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arXiv · 1811.05233

Massively Distributed SGD: ImageNet/ResNet-50 Training in a Flash

Hiroaki Mikami·Hisahiro Suganuma·Pongsakorn U-chupala·Yoshiki Tanaka·Yuichi Kageyama
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Abstract

Scaling the distributed deep learning to a massive GPU cluster level is challenging due to the instability of the large mini-batch training and the overhead of the gradient synchronization. We address the instability of the large mini-batch training with batch-size control and label smoothing. We address the overhead of the gradient synchronization with 2D-Torus all-reduce. Specifically, 2D-Torus all-reduce arranges GPUs in a logical 2D grid and performs a series of collective operation in different orientations. These two techniques are implemented with Neural Network Libraries (NNL). We have successfully trained ImageNet/ResNet-50 in 122 seconds without significant accuracy loss on ABCI cluster.

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Hiroaki Mikami, Hisahiro Suganuma, Pongsakorn U-chupala, Yoshiki Tanaka, Yuichi Kageyama. 2018-11-13. Massively Distributed SGD: ImageNet/ResNet-50 Training in a Flash. https://arxiv.org/abs/1811.05233

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