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

• Published in: BMC research notes Vol. 2; no. 1; p. 73
• Main Authors: Liu, Yongchao, Maskell, Douglas L, Schmidt, Bertil
• Format: Journal Article
• Language: English
• Published: London BioMed Central 06.05.2009; BioMed Central Ltd; BMC
• Subjects: Algorithms; Analysis; Biomedical and Life Sciences; Biomedicine; Life Sciences; Medical research; Medicine, Experimental; Medicine/Public Health; Nucleotide sequence; Technical Note; Technology application; Constant Memory; Query Sequence; Subject Sequence; Global Memory; Thread Block
• ISSN: 1756-0500; 1756-0500
• DOI: 10.1186/1756-0500-2-73

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.