CUDASW++: optimizing Smith-Waterman sequence database searches for CUDA-enabled graphics processing units
By Yongchao Liu, Douglas Leslie Maskell, Bertil Schmidt. 2009
303 citations recorded by the source corpus.
BackgroundThe 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.FindingsOur 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.ConclusionCUDASW++ 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.
Subfields: Molecular Biology, Spectroscopy
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In Molecular Biology
- Eric E. Schadt
- Garry P. Nolan
- Torbjørn Rognes
- Lawrence Lee
- J. Sorenson
- Michael D. Linderman
- Léon-Charles Tranchevent
- Yves Moreau
In Spectroscopy
Citations in the corpus
- Faster Smith-Waterman database searches with inter-sequence SIMD parallelisation (2011, methodology): “However, in the GPU-based tools [15,16] this approach is common.”
- Computational solutions to large-scale data management and analysis (2010, methodology): “They include CUDASW++, a GPU version of the Smith–Waterman sequence database search algorith”