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Publication Detail
GP on SPMD parallel Graphics Hardware for mega Bioinformatics Data Mining
  • Publication Type:
    Journal article
  • Publication Sub Type:
  • Authors:
    Langdon WB, Harrison AP
  • Publication date:
  • Pagination:
    1169, 1183
  • Journal:
    Soft Computing
  • Volume:
  • Article number:
  • Notes:
    Special Issue on Distributed Bioinspired Algorithms keywords: genetic algorithms, genetic programming, breast cancer, decorin, C17orf81, S-adenosylhomocysteine hydrolase, fibulin 1, Lance Miller’s Uppsala GEO GSE3494 tumour biopsy, Affymetrix HG-U133A, HG-U133B, data mining, consumer graphics hardware, GPU, Graphics Processing Unit, SIMD, parallel computing, genetic programming, soft computing, evolutionary algorithm, RapidMind Ubuntu GCC C++ size: 11 pages notes: http://www.cs.ucl.ac.uk/staff/W.Langdon/ftp/gp-code/gpu_gp_2.tar.gz
We demonstrate a SIMD C++ genetic programming system on a single 128 node parallel nVidia GeForce 8800 GTX GPU under RapidMind’s GPGPU Linux software by predicting ten year+ outcome of breast cancer from a dataset containing a million inputs. NCBI GEO GSE3494 contains hundreds of Affymetrix HG-U133A and HG-U133B GeneChip biopsies. Multiple GP runs each with a population of 5 million programs winnow useful variables from the chaff at more than 500 million GPops per second. Sources available via FTP.
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