Applying GPUs for Smith-Waterman Sequence Alignment Acceleration

The Smith-Waterman algorithm is a common localsequence alignment method which gives a high accuracy.However, it needs a high capacity of computation and a largeamount of storage memory, so implementations based oncommon computing systems are impractical. Here, we presentour implementation of the Smith-Waterman algorithm on acluster including graphics cards (GPU cluster) –swGPUCluster. The algorithm implementation is tested on acluster of two nodes: a node is equipped with two dual graphicscards NVIDIA GeForce GTX 295, the other node includes adual graphics cards NVIDIA GeForce 295 and a Tesla C1060card. Depending on the length of query sequences, theswGPUCluster performance increases from 37.33 GCUPS to46.71 GCUPS. This result demonstrates the great computingpower of GPUs and their high applicability in thebioinformatics field