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, Ming Chiang Chen

研究成果: Conference contribution同行評審

1 引文 斯高帕斯(Scopus)

摘要

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.

原文English
主出版物標題Proceedings - 2018 IEEE/ACIS 19th International Conference on Software Engineering, Artificial Intelligence, Networking and Parallel/Distributed Computing, SNPD 2018
編輯Ha Jin Hwang, Lizhi Cai, Gun Huck Yeom, Tokuro Matsuo, Haeng Kon Kim, Hyun Yeo, Chung Sun Hong, Naoki Fukuta, Takayuki Ito, Huaikou Miao
發行者Institute of Electrical and Electronics Engineers Inc.
頁面87-92
頁數6
ISBN(列印)9781538658895
DOIs
出版狀態Published - 20 8月 2018
事件19th IEEE/ACIS International Conference on Software Engineering, Artificial Intelligence, Networking and Parallel/Distributed Computing, SNPD 2018 - Busan, Korea, Republic of
持續時間: 27 6月 201829 6月 2018

出版系列

名字Proceedings - 2018 IEEE/ACIS 19th International Conference on Software Engineering, Artificial Intelligence, Networking and Parallel/Distributed Computing, SNPD 2018

Conference

Conference19th IEEE/ACIS International Conference on Software Engineering, Artificial Intelligence, Networking and Parallel/Distributed Computing, SNPD 2018
國家/地區Korea, Republic of
城市Busan
期間27/06/1829/06/18

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