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Data Parallel Three-Dimensional Cahn-Hilliard Field Equation Simulation on GPUs with CUDA

D.P. Playne and K.A. Hawick
Computer Science, Institute for Information and Mathematical Sciences, Massey University, North Shore 102-904, Auckland, New Zealand
Proc. 2009 International Conference on Parallel and Distributed Processing Techniques and Applications (PDPTA’09) Las Vegas, USA

@conference{playne2009data,

   title={Data Parallel Three-Dimensional Cahn-Hilliard Field Equation Simulation on GPUs with CUDA},

   author={Playne, D. and Hawick, K.},

   booktitle={Proc. 2009 International Conference on Parallel and Distributed Processing Techniques and Applications (PDPTA’09) Las Vegas, USA., no. CSTN-073},

   pages={13–16},

   year={2009}

}

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Computational scientific simulations have long used parallel computers to increase their performance. Recently graphics cards have been utilised to provide this functionality. GPGPU APIs such as NVidia’s CUDA can be used to harness the power of GPUs for purposes other than computer graphics. GPUs are designed for processing twodimensional data. In previous work we have presented several two-dimensional Cahn-Hilliard simulations that each utilise different CUDA memory types and compared their results. In this paper we extend these ideas to three dimensions. As GPUs are not intended for processing threedimensional data arrays, the performance of the memory optimisations is expected to change. Here we present several three-dimensional Cahn-Hilliard simulations to explore the challenges and the performance of the different memory types in three-dimensions. The results show that the simulation design with the best performance in threedimensions uses a different memory type to the optimal two-dimensional simulation.
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