Deep Learning at 15PF: Supervised and Semi-Supervised
Classification for Scientific Data
SessionDeep Learning
Authors
Event Type
Paper
Accelerators
Applications
Deep Learning
Machine Learning
TimeTuesday, November 14th10:30am -
11am
Location402-403-404
DescriptionThis paper presents the first, 15-PetaFLOP Deep
Learning system for solving scientific pattern
classification problems on contemporary HPC
architectures. We develop supervised convolutional
architectures for discriminating signals in high-energy
physics data as well as semi-supervised architectures
for localizing and classifying extreme weather in
climate data. Our Intelcaffe-based implementation
obtains ~2TFLOP/s on a single Cori Phase-II Xeon-Phi
node. We use a hybrid strategy employing synchronous
node-groups, while using asynchronous communication
across groups. We use this strategy to scale training of
a single model to ~9600 Xeon-Phi nodes; obtaining peak
performance of 11.73-15.07 PFLOP/s and sustained
performance of 11.41-13.27 PFLOP/s. At scale, our HEP
architecture produces state-of-the-art classification
accuracy on a dataset with 10M images, exceeding that
achieved by selections on high-level physics-motivated
features. Our semi-supervised architecture successfully
extracts weather patterns in a 15TB climate dataset. Our
results demonstrate that Deep Learning can be optimized
and scaled effectively on many-core, HPC systems.
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