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ORCID3rd DLS@SC 2019: Denver, CO, USA
- Third IEEE/ACM Workshop on Deep Learning on Supercomputers, DLS@SC 2019, Denver, CO, USA, November 17, 2019. IEEE 2019, ISBN 978-1-7281-6011-5
Technical Papers
- Liu Yang, Prabhat, George E. Karniadakis, Sean Treichler, Thorsten Kurth, Keno Fischer, David A. Barajas-Solano
, Joshua Romero, Valentin Churavy, Alexandre M. Tartakovsky, Michael Houston:
Highly-Ccalable, Physics-Informed GANs for Learning Solutions of Stochastic PDEs. 1-11 - Hyungro Lee, Matteo Turilli, Shantenu Jha, Debsindhu Bhowmik, Heng Ma, Arvind Ramanathan:
DeepDriveMD: Deep-Learning Driven Adaptive Molecular Simulations for Protein Folding. 12-19 - Zhengchun Liu, Tekin Bicer, Rajkumar Kettimuthu, Ian T. Foster:
Deep Learning Accelerated Light Source Experiments. 20-28 - Benjamin Parsons, Jing-Ru Cheng:
Deep Learning for Gap Crossing Ability of Ground Vehicles. 29-35 - Mark Coletti, Dalton D. Lunga, Jeffrey K. Bassett, Amy N. Rose:
Evolving Larger Convolutional Layer Kernel Sizes for a Settlement Detection Deep-Learner on Summit. 36-44 - Chris A. Mattmann, Zhao Zhang:
Deep Facial Recognition using Tensorflow. 45-51 - Wushi Dong, Nicola J. Ferrier, Narayanan Kasthuri, Peter Littlewood, Murat Keçeli, Rafael Vescovi, Hanyu Li, Corey Adams, Elise Jennings, Samuel Flender, Thomas D. Uram, Venkatram Vishwanath:
Scaling Distributed Training of Flood-Filling Networks on HPC Infrastructure for Brain Mapping. 52-61 - Alessandro Rigazzi:
DC-S3GD: Delay-Compensated Stale-Synchronous SGD for Large-Scale Decentralized Neural Network Training. 62-68 - Zhao Zhang, Lei Huang, J. Gregory Pauloski, Ian T. Foster:
Aggregating Local Storage for Scalable Deep Learning I/O. 69-75 - Arpan Jain, Ammar Ahmad Awan, Hari Subramoni, Dhabaleswar K. Panda:
Scaling TensorFlow, PyTorch, and MXNet using MVAPICH2 for High-Performance Deep Learning on Frontera. 76-83 - Junqi Yin, Shubhankar Gahlot, Nouamane Laanait, Ketan Maheshwari, Jack Morrison, Sajal Dash, Mallikarjun Shankar:
Strategies to Deploy and Scale Deep Learning on the Summit Supercomputer. 84-94
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