The Smith-Waterman algorithm is a common local sequence alignment method which gives a high accuracy. However, it needs a high capacity of computation and a large amount of storage memory, so implementations based on common computing systems are impractical. Here, we present our implementation of the Smith-Waterman algorithm on a cluster including graphics cards (GPU cluster) -swGPUCluster. The algorithm implementation is tested on a cluster of two nodes: a node is equipped with two dual graphics cards NVIDIA GeForce GTX 295, the other node includes a dual graphics cards NVIDIA GeForce 295 and a Tesla C1060 card. Depending on the length of query sequences, the swGPUCluster performance increases from 37.33 GCUPS to 46.71 GCUPS. This result demonstrates the great computing power of GPUs and their high applicability in the bioinformatics field.
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.
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