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A Parallel Implementation for Determining Genomic Distances Under Deletion and Insertion

A Parallel Implementation for Determining Genomic Distances Under Deletion and Insertion

  • Vijaya Smitha Kolli20,
  • Hui Liu20,
  • Michelle Hong Pan21 &
  • …
  • Yi Pan20 
  • Conference paper
  • 964 Accesses

Part of the Lecture Notes in Computer Science book series (LNTCS,volume 3515)

Abstract

As the need for comparing genomes of different species has grown dramatically with the fast progress of the Human Genome Project, the evolution at the level of whole genomes has attracted more and more attention from both biologists and computer scientists. They are especially interested in the scenarios in which the genome evolves through insertions, deletions, and movements of genes along its chromosomes. Marron et al proposed a polynomial-time approximation algorithm to compute (near) minimum edit distances under inversions, deletions, and unrestricted insertions. Our work is based on their algorithm, which carries out lots of comparisons and sorting to calculate the edit distance. These comparisons and sorting are extremely time-consuming, and they result in decrease of computational efficiency. We believe the time of the algorithm can be improved through parallelization. We parallelize their algorithm via OpenMP using Intel C++ compiler for Linux 7.1, and compare three levels of parallelism: coarse grain, fine grain and combination of both. The experiments are conducted for a varying number of threads and different lengths of the gene sequences. The experimental results show that either coarse grain parallelism or fine grain parallelism alone does not improve the performance of the algorithm very much. However, the use of combination of both fine grain and coarse grain parallelism improves the performance of the algorithm drastically.

Keywords

  • Parallel Implementation
  • Edit Distance
  • Sequential Algorithm
  • Average Execution Time
  • Chunk Size

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

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Author information

Authors and Affiliations

  1. Department of Computer Science, Georgia State University, Atlanta, GA, 30303, USA

    Vijaya Smitha Kolli, Hui Liu & Yi Pan

  2. Career Development Division, Public Health Informatics Fellow Program, Centers for Disease Control and Prevention, Office of Workforce and Career Development, 1600 Clifton Rd, Atlanta, GA, 30333, USA

    Michelle Hong Pan

Authors
  1. Vijaya Smitha Kolli
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  2. Hui Liu
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  3. Michelle Hong Pan
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  4. Yi Pan
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Editor information

Editors and Affiliations

  1. Department of Mathematics and Computer Science, Emory University, Atlanta, Georgia, USA

    Vaidy S. Sunderam

  2. Department of Mathematics and Computer Science, University of Amsterdam, Kruislaan 403, 1098, Amsterdam, SJ, The Netherlands

    Geert Dick van Albada

  3. Faculty of Sciences, Section of Computational Science, University of Amsterdam, Kruislaan 403, 1098, Amsterdam, SJ, The Netherlands

    Peter M. A. Sloot

  4. Computer Science Department, University of Tennessee, 37996-3450, TN, Knoxville, USA

    Jack J. Dongarra

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© 2005 Springer-Verlag Berlin Heidelberg

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Kolli, V.S., Liu, H., Pan, M.H., Pan, Y. (2005). A Parallel Implementation for Determining Genomic Distances Under Deletion and Insertion. In: Sunderam, V.S., van Albada, G.D., Sloot, P.M.A., Dongarra, J.J. (eds) Computational Science – ICCS 2005. ICCS 2005. Lecture Notes in Computer Science, vol 3515. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11428848_127

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  • DOI: https://doi.org/10.1007/11428848_127

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-26043-1

  • Online ISBN: 978-3-540-32114-9

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