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Multiple biological sequence alignment in heterogeneous multicore clusters with user-selectable task allocation policies

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Abstract

Multiple Sequence Alignment (MSA) is an important problem in Bioinformatics that aims to align more than two sequences in order to emphasize similarity regions. This problem is known to be NP-Hard, so heuristic methods are used to solve it. DIALIGN-TX is an iterative heuristic method for MSA that generates alignments by concatenating ungapped regions with high similarity. Usually, the first phase of MSA algorithms is parallelized by distributing several independent tasks among the nodes. Even though heterogeneous multicore clusters are becoming very common nowadays, very few task allocation policies were proposed for this type of architecture. This paper proposes an MPI/OpenMP master/slave parallel strategy to run DIALIGN-TX in heterogeneous multicore clusters, with several allocation policies. We show that an appropriate choice of the master node has great impact on the overall system performance. Also, the results obtained in a heterogeneous multicore cluster composed of 4 nodes (30 cores), with real sequence sets show that the execution time can be drastically reduced when the appropriate allocation policy is used.

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Authors and Affiliations

  1. Department of Computer Science, University of Brasilia (UnB), Brasilia, Brazil

    Emerson de Araujo Macedo, Alba Cristina Magalhaes Alves de Melo & Gerson Henrique Pfitscher

  2. School of Electrical Engineering and Computer Science, University of Ottawa, Ottawa, Canada

    Azzedine Boukerche

  3. Kuwait University, Kuwait City, Kuwait

    Azzedine Boukerche

Authors
  1. Emerson de Araujo Macedo
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  2. Alba Cristina Magalhaes Alves de Melo
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  3. Gerson Henrique Pfitscher
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  4. Azzedine Boukerche
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Corresponding author

Correspondence to Alba Cristina Magalhaes Alves de Melo.

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de Araujo Macedo, E., Magalhaes Alves de Melo, A.C., Pfitscher, G.H. et al. Multiple biological sequence alignment in heterogeneous multicore clusters with user-selectable task allocation policies. J Supercomput 63, 740–756 (2013). https://doi.org/10.1007/s11227-012-0768-8

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  • DOI: https://doi.org/10.1007/s11227-012-0768-8

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