Das Frühjahrssemester 2021 findet sicher bis Ostern online statt. Ausnahmen: Veranstaltungen, die nur mit Präsenz vor Ort durchführbar sind. Bitte beachten Sie die Informationen der Dozierenden.

Torsten Hoefler: Katalogdaten im Frühjahrssemester 2017

NameHerr Prof. Dr. Torsten Hoefler
LehrgebietSkalierbares Parallelrechnen
Adresse
Inst. f. Hochleistungsrechnersyst.
ETH Zürich, CAB F 75
Universitätstrasse 6
8092 Zürich
SWITZERLAND
Telefon+41 44 632 63 44
E-Mailtorsten.hoefler@inf.ethz.ch
URLhttp://htor.inf.ethz.ch
DepartementInformatik
BeziehungOrdentlicher Professor

NummerTitelECTSUmfangDozierende
252-0029-00LParallele Programmierung Information 7 KP4V + 2UT. Hoefler, M. Vechev
KurzbeschreibungEinfuehrung in das parallele Programmieren: nicht-deterministische und deterministische Programme, Modelle fuer parallele Programme, Synchronization, Kommunikation und Fairness.
LernzielEinfuehrung in das parallele Programmieren: nicht-deterministische und deterministische Programme, Modelle fuer parallele Programme, Synchronization, Kommunikation und Fairness. Uebungen beschaeftigen sich mit Threads in moderne Programmiersprachen
(Java, C#) und die Ausfuehrung von parallelen Programmen auf
Multi-Prozessor/Multi-Core basierten Systemen.
252-0062-00LOperating Systems and Networks Information 8 KP4V + 3UT. Hoefler, A. Perrig
KurzbeschreibungThis is an introductory course on computer networks and operating
systems, with a particular focus on networking in the Internet and
monolithic operating systems like Linux and Windows. Network and OS
programming at different levels is an integral part of the course.
LernzielThis course is intended as an introduction to both computer networking
and operating systems for computer scientists. Students will get a
comprehensive overview of the key protocols and the general
architecture of the Internet, as one example of more general principles in
network design, and acquire hands-on experience in programming
different aspects of a computer network. In addition, the course
provides a full introduction to modern operating system design, including
memory management, scheduling, I/O, protection, and so on. The
architecture of Unix-like operating systems (such as Linux) is used as
an example of more general principles in OS design.
SkriptThe slides for each lecture will be made available in the web pages of the course, along with additional reference material.
LiteraturThe networking material will be based on the following text book:

Computer Networks (5th Edition)
Andrew S. Tanenbaum, David J. Wetherall
Prentice Hall; 5 edition (October 7, 2010)

In addition, the following textbook provides useful background for the operating systems material in the course:

Modern Operating Systems (3rd Edition)
Andrew S. Tanenbaum
Prentice-Hall, 2007
263-3840-00LHardware Architectures for Machine Learning Information 2 KP2SG. Alonso, T. Hoefler, O. Mutlu, C. Zhang
KurzbeschreibungThe seminar covers recent results in the increasingly important field of hardware acceleration for data science and machine learning, both in dedicated machines or in data centers.
LernzielThe seminar aims at students interested in the system aspects of machine learning, who are willing to bridge the gap across traditional disciplines: machine learning, databases, systems, and computer architecture.
InhaltThe seminar is intended to cover recent results in the increasingly important field of hardware acceleration for data science and machine learning, both in dedicated machines or in data centers.
Voraussetzungen / BesonderesThe seminar should be of special interest to students intending to complete a master's thesis or a doctoral dissertation in related topics.