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A short guide to CUDA C: For physicists with multi-core graphics cards

David Plotzki
Computer-Oriented Quantum Field Theory Research Group, Institute for Theoretical Physics, Faculty of Physics and Earth Sciences, University of Leipzig
University of Leipzig, 2012

@article{plotzki2012short,

   title={A short guide to CUDA C},

   author={Plotzki, D.},

   year={2012}

}

The purpose of this guide is to give a quick introduction to CUDA C, NVIDIA’s extension for the C programming language to allow running code parallely on graphics cards. Moreover, the technique is applied to problems in computational physics, namely the generation of random numbers and simulations of the q-state Potts model. If you seek a complete documentation with more profound information, please refer to the NVIDIA CUDA C Programming Guide [1]. CUDA is also available for Fortran. The following is based on version 4.2 of the CUDA toolkit. Newer versions will most probably be compatible to the methods and code shown here. It is assumed that you already have CUDA installed and running. For help, please refer to NVIDIA’s Getting Started Guide [2] for your operating system.
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