GPU异构计算环境中长短时记忆网络模型的应用及优化

Application and optimization of long short-term memory network model in GPU heterogeneous computing environment

随着深度学习的广泛应用及算力资源的异构化,在GPU异构计算环境下的深度学习加速成为又一研究热点。文章探讨了在GPU异构计算环境中如何应用长短时记忆网络模型,并通过优化策略提高其性能。首先,介绍了长短时记忆网络模型的基本结构(包括门控循环单元、丢弃法、Adam与双向长短时记忆网络等);其次,提出了在GPU上执行的一系列优化方法,如CuDNN库的应用及并行计算的设计等。最终,通过实验分析了以上优化方法在训练时间、验证集性能、测试集性能、超参数和硬件资源使用等方面的差异。

With the widespread application of deep learning and the isomerization of computing resources,the acceleration of deep learning in GPU heterogeneous computing environments has become another research hotspot.This article explores how to apply long short-term memory network models in GPU heterogeneous computing environments and improve their performance through optimization strategies.Firstly,the basic structure of the long short-term memory network model was introduced,including gate recurrent unit,Dropout,Adam,and BiLSTM.Secondly,a series of optimization methods were proposed for execution on GPUs,such as the application of CuDNN library and the design of parallel computing.Finally,the differences in training time,validation set performance,test set performance,hyperparameters,and hardware resource utilization among the above optimization methods were analyzed through experiments.