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This paper presents the first, 15-PetaFLOP Deep Learning system for solvingscientific pattern classification problems on contemporary HPC architectures.We develop supervised convolutional architectures for discriminating signals inhigh-energy physics data as well as semi-supervised architectures forlocalizing and classifying extreme weather in climate data. OurIntelcaffe-based implementation obtains $\sim$2TFLOP/s on a single CoriPhase-II Xeon-Phi node. We use a hybrid strategy employing synchronousnode-groups, while using asynchronous communication across groups. We use thisstrategy to scale training of a single model to $\sim$9600 Xeon-Phi nodes;obtaining peak performance of 11.73-15.07 PFLOP/s and sustained performance of11.41-13.27 PFLOP/s. At scale, our HEP architecture produces state-of-the-artclassification accuracy on a dataset with 10M images, exceeding that achievedby selections on high-level physics-motivated features. Our semi-supervisedarchitecture successfully extracts weather patterns in a 15TB climate dataset.Our results demonstrate that Deep Learning can be optimized and scaledeffectively on many-core, HPC systems.

Deep Learning at 15PF: Supervised and Semi-Supervised Classification for Scientific Data