Astrophysical data mining with GPU. A case study: genetic classification
  of globular clusters
  We present a multi-purpose genetic algorithm, designed and implemented with
GPGPU / CUDA parallel computing technology. The model was derived from our CPU
serial implementation, named GAME (Genetic Algorithm Model Experiment). It was
successfully tested and validated on the detection of candidate Globular
Clusters in deep, wide-field, single band HST images. The GPU version of GAME
will be made available to the community by integrating it into the web
application DAMEWARE (DAta Mining Web Application REsource
(http://dame.dsf.unina.it/beta_info.html), a public data mining service
specialized on massive astrophysical data. Since genetic algorithms are
inherently parallel, the GPGPU computing paradigm leads to a speedup of a
factor of 200x in the training phase with respect to the CPU based version.
