Improving the performance of GPU-based genetic programming through exploitation of on-chip memory
Created by W.Langdon from
gp-bibliography.bib Revision:1.8098
- @Article{journals/soco/Chitty16,
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author = "Darren M. Chitty",
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title = "Improving the performance of {GPU}-based genetic
programming through exploitation of on-chip memory",
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journal = "Soft Computing",
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year = "2016",
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number = "2",
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volume = "20",
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pages = "661--680",
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month = feb,
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keywords = "genetic algorithms, genetic programming, GPU, GPGPU,
Many-core GPU, Parallel programming",
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ISSN = "1432-7643",
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bibdate = "2016-01-19",
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bibsource = "DBLP,
http://dblp.uni-trier.de/db/journals/soco/soco20.html#Chitty16",
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URL = "http://dx.doi.org/10.1007/s00500-014-1530-3",
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DOI = "doi:10.1007/s00500-014-1530-3",
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size = "20 pages",
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abstract = "Genetic Programming (GP) (Koza, Genetic programming,
MIT Press, Cambridge, 1992) is well-known as a
computationally intensive technique. Subsequently,
faster parallel versions have been implemented that
harness the highly parallel hardware provided by
graphics cards enabling significant gains in the
performance of GP to be achieved. However, extracting
the maximum performance from a graphics card for the
purposes of GP is difficult. A key reason for this is
that in addition to the processor resources, the fast
on-chip memory of graphics cards needs to be fully
exploited. Techniques will be presented that will
improve the performance of a graphics card
implementation of tree-based GP by better exploiting
this faster memory. It will be demonstrated that both
L1 cache and shared memory need to be considered for
extracting the maximum performance. Better GP program
representation and use of the register file is also
explored to further boost performance. Using an NVidia
Kepler 670GTX GPU, a maximum performance of 36 billion
Genetic Programming Operations per Second is
demonstrated.",
- }
Genetic Programming entries for
Darren M Chitty
Citations