Abstract
Training a deep neural network usually requires a high computational cost. Nowadays, the most common way to carry out this task is through the use of GPUs due to their efficiency implementing complicated algorithms for this kind of tasks. However, training several neural networks, each with different hyperparameters, is still a very heavy task. Typically, clusters include one or more GPUs that could be used for deep learning. This paper proposes and analyzes a distributed parallel procedure to train multiple Convolutional Neural Networks (CNNs) for EEG classification, in a heterogeneous CPU-GPU cluster and in a Desktop PC. The procedure is implemented in C++ and with the MPI library to dynamically distribute the hyperparameters among the nodes, which are responsible for training the corresponding CNN by using Python, Keras, and TensorFlow. The proposed algorithm has been analyzed considering running times and energy measures, showing that when more nodes are used, the procedure scales linearly and the lowest running time is obtained. However, the desktop PC provides the best energy results.
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Acknowledgements
This research was funded by grants TIN2015-67020-P (Spanish “Ministerio de Economía y Competitividad”), PGC2018-098813-B-C31 (Spanish “Ministerio de Ciencia, Innovación y Universidades”), and ERDF funds. We would also like to thank the BCI Laboratory of the University of Essex for allowing us to use their databases. The Titan Xp used for this research was donated by the NVIDIA Corporation.
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Escobar, J.J., Ortega, J., Damas, M., Kızıltepe, R.S., Gan, J.Q. (2019). Energy-Time Analysis of Convolutional Neural Networks Distributed on Heterogeneous Clusters for EEG Classification. In: Rojas, I., Joya, G., Catala, A. (eds) Advances in Computational Intelligence. IWANN 2019. Lecture Notes in Computer Science(), vol 11507. Springer, Cham. https://doi.org/10.1007/978-3-030-20518-8_74
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DOI: https://doi.org/10.1007/978-3-030-20518-8_74
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