The advent of GPGPU technologies has allowed for sensible speed-ups in many high-dimension, memory-intensive computational problems. In this paper we demonstrate the e ectiveness of such techniques by describing two applications of GPGPU computing to two di erent subfields of computer graphics, namely computer vision and mesh processing. In the first case, CUDA technology is employed to accelerate the computation of approximation of motion between two images, known also as optical flow. As for mesh processing, we exploit the massively parallel architecture of CUDA devices to accelerate the face clustering procedure that is employed in many recent mesh segmentation algorithms. In both cases, the results obtained so far are presented and thoroughly discussed, along with the expected future development of the work.
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Stefano Marras, Claudio Mura, Enrico Gobbetti, Riccardo Scateni, and Roberto Scopigno. Two examples of GPGPU acceleration of memory-intensive algorithms. In Eurographics Italian Chapter Conference. Pages 49-56. Eurographics Association, November 2010.
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@InProceedings{Marras:2010:TEG, author = {Stefano Marras and Claudio Mura and Enrico Gobbetti and Riccardo Scateni and Roberto Scopigno}, title = {Two examples of GPGPU acceleration of memory-intensive algorithms}, booktitle = {Eurographics Italian Chapter Conference}, pages = {49--56}, publisher = {Eurographics Association}, address = {Conference held in Genoa, Italy}, month = {November}, year = {2010}, abstract = { The advent of GPGPU technologies has allowed for sensible speed-ups in many high-dimension, memory-intensive computational problems. In this paper we demonstrate the e ectiveness of such techniques by describing two applications of GPGPU computing to two di erent subfields of computer graphics, namely computer vision and mesh processing. In the first case, CUDA technology is employed to accelerate the computation of approximation of motion between two images, known also as optical flow. As for mesh processing, we exploit the massively parallel architecture of CUDA devices to accelerate the face clustering procedure that is employed in many recent mesh segmentation algorithms. In both cases, the results obtained so far are presented and thoroughly discussed, along with the expected future development of the work.}, url = {http://vic.crs4.it/vic/cgi-bin/bib-page.cgi?id='Marras:2010:TEG'}, }
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