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dc.contributor.author
Rucci, Enzo
dc.contributor.author
Garcia Sanchez, Carlos
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Botella Juan, Guillermo
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de Giusti, Armando Eduardo
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Naiouf, Ricardo Marcelo
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Prieto Matias, Manuel
dc.date.available
2021-04-29T18:55:13Z
dc.date.issued
2019-04-10
dc.identifier.citation
Rucci, Enzo; Garcia Sanchez, Carlos; Botella Juan, Guillermo; de Giusti, Armando Eduardo; Naiouf, Ricardo Marcelo; et al.; SWIMM 2.0: Enhanced Smith–Waterman on Intel’s Multicore and Manycore Architectures Based on AVX-512 Vector Extensions; Springer/Plenum Publishers; International Journal Of Parallel Programming; 47; 2; 10-4-2019; 296-316
dc.identifier.issn
0885-7458
dc.identifier.uri
http://hdl.handle.net/11336/131066
dc.description.abstract
The well-known Smith–Waterman (SW) algorithm is the most commonly used method for local sequence alignments, but its acceptance is limited by the computational requirements for large protein databases. Although the acceleration of SW has already been studied on many parallel platforms, there are hardly any studies which take advantage of the latest Intel architectures based on AVX-512 vector extensions. This SIMD set is currently supported by Intel’s Knights Landing (KNL) accelerator and Intel’s Skylake (SKL) general purpose processors. In this paper, we present an SW version that is optimized for both architectures: the renowned SWIMM 2.0. The novelty of this vector instruction set requires the revision of previous programming and optimization techniques. SWIMM 2.0 is based on a massive multi-threading and SIMD exploitation. It is competitive in terms of performance compared with other state-of-the-art implementations, reaching 511 GCUPS on a single KNL node and 734 GCUPS on a server equipped with a dual SKL processor. Moreover, these successful performance rates make SWIMM 2.0 the most efficient energy footprint implementation in this study achieving 2.94 GCUPS/Watts on the SKL processor.
dc.format
application/pdf
dc.language.iso
eng
dc.publisher
Springer/Plenum Publishers
dc.rights
info:eu-repo/semantics/restrictedAccess
dc.rights.uri
https://creativecommons.org/licenses/by-nc-sa/2.5/ar/
dc.subject
BIOINFORMATICS
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INTEL-AVX512
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INTEL-KNL
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SIMD
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SMITH–WATERMAN
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XEON-PHI
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Otras Ciencias de la Computación e Información
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Ciencias de la Computación e Información
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CIENCIAS NATURALES Y EXACTAS
dc.title
SWIMM 2.0: Enhanced Smith–Waterman on Intel’s Multicore and Manycore Architectures Based on AVX-512 Vector Extensions
dc.type
info:eu-repo/semantics/article
dc.type
info:ar-repo/semantics/artículo
dc.type
info:eu-repo/semantics/publishedVersion
dc.date.updated
2021-04-28T20:06:53Z
dc.identifier.eissn
1573-7640
dc.journal.volume
47
dc.journal.number
2
dc.journal.pagination
296-316
dc.journal.pais
Estados Unidos
dc.journal.ciudad
Nueva York
dc.description.fil
Fil: Rucci, Enzo. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - La Plata; Argentina. Universidad Nacional de La Plata. Facultad de Informática. Instituto de Investigación en Informática Lidi; Argentina
dc.description.fil
Fil: Garcia Sanchez, Carlos. Universidad Complutense de Madrid; España
dc.description.fil
Fil: Botella Juan, Guillermo. Universidad Complutense de Madrid; España
dc.description.fil
Fil: de Giusti, Armando Eduardo. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - La Plata; Argentina. Universidad Nacional de La Plata. Facultad de Informática. Instituto de Investigación en Informática Lidi; Argentina
dc.description.fil
Fil: Naiouf, Ricardo Marcelo. Universidad Nacional de La Plata. Facultad de Informática. Instituto de Investigación en Informática Lidi; Argentina
dc.description.fil
Fil: Prieto Matias, Manuel. Universidad Complutense de Madrid; España
dc.journal.title
International Journal Of Parallel Programming
dc.relation.alternativeid
info:eu-repo/semantics/altIdentifier/doi/https://doi.org/10.1007/s10766-018-0585-7
dc.relation.alternativeid
info:eu-repo/semantics/altIdentifier/url/https://link.springer.com/article/10.1007/s10766-018-0585-7
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