Show simple item record

dc.contributor.authorYang, Po
dc.contributor.authorDong, Feng
dc.contributor.authorCodreanu, Valeriu
dc.contributor.authorWilliams, David
dc.contributor.authorRoerdink, Jos B.T.M.
dc.contributor.authorLiu, Baoquan
dc.contributor.authorAnvari-Moghaddam, Amjad
dc.contributor.authorMin, Geyong
dc.date.accessioned2020-11-17T11:18:10Z
dc.date.available2020-11-17T00:00:00Z
dc.date.available2020-11-17T11:18:10Z
dc.date.issued2017-07-24
dc.identifier.citationYang P, Dong F, Codreanu V, Williams D, Roerdink J, Liu B, Anvari-Moghaddam A, Min G (2018) 'Improving utility of GPU in accelerating industrial applications with user-centered automatic code translation', IEEE Transactions on Industrial Informatics, 14 (4), pp.1347-1360.en_US
dc.identifier.issn1551-3203
dc.identifier.doi10.1109/TII.2017.2731362
dc.identifier.urihttp://hdl.handle.net/10547/624622
dc.description.abstractSmall to medium enterprises (SMEs), particularly those whose business is focused on developing innovative produces, are limited by a major bottleneck in 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 GPUprogramming skills, the explosion of GPU power has not been fully utilized in general SME applications by inexperienced users. Also, the existing automatic CPU-to-GPU code translators are mainly designed for research purposes with poor user interface design and are 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 the GPU capability in accelerating general SME applications. This system designs and implements a directive programming model with a new kernel generation scheme and memory management hierarchy to optimize its performance. A web service interface is designed for inexperienced users to easily and flexibly invoke the automatic resource translator. Our experiments with nonexpert GPU users in four SMEs reflect that a GPSME system can efficiently accelerate real-world applications with at least 4× and have a better applicability, usability, and learnability than the existing automatic CPU-to-GPU source translators.en_US
dc.language.isoenen_US
dc.publisherIEEE Computer Societyen_US
dc.relation.urlhttps://ieeexplore.ieee.org/document/7990251en_US
dc.rightsGreen - can archive pre-print and post-print or publisher's version/PDF
dc.rightsAttribution-NonCommercial-NoDerivatives 4.0 International*
dc.rights.urihttp://creativecommons.org/licenses/by-nc-nd/4.0/*
dc.subjectautomatic translationen_US
dc.subjectgraphics processing unit (GPU)en_US
dc.subjectusabilityen_US
dc.subjectSubject Categories::G400 Computer Scienceen_US
dc.titleImproving utility of GPU in accelerating industrial applications with user-centered automatic code translationen_US
dc.typeArticleen_US
dc.contributor.departmentUniversity of Bedfordshireen_US
dc.contributor.departmentSURFsaraen_US
dc.contributor.departmentUniversity of Groningenen_US
dc.contributor.departmentAalborg Universityen_US
dc.contributor.departmentUniversity of Exeteren_US
dc.identifier.journalIEEE Transactions on Industrial Informaticsen_US
dc.date.updated2020-11-17T10:37:15Z
dc.description.note


Files in this item

Thumbnail
Name:
GPSME_TII_R2.pdf
Size:
832.2Kb
Format:
PDF

This item appears in the following Collection(s)

Show simple item record

Green - can archive pre-print and post-print or publisher's version/PDF
Except where otherwise noted, this item's license is described as Green - can archive pre-print and post-print or publisher's version/PDF