Hasso-Plattner-Institut25 Jahre HPI
Hasso-Plattner-Institut25 Jahre HPI
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Hardware-Conscious Data Processing (Sommersemester 2023)

Dozent: Prof. Dr. Tilmann Rabl (Data Engineering Systems) , Lawrence Benson (Data Engineering Systems)
Website zum Kurs: https://hpi.de/rabl/teaching/summer-term-2023/hardware-conscious-data-processing.html

Allgemeine Information

  • Semesterwochenstunden: 4
  • ECTS: 6
  • Benotet: Ja
  • Einschreibefrist: 01.04.2023 - 07.05.2023
  • Lehrform: Vorlesung
  • Belegungsart: Wahlpflichtmodul
  • Lehrsprache: Englisch

Studiengänge, Modulgruppen & Module

IT-Systems Engineering MA
  • OSIS: Operating Systems & Information Systems Technology
    • HPI-OSIS-K Konzepte und Methoden
  • OSIS: Operating Systems & Information Systems Technology
    • HPI-OSIS-T Techniken und Werkzeuge
  • OSIS: Operating Systems & Information Systems Technology
    • HPI-OSIS-S Spezialisierung
  • SAMT: Software Architecture & Modeling Technology
    • HPI-SAMT-K Konzepte und Methoden
  • SAMT: Software Architecture & Modeling Technology
    • HPI-SAMT-T Techniken und Werkzeuge
  • SAMT: Software Architecture & Modeling Technology
    • HPI-SAMT-S Spezialisierung
Data Engineering MA
Digital Health MA
Cybersecurity MA
Software Systems Engineering MA

Beschreibung

Hardware development continuously advances, with different technologies improving at different pace. While the amount of transistors in a CPU package are growing, the single core performance is stagnating due to physical limitations. These trends require changes in data processing to keep database management systems efficient. In this lecture, we will take a look at current computer architectures and accelerator technologies and how they can be used for efficient data processing. We will cover CPU and memory architecture; the storage hierarchy; modern memory technolgoies, such as NVM and NVMe; fast interconnects, such as Infiniband, RDMA, and NVLink; and accelerators, such as GPUs and FPGAs. The course has a significant practical part, where the students learn to implement data structures and algorithms tailored to hardware concious data processing.

Voraussetzungen

This course is aimed towards students with knowledge in database and/or big data systems. Ideally, students have attended at least one of Big Data Systems, Distributed Data Management, Database Systems II, or similar. The programming tasks are all in C++. We provide a small example task (see Example Coding Task in Moodle) which students can do before the course to see whether they are comfortable with C++. If you are able to solve this task, you will probably be able to pass the course.

Literatur

  • Structured Computer Organization, Andrew S. Tanenbaum, Todd Austin, 2012, 978-0132916523
  • A Course in In-Memory Data Management: The Inner Mechanics of In-Memory Databases, Hasso Plattner, 2014, 978-3642552694

Leistungserfassung

The programming tasks determine 100% of the grade, there is no final exam. In addition to the graded tasks, each student will present their solution for one task in a short individual meeting with the teaching team. We will randomly select students for each current task throughout the semester. This discussion will make up 20% of the final grade. The programming tasks will be 20% each.

Termine

The lectures will be held in presence on Tuesdays (L.E-03) and Wednesdays (L.E-03) at 11:00 h. The first session will be on 18.04.2023.

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