Applying GPUs for Smith-Waterman Sequence Alignment Acceleration

Phong H.Pham ., Tan N. Duong ., Ngoc M.Ta ., Cuong Q.Tran ., Duc H.Nguyen ., Thuy T.Nguyen ., Hung D.Le .

Abstract


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.


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