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Title: Gbooster : towards acceleration of GPU-intensive mobile applications
Authors: Wen, E
Seah, WKG
Ng, B
Liu, X
Cao, J 
Liu, X 
Issue Date: 2017
Source: 2017 IEEE 37th International Conference on Distributed Computing Systems (ICDCS), 5-8 June 2017, Atlanta, Georgia, p. 1408-1418
Abstract: The performance of GPUs on mobile devices is generally the bottleneck of multimedia mobile applications (e.g., 3D games and virtual reality). Previous attempts to tackle the issue mainly migrate GPU computation to servers residing in remote cloud centers. However, the costly network delay is especially undesirable for highly-interactive multimedia applications since a fast response time is critical for user experience. In this paper, we propose GBooster, a system that accelerates multimedia mobile applications by transparently offloading GPU tasks onto neighboring multimedia devices such as Smart TVs and Gaming Consoles. Specifically, GBooster intercepts and redirects system graphics calls by utilizing the Dynamic Linker Hooking technique, which requires no modification of the applications and the mobile systems. In addition, a major concern for offloading is the high energy consumption incurred by network transmissions. To address this concern, GBooster is designed to intelligently switch between the low-power Bluetooth and the high-throughput WiFi based on the traffic demand. We implement GBooster on the Android system and evaluate its performance. The results demonstrate that it can boost applications' frame rates by up to 85%. In terms of power consumption, GBooster can preserve up to 70% energy compared with local execution.
Publisher: Institute of Electrical and Electronics Engineers
ISBN: 978-1-5386-1792-2 (Electronic)
978-1-5386-1793-9 (Print on Demand(PoD))
DOI: 10.1109/ICDCS.2017.73
Rights: © 2017 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.
The following publication E. Wen, W. K. G. Seah, B. Ng, X. Liu, J. Cao and X. Liu, "GBooster: Towards Acceleration of GPU-Intensive Mobile Applications," 2017 IEEE 37th International Conference on Distributed Computing Systems (ICDCS), Atlanta, GA, USA, 2017, pp. 1408-1418 is available at https://doi.org/10.1109/ICDCS.2017.73.
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