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dc.contributor.authorAndrade, Xavier-
dc.contributor.authorAlberdi, Juan M.-
dc.contributor.authorStrubbe, David A.-
dc.contributor.authorOliveira, Micael J. T.-
dc.contributor.authorNogueira, Fernando-
dc.contributor.authorCastro, Alberto-
dc.contributor.authorMuguerza, Javier-
dc.contributor.authorArruabarrena, Agustín-
dc.contributor.authorLouie, Steven G.-
dc.contributor.authorAspuru-Guzik, Alán-
dc.contributor.authorRubio, Angel-
dc.contributor.authorMarques, Miguel A. L.-
dc.identifierdoi: 10.1088/0953-8984/24/23/233202-
dc.identifierissn: 0953-8984-
dc.identifiere-issn: 1361-648X-
dc.identifier.citationJournal of Physics Condensed Matter 24(23): 233202 (2012)-
dc.description.abstractOctopus is a general-purpose density-functional theory (DFT) code, with a particular emphasis on the time-dependent version of DFT (TDDFT). In this paper we present the ongoing efforts to achieve the parallelization of octopus. We focus on the real-time variant of TDDFT, where the time-dependent Kohn–Sham equations are directly propagated in time. This approach has great potential for execution in massively parallel systems such as modern supercomputers with thousands of processors and graphics processing units (GPUs). For harvesting the potential of conventional supercomputers, the main strategy is a multi-level parallelization scheme that combines the inherent scalability of real-time TDDFT with a real-space grid domain-partitioning approach. A scalable Poisson solver is critical for the efficiency of this scheme. For GPUs, we show how using blocks of Kohn–Sham states provides the required level of data parallelism and that this strategy is also applicable for code optimization on standard processors. Our results show that real-time TDDFT, as implemented in octopus, can be the method of choice for studying the excited states of large molecular systems in modern parallel architectures-
dc.description.sponsorshipXA and AA acknowledge hardware donations by Advanced Micro Devices, Inc. (AMD) and Nvidia Corp., and support from the US NSF CDI program (PHY-0835713). MALM acknowledges support from the French ANR (ANR-08-CEXC8-008-01) and computational resources from GENCI (project x2011096017). JAR acknowledges the scholarship of the University of the Basque Country UPV/EHU. DAS acknowledges support from the US NSF IGERT and Graduate Research Fellowship Programs, and by NSF Grant No. DMR 10-1006184. SGL was supported by NSF Grant No. DMR10-1006184 and by the Director, Office of Science, Office of Basic Energy Sciences, Materials Sciences and Engineering Division, US Department of Energy under Contract No. 10DE-AC02-05CH11231. The authors were partially supported by the European Research Council Advanced Grant DYNamo (ERC-2010-AdG—Proposal No. 267374), Spanish Grants (FIS2011-65702-C02-01 and PIB2010US-00652), ACI-Promociona (ACI2009-1036), Grupos Consolidados UPV/EHU del Gobierno Vasco (IT-319-07), University of the Basque Country UPV/EHU general funding for research groups ALDAPA (GIU10/02), Consolider nanoTHERM (Grant No. CSD2010-00044) and European Commission projects CRONOS (280879-2 CRONOS CP-FP7) and THEMA (FP7-NMP-2008-SMALL-2, 228539).-
dc.publisherInstitute of Physics Publishing-
dc.titleTime-dependent density-functional theory in massively parallel computer architectures: the octopus project-
dc.description.versionPeer Reviewed-
dc.contributor.funderAgence Nationale de la Recherche (France)-
dc.contributor.funderNational Science Foundation (US)-
dc.contributor.funderAdvanced Micro Devices-
dc.contributor.funderNVIDIA Corporation-
dc.contributor.funderUniversidad del País Vasco-
dc.contributor.funderEuropean Commission-
dc.contributor.funderEusko Jaurlaritza-
dc.contributor.funderMinisterio de Economía y Competitividad (España)-
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