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Publication Detail
Genetically Improved CUDA kernels for StereoCamera
  • Publication Type:
    Report
  • Authors:
    Langdon WB, Harman M
  • publication date:
    2014
  • Place of publication:
    Department of Computer Science, University College London
  • Report number:
    RN/14/02
  • Addresses:
    Gower Street, London WC1E 6BT, UK
  • Notes:
    keywords: genetic algorithms, genetic programming, GI, GP, gismoe, SBSE, software optimisation, nVidia, GPU, GPGPU, Tesla, GeForce GTX 580, evolutionary programming, software engineering size: 24 pages notes: GP code ftp://ftp.cs.ucl.ac.uk//genetic/gp-code/StereoCamera_1_1.tar.gz Training images ftp://ftp.cs.ucl.ac.uk//genetic/gp-code/StereoImages.tar.gz StereoCamera v1.0b with bugfix and tuned for K20c Tesla at ftp://ftp.cs.ucl.ac.uk//genetic/gp-code/StereoCamera_v1_1c.zip Cf \citelangdon:2014:EuroGP
Abstract
Genetic Programming (GP) may dramatically increase the performance of software written by domain experts. GP and autotuning are used to optimise and refactor legacy GPGPU C code for modern parallel graphics hardware and software. Speed ups of more than six times on recent nVidia GPU cards are reported compared to the original kernel on the same hardware.
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