Ce Zhang: Catalogue data in Spring Semester 2021
|Name||Prof. Dr. Ce Zhang|
Institut für Computing Platforms
ETH Zürich, STF K 513
|Telephone||+41 44 632 75 29|
|252-0063-00L||Data Modelling and Databases||7 credits||4V + 2U||C. Zhang|
|Abstract||Data modelling (Entity Relationship), relational data model, relational design theory (normal forms), SQL, database integrity, transactions and advanced database engines|
|Objective||Introduction to relational databases and data management. Basics of SQL programming and transaction management.|
|Content||The course covers the basic aspects of the design and implementation of databases and information systems. The courses focuses on relational databases as a starting point but will also cover data management issues beyond databases such as: transactional consistency, replication, data warehousing, other data models, as well as SQL.|
|Literature||Kemper, Eickler: Datenbanksysteme: Eine Einführung. Oldenbourg Verlag, 7. Auflage, 2009.|
Garcia-Molina, Ullman, Widom: Database Systems: The Complete Book. Pearson, 2. Auflage, 2008.
|252-0817-00L||Distributed Systems Laboratory||10 credits||9P||G. Alonso, T. Hoefler, A. Klimovic, 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.|
|252-3510-00L||Computing Platforms |
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, M. J. Giardino, I. Müller, C. Zhang|
|Abstract||The seminar covers core concepts and ideas in the general area of computer systems, ranging from software and hardware architectures to system design for operating systems, data processing systems, and distributed systems.|
|Objective||The seminar will cover core concepts and ideas in the general area of computer systems, ranging from software and hardware architectures to system design for operating systems, data processing systems, and distributed systems. The focus will be on fundamental ideas that apply across systems and application areas but with an emphasis on those ideas that apply to cloud platforms and hardware acceler|
|Content||The seminar will consist on student presentations based on a list of papers that will be provided at the beginning of the course. Presentations will be done in teams. Presentations will be arranged in slots of 30 minutes talk plus 15 minutes questions. Grades will be assigned based on quality of the presentation, coverage of the topic including material not in the original papers, participation during the seminar, and ability to understand, present, and criticize the underlying technology.|
|263-0007-00L||Advanced Systems Lab |
Only for master students, otherwise a special permission by the study administration of D-INFK is required.
|8 credits||3V + 2U + 2A||M. Püschel, C. Zhang|
|Abstract||This course introduces the student to the foundations and state-of-the-art techniques in developing high performance software for mathematical functionality occurring in various fields in computer science. The focus is on optimizing for a single core and includes optimizing for the memory hierarchy, for special instruction sets, and the possible use of automatic performance tuning.|
|Objective||Software performance (i.e., runtime) arises through the complex interaction of algorithm, its implementation, the compiler used, and the microarchitecture the program is run on. The first goal of the course is to provide the student with an understanding of this "vertical" interaction, and hence software performance, for mathematical functionality. The second goal is to teach a systematic strategy how to use this knowledge to write fast software for numerical problems. This strategy will be trained in several homeworks and a semester-long group project.|
|Content||The fast evolution and increasing complexity of computing platforms pose a major challenge for developers of high performance software for engineering, science, and consumer applications: it becomes increasingly harder to harness the available computing power. Straightforward implementations may lose as much as one or two orders of magnitude in performance. On the other hand, creating optimal implementations requires the developer to have an understanding of algorithms, capabilities and limitations of compilers, and the target platform's architecture and microarchitecture.|
This interdisciplinary course introduces the student to the foundations and state-of-the-art techniques in high performance mathematical software development using important functionality such as matrix operations, transforms, filters, and others as examples. The course will explain how to optimize for the memory hierarchy, take advantage of special instruction sets, and other details of current processors that require optimization. The concept of automatic performance tuning is introduced. The focus is on optimization for a single core; thus, the course complements others on parallel and distributed computing.
Finally a general strategy for performance analysis and optimization is introduced that the students will apply in group projects that accompany the course.
|Prerequisites / Notice||Solid knowledge of the C programming language and matrix algebra.|
|263-3300-00L||Data Science Lab |
Only for Data Science MSc.
|14 credits||9P||C. Zhang, V. Boeva, R. Cotterell, J. Vogt, F. Yang|
|Abstract||In this class, we bring together data science applications|
provided by ETH researchers outside computer science and
teams of computer science master's students. Two to three
students will form a team working on data science/machine
learning-related research topics provided by scientists in
a diverse range of domains such as astronomy, biology,
social sciences etc.
|Objective||The goal of this class if for students to gain experience|
of dealing with data science and machine learning applications
"in the wild". Students are expected to go through the full
process starting from data cleaning, modeling, execution,
debugging, error analysis, and quality/performance refinement.
|Prerequisites / Notice||Prerequisites: At least 8 KP must have been obtained under Data Analysis and at least 8 KP must have been obtained under Data Management and Processing.|