Accelerating Smith-Waterman Local Sequence Alignment on GPU Cluster

Abstract

With a high accuracy, the Smith-Waterman local sequence alignment algorithm requires a very large amount of memory and computation, making implementations on common computing systems become less practical. In this paper, we present swGPUCluster an implementation of the Smith-Waterman algorithm on a cluster equipped with NVIDIA GPU graphics cards (called a GPU cluster) . Our test was performed on a cluster of two nodes, one node is equipped with a dual graphics card NVIDIA GeForce GTX 295, a Tesla C1060 card, and the remaining node is equipped with 2 dual graphics cards NVIDIA GeForce GTX 295. Results show that the performance has increased significantly compared with the previous best implementations such as SWPS3 or CUDASW++. The performance of swGPUCluster has increased along with the lengths of query sequences, from 37.328 GCUPS to 46.706 GCUPS. These results demonstrate the great computing power of graphics cards and their high applicability in solving bioinformatics problems.