Repository logo
 
Publication

A high-performance computing framework for Monte Carlo ocean color simulations

2017-02Journal article Open access
Simple item page
dc.contributor.authorKajiyama, Tamito
dc.contributor.authorD'Alimonte, Davide
dc.contributor.authorCunha, Jose C.
dc.date.accessioned2018-12-07T14:52:56Z
dc.date.available2018-12-07T14:52:56Z
dc.date.issued2017-02
dc.description.abstractThis paper presents a high-performance computing (HPC) framework for Monte Carlo (MC) simulations in the ocean color (OC) application domain. The objective is to optimize a parallel MC radiative transfer code named MOX, developed by the authors to create a virtual marine environment for investigating the quality of OC data products derived from in situ measurements of in-water radiometric quantities. A consolidated set of solutions for performance modeling, prediction, and optimization is implemented to enhance the efficiency of MC OC simulations on HPC run-time infrastructures. HPC, machine learning, and adaptive computing techniques are applied taking into account a clear separation and systematic treatment of accuracy and precision requirements for large-scale MC OC simulations. The added value of the work is the integration of computational methods and tools for MC OC simulations in the form of an HPC-oriented problem-solving environment specifically tailored to investigate data acquisition and reduction methods for OC field measurements. Study results highlight the benefit of close collaboration between HPC and application domain researchers to improve the efficiency and flexibility of computer simulations in the marine optics application domain. (C) 2016 The Authors. Concurrency and Computation: Practice and Experience Published by John Wiley & Sons Ltd.
dc.description.sponsorshipPortuguese Foundation for Science and Technology (FCT/MEC) [PEst-OE/EEI/UI0527/2011]; ESA [22576/09/I-OL, ARG/003-025/1406/CIMA]; NOVA LINCS [UID/CEC/04516/2013]
dc.description.versioninfo:eu-repo/semantics/publishedVersion
dc.identifier.doi10.1002/cpe.3860
dc.identifier.issn1532-0626
dc.identifier.issn1532-0634
dc.identifier.urihttp://hdl.handle.net/10400.1/11275
dc.language.isoeng
dc.peerreviewedyes
dc.publisherWiley-Blackwell
dc.rights.urihttp://creativecommons.org/licenses/by/4.0/
dc.subjectLarge-scale
dc.subjectNeural-network
dc.subjectEuropean seas
dc.subjectScientific applications
dc.subjectEarth simulator
dc.subjectMeris data
dc.subjectParallel
dc.subjectAlgorithms
dc.subjectProducts
dc.subjectSystem
dc.titleA high-performance computing framework for Monte Carlo ocean color simulations
dc.typejournal article
dspace.entity.typePublication
oaire.awardURIinfo:eu-repo/grantAgreement/FCT/5876/UID%2FCEC%2F04516%2F2013/PT
oaire.citation.issue4
oaire.citation.startPagee3860
oaire.citation.titleConcurrency and Computation: Practice and Experience
oaire.citation.volume29
oaire.fundingStream5876
person.familyNameKajiyama
person.familyNameD'Alimonte
person.givenNameTamito
person.givenNameDavide
person.identifier.orcid0000-0002-3148-9705
person.identifier.orcid0000-0001-7217-7057
person.identifier.ridI-6531-2013
project.funder.identifierhttp://doi.org/10.13039/501100001871
project.funder.nameFundação para a Ciência e a Tecnologia
rcaap.rightsopenAccess
rcaap.typearticle
relation.isAuthorOfPublication648d8403-c7b7-4090-bb0d-fa63fe2c1f01
relation.isAuthorOfPublication8820679e-dc95-4070-b68f-b7bc79e4aff7
relation.isAuthorOfPublication.latestForDiscovery8820679e-dc95-4070-b68f-b7bc79e4aff7
relation.isProjectOfPublication37bdfcd7-b84f-4d11-8fa5-25a959cd5438
relation.isProjectOfPublication.latestForDiscovery37bdfcd7-b84f-4d11-8fa5-25a959cd5438

Files

Original bundle
Now showing 1 - 1 of 1
Thumbnail Image
Name:
H11275.pdf
Size:
2.07 MB
Format:
Adobe Portable Document Format
Download

Collections

CIM2-Artigos (em revistas ou actas indexadas)