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dc.contributor.authorArcucci, Rossella
dc.contributor.authorBasciano, Davide
dc.contributor.authorCilardo, Alessandro
dc.contributor.authorD'Amore, Luisa
dc.contributor.authorMantovani, Filippo
dc.contributor.otherBarcelona Supercomputing Center
dc.date.accessioned2018-04-12T14:25:30Z
dc.date.available2018-04-12T14:25:30Z
dc.date.issued2018-03-23
dc.identifier.citationArcucci, R. [et al.]. Energy Analysis of a 4D Variational Data Assimilation Algorithm and Evaluation on ARM-Based HPC Systems. A: International Conference on Parallel Processing and Applied Mathematics. "PPAM 2017: Parallel Processing and Applied Mathematics". Springer, 2018, p. 37-47.
dc.identifier.isbn978-3-319-78053-5
dc.identifier.urihttp://hdl.handle.net/2117/116207
dc.description.abstractDriven by the emerging requirements of High Performance Computing (HPC) architectures, the main focus of this work is the interplay of computational and energetic aspects of a Four Dimensional Variational (4DVAR) Data Assimilation algorithm, based on Domain Decomposition (named DD-4DVAR). We report first results on the energy consumption of the DD-4DVAR algorithm on embedded processor and a mathematical analysis of the energy behavior of the algorithm by assuming the architectures characteristics as variable of the model. The main objective is to capture the essential operations of the algorithm exhibiting a direct relationship with the measured energy. The experimental evaluation is carried out on a set of mini-clusters made available by the Barcelona Supercomputing Center.
dc.description.sponsorshipThe research has received funding from European Commission under H2020-MSCA-RISE NASDAC project (grant agreement no. 691184) FP7 Mont-Blanc and Mont-Blanc 2 (grant agreements no. 288777 and 610402), H2020-FET Mont-Blanc 3 (grant agreement 671697).
dc.format.extent11 p.
dc.language.isoeng
dc.publisherSpringer
dc.subjectÀrees temàtiques de la UPC::Informàtica
dc.subject.lcshHigh performance computing
dc.subject.otherData assimilation
dc.subject.other4DVar
dc.subject.otherDecomposition
dc.subject.otherEmbedded processor architectures
dc.subject.otherEnergy consumption
dc.titleEnergy Analysis of a 4D Variational Data Assimilation Algorithm and Evaluation on ARM-Based HPC Systems
dc.typeConference lecture
dc.subject.lemacSupercomputadors
dc.identifier.doi10.1007/978-3-319-78054-2_4
dc.description.peerreviewedPeer Reviewed
dc.relation.publisherversionhttps://link.springer.com/chapter/10.1007/978-3-319-78054-2_4
dc.rights.accessOpen Access
dc.description.versionPostprint (author's final draft)
dc.relation.projectidinfo:eu-repo/grantAgreement/EC/H2020/691184
dc.relation.projectidinfo:eu-repo/grantAgreement/EC/FP7/288777/EU/Mont-Blanc, European scalable and power efficient HPC platform based on low-power embedded technology/MONT-BLANC
dc.relation.projectidinfo:eu-repo/grantAgreement/EC/FP7/610402/EU/Mont-Blanc 2, European scalable and power efficient HPC platform based on low-power embedded technology/MONT-BLANC 2
dc.relation.projectid671697
upcommons.citation.contributorInternational Conference on Parallel Processing and Applied Mathematics
upcommons.citation.publishedtrue
upcommons.citation.publicationNamePPAM 2017: Parallel Processing and Applied Mathematics
upcommons.citation.volume10778
upcommons.citation.startingPage37
upcommons.citation.endingPage47


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