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dc.contributor.authorAbujas‐Pereir, Jerónimoes_ES
dc.contributor.authorMartin‐Bragado, Ignacioes_ES
dc.contributor.authorPina, Carlos M.es_ES
dc.contributor.authorPizarro, J.es_ES
dc.contributor.authorGalindo, P. L.es_ES
dc.date.accessioned2019-06-21T07:18:12Z-
dc.date.available2019-06-21T07:18:12Z-
dc.date.issued2016-10-
dc.identifier.citationCrystal Research and Technology 51(10): 575-585 (2016)es_ES
dc.identifier.issn0232-1300-
dc.identifier.urihttp://hdl.handle.net/10261/184546-
dc.description.abstractThis work presents a parallel approach of the Kinetic Monte Carlo (KMC) algorithm using a distributed memory architecture. The resulting computer software was tested by conducting crystal growth simulations on barite (001) face. Execution times, simulated times and crystallization velocities are compared with a shared memory parallel KMC software (MMonCa). Finally, a ≈ 1 μm2 crystal growth simulation is performed and compared with atomic force microscopy crystal growth experiments. The capability of this approach is demonstrated: a) a significant reduction of parallel overhead is achieved when comparing to the shared memory parallel version of the software, b) a distributed memory approach achieves an increase in memory resources enough to perform simulations with lattice sizes about 1 μm2, allowing the study of larger structures than those in shared memory or sequential implementations, c) this approach should be used only with large scale simulations to take advantage of the distributed memory architecture, d) further improvements are needed for parallel KMC to be faster than serial KMC in small scale simulations, e) the KMC algorithm used is able to adequately simulate two‐dimensional nucleation on large areas of barite (001) faces.es_ES
dc.description.sponsorshipWe would like to acknowledge funding from the European Union (project SEP‐210135800), Spanish Government (projects MAT2013‐47102‐C2‐1R, CTM2013‐49796‐EXP, TEC2014‐53727‐C2‐2‐R and CSD2009‐00013), Junta de Andalucía Regional Government (project TEP3055/2012) and SCCYT‐UCA for technical support.es_ES
dc.language.isoenges_ES
dc.publisherWiley-VCHes_ES
dc.relationMINECO/ICTI2013-2016/MAT2013‐47102‐C2‐1Res_ES
dc.relationMINECO/ICTI2013-2016/CTM2013‐49796‐EXPes_ES
dc.relationMINECO/ICTI2013-2016/TEC2014‐53727‐C2‐2‐Res_ES
dc.rightsclosedAccesses_ES
dc.subjectKinetic Monte Carloes_ES
dc.subjectDistributed computinges_ES
dc.subjectSurface structurees_ES
dc.subjectCrystal growth simulationes_ES
dc.titleA distributed‐memory parallel lattice Kinetic Monte Carlo algorithm for crystal growth applied to barite (001) facees_ES
dc.typeartículoes_ES
dc.identifier.doi10.1002/crat.201600141-
dc.description.peerreviewedPeer reviewedes_ES
dc.relation.publisherversionhttps://doi.org/10.1002/crat.201600141es_ES
dc.identifier.e-issn1521-4079-
dc.contributor.funderEuropean Commissiones_ES
dc.contributor.funderMinisterio de Economía y Competitividad (España)es_ES
dc.contributor.funderJunta de Andalucíaes_ES
dc.contributor.funderUniversidad de Cádizes_ES
dc.relation.csices_ES
oprm.item.hasRevisionno ko 0 false*
dc.identifier.funderhttp://dx.doi.org/10.13039/501100003329es_ES
dc.identifier.funderhttp://dx.doi.org/10.13039/501100000780es_ES
dc.identifier.funderhttp://dx.doi.org/10.13039/501100008723es_ES
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