AI-Cafe: How can HPC technologies help AI?


Description : Centers of Excellence in HPC promote the use of upcoming exascale and extreme performance computing capabilities and scale up existing parallel codes towards exascale scaling performance. The Centers of Excellence cover tools and optimize HPC applications along multiple areas of knowledge. Furthermore, they address the skills gap in computational science in the targeted domains by specialized training for increased adoption of advanced HPC in industry and academia.
The following session aims to bring together the Centers of Excellence to discuss how HPC and AI can benefit from each other. Several case studies will be presented on how AI is leveraging HPC technologies to solve real-live problems. Opportunities and challenges in the AI sector will be discussed, how the Centres of Excellence can help to tackle them thanks to HPC technologies and which opportunities are emerging. It is a good opportunity to get a common understanding of the ongoing activities within the different projects and foster collaboration opportunities, including education and training offered by Centres of Excellence.
-    Introduction (FocusCoE)
-    AI in Scientific Workflows on High-Performance Computers (RAISE)
-    AI for future energy system  (COEC)
-    Generating directed Social Network Graphs (HIDALGO9
-    Panel Discussion & Q&A
The Speakers are Temistocle Grenga, Andreas  Lintermann and 
Christoph Schweimer

Temistocle Grenga (CoEC): Temistocle Grenga is a group leader and researcher at the Institute for Combustion Technology (ITV) at  RWTH Aachen University (Germany) since 2018. Previously he was at the University of Notre Dame (USA), where he received his PhD degree in aerospace and mechanical engineering, and at Princeton University (USA). His work focuses on numerical simulation and modeling of multiphase turbulent reacting flows. He coordinates ITV activities at CoEC.  

Andreas Linterman (RAISE): Lintermann coordinates CoE RAISE since 2020 and leads the Simulation & Data Lab “Highly Scalable Fluids & Solids Engineering” at the Jülich Supercomputing Centre, Forschungszentrum Jülich, Germany, since 2014. He received his diploma degree in computer science and his PhD degree in engineering from RWTH Aachen University, Germany. His group develops multi-physics codes in the field of engineering and combines them with novel AI technologies making use of modular supercomputing architectures.

Christoph Schweimer (HiDALGO): Christoph Schweimer joined the Know-Center as a Data Scientist in 2019 to work in the HiDALGO project on AI-related tasks which support the workflow of the use cases. He received his M.Sc. in mathematics from the University of Salzburg in 2017