A Parallel Smith-Waterman Algorithm Based on Divide and Conquer
ICA3PP '02 Proceedings of the Fifth International Conference on Algorithms and Architectures for Parallel Processing
Sourcebook of parallel computing
Sourcebook of parallel computing
Parallel Programming: Techniques and Applications Using Networked Workstations and Parallel Computers (2nd Edition)
Distributed and Parallel Systems: Cluster and Grid Computing (Kluwer International Series in Engineering & Computer Science)
Principles of Concurrent and Distributed Programming (2nd Edition) (Prentice-Hall International Series in Computer Science)
Distributed Data Management for Grid Computing
Distributed Data Management for Grid Computing
CCGRID '07 Proceedings of the Seventh IEEE International Symposium on Cluster Computing and the Grid
Cloud Computing: Web-Based Applications That Change the Way You Work and Collaborate Online
Cloud Computing: Web-Based Applications That Change the Way You Work and Collaborate Online
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DNA sequence alignment is one of the most important operations of computational biology. In 1981, Smith and Waterman developed a method for sequences local alignment. Due to its computational power and memory requirements, various heuristics have been developed to reduce execution time at the expense of a loss of accuracy in the result. This is why heuristics do not ensure that the best alignment is found. For this reason, it is interesting to study how to apply the computer power of different parallel platforms to speed up the sequence alignment process without losing result accuracy. In this article, a new parallelization strategy (HI-M) of Smith-Waterman algorithm on a multi-core cluster is presented, configuring a pipeline with a hybrid communication model. Additionally, a performance analysis is carried out and compared with two previously presented parallel solutions. Finally, experimental results are presented, as well as future research lines.