The 32nd ISC High Performance conference, slated for June 18-22 in Frankfurt, Germany, will feature keynote addresses by Dr. Jennifer Tour Chayes, managing director of Microsoft Research New England and Microsoft Research NYC, and Professor Dr. Thomas Sterling, director, Center for Research in Extreme Scale Technologies (CREST), Indiana University (IU). The three-day computing conference is expected to draw more than 3,000 attendees, including researchers, business leaders and scientists, and will feature more than 400 expert speakers and 150 exhibitors.
During her keynote, Dr. Chayes, scientist and one of the inventors of the field of graphons (graph functions now widely used for machine learning of massive networks), will present on “Network Science: From the Massive Online Networks to Cancer Genomics.” In particular, she plans to highlight two particular applications: 1. very efficient machine learning algorithms for doing collaborative filtering on massive sparse networks of users and products, like the Netflix network; and 2. inference algorithms on cancer genomic data to suggest possible drug targets for certain kinds of cancer.
Dr. Sterling, professor of Informatics & Computing, School of Informatics & Computing, at IU, will give a talk focused on “HPC Achievement and Impact in 2017.” He is slated to provide a “rapid-fire” summary of the major accomplishments of the last year and the challenges of the next, defining the trajectory in supercomputing and its application in the immediate future.
As part of the conference, ISC High Performance will offer a day for the industrial HPC user community, specifically addressing challenges in the industrial manufacturing, transport and logistics sectors.
The Industrial Day, scheduled for June 20, is chaired by HPC experts Dr. Alfred Geiger of T-Systems and Dr. Marie-Christine Sawley of Intel Data Center Group. It will focus on three areas:
- benefits of exascale computing for industrial users,
- how to purchase HPC infrastructure, and
- use cases for high performance data analytics, including machine/deep learning, artificial intelligence (AI) and the Internet of Things
During the Industrial Day, Professor Dr. Norbert Kroll of the German Aerospace Center, Institute of Aerodynamics and Flow Technology, will deliver a keynote address on “High Performance Computational Fluid Dynamics for Future Aircraft Design,” focusing on numerical flow simulations.
ISC High Performance will devote June 21 to discuss recent advances in AI based on deep learning technology.
The program is chaired by Dr. Janis Keuper, senior scientist at The Fraunhofer Institute for Industrial Mathematics, and Dr. Damian Borth, director of the deep learning competence center at the German Research Center for Artificial Intelligence.
Two of this year’s presentations in the Distinguished Talk series will focus on data analytics in manufacturing and scientific applications. Cybernetics expert, Dr. Sabine Jeschke, who heads the Cybernetics Lab at the RWTH Aachen University, will deliver a talk titled “Robots in Crowds – Robots and Clouds.” Her presentation will be followed by one from physicist Kerstin Tackmann, from the German Electron Synchrotron (DESY) research center, who will discuss big data and machine learning techniques used for the ATLAS experiment at the Large Hadron Collider.
Beyond the keynote presentations and special sessions, the conference will include tutorials, a general track, research track, the ISC Exhibition including the Student Cluster Competition, and various workshops.
Focus topics for the conference include the following:
- Exascale System Developments (Architecture & Concepts)
- Processor Technologies for HPC and AI
- Memory Outlook
- Interconnects for HPC Systems
- Programming Models (Concepts for Exascale)
HPC Applications & Algorithms
- Life Sciences
- Energy Exploration
- Turbulences & Combustion
- Advanced Material Science
- Algorithms for Extreme Scale in Practice
- Large Scale Engineering & Cloud Computing
HPC Trends & Challenges
- High Performance Visualization
- Big Data Experiments & Big Data Analysis
- Quantum Annealing for Combinatorial Optimization Problems
Sources: Press materials received from the company and additional information gleaned from the company’s website.