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arXiv · 2103.08802

Parareal Neural Networks Emulating a Parallel-in-time Algorithm

Chang-Ock Lee·Youngkyu Lee·Jongho Park
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Abstract

As deep neural networks (DNNs) become deeper, the training time increases. In this perspective, multi-GPU parallel computing has become a key tool in accelerating the training of DNNs. In this paper, we introduce a novel methodology to construct a parallel neural network that can utilize multiple GPUs simultaneously from a given DNN. We observe that layers of DNN can be interpreted as the time step of a time-dependent problem and can be parallelized by emulating a parallel-in-time algorithm called parareal. The parareal algorithm consists of fine structures which can be implemented in parallel and a coarse structure which gives suitable approximations to the fine structures. By emulating it, the layers of DNN are torn to form a parallel structure which is connected using a suitable coarse network. We report accelerated and accuracy-preserved results of the proposed methodology applied to VGG-16 and ResNet-1001 on several datasets.

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Chang-Ock Lee, Youngkyu Lee, Jongho Park. 2021-03-16. Parareal Neural Networks Emulating a Parallel-in-time Algorithm. https://doi.org/10.1109/tnnls.2022.3206797

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