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dc.contributor.authorWang, Zhuowei
dc.contributor.authorCheng, Lianglun
dc.contributor.authorWang, Hao
dc.contributor.authorZhao, Wuqing
dc.contributor.authorSong, Xiaoyu
dc.date.accessioned2019-10-11T08:30:16Z
dc.date.available2019-10-11T08:30:16Z
dc.date.created2019-10-10T12:21:06Z
dc.date.issued2019
dc.identifier.citationIEEE transactions on industrial electronics (1982. Print). 2019, .nb_NO
dc.identifier.issn0278-0046
dc.identifier.urihttp://hdl.handle.net/11250/2621503
dc.description.abstractEnergy saving and optimization play an increasingly important role in industrial electronic systems. A heterogeneous embedded system is composed of a general-purpose central processing unit (CPU) with an enhanced module of graphics processing units (GPU). This paper explores the effective strategies of task granularity and software prefetching for energy optimization. We propose a novel energy optimization model for GPU-based embedded systems by harnessing a communication-based pipeline spatial and temporal relation. We analyze the characteristics of a multiple thread execution of parallel GPUs. We present an effective algorithm for the dynamic power optimization with the adaptively adjusted distance of software prefetching. The experimental results show that the dynamic energy consumption can be saved by 22.1% and 21.8% respectively under two prefetching strategies (register and shared memory) without loss of performance. We demonstrate the effectiveness of the proposed methods for energy saving and consumption reduction of performance driven computing in industrial scenarios.nb_NO
dc.language.isoengnb_NO
dc.publisherInstitute of Electrical and Electronics Engineers (IEEE)nb_NO
dc.titleEnergy Optimization by Software Prefetching for Task Granularity in GPU-based Embedded Systemsnb_NO
dc.typeJournal articlenb_NO
dc.typePeer reviewednb_NO
dc.description.versionacceptedVersionnb_NO
dc.source.pagenumber11nb_NO
dc.source.journalIEEE transactions on industrial electronics (1982. Print)nb_NO
dc.identifier.doi10.1109/TIE.2019.2945308
dc.identifier.cristin1735936
dc.description.localcode© 2019 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.nb_NO
cristin.unitcode194,63,10,0
cristin.unitnameInstitutt for datateknologi og informatikk
cristin.ispublishedfalse
cristin.fulltextpostprint
cristin.qualitycode2


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