Deep learning is a promising tool to determine the physical model that
describes our universe. To handle the considerable computational cost of this
problem, we present CosmoFlow: a highly scalable deep learning application
built on top of the TensorFlow framework. CosmoFlow uses efficient
implementations of 3D convolution and pooling primitives, together with
improvements in threading for many element-wise operations, to improve training
performance on Intel(C) Xeon Phi(TM) processors. We also utilize the Cray PE
Machine Learning Plugin for efficient scaling to multiple nodes. We demonstrate
fully synchronous data-parallel training on 8192 nodes of Cori with 77%
parallel efficiency, achieving 3.5 Pflop/s sustained performance. To our
knowledge, this is the first large-scale science application of the TensorFlow
framework at supercomputer scale with fully-synchronous training. These
enhancements enable us to process large 3D dark matter distribution and predict
the cosmological parameters $\Omega_M$, $\sigma_8$ and n$_s$ with unprecedented
accuracy.

使用深度学习技术在超级计算机上搭建了基于 TensorFlow 框架的高度可伸缩的宇宙学应用 CosmoFlow，通过使用 3D 卷积和池化原语的高效实现，平台多线程计算操作等技术手段，在 Cori 系统上训练具有 77％并行效率的 8192 个节点，取得 3.5 Pflop /s 的持续性能，成功处理了大规模的 3D 暗物质分布并预测了宇宙学参数 ΩM，σ8 和 ns 的精度空前的结果。