An Improved Distance Matrix Computation Algorithm for Multicore Clusters
| Autor: | Naglaa M. Reda, Fayed F. M. Ghaleb, Mohammed W. Al-Neama |
|---|---|
| Jazyk: | angličtina |
| Rok vydání: | 2014 |
| Předmět: |
Multi-core processor
Speedup General Immunology and Microbiology Article Subject Computer science Computation lcsh:R Computational Biology lcsh:Medicine General Medicine Sequence Analysis DNA Computing Methodologies General Biochemistry Genetics and Molecular Biology Scheduling (computing) Distance matrix Scalability SSE2 Algorithm Sequence Alignment Algorithms Software Research Article |
| Zdroj: | BioMed Research International, Vol 2014 (2014) BioMed Research International BASE-Bielefeld Academic Search Engine |
| ISSN: | 2314-6141 2314-6133 |
| Popis: | Distance matrix has diverse usage in different research areas. Its computation is typically an essential task in most bioinformatics applications, especially in multiple sequence alignment. The gigantic explosion of biological sequence databases leads to an urgent need for accelerating these computations.DistVectalgorithm was introduced in the paper of Al-Neama et al. (in press) to present a recent approach for vectorizing distance matrix computing. It showed an efficient performance in both sequential and parallel computing. However, the multicore cluster systems, which are available now, with their scalability and performance/cost ratio, meet the need for more powerful and efficient performance. This paper proposesDistVect1as highly efficient parallel vectorized algorithm with high performance for computing distance matrix, addressed to multicore clusters. It reformulatesDistVect1vectorized algorithm in terms of clusters primitives. It deduces an efficient approach of partitioning and scheduling computations, convenient to this type of architecture. Implementations employ potential of both MPI and OpenMP libraries. Experimental results show that the proposed method performs improvement of around 3-fold speedup upon SSE2. Further it also achieves speedups more than 9 orders of magnitude compared to the publicly available parallel implementation utilized in ClustalW-MPI. |
| Databáze: | OpenAIRE |
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