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On the parallelisation of bioinformatics applications.

O Trelles1

  • 1Computer Architecture Department, University of Malaga, Spain. ots@ac.uma.es

Briefings in Bioinformatics
|July 24, 2001
PubMed
Summary
This summary is machine-generated.

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This study explores parallel computing strategies for bioinformatics software, addressing challenges in diverse algorithms from database searching to phylogenetic tree construction across various architectures.

Area of Science:

  • Bioinformatics
  • Computational Biology
  • High-Performance Computing

Background:

  • Bioinformatics software often involves computationally intensive algorithms.
  • These algorithms exhibit diverse computational patterns, from regular to highly irregular.
  • Efficient execution requires parallelization strategies tailored to different hardware architectures.

Purpose of the Study:

  • To survey computational strategies for parallelizing widely used bioinformatics software.
  • To discuss fine- and coarse-grained parallelization for diverse applications.
  • To outline computational issues, machine models, programming approaches, and scheduling for parallel architectures.

Main Methods:

  • Review of existing parallelization strategies for bioinformatics algorithms.

Related Experiment Videos

  • Analysis of computational patterns in database searching and phylogenetic tree construction.
  • Discussion of shared, distributed, and shared/distributed memory architectures.

    • Main Results:

    • Identification of computational challenges in parallelizing bioinformatics software.
    • Categorization of parallel strategies (fine- and coarse-grained) for diverse applications.
    • Overview of programming models and scheduling for various computer architectures.

    Conclusions:

    • Parallel computing is essential for handling computationally expensive bioinformatics tasks.
    • Diverse algorithms necessitate tailored parallelization approaches.
    • Understanding hardware architectures and programming models is key for efficient bioinformatics software parallelization.