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GPU Programlamada CUDA Platformu Kullanılan Paralel Görüntü İşleme Çalışmalarının İncelenmesi

Year 2020, , 737 - 754, 01.09.2020
https://doi.org/10.2339/politeknik.563767

Abstract

Görüntü işleme pek çok alanda
kullanılmaktadır. Görüntü işleme teknikleri gün geçtikçe görüntülerin
çözünürlüklerinin artmasıyla daha fazla işlemci gücüne ihtiyaç duymaktadır.
Görüntü işleme sürecini hızlandırmak için paralel görüntü işleme teknikleri
kullanılmaktadır. GPU programlama günümüzde çok kullanılan ve tercih edilen
paralel görüntü işleme tekniklerinden biridir. CUDA ise GPU programlamada en
çok kullanılan platformdur. Bu çalışmanın temel amacı araştırmacılara ve konuya
yeni başlayanlara görüntü işleme uygulamalarında GPU ve CUDA gibi donanım ve
yazılım teknolojilerinin kullanımı konusunda bir başvuru kaynağı sağlamaktır.
Bu amaç kapsamında çalışmada GPU ve CUDA kullanılarak yapılan görüntü işleme
çalışmaları incelenmiş ve değerlendirilmiştir. GPU ve CUDA kullanan görüntü
işleme çalışmaları, görüntü geriçatma, görüntü iyileştirme, görüntü bölütleme,
görüntü çakıştırma ve görüntü sınıflandırma olmak üzere beş bölümde incelenmiş
ve değerlendirilmiştir. Elde edilen sonuçlar doğrultusunda, GPU ve CUDA
kullanımının avantajları ve bu teknolojilerin kullanıldığı görüntü işleme
uygulamalarında dikkat edilmesi gereken hususlar belirlenmiştir.  

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A Survey on Parallel Image Processing Studies Using CUDA Platform in GPU Programming

Year 2020, , 737 - 754, 01.09.2020
https://doi.org/10.2339/politeknik.563767

Abstract

Image processing is used in a variety of fields. Image
processing techniques need high processor performance due to increased image
resolution day by day. Parallel processing techniques are used to satisfy the
requirements related to high performance in real time image processing
applications. Recently, GPU programming is one of the most commonly used and
preferred methods in parallel processing. CUDA is the most popular platform in
GPU programming. In this survey the studies where CUDA platform was used for
image processing are presented and evaluated. 
The major purpose of this survey is to provide a comprehensive reference
source for the starters or researchers involved in use of CUDA platform in GPU
programming for image processing techniques. Studies using CUDA platform in GPU
programming have been classified under 5 areas; image reconstruction, image
enhancement, image segmentation, image registration and image classification.
Advantages of using CUDA in GPU programming for image processing and issues to
pay attention in applications have also been underlined.

References

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  • Ruetsch G., Oster B., Getting Started with CUDA, NVISION08: The World of Visual Computing, California USA, August 25-27, (2008).
  • NVIDIA CUDA Compute Unified Device Architecture Programming Guide, Version 1.0, (2007)
  • NVIDIA Accelerated Computing. GPU-Accelerated Libraries. Yayın tarihi 2017. Erişim tarihi Nisan 10, (2017).
  • Es A., İşler V., “Accelerated regular grid traversals using extended anisotropic chessboard distance fields on a parallel stream processor”, Journal of Parallel and Distributed Computing, 67: 1201-1217, (2007)
  • Göddeke D., Strzodka R., Mohd-Yusof J., McCormick P., Buijssen S.H.M., Grajewski M., Turek S., “Exploring weak scalability for FEM calculations on a GPU-enhanced cluster”, Parallel Computing, 33: 685-699, (2007)
  • Zwart S.F.P., Belleman R.G., Geldof P.M., “High-performance direct gravitational N-body simulations on graphics processing units”, New Astronomy, 12: 641-650, (2007)
  • Anderson A.G., Goddard III W.A., Schröder P., “Quantum Monte Carlo on graphical processing units”, Computer Physics Communications, 177: 298–306, (2007)
  • Martinsen P., Blaschke J., Künnemeyer R., Jordan R., “Accelerating Monte Carlo simulations with an NVIDIA graphics processor”, Computer Physics Communications, 180: 1983–1989, (2009).
  • Che S., Boyer M., Meng J., Tarjan D., Sheaffer J.W., SKadron K., “A performance study of general-purpose applications on graphics processors using CUDA”, Journal of Parallel and Distributed Computing, 68: 1370–1380, (2008)
  • Diez D.C., Mueller H., Frangakis A.S., “Implementation and performance evaluation of reconstruction algorithms on graphics processors”, Journal of Structural Biology, 157: 288-295, (2007)
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  • Schenk O., Christen M., Burkhart H., “Algorithmic performance studies on graphics processing units”, Journal of Parallel and Distributed Computing, 68: 1360-1369, (2008)
  • Diez D.C., Moser D., Schoenegger A., Pruggnaller S., Frangakis A.S., “Performance evaluation of image processing algorithms on the GPU”, Journal of Structural Biology, 164: 153–160, (2008)
  • Belleman R.G., Bedorf J., Zwart S.F.P., “High performance direct gravitational N-body simulations on graphics processing units II: An implementation in CUDA”, New Astronomy, 13: 103-112, (2008)
  • Ram M.P.M., Kurfess T.R., Tucker T.M., “Least-squares fitting of analytic primitives on a GPU”, Journal of Manufacturing Systems, 27: 130-135, (2008)Ryoo S., Rodrigues C.I., Stone S.S., Stratton J.A., Ueng S., “Program optimization carving for GPU computing”, Journal of Parallel and Distributed Computing, 68: 1389-1401, (2008)
  • Walsh S.D.C., Saar M.O., Bailey P., Lilja D.J., “Accelerating geoscience and engineering system simulations on graphics hardware”, Computers & Geosciences, 35: 2353–2364, (2009)
  • Komatitsch D., Michea D., Erlebacker G., “Porting a high-order finite-element earthquake modeling application to NVIDIA graphics cards using CUDA”, Journal of Parallel and Distributed Computing, 69: 451-460, (2009)
  • Vazquez F., Garzon E.M., Femandez J.J., “A matrix approach to tomographic reconstruction and its implementation on GPUs”, Journal of Structural Biology, 170: 146–151, (2010)
  • Noel P.B., Walczak A.M., Xu J., Corso J.J. Hoffmann K.R., Schafer S., “GPU-based cone beam computed tomography”, Computer Methods and Programs in Biomedicine, 98: 271–277, (2010)
  • Zheng S.Q., Branlund E., Kesthelyi B., Braunfeld M.B., Cheng Y., Sedat J.W., Agard D.A., “A distributed multi-GPU system for high speed electron microscopic tomographic reconstruction”, Ultramicroscopy, 111: 1137–1143, (2011)
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There are 99 citations in total.

