ACM SIGOPS Operating Systems Review
In search of clusters: the coming battle in lowly parallel computing
In search of clusters: the coming battle in lowly parallel computing
Algorithms for the Longest Common Subsequence Problem
Journal of the ACM (JACM)
JIAJIA: A Software DSM System Based on a New Cache Coherence Protocol
HPCN Europe '99 Proceedings of the 7th International Conference on High-Performance Computing and Networking
Using a DSM application to locally align DNA sequences
CCGRID '04 Proceedings of the 2004 IEEE International Symposium on Cluster Computing and the Grid
A parallel strategy for biological sequence alignment in restricted memory space
Journal of Parallel and Distributed Computing
BSB'07 Proceedings of the 2nd Brazilian conference on Advances in bioinformatics and computational biology
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Biological sequence comparison is one of the most important and basic problems in computational biology. Due to its high demands for computational power and memory, it is a very challenging task. Most of sequence comparison methods used are based on heuristics, which are faster but there are no guarantees that the best alignments will be produced. On the other hand, the algorithm proposed by Smith-Waterman obtains the best local alignments at the expense of very high computing power and huge memory requirements. In this article, we present and evaluate our experiments with three parallel 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 speedups and indicate that impressive improvements can be achieved, depending on the strategy used. Also, we present some theoretical remarks on how to reduce the amount of memory used.