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Performance of convolution neural network based on multiple GPUs with different data communication models
Conference paper

Performance of convolution neural network based on multiple GPUs with different data communication models

Che-Lun Hung, Yi-Yang Lin, Chuan Yi Tang, Chilung Wang and Ming-Chiang Chen
Proceedings - 2018 IEEE/ACIS 19th International Conference on Software Engineering, Artificial Intelligence, Networking and Parallel/Distributed Computing, SNPD 2018, pp.87-92
08/2018
DOI: https://doi.org/10.1109/SNPD.2018.8441056

Abstract

Convolution Neural Networks Deep Learning GPU Multiple GPUs Artificial Intelligence Computer Networks and Communications Hardware and Architecture Software Control and Optimization Information Systems and Management
Recently, deep learning technologies have been utilized in many scientific domains successfully. Convolution neural networks are common used in image understanding problems. However, to train a convolution neural network model with huge amount of images is time-consuming task. Most of deep learning frameworks, such as Caffe, TensorFlow, Torch, Keras, MxNet, and so forth, support GPU to train model fast; especially executing these models on multiple GPUs. In this work, we present the comparison of computation performance of AlexNet among different GPU servers and hyperparameters. The results shows that GPU servers with high bandwidth rate, NVLINK, can achieve better performance than others.

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