Improving the performance of GPU-based genetic programming through exploitation of on-chip memory

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

@Article{journals/soco/Chitty16,

• author = "Darren M. Chitty",
• title = "Improving the performance of {GPU}-based genetic programming through exploitation of on-chip memory",
• journal = "Soft Computing",
• year = "2016",
• number = "2",
• volume = "20",
• pages = "661--680",
• month = feb,
• keywords = "genetic algorithms, genetic programming, GPU, GPGPU, Many-core GPU, Parallel programming",
• ISSN = "1432-7643",
• bibdate = "2016-01-19",
• bibsource = "DBLP, http://dblp.uni-trier.de/db/journals/soco/soco20.html#Chitty16",
• URL = "http://dx.doi.org/10.1007/s00500-014-1530-3",
• DOI = "doi:10.1007/s00500-014-1530-3",
• size = "20 pages",
• 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

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