Abstract
Available GPUs provide increasingly more processing power especially for multimedia and digital signal processing. Despite the tremendous progress in hardware and thus processing power, there are and always will be applications that require using multiple GPUs either running inside the same machine or distributed in the network due to computational intensive processing algorithms.
Existing solutions for developing applications for GPUs still require a lot of hand-optimization when using multiple GPUs inside the same machine and provide in general no support for using remote GPUs distributed in the network. In this paper we address this problem and show that an open distributed multimedia middleware, like the Network-Integrated Multimedia Middleware (NMM), is able (1) to seamlessly integrate processing components using GPUs while completely hiding GPU specific issues from the application developer, (2) to transparently combine processing components using GPUs or CPUs, and (3) to transparently use local and remote GPUs for distributed processing.
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References
NVIDIA: CUDA Programming Guide 2.0 (2008)
Lohse, M., Winter, F., Repplinger, M., Slusallek, P.: Network-Integrated Multimedia Middleware (NMM). In: MM 2008: Proceedings of the 16th ACM International Conference on Multimedia, pp. 1081–1084 (2008)
Repplinger, M., Winter, F., Lohse, M., Slusallek, P.: Parallel Bindings in Distributed Multimedia Systems. In: Proceedings of the 25th IEEE International Conference on Distributed Computing Systems Workshops (ICDCS 2005), pp. 714–720. IEEE Computer Society, Los Alamitos (2005)
Allusse, Y., Horain, P., Agarwal, A., Saipriyadarshan, C.: GpuCV: An Open-Source GPU-accelerated Framework for Image Processing and Computer Vision. In: MM 2008: Proceeding of the 16th ACM International Conference on Multimedia, pp. 1089–1092. ACM, New York (2008)
Fung, J., Mann, S.: OpenVIDIA: parallel GPU computer vision. In: MULTIMEDIA 2005: Proceedings of the 13th Annual ACM International Conference on Multimedia, pp. 849–852. ACM, New York (2005)
Hartley, D.R., et al.: Biomedical Image Analysis on a Cooperative Cluster of GPUs and Multicores. In: ICS 2008: Proceedings of the 22nd Annual International Conference on Supercomputing, pp. 15–25. ACM, New York (2008)
Humphreys, G., et al.: WireGL: a scalable graphics system for clusters. In: SIGGRAPH 2001: Proceedings of the 28th Annual Conference on Computer Graphics and Interactive Techniques, pp. 129–140 (2001)
Humphreys, G., et al.: Chromium: a stream-processing framework for interactive rendering on clusters. In: SIGGRAPH 2002: Proceedings of the 29th Annual Conference on Computer Graphics and Interactive Techniques, pp. 693–702 (2002)
Eilemann, S., Pajarola, R.: The Equalizer parallel rendering framework. Technical Report IFI 2007.06, Department of Informatics, University of Zürich (2007)
Fillinger, A., et al.: The NIST Data Flow System II: A Standardized Interface for Distributed Multimedia Applications. In: IEEE International Symposium on a World of Wireless; Mobile and MultiMedia Networks (WoWMoM). IEEE, Los Alamitos (2008)
Black, A.P., et al.: Infopipes: An Abstraction for Multimedia Streaming. Multimedia Syst. 8(5), 406–419 (2002)
NVIDIA: CUDA Programming and Development. NVidia Forum (2009), http://www.forums.nvidia.com/index.php?showtopic=81300&hl=cuMemcpyHtoDAsync
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Repplinger, M., Slusallek, P. (2010). Stream Processing on GPUs Using Distributed Multimedia Middleware. In: Wyrzykowski, R., Dongarra, J., Karczewski, K., Wasniewski, J. (eds) Parallel Processing and Applied Mathematics. PPAM 2009. Lecture Notes in Computer Science, vol 6067. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-14390-8_45
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DOI: https://doi.org/10.1007/978-3-642-14390-8_45
Publisher Name: Springer, Berlin, Heidelberg
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