Systemic Computation Using Graphics Processors
Created by W.Langdon from
gp-bibliography.bib Revision:1.7954
- @InProceedings{Rouhipour:2010:ICES,
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author = "Marjan Rouhipour and Peter J Bentley and
Hooman Shayani",
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title = "Systemic Computation Using Graphics Processors",
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booktitle = "Proceedings of the 9th International Conference
Evolvable Systems: From Biology to Hardware, ICES
2010",
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year = "2010",
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editor = "Gianluca Tempesti and Andy M. Tyrrell and
Julian F. Miller",
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series = "Lecture Notes in Computer Science",
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volume = "6274",
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pages = "121--132",
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address = "York",
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month = sep # " 6-8",
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publisher = "Springer",
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keywords = "genetic algorithms, genetic programming, GPU",
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isbn13 = "978-3-642-15322-8",
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DOI = "doi:10.1007/978-3-642-15323-5_11",
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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.",
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affiliation = "BIHE University (The Bahai Institute for Higher
Education), Iran",
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notes = "GP?",
- }
Genetic Programming entries for
Marjan Rouhipour
Peter J Bentley
Hooman Shayani
Citations