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First Experiences Optimizing Smith-Waterman on Intel’s Knights Landing Processor

Enzo Rucci, Carlos Garcia, Guillermo Botella, Armando De Giusti, Marcelo Naiouf, Manuel Prieto-Matias
III-LIDI, CONICET, Facultad de Informatica, Universidad Nacional de La Plata
arXiv:1702.07195 [cs.DC], (23 Feb 2017)

@article{rucci2017first,

   title={First Experiences Optimizing Smith-Waterman on Intel’s Knights Landing Processor},

   author={Rucci, Enzo and Garcia, Carlos and Botella, Guillermo and Giusti, Armando De and Naiouf, Marcelo and Prieto-Matias, Manuel},

   year={2017},

   month={feb},

   archivePrefix={"arXiv"},

   primaryClass={cs.DC}

}

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The well-known Smith-Waterman (SW) algorithm is the most commonly used method for local sequence alignments. However, SW is very computationally demanding for large protein databases. There exist several implementations that take advantage of computing parallelization on many-cores, FPGAs or GPUs, in order to increase the alignment throughtput. In this paper, we have explored SW acceleration on Intel KNL processor. The novelty of this architecture requires the revision of previous programming and optimization techniques on many-core architectures. To the best of authors knowledge, this is the first KNL architecture assessment for SW algorithm. Our evaluation, using the renowned Environmental NR database as benchmark, has shown that multi-threading and SIMD exploitation reports competitive performance (351 GCUPS) in comparison with other implementations.
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