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Exascale Deep Learning for Climate Analytics

Thorsten Kurth, Sean Treichler, Joshua Romero, Mayur Mudigonda, Nathan Luehr, Everett Phillips, Ankur Mahesh, Michael Matheson, Jack Deslippe, Massimiliano Fatica, Prabhat, Michael Houston
Lawrence Berkeley National Laboratory, Berkeley, CA 94720, USA
arXiv:1810.01993 [cs.DC], (3 Oct 2018)

@article{kurth2018exascale,

   title={Exascale Deep Learning for Climate Analytics},

   author={Kurth, Thorsten and Treichler, Sean and Romero, Joshua and Mudigonda, Mayur and Luehr, Nathan and Phillips, Everett and Mahesh, Ankur and Matheson, Michael and Deslippe, Jack and Fatica, Massimiliano and Prabhat, and Houston, Michael},

   year={2018},

   month={oct},

   archivePrefix={"arXiv"},

   primaryClass={cs.DC}

}

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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.
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