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Accelerating the Nussinov RNA folding algorithm with CUDA/GPU

Dar-Jen Chang, C. Kimmer, Ming Ouyang
Computer Engineering and Computer Science Department, University of Louisville, Louisville, KY 40292, USA
IEEE International Symposium on Signal Processing and Information Technology (ISSPIT), 2010

@inproceedings{chang2010accelerating,

   title={Accelerating the Nussinov RNA folding algorithm with CUDA/GPU},

   author={Chang, D.J. and Kimmer, C. and Ouyang, M.},

   booktitle={Signal Processing and Information Technology (ISSPIT), 2010 IEEE International Symposium on},

   pages={120–125},

   organization={IEEE},

   year={2010}

}

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Graphics processing units (GPU) on commodity video cards have evolved into powerful computational devices. The RNA secondary structure arises from the primary structure and a backbone of canonical, Watson-Crick base pairings (A-U, C-G), and to a lesser extent, the G-U pairing. Early computational work by Nussinov formulated the problem of RNA secondary structure prediction as a maximization of the number of paired bases, which led to a simplified problem amenable to a dynamic programming solution for O(n^3) serial time. This article describes a GPU implementation of the Nussinov dynamic programming. Computation results show that the GPU implementation is up to 290 times faster than the CPU.
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