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Pairwise sequence alignment is one of the most computationally intensivekernels in genomic data analysis, accounting for more than 90% of the runtimefor key bioinformatics applications. This method is particularly expensive forthird-generation sequences due to the high computational cost of analyzingsequences of length between 1Kb and 1Mb. Given the quadratic overhead of exactpairwise algorithms for long alignments, the community primarily relies onapproximate algorithms that search only for high-quality alignments and stopearly when one is not found. In this work, we present the first GPUoptimization of the popular X-drop alignment algorithm, that we named LOGAN.Results show that our high-performance multi-GPU implementation achieves up to181.6 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 Power9processors using 168 CPU threads, with equivalent accuracy. We also demonstratea 2.3x LOGAN speed-up versus ksw2, a state-of-art vectorized algorithm forsequence alignment implemented in minimap2, a long-read mapping software. Tohighlight the impact of our work on a real-world application, we couple LOGANwith a many-to-many long-read alignment software called BELLA, and demonstratethat our implementation improves the overall BELLA runtime by up to 10.6x.Finally, we adapt the Roofline model for LOGAN and demonstrate that ourimplementation is near-optimal on the NVIDIA Tesla V100s.

LOGAN: High-Performance GPU-Based X-Drop Long-Read Alignment