GPGPU |
General-Purpose Computation Using Graphics Hardware
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IntroductionGPGPU stands for General-Purpose computation on GPUs. With the increasing programmability of commodity graphics processing units (GPUs), these chips are capable of performing more than the specific graphics computations for which they were designed. They are now capable coprocessors, and their high speed makes them useful for a variety of applications. The goal of this page is to catalog the current and historical use of GPUs for general-purpose computation.
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Hardware Efficient PDE Solvers in Quantized Image Processing This thesis by Robert Strzodka describes the design of robust quantized schemes and their hardware efficient implementation on data-stream-based architectures for PDE-based image processing. The focus lies on enhancing both performance and accuracy by an efficient use of appropriate hardware resources. Quantized schemes which, despite roundoff errors, preserve the qualitative behavior of the continuous models are constructed, and examined on different GPUs, a FPGA and a reconfigurable array processor. The pros and cons of the hardware designs and the memory gap problem are discussed in detail. (Hardware Efficient PDE Solvers in Quantized Image Processing. Robert Strzodka. PhD thesis, University of Duisburg-Essen, 2004.)
Posted: 20 Mar 2005 [GPGPU /Image And Volume Processing] # Interactive marker-less tracking of human limbs This paper by Rao et al. at UNC Charlotte describes an algorithm to track human limbs at interactive rates without using markers. 3d point cloud data is derived from a modified visual hull algorithm. This data is fed into a particle filtering algorithm that runs on the GPU. The tracking system runs at interactive rates. ( Interactive marker-less tracking of human limbs. Rao S., Hodges L.F to be submitted to Transactions on Visualization and Computer Graphics.)
Posted: 20 Mar 2005 [GPGPU /Image And Volume Processing/Computer Vision] # |
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