====== 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 ||
|| 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 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 {{ :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
===== 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.
* 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 final presentation (30% of the mark) and the report (70%).
The (ungraded) project topic presentation should cover 15 min, the final presentation should cover 25 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.
* 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 {{ :teaching:summer_term_2023:scap-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
* 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)
* 01.06.23 **Project topic presentations**
* Cloud Infrastructure with Go -- //Valerius Albert Gongjus Mattfeld//((Jonathan Decker))
* GPU Computing with Python -- //Emmanuel Tchoumkeu//((Hauke Kirchner))
* 08.06.23 **Project topic presentations**
* History and Development of System Architectures -- //Bhal Chandra//((Narges Lux))
* Benchmarking of HPC Systems -- //Md Jonaid Hossain//((Christoph Hottenroth))
* 15.06.23 **Project topic presentations**
* Backup
* 22.06.23 **Project topic presentations**
* Analyzing I/O performance when using DASK for Machine Learning -- //Zoya Masih//((Julian Kunkel)) (Topic)
* 29.06.23 **Project result presentations**
* Cloud Infrastructure with Go -- //Valerius Albert Gongjus Mattfeld//((Jonathan Decker))
* 06.07.23 **Project result presentations**
* Analyzing I/O performance when using DASK for Machine Learning -- //Zoya Masih//((Julian Kunkel))
* GPU Computing with Python -- //Emmanuel Tchoumkeu//((Hauke Kirchner))
* 13.07.23 **Project result presentations**
* Backup
* 30.09.23 **Deadline for the submission of the report**
===== Topic Distribution =====
* GPU Computing with Python -- //Emmanuel Tchoumkeu//((Hauke Kirchner))
* Cloud Infrastructure with Go -- //Valerius Albert Gongjus Mattfeld//((Jonathan Decker)) {{ :teaching:summer_term_2023:scap-student:valerius_mattfeld_report.pdf |Report}} {{ :teaching:summer_term_2023:scap-student:valerius_mattfeld_presentation.pdf |Presentation}}
* Reverse Elasticsearch Proxy providing attribute revealing encryption for secure data lakes -- //Lars Quentin//((Hendrik Nolte))
* Benchmarking of HPC Systems -- //Md Jonaid Hossain//((Christoph Hottenroth))
* History and Development of System Architectures -- //Bhal Chandra//((Narges Lux))
* State-of-the-art in machine learning in HPC-IO -- //Zoya Masih//((Julian Kunkel)) {{ :teaching:summer_term_2023:scap-student:zoya_masih_report.pdf |Report}} {{ :teaching:summer_term_2023:scap-student:zoya_masih_presentation.pdf |Presentation}}
* Containers in HPC -- //Vincent Florens Hasse//((Azat Khuziyakhmetov))