Sponsored by SIAM Activity Group on Geometric Design 

Title: Graphics Hardware as a Highend Computational ResourceShort Course Organizer(s): Tor Dokken, SINTEF ICT, Department of Applied Mathematics, Oslo Norway [email protected] Rationale: With the introduction in 2003 of standard GPUs (Graphics Processor Units) with 32 bit floating point numbers and programmable Vertex and Fragment processors, the processing power of the GPU was made available to nongraphics applications. As the GPU is aimed at computer graphics, the concepts in GPUprogramming are based on computer graphics terminology, and the strategies for programming have to be based on the architecture of the graphics pipeline. Instructor(s): Tor Dokken, Chief Scientist in SINTEF ICT, Department of Applied
Mathematics, Professor II (part time) at the Center of "Mathematics
for Applications" at the University of Oslo, and Professor II (part Trond Runar Hagen, Research Scientist in SINTEF ICT, Department of
Applied Mathematics. Graduated 2001 with a Master's degree from the
University College of Narvik, Norway in 2001. Employed by SINTEF in Course Description: At SINTEF in Norway, a 4year strategic institute project (20042007) "Graphics hardware as a highend computational resource" http://www.math.sintef.no/gpu/, aims at making GPUs available as a computational resource both to academia and industry. The project addresses application areas of GPUs within geometry, simulation, image processing and visualization, and has already demonstrated performance increases between 10x and 20x for classes of explicit finite difference schemes for solving partial differential equations. The combination of the computational power of commodity GPUs, simulation problems, representation of complex geometric structures and innovative visualization, has the potential of introducing applications on standard PCs that until now have been available only through the use of highperformance computing. The short course will start with a general introduction to the potential of GPUprogramming. Then we will address the graphics pipeline, since understanding the graphics pipeline is essential in GPUprogramming. We will explain the potential and limitation of textures, fragment shaders and vertex shaders, and illustrate alternative approaches using simple examples. The short course will be concluded by examples of more complex applications using multiple shaders both for computations and visualization. Examples will come from CAGD, simulation and image processing. Level of the Material: The material of the short course is aimed at beginners within GPUprogramming and the use of GPUs as a computational resource. Target Audience: The target audience is professionals from research and industry interested in an introduction to the potential of the GPU as a computational resource. Recommended Background: The recommended background is computer graphics and applied mathematics, but not at an expert level. For general information on general purpose processing on GPUs consult http://www.gpgpu.org Course Outline:
1. Introduction to the GPU as a computational resource. (30
minutes). 


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