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arXiv · 1909.11150

Exascale Deep Learning for Scientific Inverse Problems

Nouamane Laanait·Joshua Romero·Junqi Yin·M. Todd Young·Sean Treichler·Vitalii Starchenko·Albina Borisevich·Alex Sergeev·Michael Matheson
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Abstract

We introduce novel communication strategies in synchronous distributed Deep Learning consisting of decentralized gradient reduction orchestration and computational graph-aware grouping of gradient tensors. These new techniques produce an optimal overlap between computation and communication and result in near-linear scaling (0.93) of distributed training up to 27,600 NVIDIA V100 GPUs on the Summit Supercomputer. We demonstrate our gradient reduction techniques in the context of training a Fully Convolutional Neural Network to approximate the solution of a longstanding scientific inverse problem in materials imaging. The efficient distributed training on a dataset size of 0.5 PB, produces a model capable of an atomically-accurate reconstruction of materials, and in the process reaching a peak performance of 2.15(4) EFLOPS$_{16}$.

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Nouamane Laanait, Joshua Romero, Junqi Yin, M. Todd Young, Sean Treichler, Vitalii Starchenko, Albina Borisevich, Alex Sergeev, Michael Matheson. 2019-09-24. Exascale Deep Learning for Scientific Inverse Problems. https://arxiv.org/abs/1909.11150

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