Exascale Deep Learning for Climate Analytics

We extract pixel-level masks of extreme weather patterns using variants of Tiramisu and DeepLabv3+ neural networks. We describe improvements to the software frameworks, input pipeline, and the network training algorithms necessary to efficiently scale deep learning on the Piz Daint and Summit system...

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Vydané v:	SC18: International Conference for High Performance Computing, Networking, Storage and Analysis s. 649 - 660
Hlavní autori:	Kurth, Thorsten, Treichler, Sean, Romero, Joshua, Mudigonda, Mayur, Luehr, Nathan, Phillips, Everett, Mahesh, Ankur, Matheson, Michael, Deslippe, Jack, Fatica, Massimiliano, Prabhat, Prabhat, Houston, Michael
Médium:	Konferenčný príspevok..
Jazyk:	English
Vydavateľské údaje:	IEEE 01.11.2018
Predmet:	Computational modeling Computer architecture Convolutional codes Deep learning Meteorology Technological innovation Training
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