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

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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.

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Notes

  1. SWIMM 2.0 is available at https://github.com/enzorucci/SWIMM2.0.

  2. SWIPE is available at public repository: https://github.com/torognes/swipe.

  3. Parasail is available at public repository: https://github.com/jeffdaily/parasail.

  4. libssa is available at public repository: https://github.com/RonnySoak/libssa.

  5. FASTA format description: http://blast.ncbi.nlm.nih.gov/blastcgihelp.shtml.

  6. Swiss-Prot: http://www.uniprot.org/downloads.

  7. Environmental NR: ftp://ftp.ncbi.nih.gov/blast/db/FASTA/env_nr.gz.

  8. TrEMBL: http://www.uniprot.org/downloads.

  9. SSE4.1 and AVX2 versions using the QP technique were excluded from the analysis to improve figure readability since we found that the SP scheme always achieved the best performance, as in previous works [14].

  10. We have discarded the comparison with the SWhybrid framework [15] because we detected inconsistent alignment results in most of the experiments.

  11. The SSE4.1 and AVX2 versions using the QP technique were excluded from the analysis to improve figure readability since we found that the SP scheme always achieved the best performance, as in previous works [14].

  12. Once again, we have discarded the comparison with the SWhybrid framework [15] because we detected inconsistent alignment results in most of the experiments.

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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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Authors and Affiliations

  1. II-LIDI, CONICET, Universidad Nacional de La Plata, Buenos Aires, Argentina

    Enzo Rucci & Armando De Giusti

  2. Universidad Complutense de Madrid, Madrid, Spain

    Carlos Garcia Sanchez, Guillermo Botella Juan & Manuel Prieto-Matias

  3. III-LIDI, Universidad Nacional de La Plata, Buenos Aires, Argentina

    Marcelo Naiouf

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  1. Enzo Rucci
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Correspondence to Carlos Garcia Sanchez.

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Rucci, E., Garcia Sanchez, C., Botella Juan, G. et al. SWIMM 2.0: Enhanced Smith–Waterman on Intel’s Multicore and Manycore Architectures Based on AVX-512 Vector Extensions. Int J Parallel Prog 47, 296–316 (2019). https://doi.org/10.1007/s10766-018-0585-7

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