5,971 research outputs found
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
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.Comment: 12 pages, 5 tables, 4, figures, Super Computing Conference November
11-16, 2018, Dallas, TX, US
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