Parallel Smith-Waterman Algorithm for Local DNA Comparison in a Cluster of Workstations
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.
KeywordsResult Matrix Distribute Shared Memory Shared Memory System Local Sequence Alignment Distribute Shared Memory System
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- 1.Setubal, J.C., Meidanis, J.: Introduction to Computational Molecular Biology. Brooks/Cole Publishing Company (1997)Google Scholar
- 2.Smith, T.F., Waterman, M.S.: Identification of common molecular sub-sequences. Journal of Molecular Biology, 195–197 (1981)Google Scholar
- 4.Pearson, W.R., Lipman, D.L.: Improved tools for biological sequence comparison. In: Proc. National Academy Of Science, pp. 2444–2448. NAS (1988)Google Scholar
- 5.Martins, W.S., Del Cuvillo, J.B., Useche, F.J., Theobald, K.B., Gao, G.R.: A multithread parallel implementation of a dynamic programming algorithm for sequence comparison. In: Symp. on Computer Architecture and HPC (SBAC-PAD), pp. 1–8 (2001)Google Scholar
- 6.group, D.: Smith waterman homology search (2003)Google Scholar
- 7.Co., D.: Decypher smith waterman solution (2003)Google Scholar
- 8.Boukerche, A., Melo, A.C.M.A., Walter, M.E.M.T., Melo, R.C.F., Santana, M.N.P., Batista, R.B.: Performance evaluation of a local dna sequence alignment algorithm on a cluster of workstations. In: Proc. of the Int. Parallel and Distributed Processing Symposium (IPDPS 2004). IEEE Society, Los Alamitos (2004)Google Scholar
- 9.Batista, R.B., Silva, D.N., Melo, A.C.M.A., Weigang, L.: Using a dsm application to locally align dna sequences. In: Proc. of the IEEE/ACM Int. Symp. on Cluster Computing and the Grid. IEEE Computer Society Press, Los Alamitos (2004)Google Scholar
- 10.Pfister, G.: Search of Clusters - The Coming Battle for Lowly Parallel Computing. Prentice-Hall, Englewood Cliffs (1995)Google Scholar
- 12.Mosberger, D.: Memory consistency models. Operating Systems Review, 18–26 (1993)Google Scholar
- 13.Hu, S., Shi, W., Tang, Z.: Jiajia: An svm system based on a new cache coherence protocol. In: High Performance Computing and Networking (HPCN), pp. 463–472. Springer, Heidelberg (1999)Google Scholar