GPU accelerated Rna folding algorithm

Guillaume Rizk (1) , Dominique Lavenier (1)

1 SYMBIOSE - Biological systems and models, bioinformatics and sequences

Many bioinformatics studies require the analysis of RNA or DNA structures. More specifically, extensive work is done to elaborate efficient algorithms able to predict the 2-D folding structures of RNA or DNA sequences. However, the high computational complexity of the algorithms, combined with the rapid increase of genomic data, triggers the need of faster methods. Current approaches focus on parallelizing these algorithms on multiprocessor systems or on clusters, yielding to good performance but at a relatively high cost. Here, we explore the use of computer graphics hardware to speed up these algorithms which, theoretically, provide both high performance and low cost. We use the CUDA programming language to harness the power of NVIDIA graphic cards for general computation with a C-like environment. Performances on recent graphic cards achieve a ×17 speed-up.

Keywords: GPGPU; RNA; secondary structure; minimum free energy

Domains: Bioinformatics [q-bio.QM]; Quantitative Methods [q-bio.QM]

Guillaume Rizk, Dominique Lavenier. GPU accelerated Rna folding algorithm. 9th International Conference on Computational Science, May 2009, Baton Rouge, United States. pp.1031. ⟨hal-00425543⟩