Details

Primary Language Turkish
Subjects Engineering
Journal Section Review Article
Authors

Semra Aydın 0000-0002-1670-9677

Refik Samet This is me 0000-0001-8720-6834

Ömer Faruk Bay 0000-0002-6823-145X

Publication Date September 1, 2020
Submission Date May 13, 2019
Published in Issue Year 2020

Cite

APA Aydın, S., Samet, R., & Bay, Ö. F. (2020). GPU Programlamada CUDA Platformu Kullanılan Paralel Görüntü İşleme Çalışmalarının İncelenmesi. Politeknik Dergisi, 23(3), 737-754. https://doi.org/10.2339/politeknik.563767
AMA Aydın S, Samet R, Bay ÖF. GPU Programlamada CUDA Platformu Kullanılan Paralel Görüntü İşleme Çalışmalarının İncelenmesi. Politeknik Dergisi. September 2020;23(3):737-754. doi:10.2339/politeknik.563767
Chicago Aydın, Semra, Refik Samet, and Ömer Faruk Bay. “GPU Programlamada CUDA Platformu Kullanılan Paralel Görüntü İşleme Çalışmalarının İncelenmesi”. Politeknik Dergisi 23, no. 3 (September 2020): 737-54. https://doi.org/10.2339/politeknik.563767.
EndNote Aydın S, Samet R, Bay ÖF (September 1, 2020) GPU Programlamada CUDA Platformu Kullanılan Paralel Görüntü İşleme Çalışmalarının İncelenmesi. Politeknik Dergisi 23 3 737–754.
IEEE S. Aydın, R. Samet, and Ö. F. Bay, “GPU Programlamada CUDA Platformu Kullanılan Paralel Görüntü İşleme Çalışmalarının İncelenmesi”, Politeknik Dergisi, vol. 23, no. 3, pp. 737–754, 2020, doi: 10.2339/politeknik.563767.
ISNAD Aydın, Semra et al. “GPU Programlamada CUDA Platformu Kullanılan Paralel Görüntü İşleme Çalışmalarının İncelenmesi”. Politeknik Dergisi 23/3 (September 2020), 737-754. https://doi.org/10.2339/politeknik.563767.
JAMA Aydın S, Samet R, Bay ÖF. GPU Programlamada CUDA Platformu Kullanılan Paralel Görüntü İşleme Çalışmalarının İncelenmesi. Politeknik Dergisi. 2020;23:737–754.
MLA Aydın, Semra et al. “GPU Programlamada CUDA Platformu Kullanılan Paralel Görüntü İşleme Çalışmalarının İncelenmesi”. Politeknik Dergisi, vol. 23, no. 3, 2020, pp. 737-54, doi:10.2339/politeknik.563767.
Vancouver Aydın S, Samet R, Bay ÖF. GPU Programlamada CUDA Platformu Kullanılan Paralel Görüntü İşleme Çalışmalarının İncelenmesi. Politeknik Dergisi. 2020;23(3):737-54.
 
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