Benchmarking Genetically Improved BarraCUDA on Epigenetic Methylation NGS datasets and nVidia GPUs

Created by W.Langdon from gp-bibliography.bib Revision:1.4549

  author =       "William B. Langdon and Albert Vilella and 
                 Brian Yee Hong Lam and Justyna Petke and Mark Harman",
  title =        "Benchmarking Genetically Improved {BarraCUDA} on
                 Epigenetic Methylation {NGS} datasets and {nVidia}
  booktitle =    "Genetic Improvement 2016 Workshop",
  year =         "2016",
  editor =       "Justyna Petke and Westley Weimer and David R. White",
  pages =        "1131--1132",
  address =      "Denver",
  publisher_address = "New York, NY, USA",
  month =        jul # " 20-24",
  organisation = "SIGEvo",
  publisher =    "ACM",
  keywords =     "genetic algorithms, genetic programming, Genetic
                 Improvement, SBSE, GPGPU, Medicine, Bioinformatics,
                 next generation sequencing",
  URL =          "",
  URL =          "",
  DOI =          "doi:10.1145/2908961.2931687",
  size =         "2 pages",
  abstract =     "BarraCUDA uses CUDA graphics cards to map DNA reads to
                 the human genome. Previously its software source code
                 was genetically improved for short paired end next
                 generation sequences. On longer noisy epigenetics
                 strings using nVidia Titan and twin Tesla K40 the same
                 GI-ed code is more than 3 times faster than bwa-meth on
                 an 8 core CPU.",
  notes =        "See \cite{Langdon:2015:GECCO} GECCO 2016 Workshop

Genetic Programming entries for William B Langdon Albert Vilella Brian Yee Hong Lam Justyna Petke Mark Harman