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      CosmoFlow: Using Deep Learning to Learn the Universe at Scale

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          Abstract

          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.

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          Distributed asynchronous deterministic and stochastic gradient optimization algorithms

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            Evolving Deep Networks Using HPC

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              Evaluating the networking characteristics of the cray xc-40 intel knights landing-based cori supercomputer at nersc

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                Author and article information

                Journal
                14 August 2018
                Article
                1808.04728
                49f8fecc-56c0-41e4-90d1-37068d9518b5

                http://arxiv.org/licenses/nonexclusive-distrib/1.0/

                History
                Custom metadata
                12 pages, 6 pages, accepted to SuperComputing 2018
                astro-ph.CO astro-ph.IM cs.LG physics.comp-ph

                Cosmology & Extragalactic astrophysics,Mathematical & Computational physics,Artificial intelligence,Instrumentation & Methods for astrophysics

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