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ORCID3rd MLHPC@SC 2017: Denver, CO, USA
- Proceedings of the Machine Learning on HPC Environments, MLHPC@SC 2017, Denver, CO, USA, November 13, 2017. ACM 2017, ISBN 978-1-4503-5137-9
- Dominik Marek Loroch, Franz-Josef Pfreundt, Norbert Wehn
, Janis Keuper:
TensorQuant: A Simulation Toolbox for Deep Neural Network Quantization. 1:1-1:8 - Zhenyu Li, James Davis, Stephen A. Jarvis:
An Efficient Task-based All-Reduce for Machine Learning Applications. 2:1-2:8 - Guojing Cong, Brian Kingsbury, Soumyadip Gosh, George Saon, Fan Zhou:
Accelerating deep neural network learning for speech recognition on a cluster of GPUs. 3:1-3:8 - J. Travis Johnston, Steven R. Young, David Hughes, Robert M. Patton, Devin White:
Optimizing Convolutional Neural Networks for Cloud Detection. 4:1-4:9 - Sam Ade Jacobs, Nikoli Dryden, Roger A. Pearce, Brian Van Essen:
Towards Scalable Parallel Training of Deep Neural Networks. 5:1-5:9 - Saurabh Gupta, Vineet Khare:
BlazingText: Scaling and Accelerating Word2Vec using Multiple GPUs. 6:1-6:5 - Steven R. Young, Derek C. Rose, J. Travis Johnston, William T. Heller, Thomas P. Karnowski, Thomas E. Potok, Robert M. Patton, Gabriel N. Perdue, Jonathan A. Miller:
Evolving Deep Networks Using HPC. 7:1-7:7 - Ammar Ahmad Awan, Hari Subramoni, Dhabaleswar K. Panda:
An In-depth Performance Characterization of CPU- and GPU-based DNN Training on Modern Architectures. 8:1-8:8 - Weijian Zheng, Fengguang Song, Lan Lin:
Designing a Synchronization-reducing Clustering Method on Manycores: Some Issues and Improvements. 9:1-9:8 - Alexey Svyatkovskiy, Julian Kates-Harbeck, William Tang:
Training distributed deep recurrent neural networks with mixed precision on GPU clusters. 10:1-10:8
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