Dynamic parallelization for RNA structure comparison

In this paper we describe the parallelization of a dynamic programming algorithm used to find common RNA secondary structures including pseudoknots and similar structures. The sequential algorithm is recursive and uses memoization and data-driven selective allocation of the tables, in order to cope with the high space and time demands. These features, in addition to the irregular nature of the data access pattern, present particular challenges to parallelization. We present a new manager-worker approach, where workers are responsible for task creation and the manager's sole responsibility is overseeing load balancing. Special considerations are given to the management of distributed, dynamic task creation and data structures, along with general inter-process communication and load balancing on a heterogeneous computational platform. Experimental results show a modest level of speedup with a highly-scalable level of memory usage, allowing the comparison of much longer RNA molecules than is possible in the sequential implementation.