EASEA: A Generic Optimization Tool for GPU Machines in Asynchronous Island Model

Laurent A. Baumes, Frederic Kruger, Pierre Collet
ITQ, UPV-CSIC, Valencia, Spain
Computer Methods in Materials Science, Vol. 11, No. 3, 2011


   title={EASEA: A Generic Optimization Tool for GPU Machines in Asynchronous Island Model},

   author={Baumes, Laurent A. and Kruger, Frederic and Collet, Pierre},

   journal={Computer Methods in Materials Science},




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Very recently, we presented an efficient implementation of Evolutionary Algorithms (EAs) using Graphics Processing Units (GPU) for solving microporous crystal structures. Because of both the inherent complexity of zeolitic materials and the constant pressure to accelerate R&D solutions, an asynchronous island model running on clusters of machines equipped with GPU cards, i.e. the current trend for super-computers and cloud computing, is presented. This last improvement of the EASEA platform allows an effortless exploitation of hierarchical massively parallel systems. It is demonstrated that supra-linear speedup over one machine and linear speedup considering clusters of different sizes are obtained. Such an island implementation over several potentially heterogeneous machines opens new horizon for various domains of application where computation time for optimization remains the principal bottleneck.
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