Scalable Computing

Faculteit Science and Engineering
Jaar 2019/20
Vakcode WMCS16003
Vaknaam Scalable Computing
Niveau(s) master
Voertaal Engels
Periode semester II a
ECTS 5
Rooster rooster.rug.nl

Uitgebreide vaknaam Scalable Computing
Leerdoelen At the end of this module the students will be able to:
- identify and summarize existing big data processing techniques
- select a best possible technique for a given data processing problem
- construct an algorithm for a given problem on top of existing data processing frameworks and techniques
- evaluate the constructed solution and compare it with possible alternatives, both qualitatively and quantitatively
Omschrijving Over recent years the importance of the efficient data processing has drastically increased. From one side, this is caused by the additional value extracted from the data which results in huge economical and societal benefits both for companies and for the society as a whole. From the other side, demand for efficient processing is also influenced by easiness of collecting and storing large amounts of data that needs to be continuously processed and analysed. With ever increasing data, efficient data processing is not possible without fully exploiting the available computational resources. In this course we specifically look into problems that require large amount of resources, the problems, that cannot be easily solved on a single computer using traditional methods. We discuss both CPU-intensive tasks and data-intensive, and look into possible solutions for both classes of problems. The course does not concentrate on one particular method or technique, but rather aims at presenting an overview of possible solutions (to name a few: GPU computing, map/reduce, bulk synchronous parallel, etc.), their pro's and con's.
Uren per week
Onderwijsvorm Bijeenkomst (S), Hoorcollege (LC)
(Theory in 1 lecture / week. For practical assignments students are divided in groups (2-3 students per group). Each group is assigned with a project that needs to be completed by the end of the course. Weekly meetings are organized to discuss progress on ongoing projects.)
Toetsvorm Presentatie (P), Schriftelijk tentamen (WE), Verslag (R)
(Written exam 50%. Project 50% (report and presentation).)
Vaksoort master
Coördinator prof. dr. A. Lazovik
Docent(en) prof. dr. A. Lazovik ,dr. M.H.F. Wilkinson
Entreevoorwaarden
Opmerkingen
Opgenomen in
Opleiding Jaar Periode Type
MSc Astronomy: Quantum Universe  (Optional Courses in Data Science (DS)) - semester II a keuze
MSc Computing Science: Data Science and Systems Complexity  (Compulsory course units) 1 semester II a verplicht
MSc Computing Science: Intelligent Systems and Visual Computing  (Guided choice course units) - semester II a keuze
MSc Computing Science: Science Business and Policy  (Elective course units) 1 semester II a keuze
MSc Computing Science: Software Engineering and Distributed Systems  (Compulsory course units) 1 semester II a verplicht
MSc Courses for Exchange Students: AI - Computing Science - Mathematics - semester II a Computing Science
MSc Mathematics: Statistics and Big Data  (Statistics and Big Data: Guided Choice) - semester II a guided choice