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

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.

• No skills/knowledge is required
• Understanding of Linux basics and having used Linux before and being able to operate a Bash shell is beneficial
• We will provide a short crash course at the beginning of the course and link supplementary training material

• Describe approaches for the development of scalable systems and applications
• Sketch efficient algorithms and concepts
• Analyze and summarize state-of-the-art concepts, tools and research papers
• Deliver a technical presentation for a professional audience
• Explore and apply concepts or tools to improve scalability for a selected use case
• Quantify efficiency and scalability of selected use cases

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.

• Retrieval Augmented Generation (RAG) State-of-the-art and use cases
• LLM Open Source Agents
• LLM Benchmarking Frameworks and their limitations
• LLM Inference Optimization techniques
• LLM Compression and Quantization Techniques
• LLM Trustworthiness and Fact Validation
• Impact of GIL-less Cpython on performance and compatiblity
• Parallelization of async Python with trio-parallel
• Machine Learning for Predictive Maintenance on a HPC Single Node
• Understanding GPU performance, e.g., using MLCommons ML Benchmarks
• Confidential Computing (HPC/Cloud)
• Python Performance Optimization leveraging Native Implementations (Numba/CPython/PyO3/Nukita/transpyle)
• AI for monitoring
• Neuromorphic Computing
• Effective intrusion detection systems (IDS) Strategies in HPC Environments
• The compute continuum - IoT, edge and HPC computing
• Use cases for integration of edge and IoT with HPC simulations
• AI and HPDA use cases for critical infrastructure from the medical and energy domains
• Data management concepts in HPC - potential of data lakes and data warehousing
• Scalable quantum computer simulation on HPC systems
• FPGA Computing with SciEngine
• RISC-V: State of the union
• Regression Testing for HPC
• Global Optimization (of Clusters) with Genetic Algorithms
• RUST Programming for HPC application
• Benchmarking of HPC Systems
• Security in Cloud and HPC
• Infiniband DPU
• DevOps strategies in HPC
• What's new in the Kubernetes ecosystem
• Containers in HPC
• Function-as-a-service in HPC
• Object storage systems
• Encryption tools
• Scalable databases with e.g., Elasticsearch, Postgres
• Kernel compilation and configuration
• Berkeley Packet Filters (eBPF)
• Forensic tools
• Distributed computing paradigms in Cloud and HPC
• Detection of AI generated content
• Service Discovery and Traffic Management in Cloud Applications

The exam is conducted as part of the final presentation (30% of the mark) and the report (70%). When submitting the report, the submission must include the report itself as PDF, the slides used in the presentation as well as a link to any code produced as part of the course work, preferably via GitLab or GitHub.

• 30.10.25 Introduction & Scientific Presentation – Julian Kunkel, Jonathan Decker
  If you cannot attend contact us asap!
  • Introduction of the course format and requirements Slides
  • Assignment of topics to the participants on a first-come-first-served basis
  • Talk: Scientific Presentation Slides
    • Recording: https://youtu.be/NrahVjkUFls?si=SLzX8dxGpfO3SdgW
• 06.11.25 LaTeX Crash Course & Scientific Writing – Julian Kunkel, Jonathan Decker
  • Introduction to LaTeX Slides
  • Showcasing our LaTeX templates https://hps.vi4io.org/teaching/ressources/start#templates
  • Talk: Scientific Writing Slides
• 07.11.25 You have submitted your selected topic by email to jonathan.decker@uni-goettingen.de
• 13.11.25 Skip due to Tag der Lehre
• 14.11.25 You have been assigned a supervisor and presentation date
• 20.11.25 Effective Literature Search & Discussion of example reports – Julian Kunkel, Sascha Safenreider
  • Talk: Effective Literature Search Slides
  • Discussion of example reports from previous semesters
• 04.12.25 Student topic presentations
• 11.12.25 Student topic presentations
• 18.12.25 Student topic presentations
• 08.01.25 Student topic presentations
• 15.01.26 Student result presentations
• 22.01.26 Student result presentations
• 29.01.26 Student result presentations
• 05.02.26 Student result presentations
• 12.02.26 Student result presentations
• 31.03.26 Deadline for the submission of the report