Strategies to minimise the total run time of cyclic graph based genetic programming with GPUs

Created by W.Langdon from gp-bibliography.bib Revision:1.4221

  author =       "Tony E. Lewis and George D. Magoulas",
  title =        "Strategies to minimise the total run time of cyclic
                 graph based genetic programming with GPUs",
  booktitle =    "GECCO '09: Proceedings of the 11th Annual conference
                 on Genetic and evolutionary computation",
  year =         "2009",
  editor =       "Guenther Raidl and Franz Rothlauf and 
                 Giovanni Squillero and Rolf Drechsler and Thomas Stuetzle and 
                 Mauro Birattari and Clare Bates Congdon and 
                 Martin Middendorf and Christian Blum and Carlos Cotta and 
                 Peter Bosman and Joern Grahl and Joshua Knowles and 
                 David Corne and Hans-Georg Beyer and Ken Stanley and 
                 Julian F. Miller and Jano {van Hemert} and 
                 Tom Lenaerts and Marc Ebner and Jaume Bacardit and 
                 Michael O'Neill and Massimiliano {Di Penta} and Benjamin Doerr and 
                 Thomas Jansen and Riccardo Poli and Enrique Alba",
  pages =        "1379--1386",
  address =      "Montreal",
  publisher =    "ACM",
  publisher_address = "New York, NY, USA",
  month =        "8-12 " # jul,
  organisation = "SigEvo",
  keywords =     "genetic algorithms, genetic programming, cyclic
                 cartesian genetic programming, GPU, deme, parallel
  isbn13 =       "978-1-60558-325-9",
  bibsource =    "DBLP,",
  DOI =          "doi:10.1145/1569901.1570086",
  URL =          "",
  size =         "8 pages",
  abstract =     "In this paper, we describe our work to investigate how
                 much cyclic graph based Genetic Programming (GP) can be
                 accelerated on one machine using currently available
                 mid-range Graphics Processing Units (GPUs).

                 Cyclic graphs pose different problems for evaluation
                 than do trees and we describe how our CUDA based,
                 {"}population parallel{"} evaluator tackles these

                 Previous similar work has focused on the evaluation
                 alone. Unfortunately large reductions in the evaluation
                 time do not necessarily translate to similar reductions
                 in the total run time because the time spent on other
                 tasks becomes more significant. We show that this
                 problem can be tackled by having the GPU execute in
                 parallel with the Central Processing Unit (CPU) and
                 with memory transfers. We also demonstrate that it is
                 possible to use a second graphics card to further
                 improve the acceleration of one machine.

                 These additional techniques are able to reduce the
                 total run time of the GPU system by up to 2.83 times.
                 The combined architecture completes a full cyclic GP
                 run 434.61 times faster than the single-core CPU
                 equivalent. This involves evaluating at an average rate
                 of 3.85 billion GP operations per second over the
                 course of the whole run.",
  notes =        "twin GPU and dual CPU, superclocked GeForce 8800 GT,
                 CUDA 2.0

                 GECCO-2009 A joint meeting of the eighteenth
                 international conference on genetic algorithms
                 (ICGA-2009) and the fourteenth annual genetic
                 programming conference (GP-2009).

                 ACM Order Number 910092.",
  uk_research_excellence_2014 = "This paper opened up a new research
                 direction in accelerating Genetic Programming (GP) on
                 GPUs. It won a best paper award at GECCO'09.",

Genetic Programming entries for Tony Lewis George D Magoulas