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

  • William B. LangdonEmail author
Chapter
Part of the Natural Computing Series book series (NCS)

Abstract

The NCBI GEO GSE3494 breast cancer dataset contains hundreds of Affymetrix HG-U133A and HG-U133B GeneChip biopsies each with a million variables. Multiple genetic programming (GP) runs on a graphics processing unit (GPU) hardware, each with a population of five million programs both winnows (selects) useful variables from the chaff and evolves small (three inputs) data models. The SPMD CUDA interpreter exploits the GPU’s single instruction multiple data (SIMD) mode of parallel computing, even though the GP populations contain different programs. A 448 node nVidia Fermi C2050 Tesla graphics card delivers 8.5 giga GPops per second. In addition to describing our implementation, we survey current GPGPU work in bioinformatics and genetic programming.

Keywords

Genetic Programming Graphic Processing Unit Cache Line Single Instruction Multiple Data Training Case 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

Notes

Acknowledgements

I would like to thank Shigeyoshi Tsutsui, Stan Seibert, Neil Daeche (UCL) and Derek Ross (STFC Rutherford Appleton Laboratory). The two C2050s were donated by nVidia as part of the GISMO EPSRC project.

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Copyright information

© Springer-Verlag Berlin Heidelberg 2013

Authors and Affiliations

  1. 1.Department of Computer ScienceUniversity CollegeLondonUK

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