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CUDASW++: optimizing Smith-Waterman sequence database searches for CUDA-enabled graphics processing units

Yongchao Liu, Douglas Maskell, Bertil Schmidt
School of Computer Engineering, Nanyang Technological University, Singapore
BMC Research Notes, Vol. 2, No. 1. (2009), 73.

@article{liu2009cudasw++,

   title={CUDASW++: optimizing Smith-Waterman sequence database searches for CUDA-enabled graphics processing units},

   author={Liu, Y. and Maskell, D.L. and Schmidt, B.},

   journal={BMC research notes},

   volume={2},

   number={1},

   pages={73},

   issn={1756-0500},

   year={2009},

   publisher={BioMed Central Ltd}

}

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BACKGROUND:The Smith-Waterman algorithm is one of the most widely used tools for searching biological sequence databases due to its high sensitivity. Unfortunately, the Smith-Waterman algorithm is computationally demanding, which is further compounded by the exponential growth of sequence databases. The recent emergence of many-core architectures, and their associated programming interfaces, provides an opportunity to accelerate sequence database searches using commonly available and inexpensive hardware.FINDINGS:Our CUDASW++ implementation (benchmarked on a single-GPU NVIDIA GeForce GTX 280 graphics card and a dual-GPU GeForce GTX 295 graphics card) provides a significant performance improvement compared to other publicly available implementations, such as SWPS3, CBESW, SW-CUDA, and NCBI-BLAST. CUDASW++ supports query sequences of length up to 59K and for query sequences ranging in length from 144 to 5,478 in Swiss-Prot release 56.6, the single-GPU version achieves an average performance of 9.509 GCUPS with a lowest performance of 9.039 GCUPS and a highest performance of 9.660 GCUPS, and the dual-GPU version achieves an average performance of 14.484 GCUPS with a lowest performance of 10.660 GCUPS and a highest performance of 16.087 GCUPS.CONCLUSION:CUDASW++ is publicly available open-source software. It provides a significant performance improvement for Smith-Waterman-based protein sequence database searches by fully exploiting the compute capability of commonly used CUDA-enabled low-cost GPUs.
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