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

TL;DR

This paper explores optimizing the Smith-Waterman algorithm on Intel's Knights Landing processor, demonstrating competitive performance through multi-threading and SIMD techniques, marking the first assessment of SW on KNL architecture.

Contribution

It presents the first evaluation of Smith-Waterman on Intel KNL, adapting optimization techniques for many-core architectures.

Findings

Achieved 351 GCUPS performance on KNL.

Multi-threading and SIMD are effective for SW acceleration.

First assessment of SW on KNL architecture.

Abstract

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.