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DeepDownscale: a Deep Learning Strategy for High-Resolution Weather Forecast
Running high-resolution physical models is computationally expensive and essential for many disciplines.
An argument in favor of strong scaling for deep neural networks with small datasets
We evaluated our approach with up to 32 GPUs and show that weak scaling not only does not have the same accuracy as the sequential model, it also fails to converge most of time.
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