Algorithm 1033: Parallel Implementations for Computing the Minimum Distance of a Random Linear Code on Distributed-memory Architectures
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Otros documentos de la autoría: Quintana-Ortí, Gregorio; Hernando, Fernando; Igual, Francisco
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comunitat-uji-handle2:10234/7036
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Título
Algorithm 1033: Parallel Implementations for Computing the Minimum Distance of a Random Linear Code on Distributed-memory ArchitecturesFecha de publicación
2023-03Editor
Association for Computing Machinery (ACM)ISSN
0098-3500; 1557-7295Cita bibliográfica
Gregorio Quintana-Ortí, Fernando Hernando, and Francisco D. Igual. 2023. Algorithm 1033: Parallel Implementations for Computing the Minimum Distance of a Random Linear Code on Distributed-memory Architectures. ACM Trans. Math. Softw. 49, 1, Article 8 (March 2023), 24 pages. https://doi.org/10.1145/3573383Tipo de documento
info:eu-repo/semantics/articleVersión de la editorial
https://dl.acm.org/doi/full/10.1145/3573383Versión
info:eu-repo/semantics/acceptedVersionPalabras clave / Materias
Resumen
The minimum distance of a linear code is a key concept in information theory. Therefore, the time required by its computation is very important to many problems in this area. In this article, we introduce a family of ... [+]
The minimum distance of a linear code is a key concept in information theory. Therefore, the time required by its computation is very important to many problems in this area. In this article, we introduce a family of implementations of the Brouwer–Zimmermann algorithm for distributed-memory architectures for computing the minimum distance of a random linear code over 𝔽2. Both current commercial and public-domain software only work on either unicore architectures or shared-memory architectures, which are limited in the number of cores/processors employed in the computation. Our implementations focus on distributed-memory architectures, thus being able to employ hundreds or even thousands of cores in the computation of the minimum distance. Our experimental results show that our implementations are much faster, even up to several orders of magnitude, than current implementations widely used nowadays. [-]
Descripción
This is the accepted version of the work. It is posted here for your personal use. Not for redistribution. The definitive Version of Record was published inACM Transactions on Mathematical Software. Volume 49, Issue ... [+]
This is the accepted version of the work. It is posted here for your personal use. Not for redistribution. The definitive Version of Record was published inACM Transactions on Mathematical Software. Volume 49, Issue 1, https://doi.org/10.1145/3573383 [-]
Publicado en
ACM Transactions on Mathematical Software, 2023, vol. 49, no 1Entidad financiadora
Ministerio de Ciencia, Innovación y Universidades | Universitat Jaume I | Comunidad de Madrid
Identificador de la entidad financiadora
http://dx.doi.org/10.13039/501100011033
Código del proyecto o subvención
MICIU/ICTI2017-2020/RTI2018-098156-B-C54 | MICIU/ICTI2017-2020/PGC2018-096446-B-C21 | MICIU/ICTI2017-2020/PGC2018-096446-B-C22 | MICIU/ICTI2017-2020/PID2021-126576NB-I00 | MICIU/ICTI2017-2020/RTI2018-B-I00 | PB1-1B2018-10 | PR65-19/22445
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Copyright © Association for Computing Machinery (ACM)
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info:eu-repo/semantics/openAccess
http://rightsstatements.org/vocab/CNE/1.0/
info:eu-repo/semantics/openAccess
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