Workshop: Deep Learning on Supercomputers
Event TypeWorkshop
Registration Categories
Deep Learning
Scientific Computing
TimeSunday, 17 November 20199am - 5:30pm
DescriptionThe Deep Learning (DL) on Supercomputers workshop provides a forum for practitioners working on any and all aspects of DL for science and engineering in the High Performance Computing (HPC) context to present their latest research results and development, deployment, and application experiences. The general theme of this workshop series is the intersection of DL and HPC. Its scope encompasses application development in scientific scenarios using HPC platforms; DL methods applied to numerical simulation; fundamental algorithms, enhanced procedures, and software development methods to enable scalable training and inference; hardware changes with impact on future supercomputer design; and machine deployment, performance evaluation, and reproducibility practices for DL applications with an emphasis on scientific usage. This workshop will be centered around published papers. Submissions will be peer-reviewed, and accepted papers will be published as part of the Joint Workshop Proceedings by Springer.
9:00am - 9:05amDeep Learning on Supercomputers
9:05am - 10:00amKeynote:AI for HPC and HPC for AI: Bidirectional Convergence Efforts of HPC and AI on the Fugaku Supercomputer
10:00am - 10:30amDeep Learning on Supercomputers Morning Break
10:30am - 10:55amDeepDriveMD: Deep-Learning Driven Adaptive Molecular Simulations for Protein Folding
10:55am - 11:20amDeep Facial Recognition Using Tensorflow
11:20am - 11:45amDeep Learning Accelerated Light Source Experiments
11:45am - 12:10pmDC-S3GD: Delay-Compensated Stale-Synchronous SGD for Large-Scale Decentralized Neural Network Training
12:10pm - 12:30pmAggregating Local Storage for Scalable Deep Learning I/O
12:30pm - 2:00pmDeep Learning on Supercomputers Lunch Break
2:00pm - 2:30pmHighly-Scalable, Physics-Informed GANs for Learning Solutions of Stochastic PDEs
2:30pm - 3:00pmDeep Learning for Gap Crossing Ability of Ground Vehicles
3:00pm - 3:30pmDeep Learning on Supercomputers Afternoon Break
3:30pm - 4:00pmScaling Distributed Training of Flood-Filling Networks on HPC Infrastructure for Brain Mapping
4:00pm - 4:30pmEvolving Larger Convolutional Layer Kernel Sizes for a Settlement Detection Deep-Learner on Summit
4:30pm - 5:00pmScaling TensorFlow, PyTorch, and MXNet Using MVAPICH2 for High-Performance Deep Learning on Frontera
5:00pm - 5:30pmStrategies to Deploy and Scale Deep Learning on the Summit Supercomputer
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