Deep Learning
2018-11-15 13:30 - 15:00 (America/Chicago) | C140/142
Paper
Applications, Cosmology, Data Analytics, Deep Learning, Machine Learning, Programming Systems, Storage, Visualization, Tech Program Reg Pass

CosmoFlow: Using Deep Learning to Learn the Universe at Scale

Mathuriya, Bard, Mendygral, Meadows, Arnemann...

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 implem...

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Anatomy of High-Performance Deep Learning Convolutions on SIMD Architectures

Georganas, Avancha, Banerjee, Kalamkar, Henry...

Convolution layers are prevalent in many classes of deep neural networks, including Convolutional Neural Networks (CNNs) which provide state-of-the-art results for tasks like image recognition, neural machine translation, and speech recognition. The computationally expensive nature of a convolution ...

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Exploring Flexible Communications for Streamlining DNN Ensemble Training Pipelines

Pittman, Guan, Shen, Lim, Patton

Parallel training of a Deep Neural Network (DNN) ensemble on a cluster of nodes is a common practice to train multiple models in order to construct a model with a higher prediction accuracy. Existing ensemble training pipelines can perform a great deal of redundant operations, resulting in unnecessa...
