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The Deep Learning on Supercomputers Workshop


The 6th Deep Learning on Supercomputers Workshop

Program (July 2nd, 14:00–18:00, CET)

Time Title Speaker
14:00–14:10 Opening Workshop Chairs
14:10–14:35 Deep-learning approaches to Learn Interaction Patterns from Protein-Protein Interfaces Alexandre Bonvin & Manon Réau, Utrecht University
14:35–15:00 Reconstruction MRIs with Deep Learning Jonas Teuwen, Netherlands Cancer Institute (NKI)
15:00–15:25 JUWELS Booster: A Supercomputer for Large-Scale AI Research Stefan Kesselheim, Jülich Supercomputing Center
15:25–15:40 Coffee Break  
15:40–16:05 AI-enabled COVID-19 Drug Discovery Arvind Ramanathan, Argonne National Laboratory
16:05–16:30 Dataflow Optimized Systems for ML Accelerated HPC Chen Liu, SambaNova Systems
16:30–17:00 ISC’21 Break  
17:00–17:55 Keynote: High-Performance Scalable Deep Learning Torsten Hoefler, ETH Zürich
17:55–18:00 Closing Remarks Workshop Chairs

The Deep Learning (DL) on Supercomputers workshop provides a forum for practitioners working on any and all aspects of DL for scientific research 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, while the theme of this particular workshop is centered around the applications of deep learning methods in scientific research: novel uses of deep learning methods, e.g., convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial network (GAN), and reinforcement learning (RL), for both natural and social science research, and innovative applications of deep learning in traditional numerical simulation. 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 Proceeding by Springer.

Topics include but are not limited to:

As part of the reproducibility initiative, the workshop requires authors to provide information such as the algorithms, software releases, datasets, and hardware configurations used. For performance evaluation studies, we will encourage authors to use well-known benchmarks or applications with open accessible datasets: for example, MLPerf and ResNet-50 with the ImageNet-1K dataset.

Import Dates

Paper Submission

Authors are invited to submit unpublished, original work with a minimum of 6 pages and a maximum of 12 pages in single column text with LNCS style. All submissions should be in LNCS format and submitted using EasyChair tentatively.

Organizing Committee