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Genetic Improvement of GPU Software

William B. Langdon, Brian Yee Hong Lam, Marc Modat, Justyna Petke, Mark Harman
Department of Computer Science, University College London
Genetic Programming and Evolvable Machines, 2016
@article{langdon2016genetic,

   title={Genetic Improvement of GPU Software},

   author={Langdon, William B and Lam, Brian Yee Hong and Modat, Marc and Petke, Justyna and Harman, Mark},

   year={2016}

}

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We survey Genetic Improvement (GI) of general purpose computing on graphics cards. We summarise several experiments which demonstrate four themes. Experiments with the gzip program show that genetic programming (GP) can automatically port sequential C code to parallel code. Experiments with the StereoCamera program show that GI can upgrade legacy parallel code for new hardware and software. Experiments with NiftyReg and BarraCUDA show that GI can make substantial improvements to current parallel CUDA applications. Finally, experiments with the pknotsRG program show that with semi-automated approaches, enormous speed ups can sometimes be had by growing and grafting new code with genetic programming in combination with human input.
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