Modification of common Fourier computer generated hologram's representation methods from sequential to parallel computing

Makey, Ghaith; El-Daher, Moustafa Sayem; Al-Shufi, Kanj

doi:10.1016/j.ijleo.2015.02.076

Cited by 6 publications

(2 citation statements)

References 15 publications

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“…Despite some limitations, it is argued that combining both CUDA kernels and MATLAB support (using the parallel computing toolbox) can further improve and smooth the programming process. 33,62 No knowledge in CUDA is needed while exploiting the parallel computing capabilities for CGH-related computation speed-ups.…”

Section: Available Tools and Utilities For Gpusmentioning

confidence: 99%

Hardware implementations of computer-generated holography: a review

et al. 2020

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Computer-generated holography (CGH) is a technique to generate holographic interference patterns. One of the major issues related to computer hologram generation is the massive computational power required. Hardware accelerators are used to accelerate this process. Previous publications targeting hardware platforms lack performance comparisons between different architectures and do not provide enough information for the evaluation of the suitability of recent hardware platforms for CGH algorithms. We aim to address these limitations and present a comprehensive review of CGH-related hardware implementations.

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Section: Available Tools and Utilities For Gpusmentioning

confidence: 99%

Hardware implementations of computer-generated holography: a review

et al. 2020

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“…For fast CGH animation containing viewpoint parallel shifts and rotations, a Fourier transform optical system (FTOS) was implemented on the GPU [229]. Good speedups of FFT-based CGHs have also been achieved by directly using the MATLAB's Parallel Computing Toolbox [230] without knowing much parallel computing knowledge.…”

Section: Computer Generated Hologramsmentioning

confidence: 99%

Towards high-speed optical measurement of shape and deformation

Wang¹

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With the flexibly designed three variations of the framework, a real-time frame rate ranging from 30fps (frames per second) to 130fps has been achieved. Also, a reference updating scheme is employed to compensate the decorrelation when the deformation becomes large. Finally, a real-time reference-based dynamic phase retrieval algorithm called G-LS3U is proposed to extract phase distributions from fringe or speckle patterns. Different parallel computing strategies are developed and applied to both the least-squares fitting and the windowed Fourier filtering (WFF) processes. G-LS3U achieved a remarkably high processing rate at 131+ fps, making G-LS3U the fastest reference-based dynamic phase retrieval algorithm reported heretofore.

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