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Astrophysical data mining with GPU. A case study: genetic classification of globular clusters

Stefano Cavuoti, Mauro Garofalo, Massimo Brescia, Maurizio Paolillo, Antonio Pescape’, Giuseppe Longo, Giorgio Ventre
Department of Physics, University Federico II, Via Cinthia 6, I-80126 Napoli, Italy
arXiv:1304.0597 [astro-ph.IM], (2 Apr 2013)
@article{Cavuoti:2013uxa,

   author={"Cavuoti},

   title={"{Astrophysical data mining with GPU. A case study: genetic classification of globular clusters}"},

   year={"2013"},

   eprint={"1304.0597"},

   archivePrefix={"arXiv"},

   primaryClass={"astro-ph.IM"},

   SLACcitation={"%%CITATION=ARXIV:1304.0597;%%"}

}

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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), 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.
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