Parallel Implementation of Dynamic Programming Problems Using Wavefront and Rank Convergence with Full Resource Utilization

In this paper, we propose a novel approach which uses full processor utilization to compute a particular class of dynamic programming problems parallelly. This class includes algorithms such as Longest Common Subsequence and Needleman-Wunsch. In a dynamic programming, a larger problem is divided into smaller problems which are then solved, and the results are used to compute the final result. Each subproblem can be considered as a stage. If computations made in a stage are independent of the computations made in other stages, then these stages can be calculated in parallel. The idling of processors bottlenecks the performance of the currently existing parallel algorithms. In this paper, we are using rank convergence for computation of each stage ensuring full processor utilization. This increases the efficiency and speedup of the parallel algorithm.