dc.creatorRucci, Enzo
dc.creatorGarcía Sánchez, Carlos
dc.creatorBotella, Guillermo
dc.creatorDe Giusti, Armando Eduardo
dc.creatorNaiouf, Marcelo
dc.creatorPrieto-Matias, Manuel
dc.date2018-07-10
dc.date2019-10-08T12:59:03Z
dc.identifierhttp://sedici.unlp.edu.ar/handle/10915/82888
dc.identifierissn:1573-7640
dc.descriptionThe 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.descriptionFacultad de Informática
dc.formatapplication/pdf
dc.format296-316
dc.languageen
dc.rightshttp://creativecommons.org/licenses/by-nc-nd/4.0/
dc.rightsCreative Commons Attribution-NonCommercial-NoDerivatives 4.0 International (CC BY-NC-ND 4.0)
dc.subjectCiencias Informáticas
dc.subjectBioinformatics
dc.subjectSmith-Waterman
dc.subjectXeon-Phi
dc.subjectIntel-KNL
dc.subjectSIMD
dc.subjectIntel-AVX512
dc.titleSWIMM 2.0: Enhanced Smith–Waterman on Intel’s Multicore and Manycore Architectures Based on AVX-512 Vector Extensions
dc.typeArticulo
dc.typeArticulo


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