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Genetic Algorithm Modeling with GPU Parallel Computing Technology
Stefano Cavuoti1 , Mauro Garofalo2, Massimo Brescia3,1⋆ , Antonio Pescape’2 , Giuseppe Longo1,4, and Giorgio Ventre2
arXiv:1211.5481v1 [astro-ph.IM] 23 Nov 2012
Department of Physics, University Federico II, Via Cinthia 6, I-80126 Napoli, Italy Department of Computer Engineering and Systems, University Federico II, Via Claudio 21, I-80125 Napoli, Italy 3 INAF, Astronomical Observatory of Capodimonte, Via Moiariello 16, I-80131 Napoli, Italy 4 Visiting Associate, California Institute of Technology, Pasadena, CA 91125, USA
Abstract. We present a multi-purpose genetic algorithm, designed and implemented with GPGPU / CUDA parallel computing technology. The model was derived from a multi-core CPU serial implementation, named GAME, already scientiﬁcally successfully tested and validated on astrophysical massive data classiﬁcation problems, through a web application resource (DAMEWARE), specialized in data mining based on Machine Learning paradigms. Since genetic algorithms are inherently parallel, the GPGPU computing paradigm has provided an exploit of the internal training features of the model, permitting a strong optimization in terms of processing performances and scalability. Keywords: genetic algorithms, GPU programming, data mining
Computing has started to change how science is done, enabling new scientiﬁc advances through enabling new kinds of experiments. They are also generating new kinds of data of increasingly exponential complexity and volume. Achieving the goal of being able to use, exploit and share most eﬀectively these data is a huge challenge. The harder problem for the future is heterogeneity, of platforms, data and applications, rather than simply the scale of the deployed resources. Current platforms require the scientists to overcome computing barriers between them and the data . The present paper concerns the design and development of a...