SWIMM 2.0: Enhanced Smith–Waterman on Intel’s Multicore and Manycore Architectures Based on AVX-512 Vector Extensions

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 har...

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Detalles Bibliográficos
Autores principales: Rucci, Enzo, García Sánchez, Carlos, Botella, Guillermo, De Giusti, Armando Eduardo, Naiouf, Marcelo, Prieto-Matias, Manuel
Formato: Articulo
Lenguaje:Inglés
Publicado: 2018
Materias:
Ciencias Informáticas
Bioinformatics
Smith-Waterman
Xeon-Phi
Intel-KNL
SIMD
Intel-AVX512
Acceso en línea:http://sedici.unlp.edu.ar/handle/10915/82888
Aporte de:
SEDICI (UNLP) de Universidad Nacional de La Plata
id I19-R120-10915-82888
record_format dspace
institution Universidad Nacional de La Plata
institution_str I-19
repository_str R-120
collection SEDICI (UNLP)
language Inglés
topic Ciencias Informáticas
Bioinformatics
Smith-Waterman
Xeon-Phi
Intel-KNL
SIMD
Intel-AVX512
spellingShingle Ciencias Informáticas
Bioinformatics
Smith-Waterman
Xeon-Phi
Intel-KNL
SIMD
Intel-AVX512
Rucci, Enzo
García Sánchez, Carlos
Botella, Guillermo
De Giusti, Armando Eduardo
Naiouf, Marcelo
Prieto-Matias, Manuel
SWIMM 2.0: Enhanced Smith–Waterman on Intel’s Multicore and Manycore Architectures Based on AVX-512 Vector Extensions
topic_facet Ciencias Informáticas
Bioinformatics
Smith-Waterman
Xeon-Phi
Intel-KNL
SIMD
Intel-AVX512
description 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.
format Articulo
Articulo
author Rucci, Enzo
García Sánchez, Carlos
Botella, Guillermo
De Giusti, Armando Eduardo
Naiouf, Marcelo
Prieto-Matias, Manuel
author_facet Rucci, Enzo
García Sánchez, Carlos
Botella, Guillermo
De Giusti, Armando Eduardo
Naiouf, Marcelo
Prieto-Matias, Manuel
author_sort Rucci, Enzo
title SWIMM 2.0: Enhanced Smith–Waterman on Intel’s Multicore and Manycore Architectures Based on AVX-512 Vector Extensions
title_short SWIMM 2.0: Enhanced Smith–Waterman on Intel’s Multicore and Manycore Architectures Based on AVX-512 Vector Extensions
title_full SWIMM 2.0: Enhanced Smith–Waterman on Intel’s Multicore and Manycore Architectures Based on AVX-512 Vector Extensions
title_fullStr SWIMM 2.0: Enhanced Smith–Waterman on Intel’s Multicore and Manycore Architectures Based on AVX-512 Vector Extensions
title_full_unstemmed SWIMM 2.0: Enhanced Smith–Waterman on Intel’s Multicore and Manycore Architectures Based on AVX-512 Vector Extensions
title_sort swimm 2.0: enhanced smith–waterman on intel’s multicore and manycore architectures based on avx-512 vector extensions
publishDate 2018
url http://sedici.unlp.edu.ar/handle/10915/82888
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