EASEA: a generic optimization tool for GPU machines in asynchronous island model
Laurent A. Baumes1
, Frederic Kruger2, Pierre Collet2
1ITQ, UPV-CSIC, Valencia, Spain.
2Univ. Strasbourg, LSIIT, FDBT, Illkrirch, France.
DOI:
https://doi.org/10.7494/cmms.2011.3.0373
Abstract:
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.
Cite as:
Baumes, L., Kruger, F., & Collet, P. (2011). EASEA: a generic optimization tool for GPU machines in asynchronous island model. Computer Methods in Materials Science, 11(3), 489 – 499. https://doi.org/10.7494/cmms.2011.3.0373
Article (PDF):
Keywords:
GPU, Evolutionary Algorithms, Island Model, Parallelism, Zeolite Materials
References: