  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.

Distributed, Parallel, and Cluster Computing;Performance