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Related Experiment Video

Updated: May 29, 2026

Assessing Human Spatial Navigation in a Virtual Space and its Sensitivity to Exercise
06:17

Assessing Human Spatial Navigation in a Virtual Space and its Sensitivity to Exercise

Published on: January 26, 2024

Parallel game-tree search.

T A Marsland1, F Popowich

  • 1Department of Computer Science, University of Alberta, Edmonton, Alta., Canada.

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

This study details a parallel alpha-beta search algorithm for chess programs, optimizing performance by minimizing communication delays on multiprocessor systems. Experimental results highlight the impact of techniques like principal variation splitting and memory tables.

Related Experiment Videos

Last Updated: May 29, 2026

Assessing Human Spatial Navigation in a Virtual Space and its Sensitivity to Exercise
06:17

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Published on: January 26, 2024

Area of Science:

  • Computer Science
  • Artificial Intelligence
  • Parallel Computing

Background:

  • Designing efficient parallel algorithms for complex search problems like chess is challenging.
  • Communication overhead significantly impacts performance in multiprocessor systems.
  • Existing parallel search algorithms often struggle with communication delays.

Purpose of the Study:

  • To discuss design issues in parallel alpha-beta search implementation.
  • To present a tree decomposition scheme for well-ordered trees.
  • To evaluate performance enhancements for multiprocessor chess programs.

Main Methods:

  • Implementation of the principal variation splitting method.
  • Experimental analysis of refinements: progressive deepening, narrow window searching, and memory tables.
  • Development of a tree decomposition-based algorithm with modest message passing.

Main Results:

  • Refinements like progressive deepening and memory tables improve performance.
  • The proposed algorithm operates with low search overhead and minimal message passing.
  • Communication overhead is identified as a key performance bottleneck with two distinct components.

Conclusions:

  • The tree decomposition scheme effectively addresses design issues in parallel alpha-beta search.
  • Minimizing communication overhead is crucial for high-performance parallel computing in AI.
  • The presented algorithm offers a promising approach for efficient multiprocessor-based game playing programs.