计算机与现代化

• 应用与开发 • 上一篇    下一篇

深度卷积神经网络的多GPU并行框架

  

  1. 南京晓庄学院信息工程学院,江苏南京211171
  • 收稿日期:2016-03-11 出版日期:2016-11-15 发布日期:2016-11-23
  • 作者简介:杨宁(1972-),男,江苏泰州人,南京晓庄学院信息工程学院讲师,硕士,研究方向:数据库,数据挖掘,机器学习。
  • 基金资助:
    国家自然科学基金青年基金资助项目(61202136)

Multi-GPU Parallel Framework of Deep Convolutional Neural Networks

  1. College of Information Engineering, Nanjing Xiaozhuang University, Nanjing 211171, China
  • Received:2016-03-11 Online:2016-11-15 Published:2016-11-23

摘要: 近年来,深度卷积神经网络在图像识别和语音识别等领域被广泛运用,取得了很好的效果。深度卷积神经网络是层数较多的卷积神经网络,有数千万参数需要学习,计算开销大,导致训练非常耗时。针对这种情况,本文提出深度卷积神经网络的多GPU并行框架,设计并实现模型并行引擎,依托多GPU的强大协同并行计算能力,结合深度卷积神经网络在训练中的并行特点,实现快速高效的深度卷积神经网络训练。

关键词: 深度卷积神经网络, GPU, 并行框架, 图像识别, 大数据

Abstract: In recent years, deep convolutional neural network is widely used in the fields of image recognition and speech recognition, and achieves good results. Deep convolutional neural networks are the convolutional neural networks with multiple layers, tens of millions of parameters need to be learned, and computational overhead is large, so the training is very time-consuming. In view of this situation, we propose a multi-GPU parallel framework of deep convolutional neural networks, design and implement model parallel engine, relying on the powerful collaborative parallel computing ability of multi-GPU, combined with the parallel characteristics of deep convolutional neural networks in training, to achieve fast and efficient deep convolution neural networks training.

Key words: deep convolutional neural networks, graphic processing unit, parallel framework, image recognition, big data

中图分类号: