Systemic Computation Using Graphics Processors

Created by W.Langdon from gp-bibliography.bib Revision:1.7615

@InProceedings{Rouhipour:2010:ICES,

• author = "Marjan Rouhipour and Peter J Bentley and Hooman Shayani",
• title = "Systemic Computation Using Graphics Processors",
• booktitle = "Proceedings of the 9th International Conference Evolvable Systems: From Biology to Hardware, ICES 2010",
• year = "2010",
• editor = "Gianluca Tempesti and Andy M. Tyrrell and Julian F. Miller",
• series = "Lecture Notes in Computer Science",
• volume = "6274",
• pages = "121--132",
• address = "York",
• month = sep # " 6-8",
• publisher = "Springer",
• keywords = "genetic algorithms, genetic programming, GPU",
• isbn13 = "978-3-642-15322-8",
• DOI = "doi:10.1007/978-3-642-15323-5_11",
• abstract = "Previous work created the systemic computer - a model of computation designed to exploit many natural properties observed in biological systems, including parallelism. The approach has been proven through two existing implementations and many biological models and visualisations. However to date the systemic computer implementations have all been sequential simulations that do not exploit the true potential of the model. In this paper the first parallel implementation of systemic computation is introduced. The GPU Systemic Computation Architecture is the first implementation that enables parallel systemic computation by exploiting multiple cores available in graphics processors. Comparisons with the serial implementation when running a genetic algorithm at different scales show that as the number of systems increases, the parallel architecture is several hundred times faster than the existing implementations, making it feasible to investigate systemic models of more complex biological systems.",
• affiliation = "BIHE University (The Bahai Institute for Higher Education), Iran",
• notes = "GP?",

}

Genetic Programming entries for Marjan Rouhipour Peter J Bentley Hooman Shayani

Citations