Table of Contents

Seminar with Practical: Scalable Computing Systems and Applications in AI, Big Data and HPC

Key information

Contact Julian Kunkel, Jonathan Decker
Location Virtual
Time Thursday 14:15-15:45
Language English or German (individual presentation)
Module M.Inf.1238: Scalable Computing Systems and Applications in AI, BigData and HPC
SWS 3
Credits 5
Contact time 42 hours
Independent study 108 hours

As part of this seminar, you will create a presentation, work on a small-scale practical project and write a report revolving around a research topic in German or English (your choice!). Therefore, you will meet regularly with an assigned supervisor and work towards the presentation, practical project and report. You will first select a topic and a use case related to the overall topic of the course. Then, during the term you will prepare a presentation to introduce the topic and the state of the art. Next, you will realize a small-scale project by practically working on your topic. This includes evaluating performance and scalability, as well as analyzing and quantifying the contribution of your topic or tool. Finally, you present your results in another presentation.

The presentation time is 25 minutes (plus discussion) for each presentation. A short report describing your work in the practical project is expected (max 15 pages).

Please note that we plan to record sessions (lectures and seminar talks) with the intent of providing the recordings via BBB to other students but also to publish and link the recordings on YouTube for future terms. If you appear in any of the recordings via voice, camera or screen share, we need your consent to publish the recordings. See also this Slide.

Required Prior Knowledge

Learning Objectives

Topics

This is the list of topics that we will assign to students during the first meeting. You will have some room for developing the topic into the direction of your choice. Feel free to propose your own great topic.

Examination

The exam is conducted as part of the final presentation (30% of the mark) and the report (70%).

Agenda

warehousing

Topic Distribution

Student Supervisor Topic Submissions
Mohd Uwaish Patrick Höhn Understanding GPU performance e.g. using MLCommons ML Benchmarks
Claas Kochanke Jack Ogaja Performance Analysis using Scalasca and Vampir Report
Jule Anger Jonathan Decker Kubernetes for HPC Report Slides
Lukas Steinegger Christian Köhler Load Balancing or Authorization
Sonal Lakhotia Aasish Kumar Sharma Usage of data lakes and/or data warehouses/Development in data lakes and data warehousing
Robin Lösekrug Giorgi Mamulashvili Usage of data lakes and/or data warehouses
Laura Plodek Chirag Mandal Scalable Deep Learning Models
Esther Hagenkort Patrick Höhn Machine learning performance and behavior of HPC storage systems Report Slides
David Alexandre Silva Christian Boehme Quantum Neural Networks: Libraries and Applications Report