BIFOLD Colloquium 2022/05/20
Machine Learning for Remote Sensing Applications powered by Modular Supercomputing Architectures
Speaker: Dr. Gabriele Cavallaro, Forschungszentrum Jülich
Venue: TU Berlin, Architekturgebäude, Straße des 17. Juni 152, 10623 Berlin, Room: A151
Date & time: May 20/2022, 2 pm
Title: Machine Learning for Remote Sensing Applications powered by Modular Supercomputing Architectures
Supercomputers are unique computing environments with extremely high computational capabilities. They are able to solve problems and perform calculations which require more speed and power than traditional computers are capable of. In particular, they represent a concrete solution for data-intensive applications as they can boost the performance of processing workflows with more efficient access to and scalable processing of extremely large data sets.
This talk will first give an overview of the work and research activities of the ‘‘AI and ML for Remote Sensing’’ Simulation and Data Lab hosted at the Jülich Supercomputing Centre (JSC). Then, it will introduce the Modular Supercomputing Architecture (MSA) systems that are operated by the JSC. An MSA is a computing environment that integrates heterogeneous High Performance Computing (HPC) systems, which can include different types of accelerators (e.g., GPUs, FPGAs) and cutting-edge computing technologies (e.g., quantum and neuromorphic computing) and that is “modularized” by its software stack. The presentation will finally include different examples from Remote Sensing applications that can exploit MSA to drastically reduce the time to solution and provide users with timely and valuable information.
Dr. Gabriele Cavallaro is the Head of the ‘‘AI and ML for Remote Sensing’’ Simulation and Data Lab at the Jülich Supercomputing Centre, Forschungszentrum Jülich Germany. He is currently the Chair of the ‘‘High-Performance and Disruptive Computing in Remote Sensing’’ (HDCRS) Working Group of the IEEE GRSS ESI Technical Committee and Visiting Scientist at the Φ-Lab of the European Space Agency. His research interests cover remote sensing data processing with parallel machine learning methods that scale on cutting-edge distributed computing technologies.