search-icon
Paper
:
CosmoFlow: Using Deep Learning to Learn the Universe at Scale
Event Type
Paper
Registration Categories
TP
Tags
Applications
Cosmology
Data Analytics
Deep Learning
Machine Learning
Programming Systems
Storage
Visualization
TimeThursday, November 15th2pm - 2:30pm
LocationC140/142
DescriptionDeep 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 Xeon Phi 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.
Back To Top Button