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Kurth, Thorsten; Zhang, Jian; Satish, Nadathur; Mitliagkas, Ioannis; Racah, Evan; Patwary, Mostofa Ali; Malas, Tareq; Sundaram, Narayanan; Bhimji, Wahid; Smorkalov, Mikhail; Deslippe, Jack; Shiryaev, Mikhail; Sridharan, Srinivas; Prabhat; Dubey, Pradeep (2017)
Languages: English
Types: Preprint
Subjects: Computer Science - Performance, Computer Science - Computer Vision and Pattern Recognition, Computer Science - Learning
This 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 $\sim$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 $\sim$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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