics depending on the type of processor that it is assigned to. The goal is to minimize the total energy consumption. After proving the NP-hardness of this problem, we propose a constant approximation algorithm for the case when processors can scale to any continuous speed. When processors have a set of discrete speeds, we propose a heuristic algorithm and compare with some classical scheduling algorithms experimentally. Then, we extend this heuristic to the online case where jobs arrive over time. Our simulation results show that the proposed heuristic algorithms are effective and can achieve near-optimal performance.
Starting Page	1
Ending Page	11
Page Count	11
File Format	PDF
ISBN	9781450350365
DOI	10.1145/3077839.3077855
Language	English
Publisher	Association for Computing Machinery (ACM)
Publisher Date	2017-05-16
Publisher Place	New York
Access Restriction	Subscribed
Subject Keyword	Dynamic voltage frequency scaling Scheduling Energy efficient
Content Type	Text
Resource Type	Article

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