GPGPU: general purpose computation on graphics hardware

David Luebke, Mark Harris, Jens Krüger, Tim Purcell, Naga Govindaraju, Ian Buck, Cliff Woolley, Aaron Lefohn
In SIGGRAPH ’04: ACM SIGGRAPH 2004 Course Notes (2004), 33.


   title={GPGPU: general-purpose computation on graphics hardware},

   author={Luebke, D. and Harris, M. and Govindaraju, N. and Lefohn, A. and Houston, M. and Owens, J. and Segal, M. and Papakipos, M. and Buck, I.},

   booktitle={Proceedings of the 2006 ACM/IEEE conference on Supercomputing},






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The graphics processor (GPU) on today’s commodity video cards has evolved into an extremely powerful and flexible processor. The latest graphics architectures provide tremendous memory bandwidth and computational horsepower, with fully programmable vertex and pixel processing units that support vector operations up to full IEEE floating point precision. High level languages have emerged for graphics hardware, making this computational power accessible. Architecturally, GPUs are highly parallel streaming processors optimized for vector operations, with both MIMD (vertex) and SIMD (pixel) pipelines. Not surprisingly, these processors are capable of general-purpose computation beyond the graphics applications for which they were designed. Researchers have found that exploiting the GPU can accelerate some problems by over an order of magnitude over the CPU.However, significant barriers still exist for the developer who wishes to use the inexpensive power of commodity graphics hardware, whether for in-game simulation of physics of for conventional computational science. These chips are designed for and driven by video game development; the programming model is unusual, the programming environment is tightly constrained, and the underlying architectures are largely secret. The GPU developer must be an expert in computer graphics and its computational idioms to make effective use of the hardware, and still pitfalls abound. This course provides a detailed introduction to general purpose computation on graphics hardware (GPGPU). We emphasize core computational building blocks, ranging from linear algebra to database queries, and review the tools, perils, and tricks of the trade in GPU programming. Finally we present some interesting and important case studies on general-purpose applications of graphics hardware.The course presenters are experts on general-purpose GPU computation from academia and industry, and have presented papers and tutorials on the topic at SIGGRAPH, Graphics Hardware, Game Developers Conference, and elsewhere.
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