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dc.contributor.authorFarres, Albert
dc.contributor.authorDuran, A.
dc.contributor.authorRosas, Claudia
dc.contributor.authorHanzich, Mauricio
dc.contributor.authorYount, C.
dc.contributor.authorFernandez, S.
dc.contributor.otherBarcelona Supercomputing Center
dc.identifier.citationFarres, A. [et al.]. Optimizing Fully Anisotropic Elastic Propagation on 2nd Generation Intel Xeon Phi Processors. A: 79th EAGE Conference and Exhibition 2017 (Session: Computational Geosciences). "79th EAGE Conference and Exhibition 2017". 2017.
dc.description.abstractThis work shows several optimization strategies evaluated and applied to an elastic wave propagation engine, based on a Fully Staggered Grid, running on the latest Intel Xeon Phi processors, the second generation of the product (code-named Knights Landing). Our fully optimized code shows a speed-up of about 4x when compared with the same algorithm optimized for the previous generation processor.
dc.description.sponsorshipAuthors also thank Repsol for the permission to publish the present research, carried out at the Repsol-BSC Research Center. This work has received funding from the European Union's Horizon 2020 Programme (2014-2020) and from the Brazilian Ministry of Science, Technology and Innovation through Rede Nacional de Pesquisa (RNP) under the HPC4E Project (, grant agreement n.◦ 689772. * Other brands and names are the property of their respective owners.
dc.format.extent6 p.
dc.rightsAttribution-NonCommercial-NoDerivs 3.0 Spain
dc.subjectÀrees temàtiques de la UPC::Enginyeria electrònica
dc.subject.lcshWave mechanics
dc.subject.other3D full-wave field modelling-based applications
dc.subject.otherWave propagation
dc.subject.otherIntel Xeon Phi x200 processor family
dc.titleOptimizing Fully Anisotropic Elastic Propagation on 2nd Generation Intel Xeon Phi Processors
dc.typeConference lecture
dc.subject.lemacMecànica ondulatòria
dc.description.peerreviewedPeer Reviewed
dc.rights.accessOpen Access
dc.description.versionPostprint (author's final draft)
dc.relation.projectidinfo:eu-repo/grantAgreement/EC/H2020/689772/EU/HPC for Energy/HPC4E
upcommons.citation.contributor79th EAGE Conference and Exhibition 2017 (Session: Computational Geosciences)
upcommons.citation.publicationName79th EAGE Conference and Exhibition 2017
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