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Related Experiment Videos

Parallel hardware for sequence comparison and alignment

R Hughey1

  • 1University of California, Santa Cruz 95064, USA. rph@cse.ucsc.edu

Computer Applications in the Biosciences : CABIOS
|December 1, 1996
PubMed
Summary
This summary is machine-generated.

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This study reviews high-performance sequence analysis methods. It compares supercomputers and specialized co-processors for computational biology tasks, noting hardware comparison challenges.

Area of Science:

  • Computational biology
  • Bioinformatics
  • High-performance computing

Background:

  • Sequence comparison is a fundamental tool in computational biology.
  • The standard algorithm has a time complexity of O(n2), suitable for parallel processing.
  • Evaluating hardware performance for sequence analysis presents challenges.

Purpose of the Study:

  • To review and compare high-performance sequence analysis techniques.
  • To assess the efficacy of general-purpose supercomputers versus specialized co-processors.
  • To highlight the difficulties in comparing hardware based on published performance data.

Main Methods:

  • Review of existing literature on high-performance sequence analysis.
  • Comparative analysis of computational strategies on different hardware architectures.

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  • Discussion of performance metrics and their limitations.

    • Main Results:

    • Sequence comparison algorithms can be effectively mapped to linear processor arrays.
    • Both supercomputers and reconfigurable/programmable co-processors offer high-performance solutions.
    • Direct hardware comparison is complicated by variations in reported performance metrics.

    Conclusions:

    • High-performance sequence analysis can be achieved using diverse computing architectures.
    • The choice of hardware depends on specific research needs and performance evaluation criteria.
    • Standardized benchmarking is crucial for accurate hardware assessment in bioinformatics.