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A Practical Guide to Phylogenetics for Nonexperts
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Design, analysis, and implementation of a parallel tree search algorithm.

S G Akl1, D T Barnard, R J Doran

  • 1MEMBER, IEEE, Department of Computing and Information Science, Queen's University, Kingston, Ont., Canada.

IEEE Transactions on Pattern Analysis and Machine Intelligence
|August 27, 2011
PubMed
Summary
This summary is machine-generated.

Parallel processing significantly speeds up decision tree searches using the alpha-beta algorithm. This optimization reduces search time by enabling simultaneous activity across different tree sections, demonstrating a key time-storage tradeoff.

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Area of Science:

  • Computer Science
  • Artificial Intelligence
  • Algorithm Optimization

Background:

  • Decision tree search algorithms are fundamental in artificial intelligence and computer science.
  • Traditional alpha-beta algorithms can be computationally intensive for large search spaces.
  • Parallel computing offers potential for accelerating complex search tasks.

Purpose of the Study:

  • To adapt the alpha-beta algorithm for parallel execution in decision tree searches.
  • To evaluate the performance benefits of parallelizing the alpha-beta algorithm.
  • To analyze the storage requirements and time-storage tradeoffs of the parallelized algorithm.

Main Methods:

  • Modified the standard alpha-beta algorithm to support parallel processing across different tree segments.
  • Implemented the parallelized algorithm using the GASP IV simulation language.
  • Conducted preliminary experiments to collect runtime, nodes scored, and nodes visited statistics.

Main Results:

  • Demonstrated substantial reductions in search time through the implementation of parallelism.
  • Derived an analytic expression quantifying the storage requirements for the parallel algorithm.
  • Observed a clear tradeoff between computational time and storage space.

Conclusions:

  • Parallelizing the alpha-beta algorithm offers significant performance improvements for decision tree searches.
  • The simulation environment effectively models parallel software and hardware execution.
  • The study highlights the classic time-storage tradeoff inherent in algorithm design.