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Parallel and Distributed Computational Intelligence pp 113–141Cite as

Large Scale Bioinformatics Data Mining with Parallel Genetic Programming on Graphics Processing Units

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Part of the book series: Studies in Computational Intelligence ((SCI,volume 269))

Abstract

A suitable single instruction multiple data GP interpreter can achieve high (Giga GPop/second) performance on a SIMD GPU graphics card by simultaneously running multiple diverse members of the genetic programming population. SPMD dataflow parallelisation is achieved because the single interpreter treats the different GP programs as data. On a single 128 node parallel nVidia GeForce 8800 GTX GPU, the interpreter can out run a compiled approach, where data parallelisation comes only by running a single program at a time across multiple inputs.

The RapidMind GPGPU Linux C++ system has been demonstrated 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 five million programs winnow useful variables from the chaff at more than 500 million GPops per second. Sources available via FTP.

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Authors and Affiliations

  1. King’s College, London

    William B. Langdon

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  1. William B. Langdon
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  1. University of Extremadura, C/ Sta Teresa de Jornet, 38, Merida, Spain

    Francisco Fernández de Vega

  2. Yahoo! Inc., 701 First Avenue, 94087, Sunnyvale, CA, USA

    Erick Cantú-Paz

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Langdon, W.B. (2010). Large Scale Bioinformatics Data Mining with Parallel Genetic Programming on Graphics Processing Units. In: de Vega, F.F., Cantú-Paz, E. (eds) Parallel and Distributed Computational Intelligence. Studies in Computational Intelligence, vol 269. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-10675-0_6

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  • DOI: https://doi.org/10.1007/978-3-642-10675-0_6

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