Parallel strategies for the local biological sequence alignment in a cluster of workstations

Recently, many organisms have had their DNA entirely sequenced. This reality presents the need for comparing long DNA sequences, which is a challenging task due to its high demands for computational power and memory. Sequence comparison is a basic operation in DNA sequencing projects, and most sequence comparison methods currently in use are based on heuristics, which are faster but offer no guarantees of producing the best alignments possible. In order to alleviate this problem, Smith-Waterman proposed an algorithm. This algorithm obtains the best local alignments but at the expense of very high computing power and huge memory requirements. In this article, we present and evaluate our experiments involving three strategies to run the Smith-Waterman algorithm in a cluster of workstations using a Distributed Shared Memory System. Our results on an eight-machine cluster presented very good speed-up and indicate that impressive improvements can be achieved depending on the strategy used. In addition, we present a number of theoretical remarks concerning how to reduce the amount of memory used. (C) 2006 Elsevier Inc. All rights reserved.