Yang, P, Dong, F, Codreanu, V, Williams, D, Roerdink, J and Liu, B (2017) Improving Utility of GPU in Accelerating Industrial Applications with User-centred Automatic Code Translation. IEEE Transactions on Industrial Informatics, 14 (4). pp. 1347-1360. ISSN 1941-0050

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Abstract

SMEs (Small and medium-sized enterprises), particularly those whose business is focused on developing innovative produces, are limited by a major bottleneck on the speed of computation in many applications. The recent developments in GPUs have been the marked increase in their versatility in many computational areas. But due to the lack of specialist GPU (Graphics processing units) programming skills, the explosion of GPU power has not been fully utilized in general SME applications by inexperienced users. Also, existing automatic CPU-to-GPU code translators are mainly designed for research purposes with poor user interface design and hard-to-use. Little attentions have been paid to the applicability, usability and learnability of these tools for normal users. In this paper, we present an online automated CPU-to-GPU source translation system, (GPSME) for inexperienced users to utilize GPU capability in accelerating general SME applications. This system designs and implements a directive programming model with new kernel generation scheme and memory management hierarchy to optimize its performance. A web-service based interface is designed for inexperienced users to easily and flexibly invoke the automatic resource translator. Our experiments with non-expert GPU users in 4 SMEs reflect that GPSME system can efficiently accelerate real-world applications with at least 4x and have a better applicability, usability and learnability than existing automatic CPU-to-GPU source translators.

Item Type:	Article
Additional Information:	(c) 2017 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other users, 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 components of this work in other works.
Uncontrolled Keywords:	08 Information And Computing Sciences, 09 Engineering, 10 Technology
Subjects:	H Social Sciences > HF Commerce > HF5001 Business Q Science > QA Mathematics > QA75 Electronic computers. Computer science
Divisions:	Computer Science & Mathematics
Publisher:	Institute of Electrical and Electronics Engineers
Date Deposited:	26 Jun 2017 08:41
Last Modified:	04 Sep 2021 11:24
DOI or ID number:	10.1109/TII.2017.2731362
URI:	https://researchonline.ljmu.ac.uk/id/eprint/6736

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