CosmoFlow：使用深度学习大规模学习宇宙
CosmoFlow: Using Deep Learning to Learn the Universe at Scale
摘要：
深度学习是一种很有前途的工具，可以用来确定描述我们宇宙的物理模型。为了处理这个问题的巨大计算成本，我们提出了CosmoFlow：一个基于TensorFlow框架的高度可扩展的深度学习应用程序。CosmoFlow使用3D卷积和池基元的高效实现，以及对许多元素操作的线程化改进，来提高英特尔（C）至强融核（TM）处理器上的训练性能。我们还利用Cray PE机器学习插件来有效地扩展到多个节点。我们在8192个Cori节点上展示了完全同步的数据并行训练，并行效率为77%，实现了3.5 Pflop/s的持续性能。据我们所知，这是TensorFlow框架在超级计算机规模上的第一个大规模科学应用，具有完全同步的训练。这些增强使我们能够处理大的三维暗物质分布，并以前所未有的精度预测宇宙学参数$Ω_M$，$σ_8$和n $_s$。
英文摘要：
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 $Ω_M$, $σ_8$ and n$_s$ with unprecedented accuracy.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
