Creating and Debugging Performance CUDA C

Various practical ways of testing, locating and removing bugs in parallel general-purpose computation on graphics hardware GPGPU applications are described. Some of these are generic whilst other relate directly to stochastic bioinspired techniques, such as genetic programming. We pass on software engineering lessons learnt during CUDA C programming and ways to obtain high performance from nVidia GPU and Tesla cards including examples of both successful and less successful recent applications.

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