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CosmoFlow

swMATH ID: 42355
Software Authors: Amrita Mathuriya, Deborah Bard, Peter Mendygral, Lawrence Meadows, James Arnemann, Lei Shao, Siyu He, Tuomas Karna, Daina Moise, Simon J. Pennycook, Kristyn Maschoff, Jason Sewall, Nalini Kumar, Shirley Ho, Mike Ringenburg, Prabhat, Victor Lee
Description: CosmoFlow: Using Deep Learning to Learn the Universe at Scale. 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
Homepage: https://arxiv.org/abs/1808.04728
Related Software: SuperNNova; TensorFlow; Scikit; AlexNet; SqueezeNet; ImageNet; cuDNN; ZDOCK; mctoolbox; FFTW
Referenced in: 2 Publications

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