Please note: This seminar will take place online.
Tilmann Rabl, Chair for Data Engineering Systems
Hasso Plattner Institute
GPUs are one of the main drivers of modern AI applications. For database processing, however, they have been mostly disregarded, because of limited memory capacity and limited interconnect bandwidth for ad hoc data transfers. Recent developments in interconnect technology, such as NVLink, enable orders of magnitude faster transfers than the long standing PCI-e 3.0 standard. In this talk, we will give an overview of GPU topologies, modern GPU interconnects, and basic processing on GPU. Based on this, we will present how to scale Join Processing and Sorting beyond GPU memory capacity and on multiple GPUs by utilizing modern GPU interconnects.
Bio: Tilmann Rabl received his Ph.D. from the University of Passau in 2011. After finishing his PhD thesis on the subject of scalability and data allocation in cluster databases, he continued his work as a postdoctoral researcher at the Middleware Systems Research Group at the University of Toronto. In 2015, he joined the Database Systems and Information Management group at Technische Universität Berlin as a senior researcher and visiting professor and held the position of Vice Director of the Intelligent Analytics for Massive Data group at the German Research Center for Artificial Intelligence. Since 2019, he has held the chair for Data Engineering Systems at the Digital Engineering Faculty of the University of Potsdam and the Hasso Plattner Institute. His research focuses on efficiency of database systems, real-time analytics, hardware efficient data processing, and benchmarking.