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Article

Significance of Parallel Computing on the Performance of Digital Image Correlation Algorithms in MATLAB

by 1,2,†, 3,*,† and 4
1
School of Engineering, RMIT University, Melbourne, VIC 3000, Australia
2
Department of Aerospace Engineering, Aachen University of Applied Sciences, 52066 Aachen, Germany
3
Department of Mechanical Engineering, Kingston University, London SW15 3DW, UK
4
School of Engineering and Information Technology, University of New South Wales, Canberra, ACT 2612, Australia
*
Author to whom correspondence should be addressed.
Equal contributions.
Received: 14 January 2021 / Revised: 20 February 2021 / Accepted: 25 February 2021 / Published: 3 March 2021
Digital Image Correlation (DIC) is a powerful tool used to evaluate displacements and deformations in a non-intrusive manner. By comparing two images, one from the undeformed reference states of the sample and the other from the deformed target state, the relative displacement between the two states is determined. DIC is well-known and often used for post-processing analysis of in-plane displacements and deformation of the specimen. Increasing the analysis speed to enable real-time DIC analysis will be beneficial and expand the scope of this method. Here we tested several combinations of the most common DIC methods in combination with different parallelization approaches in MATLAB and evaluated their performance to determine whether the real-time analysis is possible with these methods. The effects of computing with different hardware settings were also analyzed and discussed. We found that implementation problems can reduce the efficiency of a theoretically superior algorithm, such that it becomes practically slower than a sub-optimal algorithm. The Newton–Raphson algorithm in combination with a modified particle swarm algorithm in parallel image computation was found to be most effective. This is contrary to theory, suggesting that the inverse-compositional Gauss–Newton algorithm is superior. As expected, the brute force search algorithm is the least efficient method. We also found that the correct choice of parallelization tasks is critical in attaining improvements in computing speed. A poorly chosen parallelization approach with high parallel overhead leads to inferior performance. Finally, irrespective of the computing mode, the correct choice of combinations of integer-pixel and sub-pixel search algorithms is critical for efficient analysis. The real-time analysis using DIC will be difficult on computers with standard computing capabilities, even if parallelization is implemented, so the suggested solution would be to use graphics processing unit (GPU) acceleration. View Full-Text
Keywords: digital image correlation; real-time processing; Newton–Raphson method; particle swarm optimization; inverse-compositional Gauss–Newton method; parallel computation digital image correlation; real-time processing; Newton–Raphson method; particle swarm optimization; inverse-compositional Gauss–Newton method; parallel computation
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MDPI and ACS Style

Thoma, A.; Moni, A.; Ravi, S. Significance of Parallel Computing on the Performance of Digital Image Correlation Algorithms in MATLAB. Designs 2021, 5, 15. https://0-doi-org.brum.beds.ac.uk/10.3390/designs5010015

AMA Style

Thoma A, Moni A, Ravi S. Significance of Parallel Computing on the Performance of Digital Image Correlation Algorithms in MATLAB. Designs. 2021; 5(1):15. https://0-doi-org.brum.beds.ac.uk/10.3390/designs5010015

Chicago/Turabian Style

Thoma, Andreas; Moni, Abhijith; Ravi, Sridhar. 2021. "Significance of Parallel Computing on the Performance of Digital Image Correlation Algorithms in MATLAB" Designs 5, no. 1: 15. https://0-doi-org.brum.beds.ac.uk/10.3390/designs5010015

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