Exploiting parallelization for RNA secondary structure prediction in cluster

Abstract

RNA structure prediction remains one of the most compelling, yet elusive areas of computational biology. Many computational methods have been proposed in an attempt to predict RNA secondary structures. A popular dynamic programming (DP) algorithm uses a stochastic context-free grammar to model RNA secondary structures, its time complexity is O(N4) and spatial complexity is O(N3). In this paper, a parallel algorithm, which is time-wise and space-wise optimal with respect to the usual sequential DP algorithm, can be implemented using O(N4/P) time and O(N 3/P) space in cluster. High efficient utilization of processors and good load balancing are achieved through dynamic mapping of DP matrix to processors. As experiments shown, dynamic mapping algorithm has good speedup. © Springer-Verlag Berlin Heidelberg 2005.