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dc.contributor.authorWang, Zhuowei
dc.contributor.authorXiong, Naixue
dc.contributor.authorWang, Hao
dc.contributor.authorCheng, Lianglun
dc.contributor.authorZhao, Wuqing
dc.date.accessioned2019-04-02T11:09:10Z
dc.date.available2019-04-02T11:09:10Z
dc.date.created2018-12-19T14:39:36Z
dc.date.issued2018
dc.identifier.citationCluster Computing. 2018, 1-17.nb_NO
dc.identifier.issn1386-7857
dc.identifier.urihttp://hdl.handle.net/11250/2592905
dc.description.abstractPower consumption reduction is the primary problem for the design and implementation of heterogeneous parallel systems. As it is difficult to make progress in the low-power optimization in the hardware layer to meet the increasing need for power optimization, more attention has been paid to low-power optimization in the hardware layer. The relationship between the execution time and dynamic power consumption of programs divided between homogeneous and heterogeneous computing sections is analysed. In addition, the communication power consumption for data transmission and dynamic multi-task allocation are described. Afterwards, this study establishes a power model for the whole procedure of heterogeneous parallel systems. By using this model, a selection algorithm is designed for the optimal frequency of processors with optimal power consumption under time constraints, optimal descent-based time allocation algorithms in multiple computing sections, and profiling dynamic analysis-based integral linear programming at algorithm-level, separately. Finally, the validity of the power optimization algorithm is ascertained using typical applications.nb_NO
dc.language.isoengnb_NO
dc.publisherSpringernb_NO
dc.subjectWhole procedure, Heterogeneous parallel systems, Algorithm-level, Low-power optimizationnb_NO
dc.titleWhole procedure heterogeneous multiprocessors low-power optimization at algorithm-levelnb_NO
dc.typeJournal articlenb_NO
dc.typePeer reviewednb_NO
dc.description.versionsubmittedVersionnb_NO
dc.source.pagenumber1-17nb_NO
dc.source.journalCluster Computingnb_NO
dc.identifier.doi10.1007/s10586-018-1920-x
dc.identifier.cristin1645736
dc.description.localcodehis is a pre-print of an article published in Cluster Comput (2018). The final authenticated version is available online at: https://doi.org/10.1007/s10586-018-1920-xnb_NO
cristin.unitcode194,63,55,0
cristin.unitnameInstitutt for IKT og realfag
cristin.ispublishedtrue
cristin.fulltextpreprint
cristin.qualitycode1


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