Towards Exascale Deep Learning for Climate Science

Monday, December 10, 2018 - 08:00
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We develop an advanced Deep Learning architecture to segment pixel-level masks of extreme weather patterns (hurricanes and atmospheric rivers). We scale this architecture on the largest GPU system in the world: the OLCF Summit system. We train the network on 15360 Volta GPUs, and obtain a peak performance of 263 PF/s. The network trains to convergence in 100 minutes. We develop a number of innovations spanning software frameworks, I/O, and machine learning algorithms to pull off this unprecedented level of performance.

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