Enhancing Energy Production with Exascale HPC Methods

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dc.contributor.authorCamata, José J.
dc.contributor.authorCela, José M.
dc.contributor.authorCosta, Danilo
dc.contributor.authorCoutinho, Alvaro LGA
dc.contributor.authorFernández-Galisteo, Daniel
dc.contributor.authorJiménez, Carmen
dc.contributor.authorKourdioumov, Vadim
dc.contributor.authorMattoso, Marta
dc.contributor.authorMayo-García, Rafael
dc.contributor.authorMiras, Thomas
dc.contributor.authorMoríñigo, José A.
dc.contributor.authorNavarro, Jorge
dc.contributor.authorNavaux, Philippe O.A.
dc.contributor.authorOliveira, Daniel de
dc.contributor.authorRodríguez-Pascual, Manuel
dc.contributor.authorSilva, Vítor
dc.contributor.authorSouza, Renan
dc.contributor.authorValduriez, Patrick
dc.contributor.otherBarcelona Supercomputing Center
dc.date.accessioned2017-01-27T10:11:21Z
dc.date.available2017-01-27T10:11:21Z
dc.date.issued2016
dc.description.abstractHigh Performance Computing (HPC) resources have become the key actor for achieving more ambitious challenges in many disciplines. In this step beyond, an explosion on the available parallelism and the use of special purpose processors are crucial. With such a goal, the HPC4E project applies new exascale HPC techniques to energy industry simulations, customizing them if necessary, and going beyond the state-of-the-art in the required HPC exascale simulations for different energy sources. In this paper, a general overview of these methods is presented as well as some specific preliminary results.
dc.description.sponsorshipThe research leading to these results has received funding from the European Union's Horizon 2020 Programme (2014-2020) under the HPC4E Project (www.hpc4e.eu), grant agreement n° 689772, the Spanish Ministry of Economy and Competitiveness under the CODEC2 project (TIN2015-63562-R), and from the Brazilian Ministry of Science, Technology and Innovation through Rede Nacional de Pesquisa (RNP). Computer time on Endeavour cluster is provided by the Intel Corporation, which enabled us to obtain the presented experimental results in uncertainty quantification in seismic imaging
dc.description.versionPostprint (author's final draft)
dc.format.extent14 p.
dc.identifier.citationCamata, José J. [et al.]. Enhancing Energy Production with Exascale HPC Methods. A: 2 016 LatinAmerican High Performance Computing Conference (CARLA), México, Aug 29-Sep 02, 2016. "". 2016.
dc.identifier.urihttps://hdl.handle.net/2117/100183
dc.language.isoeng
dc.relation.projectidinfo:eu-repo/grantAgreement/EC/H2020/689772/EU/HPC for Energy/HPC4E
dc.relation.projectidinfo:eu-repo/grantAgreement/MINECO//TIN2015-63562-R/ES/DESARROLLOS COMPUTACIONALES PARA EL RETO DE LA EXAESCALA 2/
dc.relation.publisherversionhttps://link.springer.com/chapter/10.1007/978-3-319-57972-6_17
dc.rights.accessOpen Access
dc.subjectÀrees temàtiques de la UPC::Energies
dc.subject.lcshHigh performance computing
dc.subject.lcshEnergy sources
dc.subject.lemacFonts d'energia
dc.subject.lemacSupercomputadors
dc.subject.otherHigh Performance Computing (HPC)
dc.subject.otherNew energy sources
dc.titleEnhancing Energy Production with Exascale HPC Methods
dc.typeConference lecture
dspace.entity.typePublication
local.citation.contributor2 016 LatinAmerican High Performance Computing Conference (CARLA), México, Aug 29-Sep 02, 2016

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