Data Science and Artificial Intelligence

High Performance Computing

Integrated course, 2.50 ECTS

 

Course content

Part 1: Basics
- Overview and definition of terms
- Processor architectures (CPU, GPU, TPU, ...) and relevant interfaces
Part 2: Hardware virtualization
- Platform virtualization
- Relevant cluster frameworks in the context of hardware virtualization
- Storage virtualization
Part 3: Operating system virtualization
- Container virtualization
- Relevant cluster frameworks in the context of operating system virtualization

Learning outcomes

The students know and understand the basics of different processor architectures as well as hardware and operating system virtualization. They will be able to assess the advantages and disadvantages of selected frameworks for virtualization and to set up or configure them accordingly for different use cases.

Recommended or required reading and other learning resources / tools

Recommended literature or books:
- Deimeke, D., Kania, S. et al (2018). Linux server: The comprehensive manual. Incl. Samba, Kerberos, databases, KVM and Docker, Ansible and much more (2019 edition). Reihnwerk Computing, 5th edition.
- Dille, N., Grote, M. et al (2017). Microsoft Hyper-V: Das Handbuch für Administratoren. Aktuell zu Windows Server 2016. Rheinwerk Computing, 3. Auflage.
- Gilbert N. (2008). Agent-based models. Series: Quantitative Applications in the Social Sciences 153. Sage Publications, 2nd edition.
- Kania, S., Wolf, J. (2019). Shell-Programmierung: Das umfassende Handbuch. Für Bourne-, Korn- und Bourne-Again-Shell (bash). Ideal für alle UNIX-Administratoren (Linux, macOS). Rheinwerk Computing, 6. Auflage.
- Karau, H., Kowinsky, A. et al (2015). Learning Spark: Lightning-Fast Data Analysis. O'Reilly and Associates, 1st edition.
- Kofler, M., Spenneberg, R. (2012). KVM für die Server-Virtualisierung - Von Konfiguration und Administration bis Clustering und Cloud. Addison-Wesley Verlag, 1. Auflage.
- Liebel, O. (2018). Skalierbare Container-Infrastrukturen: Das Handbuch für Admins & DevOps-Teams, inkl. Docker und Container-Orchestrierung mit Kubernetes und OpenShift. Rheinwerk Computing, 2. Auflage.
- Meier, J. H. (2018). Citrix XenApp und XenDesktop 7.15 LTSR: Das Praxishandbuch für Administratoren. Reihnwerk Computing, 6. Auflage.
- Öggl, B., Kofler, M. (2018). Docker: Das Praxisbuch für Entwickler und DevOps-Teams. Für Windows, Mac und Linux. Reihnwerk Computing, 1. Auflage.
- Railsback S. F., Grimm V. (2012). Agent-Based and Individual-Based Modeling. Princeton University Press, 2nd edition.
- Sayama, H. (2015). Introduction to the Modeling and Analysis of Complex Systems. Open SUNY Textbooks, 1st edition (print edition).
- White, T. (2015). Hadoop: The Definitive Guide. O'Reilly and Associates, 4th edition.
- Wilensky U., Rand W. (2015). An Introduction to Agent-Mased Modeling. Modeling Natural, Social, and Engineered Complex Systems with NetLogo. MIT Press, 1st edition.
- Wöhrmann, B., Baumgart, G. et al (2018). VMware vSphere 6.7: The comprehensive guide to virtualization with vSphere. Rheinwerk Computing, 5th edition.
Recommended journals or selected articles:
- Grimm V. et al. (2006): A standard protocol for describing individual-based and agent-based models. Ecological Modeling 198, Elsevier, pp. 115-126.
- Grimm V. et al. (2010): The ODD protocol: A review and first update. Evological Modeling 221, Elsevier, pp. 2760-2768.
- Railsback, S., Ayllon, D. et al. (2017): Improving Execution Speed of Models Implemented in NetLogo. In: Journal of Artificial Societies and Social Simulation20 (1) 3, 2017.

Other relevant journals and articles will be announced in the courses.

Typical software for this module:
NetLogo, Python / Spyder / PyCharm, R / RStudio, CUDA, OpenGL, Xen / OpenXen, KVM, VMware vSphere, Microsoft Hyper-V, OpenStack, Apache CloudStack, Apache Hadoop, Apache Cassandra, Ceph, GlusterFS, Apache Spark, Docker, LXC, Kubernetes, OpenShift, DockerSwarm etc.

Mode of delivery

1,25 ECTS Lecture, 1,25 ECTS Exercise

Prerequisites and co-requisites

module 2 and 5

Assessment methods and criteria

Lecture: final exam, Exercise: examination character