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Optimizations of a Generic Holographic Projection Model for GPU’s

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Computational Science – ICCS 2021 (ICCS 2021)

Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 12745))

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Abstract

Holographic projections are volumetric projections that make use of the wave-like nature of light and may find use in applications such as volumetric displays, 3D printing, lithography and LIDAR. Modelling different types of holographic projectors is straightforward but challenging due to the large number of samples that are required. Although computing capabilities have improved, recent simulations still have to make trade-offs between accuracy, performance and level of generalization. Our research focuses on the development of optimizations that make optimal use of modern hardware, allowing larger and higher-quality simulations to be run. Several algorithms are proposed; (1) a brute force algorithm that can reach 20% of the theoretical peak performance and reached a \(43{\times }\) speedup w.r.t. a previous GPU implementation and (2) a Monte Carlo algorithm that is another magnitude faster but has a lower accuracy. These implementations help researchers to develop and test new holographic devices.

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Notes

  1. 1.

    A sum of N random vectors can be represented by a two-dimensional random walk of N steps. The corresponding distribution is derived by (and named after) Rayleigh and is given by \( p(\ell ) = \frac{2\ell }{N} \exp \left( -\ell ^2/N\right) \), where \(\ell \) is the length or absolute value of the phasor-sum[20]. The expected value of this distribution is proportional to \(\sqrt{N}\). This distribution requires the input phasors to have unit length, but more refined solutions that allow for arbitrary amplitude and phase distributions exist as well [4].

  2. 2.

    The exact number of floating point operations is difficult to determine because compilers can restructure code and certain implementations are hardware-dependent. For example fused multiply-add operations and hardware-units for special arithmetic such as the square root [7].

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Authors and Affiliations

  1. Informatics Institute, University of Amsterdam, Amsterdam, The Netherlands

    Mark Voschezang

  2. Nikhef, Amsterdam, The Netherlands

    Mark Voschezang & Martin Fransen

Authors
  1. Mark Voschezang
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  2. Martin Fransen
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Correspondence to Mark Voschezang .

Editor information

Editors and Affiliations

  1. AGH University of Science and Technology, Krakow, Poland

    Maciej Paszynski

  2. Ludwig-Maximilians-Universität München, Munich, Germany

    Dieter Kranzlmüller

  3. University of Amsterdam, Amsterdam, The Netherlands

    Valeria V. Krzhizhanovskaya

  4. University of Tennessee at Knoxville, Knoxville, TN, USA

    Jack J. Dongarra

  5. University of Amsterdam, Amsterdam, The Netherlands

    Peter M.A. Sloot

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Voschezang, M., Fransen, M. (2021). Optimizations of a Generic Holographic Projection Model for GPU’s. In: Paszynski, M., Kranzlmüller, D., Krzhizhanovskaya, V.V., Dongarra, J.J., Sloot, P.M. (eds) Computational Science – ICCS 2021. ICCS 2021. Lecture Notes in Computer Science(), vol 12745. Springer, Cham. https://doi.org/10.1007/978-3-030-77970-2_11

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  • DOI: https://doi.org/10.1007/978-3-030-77970-2_11

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-77969-6

  • Online ISBN: 978-3-030-77970-2

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