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GPU acceleration for optical measurement

Author: Tianyi Wang; Qian Kemao; Society of Photo-optical Instrumentation Engineers,
Publisher: Bellingham, Washington (1000 20th St. Bellingham WA 98225-6705 USA) : SPIE, 2017.
Series: SPIE spotlight, SL34.
Edition/Format:   eBook : Document : EnglishView all editions and formats
Summary:
Optical measurement techniques have been successfully applied to measure various physical quantities in experimental mechanics, material science, biomechanics, etc. To faciliate larger amounts of data and increased accuracy, graphics processing units (GPUs) has entered the mainstream due to their cost-effectiveness, short development cycle, and transparent scalability. This Spotlight not only demonstrates the power  Read more...
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Details

Genre/Form: Electronic books
Material Type: Document, Internet resource
Document Type: Internet Resource, Computer File
All Authors / Contributors: Tianyi Wang; Qian Kemao; Society of Photo-optical Instrumentation Engineers,
ISBN: 9781510617346 1510617345 1510617353 9781510617353 1510617361 9781510617360
OCLC Number: 1016979586
Description: 1 online resource (vi, 44 pages) : illustrations.
Contents: Preface --
1. Introduction --
2. CUDA basics for GPU programming --
3. Parallel WFF2 algorithm for fringe pattern analysis --
4. Parallel IC-GN algorithm for DIC --
5. Conclusion and further reading --
Acknowledgments --
References.
Series Title: SPIE spotlight, SL34.
Other Titles: Graphics processing unit acceleration for optical measurement.
Responsibility: by Tianyi Wang and Qian Kemao.

Abstract:

Optical measurement techniques have been successfully applied to measure various physical quantities in experimental mechanics, material science, biomechanics, etc. To faciliate larger amounts of data and increased accuracy, graphics processing units (GPUs) has entered the mainstream due to their cost-effectiveness, short development cycle, and transparent scalability. This Spotlight not only demonstrates the power of GPUs to accelerate optical measurement algorithms but also provides a hands-on approach for the use of Compute Unified Device Architecture (CUDA) to accelerate existing sequential algorithms on CUDA-capable GPUs. Readers familiar with basic C/C++ programming can integrate CUDA with their existing optical algorithms for higher computing performance.

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