Automatic Evolution of Parallel Sorting Programs on Multi-cores

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

  author =       "Gopinath Chennupati and R. Muhammad Atif Azad and 
                 Conor Ryan",
  title =        "Automatic Evolution of Parallel Sorting Programs on
  booktitle =    "18th European Conference on the Applications of
                 Evolutionary Computation",
  year =         "2015",
  editor =       "Antonio M. Mora and Giovanni Squillero",
  series =       "LNCS",
  volume =       "9028",
  publisher =    "Springer",
  pages =        "706--717",
  address =      "Copenhagen",
  month =        "8-10 " # apr,
  organisation = "EvoStar",
  keywords =     "genetic algorithms, genetic programming, Grammatical
                 evolution, Automatic parallelisation, Recursion,
                 Program synthesis, OpenMP, Evolutionary
                 parallelization: Poster",
  isbn13 =       "978-3-319-16548-6",
  DOI =          "doi:10.1007/978-3-319-16549-3_57",
  abstract =     "Sorting algorithms that offer the potential for
                 data-parallel execution on parallel architectures are
                 an excellent tool for the current generation of
                 multi-core processors that often require skilled
                 parallelisation knowledge to fully realize the
                 potential of the hardware.

                 We propose to automate the evolution of natively
                 parallel programs using the Grammatical Evolution (GE)
                 approach to use the computational potential of
                 multi-cores. The proposed system, Multi-core
                 Grammatical Evolution for Parallel Sorting (MCGE-PS),
                 applies GE mapping along with explicit OpenMP #pragma
                 compiler directives to automatically evolve data-level
                 parallel iterative sorting algorithms. MCGE-PS is
                 assessed on the generation of four non-recursive
                 sorting programs in C. We show that it generated
                 programs that can solve the problem that are also
                 parallel. On a high performance Intel processor,
                 MCGE-PS significantly reduced the execution time of the
                 evolved programs for all the benchmark problems.",
  notes =        "EvoPAR EvoApplications2015 held in conjunction with
                 EuroGP'2015, EvoCOP2015 and EvoMusArt2015

Genetic Programming entries for Gopinath Chennupati R Muhammad Atif Azad Conor Ryan