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GPU Computing

SINTEF has a long history of working with cutting-edge methods for GPU computing. From the early beginnings with register combiners, through OpenGL shaders and becoming an early certified CUDA Research Center, to maintaining expertise after the use of many-core accelerators became more commonplace.

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This page describes old research activity conducted in our previous "Heterogeneous computing" research group. The content is outdated and will be updated.


In the SCORE project we were responsible for GPU acceleration of the SPH simulations. In addition to this, we were heavily involved in the general SCORE code design and code development. The end result was an efficient GPU implementation supporting many complex simulations. 


Dambreak simulation with SPH on GPU
Simulating impact of copper bar using SPH on GPU

K. O. Lye, C. Dyken, J. S. Seland, F. O. Bjørnson, J. O. Busklein, T. Coudert, M. Føre, P. Klebert, A. Lavrov, B. Lund, J. O. Olsen, C. Pákozdi, P. Skjetne, W. Yang. Effective memory layout and accesses for the SPH method on the GPU, in: Proceedings 8th SPHERIC Workshop held in Trondheim (Norway), 2013. [ResearchGate]

Simulation on GPU

Shallow water simulation is performed by solving a hyperbolic conservation law. These kind of simulations are perticularly suitable for GPU-based implementation, because they can be solved by using explisite schemes. We have therefore developed a highly optimized Cuda implementation.

Medical Ultrasound

Medical ultrasound machines use advanced signal and image processing to produce the best possible image quality. GPUs is the prefferred computational resource due to their flexibility and floating point performance. We help our industrial partner to make good design choises and optimize their algorithms.