EASEA: a generic optimization tool for GPU machines in asynchronous island model

Created by W.Langdon from gp-bibliography.bib Revision:1.8051

@Article{krueg11ease,

• author = "Laurent A. Baumes and Frederic Kruger and Pierre Collet",
• title = "{EASEA}: a generic optimization tool for {GPU} machines in asynchronous island model",
• journal = "Computer Methods in Materials Science",
• year = "2011",
• volume = "11",
• number = "3",
• pages = "489--499",
• keywords = "genetic algorithms, genetic programming, GPGPU, Evolutionary Algorithms, Island Model, Parallelism, Zeolite Materials",
• ISSN = "1641-8581",
• publisher = "The AGH University of Science and Technology Press, Open Access",
• URL = "http://icube-publis.unistra.fr/docs/7407/baumes.pdf",
• URL = "http://cmms-editorial.agh.edu.pl/abstract.php?p_id=373",
• size = "11 pages",
• abstract = "Very recently, we presented an efficient implementation of Evolutionary Algorithms (EAs) using Graphics Processing Units (GPU) for solving microporous crystal structures. Because of both the inherent complexity of zeolitic materials and the constant pressure to accelerate R and D solutions, an asynchronous island model running on clusters of machines equipped with GPU cards, i.e. the current trend for super-computers and cloud computing, is presented. This last improvement of the EASEA platform allows an effortless exploitation of hierarchical massively parallel systems. It is demonstrated that supra-linear speedup over one machine and linear speedup considering clusters of different sizes are obtained. Such an island implementation over several potentially heterogeneous machines opens new horizon for various domains of application where computation time for optimisation remains the principal bottleneck.",
• notes = "Address LSIIT , Illkirch, FRA",

}

Genetic Programming entries for Laurent Allan Baumes Frederic Kruger Pierre Collet

Citations