Torsten Hoefler: Catalogue data in Spring Semester 2020
|Name||Prof. Dr. Torsten Hoefler|
|Field||Scalable Parallel Computing|
Inst. f. Hochleistungsrechnersyst.
ETH Zürich, CAB F 75
|Telephone||+41 44 632 63 44|
|252-0029-00L||Parallel Programming||7 credits||4V + 2U||T. Hoefler, H. Lehner, M. Schwerhoff|
|Abstract||Introduction to parallel programming: deterministic and non-deterministic programs, models for parallel computation, synchronization, communication, and fairness.|
|Objective||The student should learn how to write a correct parallel program, how to measure its efficiency, and how to reason about a parallel program. Student should become familiar with issues, problems, pitfalls, and solutions related to the construction of parallel programs. Labs provide an opportunity to gain experience with threads, libraries for thread management in modern programming lanugages (e.g., Java, C#) and with the execution of parallel programs on multi-processor/multi-core computers.|
|252-0817-00L||Distributed Systems Laboratory |
In the Master Programme max. 10 credits can be accounted by Labs
on top of the Interfocus Courses. Additional Labs will be listed on the Addendum.
|10 credits||9P||G. Alonso, T. Hoefler, F. Mattern, A. Singla, R. Wattenhofer, C. Zhang|
|Abstract||This course involves the participation in a substantial development and/or evaluation project involving distributed systems technology. There are projects available in a wide range of areas: from web services to ubiquitous computing including as well wireless networks, ad-hoc networks, and distributed application on mobile phones.|
|Objective||Students acquire practical knowledge about technologies from the area of distributed systems.|
|Content||This course involves the participation in a substantial development and/or evaluation project involving distributed systems technology. There are projects available in a wide range of areas: from web services to ubiquitous computing including as well wireless networks, ad-hoc networks, and distributed application on mobile phones. The objecte of the project is for the students to gain hands-on-experience with real products and the latest technology in distributed systems. There is no lecture associated to the course.|
For information of the course or projects available, please contact Prof. Mattern, Prof. Wattenhofer, Prof. Roscoe or Prof. G. Alonso.
|263-3840-00L||Hardware Architectures for Machine Learning |
The deadline for deregistering expires at the end of the second week of the semester. Students who are still registered after that date, but do not attend the seminar, will officially fail the seminar.
|2 credits||2S||G. Alonso, T. Hoefler, C. Zhang|
|Abstract||The 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.|
|Objective||The 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.|
|Content||The 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.|
|Prerequisites / Notice||The seminar should be of special interest to students intending to complete a master's thesis or a doctoral dissertation in related topics.|