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Related Experiment Videos

Parallel computation and FASTA: confronting the problem of parallel database search for a fast sequence comparison

P L Miller1, P M Nadkarni, N M Carriero

  • 1Department of Anesthesiology, Yale University School of Medicine, New Haven, CT 06510.

Computer Applications in the Biosciences : CABIOS
|January 1, 1991
PubMed
Summary
This summary is machine-generated.

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Parallelizing the FASTA algorithm for biological sequence comparison using Linda is effective, but disk I/O can become a bottleneck, limiting speed-up with more processors. Strategies to mitigate this bottleneck are discussed.

Area of Science:

  • Bioinformatics
  • Computational Biology
  • Parallel Computing

Background:

  • Biological sequence comparison is computationally intensive.
  • The FASTA algorithm is a widely used tool for sequence comparison.
  • Parallel computing offers potential speed-ups for such tasks.

Purpose of the Study:

  • To parallelize the FASTA algorithm for biological sequence comparison.
  • To investigate performance bottlenecks in parallelized algorithms.
  • To propose solutions for optimizing parallel database searches.

Main Methods:

  • Parallelization of the FASTA algorithm using Linda, a machine-independent parallel programming language.
  • Testing the parallel program on various parallel machines.
  • Analyzing performance with varying computational loads and processor counts.

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Main Results:

  • The parallel FASTA program runs on diverse parallel machines.
  • A straightforward parallelization strategy is efficient for large computations.
  • Disk I/O emerges as a bottleneck when computation is reduced, limiting scalability.

Conclusions:

  • Parallelizing FASTA with Linda is feasible across different hardware.
  • Disk I/O bottlenecks can hinder performance gains in parallel database searches.
  • Generalizable strategies are needed to overcome I/O limitations in accelerated computations.