Exascale Deep Learning for Climate Science

Monday, January 7, 2019 - 10:30
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In recent years, our group has developed Deep Learning capabilities to perform binary classification and localization tasks for extreme weather patterns. Recently, we have developed an advanced architecture to segment pixel-level masks of patterns (hurricanes and atmospheric rivers). We scale this architecture on Summit at OakRidge National Lab; the largest GPU system in the world. We train the network on 15360 Volta GPUs, and obtain a peak performance of 263 PF/s. In the AMS timeframe, we expect further scaling and code enhancements to achieve a performance level closer to 1 ExaFlop. 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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