17867

Distributed learning of CNNs on heterogeneous CPU/GPU architectures

Jose Marques, Gabriel Falcao, Luis A. Alexandre
Instituto de Telecomunicacoes, Dept. of Electrical and Computer Engineering, University of Coimbra, Polo II – Universidade de Coimbra, 3030-290 Coimbra, Portugal
arXiv:1712.02546 [cs.DC], (7 Dec 2017)

@article{marques2017distributed,

   title={Distributed learning of CNNs on heterogeneous CPU/GPU architectures},

   author={Marques, Jose and Falcao, Gabriel and Alexandre, Luis A.},

   year={2017},

   month={dec},

   archivePrefix={"arXiv"},

   primaryClass={cs.DC}

}

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Convolutional Neural Networks (CNNs) have shown to be powerful classification tools in tasks that range from check reading to medical diagnosis, reaching close to human perception, and in some cases surpassing it. However, the problems to solve are becoming larger and more complex, which translates to larger CNNs, leading to longer training times that not even the adoption of Graphics Processing Units (GPUs) could keep up to. This problem is partially solved by using more processing units and distributed training methods that are offered by several frameworks dedicated to neural network training. However, these techniques do not take full advantage of the possible parallelization offered by CNNs and the cooperative use of heterogeneous devices with different processing capabilities, clock speeds, memory size, among others. This paper presents a new method for the parallel training of CNNs that can be considered as a particular instantiation of model parallelism, where only the convolutional layer is distributed. In fact, the convolutions processed during training (forward and backward propagation included) represent from 60-90% of global processing time. The paper analyzes the influence of network size, bandwidth, batch size, number of devices, including their processing capabilities, and other parameters. Results show that this technique is capable of diminishing the training time without affecting the classification performance for both CPUs and GPUs. For the CIFAR-10 dataset, using a CNN with two convolutional layers, and 500 and 1500 kernels, respectively, best speedups achieve 3.28x using four CPUs and 2.45x with three GPUs. Modern imaging datasets, larger and more complex than CIFAR-10 will certainly require more than 60-90% of processing time calculating convolutions, and speedups will tend to increase accordingly.
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