@Article{liu09cudasw,
  author    = {Yongchao Liu and Douglas L Maskell and Bertil Schmidt},
  title     = {{CUDASW++}: Optimizing {Smith-Waterman} sequence database searches for {CUDA}-enabled graphics processing units.},
  journal   = {BMC Res Notes},
  year      = {2009},
  volume    = {2},
  pages     = {73},
  abstract  = {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.},
  doi       = {10.1186/1756-0500-2-73},
  owner     = {swinter},
  pmid      = {19416548},
  timestamp = {2010.04.09},
}

{"_id":"D9NbgSkfmfyfJSj4x","bibbaseid":"liu-maskell-schmidt-cudaswoptimizingsmithwatermansequencedatabasesearchesforcudaenabledgraphicsprocessingunits-2009","authorIDs":[],"author_short":["Liu, Y.","Maskell, D. L","Schmidt, B."],"bibdata":{"bibtype":"article","type":"article","author":[{"firstnames":["Yongchao"],"propositions":[],"lastnames":["Liu"],"suffixes":[]},{"firstnames":["Douglas","L"],"propositions":[],"lastnames":["Maskell"],"suffixes":[]},{"firstnames":["Bertil"],"propositions":[],"lastnames":["Schmidt"],"suffixes":[]}],"title":"CUDASW++: Optimizing Smith-Waterman sequence database searches for CUDA-enabled graphics processing units.","journal":"BMC Res Notes","year":"2009","volume":"2","pages":"73","abstract":"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.","doi":"10.1186/1756-0500-2-73","owner":"swinter","pmid":"19416548","timestamp":"2010.04.09","bibtex":"@Article{liu09cudasw,\n author = {Yongchao Liu and Douglas L Maskell and Bertil Schmidt},\n title = {{CUDASW++}: Optimizing {Smith-Waterman} sequence database searches for {CUDA}-enabled graphics processing units.},\n journal = {BMC Res Notes},\n year = {2009},\n volume = {2},\n pages = {73},\n abstract = {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.},\n doi = {10.1186/1756-0500-2-73},\n owner = {swinter},\n pmid = {19416548},\n timestamp = {2010.04.09},\n}\n\n","author_short":["Liu, Y.","Maskell, D. L","Schmidt, B."],"key":"liu09cudasw","id":"liu09cudasw","bibbaseid":"liu-maskell-schmidt-cudaswoptimizingsmithwatermansequencedatabasesearchesforcudaenabledgraphicsprocessingunits-2009","role":"author","urls":{},"metadata":{"authorlinks":{}}},"bibtype":"article","biburl":"https://git.bio.informatik.uni-jena.de/fleisch/literature/raw/master/group-literature.bib","creationDate":"2019-11-19T16:50:42.322Z","downloads":0,"keywords":[],"search_terms":["cudasw","optimizing","smith","waterman","sequence","database","searches","cuda","enabled","graphics","processing","units","liu","maskell","schmidt"],"title":"CUDASW++: Optimizing Smith-Waterman sequence database searches for CUDA-enabled graphics processing units.","year":2009,"dataSources":["C5FtkvWWggFfMJTFX"]}