====== Seminar: Newest Trends in High-Performance Data Analytics ======

High-Performance Data Analytics is a vehicle to extract findings from large data sets. It
is an indispensable tool in science and business but a rapidly changing field. As part of
this seminar, you will create a presentation and report revolving around a selected hot
topic in German or English. You will learn to research literature and may conduct small
experiments to provide a holistic view of the selected topic. You will meet regularly with
an assigned supervisor and work towards the presentation and report.

===== Key information =====
|| Contact || [[about:people:julian_kunkel|Julian Kunkel]], [[about:people:jonathan_decker|Jonathan Decker]] ||
|| Location || https://meet.gwdg.de/b/jul-yha-uqh-vrl ||
|| Time || Thursday 16:15-17:45 ||
|| Language || English or German (individual presentation) ||
|| Module || M.Inf.1237: Seminar Neueste Trends in High-Performance Data Analytics ||
|| SWS || 2 ||
|| Credits || 5 ||
|| Contact time || 28 hours ||
|| Independent study || 122 hours ||

As part of this seminar, you will create a presentation (and 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 and report.

This seminar is also available as a pro-seminar.
As pro-seminar, the focus will be on learning presentation techniques while in the seminar your focus must be on presenting scientific facts and leading a scientific discussion.
There are also two additional mandatory sessions for pro-seminar attendees (optional for seminar attendees).

The presentation time is 35 minutes (plus discussion).
A short report accompanying the slides 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}}.

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

  * Appraise research in the area of high-performance data analytics
  * Compose a presentation covering their selected topic in depth
  * Evaluate findings (tools or theory) of other researchers
  * Explain theory and application covering their topic

===== 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 in the direction of your choice.
Feel free to propose your own great topic.

  * Understanding GPU performance e.g. using MLCommons ML Benchmarks
  * Usage of data lakes and/or data warehouses
  * 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
  * Seagate CORTX storage system
  * FPGA Computing with SciEngine
  * RISC-V: State of the union
  * Regression Testing for HPC
  * Global Optimization (of Clusters) with Genetic Algorithms
  * Julia Programming Language for deep learning
  * RUST Programming for HPC application
  * Sustainability for data centers
  * The HPC Community
  * Benchmarking of HPC Systems
  * History and Development of System Architectures
  * Security in Cloud and HPC
  * DevOps strategies in HPC
  * Infiniband DPU
  * Convergence of HPC and High-Performance Data Analytics
  * Using Data Analytics in HPC Applications
  * GPU Computing with Python
  * Parallelization with Dask + Xarray
  * What's new in the Kubernetes ecosystem (SEDNA, Volcana, ...)
  * What's new with Spark
  * What's new with Pytorch/Tensorflow
  * Containers in HPC
  * Webassembly for Function-as-a-service
  * Function-as-a-service in HPC
  * Key-value stores for HPDA
  * Object storage systems
  * HPDA Benchmarks 
  * Performance Analysis using Scalasca and Vampir
  * Data Streaming and Workflows using Apache Airflow

===== Examination =====

The exam is conducted as part of the presentation (50% of the mark) and report (50%).
The focus for pro-seminars lies in the effective presentation while the focus for seminars is the depth of the scientific topic (slightly different marking schemes).
The presentation should cover 35 min and the report should be 10 to 15 pages (not counting cover, toc, appendix).

===== Agenda =====
  * 13.04.23 **Preliminary discussion / Vorbesprechung** -- Julian Kunkel, Jonathan Decker \\ If you cannot attend contact us asap!
    * Short introduction to the topics of the seminar.
    * Organizational matters: How to get good marks.
    * Assignment of topics to the participants on a first-come-first-served basis.
    * Talk: Professional presentation {{ :teaching:summer_term_2023:nthpda-welcome.pdf |Slides}}
  * 27.04.23 **How to create professional presentations and reports?** -- //Julian Kunkel, Jonathan Decker//
    * Introducing our report template and usage (very quick intro to LaTeX) {{ :teaching:summer_term_2023:latex-intro.pdf |Slides}}
    * Discussion of existing reports and presentations individually and in the group (15 min + 15 min discussion)
    * Feedback regarding audio/VC settings -> example on one slide -> create 1-2 own slides, e.g., introducing yourself + strengths or introduce a topic (15 minutes)
  * 15.06.23 **Student presentations**
  * 22.06.23 **Student presentations**
    * AI and HPDA use cases for critical infrastructure from the medical and energy domains -- //Bhal Chandra Ram Tripathi//((Stefanie Mühlhausen))
    * <del>Containers in HPC -- //Vincent Hasse//((Azat Khuziyakhmetov))</del>
  * 29.06.23 **Student presentations**
    * Rust for HPC applications -- //Lars Quentin//((Artur Wachtel))
    * Non-linear Dimensionality Reduction and Benchmarking -- //Emmanuel Tchoumkeu Ngatat//((Hauke Kirchner))
  * 06.07.23 **Student presentations**
    * Go Programming in HPC -- //Valerius Albert Gongjus Mattfeld//((Jonathan Decker))
    * History and Development of System Architectures -- //Humaira Azam//((Narges Lux))
  * 13.07.23 **Student presentations**
    * Sustainability in Data Centers -- //Chinaza Ogo Obiagazie//((Timon Vogt))
    * Zarr Performance (also some embedding of Dask) -- //Friedrich Schwarz//((Pavan Siligam))
  * 20.07.23 **Student presentations**
    * Non-linear Dimensionality Reduction and Benchmarking -- //Emmanuel Tchoumkeu Ngatat//((Hauke Kirchner))
  * 30.09.23 **Deadline for the submission of the report**

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

  * Rust for HPC applications -- //Lars Quentin//((Artur Wachtel)) {{ :teaching:summer_term_2023:nthpda-student:lars_quentin_report.pdf |Report}} {{ :teaching:summer_term_2023:nthpda-student:lars_quentin_presentation.pdf |Presentation}} Repo: https://github.com/lquenti/IntroPerfEng/
  * History and Development of System Architectures -- //Humaira Azam//((Narges Lux)) {{ :teaching:summer_term_2023:nthpda-student:humaira_azam_report.pdf |Report}} {{ :teaching:summer_term_2023:nthpda-student:humaira_azam_presentation.pptm |Presentation}}
  * <del>Containers in HPC -- //Vincent Hasse//((Azat Khuziyakhmetov))</del>
  * Zarr Performance (also some embedding of Dask) -- //Friedrich Schwarz//((Pavan Siligam))
  * Go Programming in HPC -- //Valerius Albert Gongjus Mattfeld//((Jonathan Decker))
  * AI and HPDA use cases for critical infrastructure from the medical and energy domains -- //Bhal Chandra Ram Tripathi//((Stefanie Mühlhausen))
  * <del>Non-linear Dimensionality Reduction and Benchmarking -- //Emmanuel Tchoumkeu Ngatat//((Hauke Kirchner))</del>
  * Sustainability in Data Centers -- //Chinaza Ogo Obiagazie//((Timon Vogt)) {{ :teaching:summer_term_2023:nthpda-student:chinaza_ogo_obiagazie_report-2.pdf |Report}}