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CUDASW++: optimizing Smith-Waterman sequence database searches for CUDA-enabled graphics processing units
| Content Provider | Open Access Library (OALib) |
|---|---|
| Author | Yongchao Liu Douglas L. Maskell Bertil Schmidt |
| Abstract | 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.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.In bioinformatics, sequence database searches are used to find the similarity between a query sequence and subject sequences in the database so as to identify evolutionary relationships. The sequence similarities can be determined by computing their optimal local alignments using the dynamic programming based Smith-Waterman (SW) algorithm [1,2]. However, the cost of this approach is expensive in terms of computing time and memory space. This is especially evident with the rapid growth of biological sequence databases demanding powerful high-performance computing solutions. Due to the computationally demanding nature of the SW algorithm, some heuristic solutions, such as FASTA [3] and BLAST [4,5], have been devised to improve the execution speed, but at the expense of sensitivity. This may result in some distantly related sequences not being detected.The recent emergence of accelerator technologies and many-core architectures, such as FPGAs, Cell/BEs and GPUs, provides the opportunity to signific |
| ISSN | 17560500 |
| Journal | BMC Research Notes |
| DOI | 10.1186/1756-0500-2-73 |
| Publisher | BioMed Central |
| Publisher Date | 2009-01-01 |
| Access Restriction | Open |
| Content Type | Text |
| Resource Type | Article |
| Subject | Medicine Biochemistry, Genetics and Molecular Biology |
