GPU parallelization of algebraic dynamic programming

Authors:
Peter Steffen;Robert Giegerich;Mathieu Giraud
Affiliations:
Technische Fakultät, Universität Bielefeld, Bielefeld, Germany and CNRS, LIFL, Université Lille 1, Villeneuve d'Ascq Cedex, France;Technische Fakultät, Universität Bielefeld, Bielefeld, Germany;CNRS, LIFL, Université Lille 1, Villeneuve d'Ascq Cedex, France
Venue:
PPAM'09 Proceedings of the 8th international conference on Parallel processing and applied mathematics: Part II
Year:
2009

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Abstract

Algebraic Dynamic Programming (ADP) is a framework to encode a broad range of optimization problems, including common bioinformatics problems like RNA folding or pairwise sequence alignment. The ADP compiler translates such ADP programs into C. As all the ADP problems have similar data dependencies in the dynamic programming tables, a generic parallelization is possible. We updated the compiler to include a parallel backend, launching a large number of independent threads. Depending on the application, we report speedups ranging from 6.1× to 25.8× on a Nvidia GTX 280 through the CUDA libraries.