A GPU-CPU heterogeneous algorithm for NGS read alignment
by Ahmad Al Kawam; Sunil Khatri; Aniruddha Datta
International Journal of Computational Biology and Drug Design (IJCBDD), Vol. 11, No. 1/2, 2018

Abstract: In the next generation sequencing (NGS) read alignment problem, millions of deoxyribonucleic acid (DNA) fragments, called reads, are mapped to a reference genome. Read alignment is typically carried out using traditional computing platforms, which have become a limiting factor in the speed of the process. The massive scale of the problem makes it an attractive target for acceleration. In this paper, we design a read alignment algorithm designed to run on a heterogeneous system composed of a graphics processing unity (GPU) and a multicore central processing unit (CPU). We introduce novel techniques for the alignment process and construct a computational pipeline of overlapped CPU and GPU stages. We compare our tool with the BWA-mem alignment tool, and the results show substantial speedups.

Online publication date: Wed, 28-Mar-2018

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