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

  • Enzo Rucci
  • Carlos Garcia Sanchez
  • Guillermo Botella Juan
  • Armando De Giusti
  • Marcelo Naiouf
  • Manuel Prieto-Matias
Article
  • 14 Downloads

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.

Keywords

Bioinformatics Smith–Waterman Xeon-Phi Intel-KNL SIMD Intel-AVX512 

Notes

Acknowledgements

This work has been supported by the EU (FEDER) and the Spanish MINECO, under Grant TIN2015-65277-R and the CAPAP-H6 network (TIN2016-81840-REDT).

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Copyright information

© Springer Science+Business Media, LLC, part of Springer Nature 2018

Authors and Affiliations

  1. 1.II-LIDI, CONICETUniversidad Nacional de La PlataBuenos AiresArgentina
  2. 2.Universidad Complutense de MadridMadridSpain
  3. 3.III-LIDIUniversidad Nacional de La PlataBuenos AiresArgentina

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