Large-scale experimental evaluation of GPU strategies for evolutionary machine learning

摘要

Graphics Processing Units (GPUs) are effective tools for improving the efficiency of many computationally demanding algorithms. GPUs have been particularly effective at speeding up the evaluation stage of evolutionary machine learning systems. The speedups obtained in these tasks, depend on many factors: dataset characteristics, the parallel strategy of the CPU code and the fit of the GPU code within the rest of the learning system. A solid understanding of all these factors is required to choose and adjust the most suitable GPU strategy in different scenarios. In this paper we present a large-scale performance evaluation of two GPU strategies for speeding up the evaluation of evolutionary machine learning systems. We use highly-tuneable synthetic problems to exhaustively explore the space of problem characteristics and determine the type of problems where each strategy performs best. The lessons learnt from this extensive evaluation are further confirmed by running experiments on a broad range of real-world datasets. Through this thorough evaluation we obtain a solid understanding of the capabilities and limitations of the evaluated GPU strategies for boosting the efficiency of evolutionary machine learning systems. (C) 2015 Elsevier Inc. All rights reserved.