Identificador persistente para citar o vincular este elemento: http://hdl.handle.net/10553/114787
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dc.contributor.authorOrtega Zamorano, Franciscoen_US
dc.contributor.authorMarcelo A. Montemurroen_US
dc.contributor.authorSergio A. Cannasen_US
dc.contributor.authorJosé M. Jerezen_US
dc.contributor.authorLeonardo Francoen_US
dc.date.accessioned2022-05-18T08:49:38Z-
dc.date.available2022-05-18T08:49:38Z-
dc.date.issued2016en_US
dc.identifier.issn1045-9219en_US
dc.identifier.urihttp://hdl.handle.net/10553/114787-
dc.description.abstractA two-dimensional Ising model with nearest-neighbors ferromagnetic interactions is implemented in a Field Programmable Gate Array (FPGA) board.Extensive Monte Carlo simulations were carried out using an efficient hardware representation of individual spins and a combined global-local LFSR random number generator. Consistent results regarding the descriptive properties of magnetic systems, like energy, magnetization and susceptibility are obtained while a speed-up factor of approximately 6 times is achieved in comparison to previous FPGA-based published works and almost $10^4$ times in comparison to a standard CPU simulation. A detailed description of the logic design used is given together with a careful analysis of the quality of the random number generator used. The obtained results confirm the potential of FPGAs for analyzing the statistical mechanics of magnetic systems.en_US
dc.languageengen_US
dc.relation.ispartofIEEE Transactions on Parallel and Distributed Systemsen_US
dc.sourceIEEE Transactions on Parallel and Distributed Systems [ISSN 1045-9219], v. 27(9), p. 2618 - 2627en_US
dc.subject330406 Arquitectura de ordenadoresen_US
dc.subject2203 Electrónicaen_US
dc.subject.otherHardware implementationen_US
dc.subject.otherLFSR random number generatoren_US
dc.subject.otherMonte Carlo simulationsen_US
dc.subject.otherIsing modelen_US
dc.titleFPGA Hardware Acceleration of Monte Carlo Simulations for the Ising Modelen_US
dc.typeinfo:eu-repo/semantics/Articleen_US
dc.typearticleen_US
dc.identifier.doi10.1109/TPDS.2015.2505725en_US
dc.identifier.scopus2-s2.0-84982113681-
dc.identifier.isiWOS:000384238000011-
dc.contributor.orcid#NODATA#-
dc.contributor.orcid#NODATA#-
dc.contributor.orcid#NODATA#-
dc.contributor.orcid#NODATA#-
dc.contributor.orcid#NODATA#-
dc.identifier.issue9-
dc.investigacionIngeniería y Arquitecturaen_US
dc.type2Artículoen_US
dc.utils.revisionen_US
dc.identifier.ulpgcNoen_US
dc.contributor.buulpgcBU-INFen_US
dc.description.sjr1,129
dc.description.jcr4,181
dc.description.sjrqQ1
dc.description.jcrqQ1
dc.description.scieSCIE
item.grantfulltextnone-
item.fulltextSin texto completo-
crisitem.author.deptGIR SIANI: Inteligencia Artificial, Robótica y Oceanografía Computacional-
crisitem.author.deptIU Sistemas Inteligentes y Aplicaciones Numéricas-
crisitem.author.orcid0000-0002-4397-2905-
crisitem.author.parentorgIU Sistemas Inteligentes y Aplicaciones Numéricas-
crisitem.author.fullNameOrtega Zamorano,Francisco-
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