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Genetic improvement of GPU software

Langdon, WB; Lam, BYH; Modat, M; Petke, J; Harman, M; (2017) Genetic improvement of GPU software. Genetic Programming and Evolvable Machines , 18 (1) pp. 5-44. 10.1007/s10710-016-9273-9. Green open access

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Abstract

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 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.

Type: Article
Title: Genetic improvement of GPU software
Open access status: An open access version is available from UCL Discovery
DOI: 10.1007/s10710-016-9273-9
Publisher version: http://doi.org/10.1007/s10710-016-9273-9
Language: English
Additional information: This version is the author accepted manuscript. For information on re-use, please refer to the publisher’s terms and conditions.
Keywords: Genetic programming, SBSEGI-GPGPU, Metaprogramming, Grammar based genetic programming NVidia, CUDA, Parallel computing, Dynamic programming, GPGPU, GGGP
UCL classification: UCL > Provost and Vice Provost Offices
UCL > Provost and Vice Provost Offices > UCL BEAMS
UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Engineering Science
UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Engineering Science > Dept of Computer Science
UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Engineering Science > Dept of Med Phys and Biomedical Eng
URI: https://discovery.ucl.ac.uk/id/eprint/1508228
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