- 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 interpretter",
- isbn13 = "978-1-60558-325-9",
- bibsource = "DBLP, http://dblp.uni-trier.de",
-
DOI = "
doi:10.1145/1569901.1570086",
-
URL = "
http://results.ref.ac.uk/Submissions/Output/457444",
- 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 problems.
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.",
