Parallelization Mechanisms of Neighbor-Joining for CUDA Enabled Devices

Abstract

Multiple Sequence Alignment (MSA) is a fundamental process in bioinformatics in which phylogenetic tree reconstruction is an essential operation. Neighbor-Joining algorithm is the best approach to reconstruct phylogenetic tree with its less time and space costs. With the rapid increase of biological sequences, it will take many hours or even days to reconstruct phylogenetic tree because of the complex computing for multiple sequence alignment. In this paper, two mechanisms for parallelizing Neighbor-Joining algorithm are proposed based on CUDA to get higher performance of lower time and space costs. Data dependency is reduced by converting the running mode and dynamic multiple granularity mechanism is used to figure out imbalance guiding tree with lower rate of resources occupation and higher efficiency. The parallelization mechanisms have achieved average speedups of 18.6 for thousands of datasets as well as far genetic relationship datasets compared to the basic method.