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• W2888193973 abstract "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." @default.
• W2888193973 created "2018-08-31" @default.
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• W2888193973 date "2018-06-01" @default.
• W2888193973 modified "2023-09-23" @default.
• W2888193973 title "Performance of Convolution Neural Network based on Multiple GPUs with Different Data Communication Models" @default.
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• W2888193973 doi "https://doi.org/10.1109/snpd.2018.8441056" @default.
• W2888193973 hasPublicationYear "2018" @default.
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