The recent emergence of long-read sequencing technologies has enabled substan-
tial improvements in accuracy and reduced computational costs. Nonetheless, pair-
wise sequence alignment remains a time-consuming step in common bioinformat-
ics pipelines, becoming a bottleneck in de novo whole-genome assembly. Speeding
up this step requires heuristics and the development of memory-frugal and efficient
implementations. A promising candidate for all of the above is Myers’ algorithm.
However, the state-of-the-art implementations face scalability challenges when deal-
ing with longer reads and large datasets. To address these challenges, we propose
SeqMatcher, a fast and memory-frugal genomics sequence aligner. By leveraging
the long registers of AVX-512, SeqMatcher reduces the data movement and memory
footprint. In a comprehensive performance evaluation, SeqMatcher achieves speed-
ups of up to 12.32x for the unbanded version and 26.70x for the banded version
compared to the non-vectorized implementation, along with energy footprint reduc-
tions of up to 2.59x. It also outperforms state-of-the-art implementations by factors
of up to 29.21x, 17.56x, 13.47x, 9.12x, and 8.81x compared to Edlib, WFA2-lib,
SeqAn, BSAlign, and QuickEd, while improving energy consumption with reduc-
tions of up to 6.78x
