  Pairwise sequence alignment is one of the most computationally intensive
kernels in genomic data analysis, accounting for more than 90% of the runtime
for key bioinformatics applications. This method is particularly expensive for
third-generation sequences due to the high computational cost of analyzing
sequences of length between 1Kb and 1Mb. Given the quadratic overhead of exact
pairwise algorithms for long alignments, the community primarily relies on
approximate algorithms that search only for high-quality alignments and stop
early when one is not found. In this work, we present the first GPU
optimization of the popular X-drop alignment algorithm, that we named LOGAN.
Results show that our high-performance multi-GPU implementation achieves up to
181.6 GCUPS and speed-ups up to 6.6x and 30.7x using 1 and 6 NVIDIA Tesla V100,
respectively, over the state-of-the-art software running on two IBM Power9
processors using 168 CPU threads, with equivalent accuracy. We also demonstrate
a 2.3x LOGAN speed-up versus ksw2, a state-of-art vectorized algorithm for
sequence alignment implemented in minimap2, a long-read mapping software. To
highlight the impact of our work on a real-world application, we couple LOGAN
with a many-to-many long-read alignment software called BELLA, and demonstrate
that our implementation improves the overall BELLA runtime by up to 10.6x.
Finally, we adapt the Roofline model for LOGAN and demonstrate that our
implementation is near-optimal on the NVIDIA Tesla V100s.
