- author = "W. B. Langdon",
- title = "Improved {CUDA} {3D} Medical Image Registration",
- booktitle = "UK Many-Core Developer Conference 2014 - UKMAC 2014",
- editor = "Mike Giles and Paul Kelly and Simon McIntosh-Smith and Graham Pullan and Filippo Spiga and David Thomas",
- year = "2014",
- month = "15 " # dec,
- address = "Cambridge",
- keywords = "genetic algorithms, genetic programming, genetic improvement, SBSE, GPGPU, NiftyReg",
-
URL = "
http://www.cs.ucl.ac.uk/staff/W.Langdon/ftp/papers/Langdon_2014_ukmac.pdf",
- size = "1 page",
- abstract = "A combination of manual and genetic improvement (GI) can optimise a critical component of NiftyReg healthcare industry software across a diverse range of six nVidia graphics processing units (GPUs). The improved K20c kernel gives a speed up >2000 fold compared to released code on a 3GHz CPU",
-
notes = "http://www.many-core.group.cam.ac.uk/ukmac2014/
One page summary of \cite{Langdon:2014:GECCO} but emphasis on result rather than GP.",
