Acceleration of Smith-Waterman using Recursive Variable Expansion

The Smith-Waterman (SW) algorithm is a local sequence alignment algorithm that attempts to align two biological sequences of varying length such that the alignment score is maximum. In this paper, we propose a new approach to reduce the time needed to perform the SW algorithm. This is done by applying the concept of recursive variable expansion, which exposes more parallelism in the algorithm than any other published method. The paper estimates the speed and hardware overhead for the newly proposed approach relative to other known acceleration methods. Using the new approach, it is possible to achieve a minimum speedup of 400 times better than the serial case for a typical sequence length of 500, which is 1.6 times higher than any other published method. The paper also shows that further speedup can be achieved using extra hardware to expose even more parallelism in the algorithm.