Exascale深度学习用于气候分析
Exascale Deep Learning for Climate Analytics
摘要：
我们使用提拉米苏和DeepLabv 3+神经网络的变体提取极端天气模式的像素级掩模。我们描述了对软件框架、输入管道和网络训练算法的改进，这些改进是在Piz Daint和Summit系统上有效扩展深度学习所必需的。Tiramisu网络可扩展到5300个P100 GPU，持续吞吐量为21.0 PF/s，并行效率为79.0%。DeepLabv 3+可扩展到27360个V100 GPU，持续吞吐量为325.8 PF/s，单精度的并行效率为90.7%。通过利用FP 16 Tensor Cores，DeepLabv 3+网络的半精度版本分别实现了1.13 EF/s和999.0 PF/s的峰值和持续吞吐量。
英文摘要：
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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