A Fast Linear-Space Sequence Alignment Algorithm with Dynamic Parallelization Framework

Exact pairwise sequence alignment algorithms using dynamic programming require quadratic space and time, and this makes these algorithms impractical for large-scale sequences. In this paper, we propose and evaluate a new Anti-Diagonal based Parallel Linear-Space Algorithm (AD-PLSA). It records similarity matrix scores and start points on special anti-diagonals instead of special rows or columns. This algorithm is able to further reduce the total amount of re-computation. It also produces balanced sub-problems with approximately even size, and this is of great benefit to the parallelization. In addition, we establish a dynamic parallelization framework for the efficient acceleration on a symmetric multiprocessor (SMP) platform. The experimental results present liner speedup for long real DNA sequences. Compared with the typical row-col algorithm, our method is able to save more than 30% of the re-computation, and run twice as fast in the backward phase.