Improving the Mapping of Smith-Waterman Sequence Database Searches onto CUDA-Enabled GPUs.
Sequence alignment lies at heart of the bioinformatics. The Smith-Waterman algorithm is one of the key sequence search algorithms and has gained popularity due to improved implementations and rapidly increasing compute power. Recently, the Smith-Waterman algorithm has been successfully mapped onto the emerging general-purpose graphics processing units (GPUs). In this paper, we focused on how to improve the mapping, especially for short query sequences, by better usage of shared memory. We performed and evaluated the proposed method on two different platforms (Tesla C1060 and Tesla K20) and compared it with two classic methods in CUDASW++. Further, the performance on different numbers of threads and blocks has been analyzed. The results showed that the proposed method significantly improves Smith-Waterman algorithm on CUDA-enabled GPUs in proper allocation of block and thread numbers.
Department of Medical Informatics, Tzu Chi University, Hualien 970, Taiwan.
Liang-Tsung
LT
Huang
Department of Computer Science and Information Engineering, National Changhua University of Education, Changhua 500, Taiwan.
Chao-Chin
CC
Wu
Department of Computer Science and Information Engineering, National Changhua University of Education, Changhua 500, Taiwan.
Lien-Fu
LF
Lai
Department of Computer Science and Information Engineering, National Changhua University of Education, Changhua 500, Taiwan.
Yun-Ju
YJ
Li
D000465:Algorithms; D019295:Computational Biology; D030561:Databases, Nucleic Acid; D030562:Databases, Protein; D006801:Humans; D016415:Sequence Alignment / Q000379:methods*; D012984:Software