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dc.contributor.authorPeredo Andrade, Oscar Francisco
dc.contributor.authorHerrero Zaragoza, José Ramón
dc.contributor.otherUniversitat Politècnica de Catalunya. Doctorat en Arquitectura de Computadors
dc.contributor.otherUniversitat Politècnica de Catalunya. Departament d'Arquitectura de Computadors
dc.date.accessioned2022-02-24T07:45:41Z
dc.date.available2022-02-24T07:45:41Z
dc.date.issued2022-03
dc.identifier.citationPeredo, O.; Herrero, J. Acceleration strategies for large-scale sequential simulations using parallel neighbour search: Non-LVA and LVA scenarios. "Computers and geosciences", Març 2022, vol. 160, article 105027, p. 1-19.
dc.identifier.issn0098-3004
dc.identifier.urihttp://hdl.handle.net/2117/362994
dc.description.abstractThis paper describes the application of acceleration techniques into existing implementations of Sequential Gaussian Simulation and Sequential Indicator Simulation. These implementations might incorporate Locally Varying Anisotropy (LVA) to capture non-linear features of the underlying physical phenomena. The imple- mentation focuses on a novel parallel neighbour search algorithm, which can be used on both non-LVA and LVA codes. Additionally, parallel shortest path executions and optimized linear algebra libraries are applied with focus on LVA codes. Execution time, speedup and accuracy results are presented. Non-LVA codes are benchmarked using two scenarios with approximately 50 million domain points each. Speedup results of 2× and 4× were obtained on SGS and SISIM respectively, where each scenario is compared against a baseline code published in Peredo et al. (2018). The aggregated contribution to speedup of both works results in 12× and 50× respectively. LVA codes are benchmarked using two scenarios with approximately 1.7 million domain points each. Speedup results of 56× and 1822× were obtained on SGS and SISIM respectively, where each scenario is compared against the original baseline sequential codes.
dc.description.sponsorshipThe authors acknowledge the donated resources from project PID2019-107255GB of the Spanish Ministerio de Economía y Competitividad, and project 2017-SGR-1414 from the Generalitat de Catalunya, Spain.
dc.format.extent19 p.
dc.language.isoeng
dc.rightsAttribution-NonCommercial-NoDerivatives 4.0 International
dc.rights.urihttps://creativecommons.org/licenses/by-nc-nd/4.0/
dc.subjectÀrees temàtiques de la UPC::Informàtica::Arquitectura de computadors::Arquitectures paral·leles
dc.subject.lcshAlgebras, Linear
dc.subject.lcshAnisotropy
dc.subject.lcshParallel processing (Electronic computers)
dc.subject.otherGeostatistics
dc.subject.otherParallel computing
dc.subject.otherAlgorithms
dc.titleAcceleration strategies for large-scale sequential simulations using parallel neighbour search: Non-LVA and LVA scenarios
dc.typeArticle
dc.subject.lemacÀlgebra lineal
dc.subject.lemacAnisotropia
dc.subject.lemacProcessament en paral·lel (Ordinadors)
dc.contributor.groupUniversitat Politècnica de Catalunya. CAP - Grup de Computació d'Altes Prestacions
dc.identifier.doi10.1016/j.cageo.2021.105027
dc.description.peerreviewedPeer Reviewed
dc.relation.publisherversionhttps://www.sciencedirect.com/science/article/pii/S0098300421003083
dc.rights.accessOpen Access
local.identifier.drac32778372
dc.description.versionPostprint (published version)
dc.relation.projectidinfo:eu-repo/grantAgreement/AEI/Plan Estatal de Investigación Científica y Técnica y de Innovación 2017-2020/PID2019-107255GB-C22/ES/UPC-COMPUTACION DE ALTAS PRESTACIONES VIII/
local.citation.authorPeredo, O.; Herrero, J.
local.citation.publicationNameComputers and geosciences
local.citation.volume160
local.citation.numberarticle 105027
local.citation.startingPage1
local.citation.endingPage19


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