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Temporal fringe pattern analysis with parallel computing.

Tuck Wah Ng1, Kar Tien Ang, Gianluca Argentini

  • 1The National University of Singapore, Faculty of Engineering, Engneering Block EA-07-32, 9 Engineering Drive 1, Singapore 117576. engngtw@nus.edu.sg

Applied Optics
|December 2, 2005
PubMed
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Parallel computing significantly reduces processing times for temporal fringe pattern analysis. This strategy, using the single-program multiple-data model, speeds up transient phenomena studies.

Area of Science:

  • Optics and Photonics
  • Computational Science

Background:

  • Temporal fringe pattern analysis is crucial for studying transient phenomena.
  • Current analysis methods often suffer from long processing times, hindering real-time applications.

Purpose of the Study:

  • To introduce and evaluate a parallel computing strategy for reducing execution times in temporal fringe pattern analysis.
  • To investigate the impact of processor configuration on analysis speed.

Main Methods:

  • Implementation of a parallel computing strategy utilizing the single-program multiple-data (SPMD) model.
  • Leveraging hyperthreading processor technology within a two-node cluster workstation.
  • Systematic variation of the number of virtual processors to assess performance.

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Main Results:

  • Execution times for temporal fringe pattern analysis were reduced by 1.6 times using four virtual processors in a two-node cluster.
  • Demonstrated feasibility of parallel computing for accelerating fringe pattern analysis.
  • Identified data transfer, read, and waiting times as key areas for further optimization.

Conclusions:

  • Parallel computing offers a viable and effective approach to significantly decrease execution times in temporal fringe pattern analysis.
  • Optimizing data handling is essential for maximizing the benefits of parallel processing with an increasing number of processors.
  • This methodology enhances the applicability of fringe pattern analysis in time-sensitive research areas.