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The massively parallel genetic algorithm for RNA folding: MIMD implementation and population variation

  1. Author:
    Shapiro, B. A.
    Wu, J. C.
    Bengali, D.
    Potts, M. J.

  2. Author Address

    NCI, Image Proc Sect, Lab Expt & Computat Biol, Div Basic Sci, Frederick Canc Res & Dev Ctr, NIH, Bldg 469, Rm 150, Frederick, MD 21702 USA. NCI, Image Proc Sect, Lab Expt & Computat Biol, Div Basic Sci, Frederick Canc Res & Dev Ctr, NIH, Frederick, MD 21702 USA. NCI, Sci Applicat Int Corp, LECB, FCRDC, Frederick, MD 21702 USA. SGI Inc, Silver Spring, MD 20904 USA. Shapiro BA NCI, Image Proc Sect, Lab Expt & Computat Biol, Div Basic Sci, Frederick Canc Res & Dev Ctr, NIH, Bldg 469, Rm 150, Frederick, MD 21702 USA.
    1. Year: 2001
  2. Journal: Bioinformatics
    1. 17
    2. 2
    3. Pages: 137-148
  4. Type of Article: Article
  1. Abstract:

    A massively parallel Genetic Algorithm (GA) has been applied to RNA sequence folding on three different computer architectures. The GA, an evolution-like algorithm that is applied to a large population of RNA structures based on a pool of helical stems derived from an RNA sequence, evolves this population in parallel. The algorithm was originally designed and developed for a 16 384 processor SIMD (Single Instruction Multiple Data) MasPar MP-2. More recently it has been adapted to a 64 processor MIMD (Multiple Instruction Multiple Data) SGI ORIGIN 2000, and a 512 processor MIMD GRAY T3E. The MIMD version of the algorithm raises issues concerning RNA structure data- layout and processor communication. In addition, the effects of population variation on the predicted results are discussed. Also presented are the scaling properties of the algorithm from the perspective of the number of physical processors utilized and the number of virtual processors (RNA structures) operated upon.

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