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Exploring NVIDIA-CUDA for video coding

Published: 22 February 2010 Publication History

Abstract

Today, world is rapidly turning to high definition multimedia. From engineering and programming point of view, this usually means more computation is needed and more memory space is required to achieve these higher qualities. In this paper we explore the use of parallelization opportunities in graphics processors to accelerate video encoding. We evaluate the NVIDIA CUDA[1] toolkit and evaluate the performance of motion estimation in video encoding. The main goal of this paper is to evaluate the capabilities of NVIDIA/CUDA and develop a process for implementing video/multimedia applications. We have discovered that the difference in performance when CUDA is not used properly can be over 100x. We show how we were able to use CUDA capabilities to reduce the motion estimation time from 7000 milli seconds to 70 milli seconds.

References

[1]
NVIDIA CUDA Programming Guide version 2.3 (7/1/2009) developer.download.nvidia.com/.../cuda/.../NVIDIA_CUDA_Programming_Guide_2.3.pdf
[2]
S. Yang, et al., "Power and Performance Analysis of Motion Estimation Based on Hardware and Software Realizations," in IEEE Trans. Computer, vol.54, pp.714--726, Jun, 2005.
[3]
C.-W. Ho, et al., "Motion Estimation for H.264/AVC Using Programmable Graphics Hardware," in Proc. IEEE Int'l Conf. on Multimedia and Expo, July 2006, pp. 2049--2052.
[4]
C.-Y. Lee, et al., "Multi-Pass and Frame Parallel Algorithm of Motion Estimation in H.264/AVC for Generic GPU," in Proc. IEEE Int'l Conf. on Multimedia and Expo, July 2007, pp. 1603--1606.
[5]
Y.-C. Lin, et al., "Multi-Pass algorithm of Motion Estimation in Video Encoding for Generic GPU," in Proc. IEEE International Symposium on Circuit and Systems, May 2006, pp. 4451--4454.
[6]
R.-X. Chen and J. Fan, "Complexity reduction for SOPCbased H.264/AVC coder via sum of absolute difference", IEEE/CIE 7th Int'l Conf' on ASIC, pp. 1277--1280, Oct. 2007
[7]
S. Ryoo, et al., "Optimization principles and application performance evaluation of a multithreaded GPU using CUDA," Proc. 13th ACM SIGPLAN Symp. on Principles and Practice of Parallel Programming, Feb. 2008, pp.73--82.
[8]
GPGPU. http://www.gpgpu.org/
[9]
K. Mueller, F. Xu, and N. Neophytou, "Why do commodity graphics hardware boards (GPUs) work so well for acceleration of computed tomography?" in SPIE Electronic Imaging Conference, San Diego, 2007, (Keynote, Computational Imaging V).
[10]
E. G. Richardson, Iain (2003). H.264 and MPEG-4 Video Compression: Video Coding for Next-generation Multimedia. Chichester: John Wiley & Sons Ltd.
[11]
Wei-Nien Chen and Hsueh-Ming Hang, 2008 "H.264/AVC motion estimation implementation on compute unified device architecture (CUDA)" in conf. ICME 2008. National Chiao-Tung University, Taiwan

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Vladimir Botchev

Colic, Kalva, and Furht discuss techniques for optimizing full search motion estimation using NVIDIA's compute unified device architecture (CUDA) and graphics coprocessors. The motion estimation technique used is motivated by the fact that it is the best candidate for efficient parallelization. The authors offer a brief but insightful description of graphics processing unit (GPU) architecture, as well as the CUDA programming model. They present the optimization strategies, followed by a series of experiments, from the unoptimized test run to the fully optimized motion search. Many comparison charts are provided, in order to emphasize the speedup benefits of the optimization. Guidelines are provided at the end of the paper; these are almost universally valid for all GPU parallelization designs-whenever parallelization is possible, of course. Overall, the paper makes a valuable contribution by showing how to approach optimization tasks using CUDA. Online Computing Reviews Service

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cover image ACM Conferences
MMSys '10: Proceedings of the first annual ACM SIGMM conference on Multimedia systems
February 2010
328 pages
ISBN:9781605589145
DOI:10.1145/1730836
Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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Publication History

Published: 22 February 2010

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Author Tags

  1. compute unified device architecture (cuda)
  2. graphics processing unit (gpu)
  3. motion estimation
  4. parallel processing
  5. video coding

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MMSYS '10
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MMSYS '10: Multimedia Systems Conference
February 22 - 23, 2010
Arizona, Phoenix, USA

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MMSys '10 Paper Acceptance Rate 25 of 59 submissions, 42%;
Overall Acceptance Rate 176 of 530 submissions, 33%

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  • (2020)AlloXProceedings of the Fifteenth European Conference on Computer Systems10.1145/3342195.3387547(1-16)Online publication date: 15-Apr-2020
  • (2018)Graph Processing on GPUsACM Computing Surveys10.1145/312857150:6(1-35)Online publication date: 3-Jan-2018
  • (2018)A fast deconvolution-based approach for single-image super-resolution with GPU accelerationJournal of Real-Time Image Processing10.1007/s11554-015-0513-714:2(501-512)Online publication date: 1-Feb-2018
  • (2017)GPU based background subtraction using CUDA: State of the Art2017 International Conference on Wireless Communications, Signal Processing and Networking (WiSPNET)10.1109/WiSPNET.2017.8299953(1201-1204)Online publication date: Mar-2017
  • (2017)LASSO approximation and application to image super-resolution with CUDA acceleration2017 2nd International Conference on Image, Vision and Computing (ICIVC)10.1109/ICIVC.2017.7984603(483-488)Online publication date: Jun-2017
  • (2017)An Accelerated H.264/AVC Encoder on Graphic Processing Unit for UAV VideosComputational Modeling of Objects Presented in Images. Fundamentals, Methods, and Applications10.1007/978-3-319-54609-4_19(251-258)Online publication date: 10-Mar-2017
  • (2015)A checkpoint compression study for high-performance computing systemsThe International Journal of High Performance Computing Applications10.1177/109434201557092129:4(387-402)Online publication date: 17-Feb-2015
  • (2015)A Special Sorting Method for Neighbor Search Procedure in Smoothed Particle Hydrodynamics on GPUsProceedings of the 2015 44th International Conference on Parallel Processing Workshops (ICPPW)10.1109/ICPPW.2015.46(81-85)Online publication date: 1-Sep-2015
  • (2014)Accelerating 2D orthogonal matching pursuit algorithm on GPUThe Journal of Supercomputing10.1007/s11227-014-1188-869:3(1363-1381)Online publication date: 1-Sep-2014
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