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...

Ausführliche Beschreibung

Bibliographische Detailangaben

Veröffentlicht in:	SC18: International Conference for High Performance Computing, Networking, Storage and Analysis S. 649 - 660
Hauptverfasser:	Kurth, Thorsten, Treichler, Sean, Romero, Joshua, Mudigonda, Mayur, Luehr, Nathan, Phillips, Everett, Mahesh, Ankur, Matheson, Michael, Deslippe, Jack, Fatica, Massimiliano, Prabhat, Prabhat, Houston, Michael
Format:	Tagungsbericht
Sprache:	Englisch
Veröffentlicht:	IEEE 01.11.2018
Schlagworte:	Computational modeling Computer architecture Convolutional codes Deep learning Meteorology Technological innovation Training
Online-Zugang:	Volltext
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