• DocumentCode
    2502959
  • Title

    A Parallel, Out-of-Core Algorithm for RNA Secondary Structure Prediction

  • Author

    Zhou, Wenduo ; Lowenthal, David K.

  • Author_Institution
    Dept. of Phys. & Astron., Georgia Univ., Athens, GA
  • fYear
    2006
  • fDate
    14-18 Aug. 2006
  • Firstpage
    74
  • Lastpage
    81
  • Abstract
    RNA pseudoknot prediction is an algorithm for RNA sequence search and alignment. An important building block towards pseudoknot prediction is RNA secondary structure prediction. The difficulty of extending the secondary structure prediction algorithm to a parallel program is (1) it has complicated data dependences, and (2) it has a large data set that typically cannot fit completely in main memory. In this paper, we propose a new out-of-core, distributed-memory algorithm for RNA secondary structure prediction. Its novelty lies in its redundant file scheme, I/O-reducing in-core buffer mechanism, and dynamic load balancing algorithm. Experimental results obtained on 16 Sun UltraSPARC Illi nodes provide evidence that our approach achieves good speedup. Furthermore, we found that counterintuitively, the size of the in-memory buffer is critical to efficiency of the parallel program
  • Keywords
    biology computing; buffer storage; distributed memory systems; macromolecules; organic compounds; parallel algorithms; parallel programming; resource allocation; I/O-reducing in-core buffer mechanism; RNA pseudoknot prediction; RNA secondary structure prediction; RNA sequence alignment; RNA sequence search; distributed-memory algorithm; dynamic load balancing; parallel out-of-core algorithm; parallel program; redundant file scheme; Astronomy; Biological system modeling; Computer science; Context modeling; Heuristic algorithms; Load management; Physics; Prediction algorithms; Predictive models; RNA;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Parallel Processing, 2006. ICPP 2006. International Conference on
  • Conference_Location
    Columbus, OH
  • ISSN
    0190-3918
  • Print_ISBN
    0-7695-2636-5
  • Type

    conf

  • DOI
    10.1109/ICPP.2006.10
  • Filename
    1690607