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

===== Key information =====

|| Contact || [[about:people:julian_kunkel|Julian Kunkel]], [[about:people:jonathan_decker|Jonathan Decker]] ||
|| Location || [[https://meet.gwdg.de/b/jul-cal-vop-2bi|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, 6 ||
|| 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 15 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 {{ :teaching:templates:dataprivacy_student_notice_slide.pdf |Slide}}.

==== Required Prior Knowledge ====

  * 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

===== Learning Objectives =====

  * 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

===== 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.

  * Performance Analysis using Scalasca and Vampir
  * Data Streaming and Workflows using Apache Airflow
  * Scalable data lakes
  * Evaluating the ARM Architecture for HPC
  * Understanding GPU performance e.g. using MLCommons ML Benchmarks
  * Usage of data lakes and/or data warehouses
  * Scalable quantum computer simulation on HPC systems
  * TBD

===== Examination =====

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

===== Agenda =====

  * 27.10.22 **Preliminary discussion / Vorbesprechung** -- //Julian Kunkel, Jonathan Decker// {{ :teaching:autumn_term_2022:scap-22-welcome.pdf |Slides}}\\ If you cannot attend contact us asap!
    * Short introduction to the topics of the seminar.
    * Short introduction of the overall timeline for seminar and practical part.
    * Organizational matters: How to get good marks.
    * Assignment of topics to the participants on a first-come-first-served basis.
    * Talk: Professional presentation
  * 03.11.22 **How to create professional presentations and reports?** -- //Julian Kunkel, Jonathan Decker//
    * Introducing our report template and usage (very quick intro to LaTeX) {{ :teaching:autumn_term_2022:nthpda-latex-intro.pdf |Slides}}
    * Discussion of existing reports and presentations individually and in the group
  * <del>10.11.22</del>
  * <del>17.11.22</del>
  * 24.11.22 **Project topic presentations** 
    * Benchmarking tree species classification with synthetic data and deep learning -- Hauke Kirchner
  * 01.12.22 **Project topic presentations**
    * <del>Usage of data lakes and/or data warehouses -- Lars Meyer</del>
    * Usage of data lakes and/or data warehouses -- Céline Thorns
  * 08.12.22 **Project topic presentations**
    * Kubernetes for HPC -- Vincenz Dumann
    * Performance of Dask -- Sebastian Mohr
    * ARM evaluation in HPC -- Tim Dettmar
  * 15.12.22 **Project topic presentations**
    * Structural comparison of proteins -- Friedrich Schwarz 
  * 22.12.22 **Project topic presentations**
  * 12.01.23 **Project result presentations**
  * 19.01.23 **Project result presentations**
  * 26.01.23 **Project result presentations**
  * 02.02.23 **Project result presentations**
    * Benchmarking tree species classification with synthetic data and deep learning -- Hauke Kirchner
    * Performance of Dask -- Sebastian Mohr
        * Presentation: https://dask-presentation.vercel.app/
    * ARM Evaluation in HPC -- Tim Dettmar
  * 09.02.23 **Project result presentations**
    * Usage of data lakes and/or data warehouses -- Céline Thorns
  * 20-24.02.23 Optional Block Seminar in the lecture-free time between the terms
  * 02.03.23 **Project result presentations**
    * Kubernetes for HPC((Supervisor: Jonathan Decker)) -- //Vincenz Dumann//
    * Structural comparison of proteins((Supervisor: Stefanie Mühlhause)) -- //Friedrich Schwarz//
  * 31.03.23 **Deadline for the submission of the report**

===== Topic Distribution =====

    * <del>Usage of data lakes and/or data warehouses((Supervisor: Hendrik Nolte)) -- //Lars Meyer//</del>
    * Usage of data lakes and/or data warehouses((Supervisor: Hendrik Nolte)) -- //Céline Thorns// {{ :teaching:autumn_term_2022:scap_celine_thorns_deltalake.pdf |Report}} {{ :teaching:autumn_term_2022:scap_celine_thorns_deltalake_code.ipynb|Code}}
    * <del>Structural comparison of proteins((Supervisor: Stefanie Mühlhause)) -- //Friedrich Schwarz//</del>
    * Performance of Dask((Supervisor: Pavan Siligam)) -- //Sebastian Mohr// {{ :teaching:autumn_term_2022:scap_sebastian_mohr_dask_performance.pdf |Report}} [[https://github.com/semohr/dask_presentation|Slides]]
    * Benchmarking tree species classification with synthetic data and deep learning((Supervisor: Dorothea Sommer)) -- //Hauke Kirchner// {{ :teaching:autumn_term_2022:scap_hauke_kirchner_benchmarking_tree_species_classification.pdf |Report}} [[https://github.com/haukekirchner/scap|Code]]
    * ARM evaluation in HPC((Supervisor: Trevor Khwam Tabougua)) -- //Tim Dettmar// {{ :teaching:autumn_term_2022:scap_tim_dettmar_arm_evaluation.pdf |Report}} {{ :teaching:autumn_term_2022:scap_tim_dettmar_arm_evaluation_slides.pdf |Slides}}
    * Kubernetes for HPC((Supervisor: Jonathan Decker)) -- //Vincenz Dumann// {{ :teaching:autumn_term_2022:scap_vincenz_dumann_benchmarking_kube_edge.pdf |Report}} [[https://gitlab.gwdg.de/vincenz.dumann/benchmarking-kube-edge|Code]]