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Graph algorithms are fundamental to many disciplines and application areas. Large graphs involving millions of vertices are common in scientific and engineering applications. Practical-time implementations using high-end computing resources have been reported but are accessible only to a few. Graphics Processing Units (GPUs) are fast emerging as inexpensive(More)
Reality world model consists of real images and depth information computed from these images. Stereoscopic reconstructions provide a sense of complete immersion, and users can select their own viewpoints at view time, independent of the actual camera positions used to capture the event. T he different visual media we have today share two shortcomings:(More)
—Graphics processing units provide a large computational power at a very low price which position them as an ubiquitous accelerator. General purpose programming on the graphics processing units (GPGPU) is best suited for regular data parallel algorithms. They are not directly amenable for algorithms which have irregular data access patterns such as list(More)
Modern Graphics Processing Units (GPUs) provide high computation power at low costs and have been described as desktop supercomputers. The GPUs expose a general, data-parallel programming model today in the form of CUDA and CAL. The GPU is presented as a massively multithreaded architecture by them. Several high-performance, general data processing(More)
Graphics Processor Units are used for many general purpose processing due to high compute power available on them. Regular, data-parallel algorithms map well to the SIMD architecture of current GPU. Irregular algorithms on discrete structures like graphs are harder to map to them. Efficient data-mapping primitives can play crucial role in mapping such(More)
The significant growth in computational power of modern Graphics Processing Units(GPUs) coupled with the advent of general purpose programming environments like NVIDA's CUDA, has seen GPUs emerging as a very popular parallel computing platform. However, despite their popularity, there is no performance model of any GPGPU programming environment. The absence(More)
Compact representation of geometry using a suitable procedural or mathematical model and a ray-tracing mode of rendering fit the programmable graphics processor units (GPUs) well. Several such representations including parametric and subdivision surfaces have been explored in recent research. The important and widely applicable category of the general(More)