Accelerating Long Read Alignment on Three Processors

Abstract

Sequence alignment is a fundamental task in bioinformatics, because many downstream applications rely on it. The recent emergence of the third-generation sequencing technology requires new sequence alignment algorithms that handle longer read lengths as well as more sequencing errors. Furthermore, the rapidly increasing volume of sequence data calls for efficient analysis solutions. To address this need, we propose to utilize commodity parallel processors to perform the long read alignment. Specifically, we propose manymap, an acceleration of the leading CPU-based long read aligner minimap2 on the CPU, the GPU, and the Intel Xeon Phi processor. We eliminate intra-loop data dependency in the base-level alignment step of the original minimap2 through redesigning memory layouts of dynamic programming (DP) matrices. This change facilitates the effective vectorization of the most time-consuming procedure in alignment. Additionally, we apply architecture-aware optimizations, such as utilizing high bandwidth memory on Xeon Phi and concurrent kernel execution on GPU. We evaluate our manymap in comparison with the extended minimap2 on a Xeon Gold 5115 CPU, a Tesla V100 GPU, and a Xeon Phi 7210 processor. Our results show that manymap outperforms minimap2 by up to 2.3 times on the overall execution time and 4.5 times on the base-level alignment step.