Genetically Improved CUDA kernels for StereoCamera

Created by W.Langdon from gp-bibliography.bib Revision:1.3872

@TechReport{Langdon_RN1402,
  author =       "W. B. Langdon and M. Harman",
  title =        "Genetically Improved CUDA kernels for StereoCamera",
  institution =  "Department of Computer Science, University College
                 London",
  year =         "2014",
  type =         "Research Note",
  number =       "RN/14/02",
  address =      "Gower Street, London WC1E 6BT, UK",
  month =        "20 " # feb,
  keywords =     "genetic algorithms, genetic programming, GI, GP,
                 gismoe, SBSE, software optimisation, nVidia, GPU,
                 GPGPU, Tesla, GeForce GTX 580, evolutionary
                 programming, software engineering",
  URL =          "http://www.cs.ucl.ac.uk/fileadmin/UCL-CS/research/Research_Notes/Langdon_RN1402.pdf",
  size =         "24 pages",
  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.",
  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 \cite{langdon:2014:EuroGP}",
}

Genetic Programming entries for William B Langdon Mark Harman

Citations