1. [Publications](/publications)
2. Exascale Deep Learning for Climate Analytics
 
 # Exascale Deep Learning for Climate Analytics

  ![Publication image](/sites/default/files/styles/wide/public/default_images/default.jpeg?itok=qUFsuJCP "Publication image")

 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 systems. The Tiramisu network scales to 5300 P100 GPUs with a sustained throughput of 21.0 PF/s and parallel efficiency of 79.0%. DeepLabv3+ scales up to 27360 V100 GPUs with a sustained throughput of 325.8 PF/s and a parallel efficiency of 90.7% in single precision. By taking advantage of the FP16 Tensor Cores, a half-precision version of the DeepLabv3+ network achieves a peak and sustained throughput of 1.13 EF/s and 999.0 PF/s respectively.



 ## Authors



Thorsten Kurth (Lawrence Berkeley National Laboratory)

Sean Treichler (NVIDIA)

Joshua Romero (NVIDIA)

Mayur Mudigonda (UC Berkeley)

Nathan Luehr (NVIDIA)

Everett Phillips (NVIDIA)

Ankur Mahesh (Lawrence Berkeley National Laboratory)

Michael Matheson (Oak Ridge National Laboratory)

Jack Deslippe (Lawrence Berkeley National Laboratory)

Massimiliano Fatica (NVIDIA)

Prabhat (Lawrence Berkeley National Laboratory)

Michael Houston (NVIDIA)

 

 

 ## Publication Date



Sunday, November 11, 2018

 

 ## Published in



[International Conference for High Performance Computing and Communications (SC'…](https://dl.acm.org/doi/10.5555/3291656.3291724)

 

 ## Research Area



[Artificial Intelligence and Machine Learning ](/research-area/machine-learning-artificial-intelligence)

[Climate Simulation](/research-area/climate-simulation)

[High Performance Computing](/research-area/high-performance-computing)

 

 

 ## External Links



[ACM Digital Library](https://dl.acm.org/doi/10.5555/3291656.3291724)

 

 

 ## Uploaded Files



[Published manuscript](https://d1qx31qr3h6wln.cloudfront.net/publications/SC_2018_Exascale_DL_Climate.pdf "Open file in new window")752.89 KB

 

 

 ## Copyright



Copyright by the Association for Computing Machinery, Inc. Permission to make digital or hard copies of part or all of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, to republish, to post on servers, or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from Publications Dept, ACM Inc., fax +1 (212) 869-0481, or <permissions@acm.org>. The definitive version of this paper can be found at ACM's Digital Library <http://www.acm.org/dl/>.