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[13] Ian Buck. Gpu computing: Programming a massively parallel processor. Code Generation and Optimization, 2007. CGO '07. International Symposium on, pages 17-17, March 2007. [ bib | DOI ]
Summary form only given. Many researchers have observed that general purpose computing with programmable graphics hardware (GPUs) has shown promise to solve many of the world's compute intensive problems, many orders of magnitude faster the conventional CPUs. The challenge has been working within the constraints of a graphics programming environment and limited language support to leverage this huge performance potential. GPU computing with CUDA is a new approach to computing where hundreds of on-chip processor cores simultaneously communicate and cooperate to solve complex computing problems, transforming the GPU into a massively parallel processor. The NVIDIA C-compiler for the GPU provides a complete development environment that gives developers the tools they need to solve new problems in computation-intensive applications such as product design, data analysis, technical computing, and game physics. In this talk, I will provide a description of how CUDA can solve compute intensive problems and highlight the challenges when compiling parallel programs for GPUs including the differences between graphics shaders vs. CUDA applications

Keywords: digital signal processing chips, parallel programming, program compilersCUDA applications, GPU computing, NVIDIA C-compiler, graphics programming, massively parallel processor programming, on-chip processor cores, programmable graphics hardware
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[20] Andrew Corrigan, John Wallin, and Matej Vesenjak. Progress on Meshless Methods, chapter Visualization of meshless simulations using Fourier volume rendering. Springer, 2008. [ bib ]
[21] Andrew Corrigan and John Wallin. Visualization of meshless simulations using Fourier volume rendering. In Proc. of the ECCOMAS Thematic Conference on Meshless Methods, pages 65-70, July 2007. [ bib ]

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