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CUDAlign: using GPU to accelerate the comparison of megabase genomic sequences

Edans Flavius O. Sandes, Alba Cristina M.A. de Melo
University of Brasilia, Brasilia, Brazil
Proceedings of the 15th ACM SIGPLAN symposium on Principles and practice of parallel programming, PPoPP ’10

@conference{sandes2010cudalign,

   title={CUDAlign: using GPU to accelerate the comparison of megabase genomic sequences},

   author={Sandes, E.F.O. and de Melo, A.C.},

   booktitle={Proceedings of the 15th ACM SIGPLAN symposium on Principles and practice of parallel computing},

   pages={137–146},

   year={2010},

   organization={ACM}

}

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Biological sequence comparison is a very important operation in Bioinformatics. Even though there do exist exact methods to compare biological sequences, these methods are often neglected due to their quadratic time and space complexity. In order to accelerate these methods, many GPU algorithms were proposed in the literature. Nevertheless, all of them restrict the size of the smallest sequence in such a way that Megabase genome comparison is prevented. In this paper, we propose and evaluate CUDAlign, a GPU algorithm that is able to compare Megabase biological sequences with an exact Smith-Waterman affine gap variant. CUDAlign was implemented in CUDA and tested in two GPU boards, separately. For real sequences whose size range from 1MBP (Megabase Pairs) to 47MBP, a close to uniform GCUPS (Giga Cells Updates per Second) was obtained, showing the potential scalability of our approach. Also, CUDAlign was able to compare the human chromosome 21 and the chimpanzee chromosome 22. This operation took 21 hours on GeForce GTX 280, resulting in a peak performance of 20.375 GCUPS. As far as we know, this is the first time such huge chromosomes are compared with an exact method.
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