ClawHMMER: A Streaming HMMer-Search Implementatio
SC '05 Proceedings of the 2005 ACM/IEEE conference on Supercomputing
Streaming Algorithms for Biological Sequence Alignment on GPUs
IEEE Transactions on Parallel and Distributed Systems
Program optimization carving for GPU computing
Journal of Parallel and Distributed Computing
HCW 2009 keynote talk: GPU computing: Heterogeneous computing for future systems
IPDPS '09 Proceedings of the 2009 IEEE International Symposium on Parallel&Distributed Processing
Harnessing the power of idle GPUs for acceleration of biological sequence alignment
IPDPS '09 Proceedings of the 2009 IEEE International Symposium on Parallel&Distributed Processing
Bio-sequence database scanning on a GPU
IPDPS'06 Proceedings of the 20th international conference on Parallel and distributed processing
GPU-ClustalW: using graphics hardware to accelerate multiple sequence alignment
HiPC'06 Proceedings of the 13th international conference on High Performance Computing
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Smith-Waterman algorithm is a classic dynamic programming algorithm to solve the problem of biological sequence alignment However, with the rapid increment of the number of DNA and protein sequences, the originally sequential algorithm is very time consuming due to there existing the same computing task computed repeatedly on large-scale data Today's GPU (graphics processor unit) consists of hundreds of processors, so it has a more powerful computation capacity than the current multicore CPU And as the programmability of GPU improved continuously, using it to do generous purpose computing is becoming very popular In order to accelerate sequence alignment, previous researchers use the parallelism of the anti-diagonal of similarity matrix to parallelize the Smith-Waterman algorithm on GPU In this paper, we design a new parallel algorithm which exploits the parallelism of the column of similarity matrix to parallelize the Smith-Waterman algorithm on a heterogeneous system based on CPU and GPU The experiment result shows that our new parallel algorithm is more efficient than that of previous, which takes full advantage of the features of both the CPU and GPU and obtains approximately 37 times speedup compared with the sequential algorithm named OSEARCH implemented on Intel dual-core E2140 processor.