Abstract
With the introduction in 2003 of standard GPUs with 32 bit floating point numbers and programmable Vertex and Fragment processors, the processing power of the GPU was made available to non-graphics applications. As the GPU is aimed at computer graphics, the concepts in GPU-programming are based on computer graphics terminology, and the strategies for programming have to be based on the architecture of the graphics pipeline. At SINTEF in Norway a 4-year strategic institute project (2004-2007) ""Graphics hardware as a high-end computational resource"", http://www.math.sintef.no/gpu/ aims at making GPUs available as a computational resource both to academia and industry. This paper addresses the challenges of GPU-programming and results of the project's first year.