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Calculating Genomic Distances in Parallel Using OpenMP

  • Vijaya Smitha Kolli
  • Hui Liu
  • Jieyue He
  • Michelle Hong Pan
  • Yi Pan
Conference paper
  • 279 Downloads
Part of the Lecture Notes in Computer Science book series (LNCS, volume 3680)

Abstract

By finding the corresponding shortest edit distance between two signed gene permutations, we can know the smallest number of insertions, deletions, and inversions required to change on string of genes into another, where insertion, deletion and inversion are the process of genome evolutions. However, it is NP-hard problem to compute the edit distance between two genomes. 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 Marron’s et al 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 the decrease of the efficiency. We believe the efficiency of the algorithm can be improved by parallelizing. We parallelize their algorithm via OpenMP on 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 length of the gene sequences. The experimental results have shown that either coarse grain parallelism or fine grain parallelism alone does not improve the performance of the algorithm very much, however, the combination of both fine grain and coarse grain parallelism have improve the performance to a great extent.

Keywords

Edit Distance Chunk Size Genomic Distance Edit Sequence OpenMP Program 
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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Copyright information

© Springer-Verlag Berlin Heidelberg 2005

Authors and Affiliations

  • Vijaya Smitha Kolli
    • 1
  • Hui Liu
    • 1
  • Jieyue He
    • 1
    • 2
  • Michelle Hong Pan
    • 3
  • Yi Pan
    • 1
  1. 1.Department of Computer ScienceGeorgia State UniversityAtlantaUSA
  2. 2.Department of Computer ScienceSoutheast UniversityNanjing, JiangsuChina
  3. 3.Centers for Disease Control and Prevention, Office of Workforce and Career Development, Career Development Division, Public Health Informatics Fellow ProgramAtlantaUSA

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