Optimal multiple sequence alignment by dynamic programming, like many highly dimensional scientific computing problems, has failed to benefit from the improvements in computing performance brought about by multi-processor systems, due to the lack of suitable scheme to manage partitioning and dependencies. A scheme for parallel implementation of the dynamic programming multiple sequence alignment is presented, based on a peer to peer design and a multidimensional array indexing method. This design results in up to 5-fold improvement compared to a previously described master/slave design, and scales favourably with the number of processors used. This study demonstrates an approach for parallelising multi-dimensional dynamic programming and similar algorithms utilizing multi-processor architectures.
@article{arxiv.2311.17530,
title = {Parallelizing Optimal Multiple Sequence Alignment by Dynamic Programming},
author = {Manal Helal and Hossam El-Gindy and Lenore Mullin and Bruno Gaeta},
journal= {arXiv preprint arXiv:2311.17530},
year = {2023}
}