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Related Experiment Videos

Using video-oriented instructions to speed up sequence comparison

A Wozniak1

  • 1INRIA, Le Chesnay, France. Andrzej.Wozniak@inria.fr

Computer Applications in the Biosciences : CABIOS
|April 1, 1997
PubMed
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This study accelerates the Smith-Waterman algorithm for sequence comparison using specialized video instructions, achieving over 18 million matrix cells per second. This novel implementation significantly enhances computational speed for large-scale sequence analysis.

Area of Science:

  • Bioinformatics
  • Computational Biology
  • Computer Science

Background:

  • The Smith-Waterman algorithm is crucial for sequence comparison in bioinformatics.
  • Existing implementations can be computationally intensive for large datasets.
  • Specialized instructions offer potential for significant performance improvements.

Purpose of the Study:

  • To implement the Smith-Waterman algorithm using specialized video instructions for enhanced performance.
  • To achieve instruction-level parallelization for faster sequence comparison.
  • To integrate the accelerated algorithm into the LASSAP software package.

Main Methods:

  • Utilized SIMD-like video instructions for parallelizing the Smith-Waterman algorithm.
  • Implemented the algorithm on ULTRA SPARC and SUN Enterprise 6000 platforms.

Related Experiment Videos

  • Integrated the optimized procedure into the LArge Scale Sequence compArison Package (LASSAP).

    • Main Results:

    • Achieved a speed-up factor of two compared to integer-based implementations.
    • Reached over 18 million matrix cells per second on a single processor.
    • Obtained nearly 200 million matrix cells per second on a 12-processor server, scanning 300 amino acids in 29 seconds.

    Conclusions:

    • The specialized video instruction implementation represents the fastest known Smith-Waterman algorithm on a workstation.
    • The accelerated procedure enhances large-scale sequence comparison tasks, including databank scanning and Z-score computation.
    • This approach offers significant speed-ups for various bioinformatics analyses.