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Load Balancing and Parallel Multiple Sequence Alignment with Tree Accumulation

  • Guangming Tan
  • Liu Peng
  • Shengzhong Feng
  • Ninghui Sun
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4128)

Abstract

Multiple sequence alignment program, ClustalW, is time consuming, however, commonly used to compare the protein sequences. ClustalW includes two main time consuming parts: pairwise alignment and progressive alignment. Due to the irregular computation based on tree in progressive alignment, available parallel programs can not achieve reasonable speedups for large scale number of sequences. In this paper, progressive alignment is reduced to tree accumulation problem. Load balancing is ignored in previous efficient parallel tree accumulations. We proposed a load balancing strategy for parallelizing tree accumulation in progressive alignment. The new parallel progressive alignment algorithm reducing to tree accumulation with load balancing reduced the overall running time greatly and achieved reasonable speedups.

Keywords

Load Balance Parallel Algorithm Internal Node Parallel Program Linear Speedup 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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References

  1. 1.
    Julie, D.T., Desmond, G.H., Toby, J.G.: Clustal W: improving the sensitivity of progressive multiple sequence alignment through sequence weighting, position-specific gap penalties and weight matrix choice. Nucleic Acids Research 22(22), 4673–4680 (1994)CrossRefGoogle Scholar
  2. 2.
    Henikoff, D.: Approximation Algorithms for NP-hard Problems. PWS publishers (1996)Google Scholar
  3. 3.
    Feng, D., Doolittle, R.F.: Progressive sequence alignment as prerequisite to correct phylogenetic trees. Journal of Molecular Evolution 25, 351–360 (1987)CrossRefGoogle Scholar
  4. 4.
    Saitou, N., Nei, M.: The neighbor-joining method: A new method for reconstructing phylogenetic trees. Molecular Biology and Evolutoin 4, 406–425 (1987)Google Scholar
  5. 5.
    Mikhailov, D., Cofer, H., Gomperts, R.: Performance optimization of ClustalW: Parallel ClustalW, HT Clustal and MULTICLUSTAL. White papers, SGI (2001)Google Scholar
  6. 6.
    Duzlevski, O.: SMP version of ClustalW 1.82, http://bioinfor.pbi.nrc.ca/clustalw-smp
  7. 7.
    Cheetham, J.J., Dehne, F., Pitre, S., Chaplin, A.R., Tailon, P.J.: Parallel CLUSTALW for PC Clusters. In: Proceedings of International Conference on Computational Science and its Applications, Montreal, Canada, May 18-21 (2003)Google Scholar
  8. 8.
    Li, K.: ClustalW-MPI: Clustalw analysis using distributed and parallel computing. Bioinformatics 19(12), 1585–1586 (2003)CrossRefGoogle Scholar
  9. 9.
    Gibbons, J., Cai, W., Skillicorn, D.: Efficient parallel algorithms for tree accumulations. Sci. Comput. Programming 23, 1–18 (1994)MATHCrossRefMathSciNetGoogle Scholar
  10. 10.
    Kwok, Y.K., Ahmad, I.: Static scheduling algorithms for allocating directed task graphs to multiprocessors. ACM Computing Surveys 31(4), 406–471 (1999)CrossRefGoogle Scholar
  11. 11.

Copyright information

© Springer-Verlag Berlin Heidelberg 2006

Authors and Affiliations

  • Guangming Tan
    • 1
    • 2
  • Liu Peng
    • 1
    • 2
  • Shengzhong Feng
    • 1
  • Ninghui Sun
    • 1
  1. 1.Institute of Computing TechnologyChinese Academy of Sciences 
  2. 2.Graduate School of Chinese Academy of Sciences 

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