Postgraduate Course: Extreme Computing (INFR11088)

Course Outline
School	School of Informatics	College	College of Science and Engineering
Credit level (Normal year taken)	SCQF Level 11 (Year 4 Undergraduate)	Availability	Available to all students
SCQF Credits	10	ECTS Credits	5
Summary	Extreme Computing deals with the principles, systems and algorithms behind Web-scale problem solving. This touches upon the technologies and techniques used by companies such as Google, FaceBook and Microsoft, using warehouse-scale computing and massive datasets. The course will be in three parts: the principles behind extreme computing (cloud computing, scaling, performance, privacy etc), supporting infrastructure (distributed file systems, replication, web services etc) and algorithms (MapReduce, case studies from Natural Language Processing, Database query evaluation, machine learning, streaming).
Course description	The course is to be conceptually split into three main areas, with each area not necessarily accounting for an equal portion of the syllabus. The three areas and the material covered in each area are as follows: - Background: Motivation for new computing paradigms; introduction and differences between cloud and cluster computing; scaling, performance, privacy, economics, security, software as service. - Infrastructure: Distributed file systems; virtualisation; replication; fault tolerance; concurrent programming; web services. - Algorithms: Hadoop (MapReduce); design and implementation of MapReduce programs; dealing with massive amounts of data; case studies using natural language processing, database query evaluation and machine learning; data stream processing.

Entry Requirements (not applicable to Visiting Students)
Pre-requisites		Co-requisites
Prohibited Combinations		Other requirements	This course is open to all Informatics students including those on joint degrees. For external students where this course is not listed in your DPT, please seek special permission from the course organiser. Maths background, including basic probability. Programming ability, and be familiar with Unix-like systems. Any programming language is fine; past students find that Python is sufficient.

Information for Visiting Students
Pre-requisites	Visiting students are required to have comparable background to that assumed by the course prerequisites listed in the Degree Regulations & Programmes of Study. If in doubt, consult the course lecturer.

Course Delivery Information

Academic year 2014/15, Available to all students (SV1)		Quota: None
Course Start	Semester 1
Timetable	Timetable
Learning and Teaching activities (Further Info)	Total Hours: 100 ( Lecture Hours 20, Supervised Practical/Workshop/Studio Hours 5, Summative Assessment Hours 2, Programme Level Learning and Teaching Hours 2, Directed Learning and Independent Learning Hours 71 )
Assessment (Further Info)	Written Exam 50 %, Coursework 50 %, Practical Exam 0 %
Additional Information (Assessment)	You should expect to spend approximately 40 hours on the coursework for this course. If delivered in semester 1, this course will have an option for semester 1 only visiting undergraduate students, providing assessment prior to the end of the calendar year.
Feedback	Not entered
Exam Information
Exam Diet	Paper Name		Hours & Minutes
Main Exam Diet S2 (April/May)			2:00

Academic year 2014/15, Part-year visiting students only (VV1)		Quota: None
Course Start	Semester 1
Timetable	Timetable
Learning and Teaching activities (Further Info)	Total Hours: 100 ( Lecture Hours 20, Supervised Practical/Workshop/Studio Hours 5, Summative Assessment Hours 2, Programme Level Learning and Teaching Hours 2, Directed Learning and Independent Learning Hours 71 )
Assessment (Further Info)	Written Exam 50 %, Coursework 50 %, Practical Exam 0 %
Additional Information (Assessment)	You should expect to spend approximately 40 hours on the coursework for this course. If delivered in semester 1, this course will have an option for semester 1 only visiting undergraduate students, providing assessment prior to the end of the calendar year.
Feedback	Not entered
Exam Information
Exam Diet	Paper Name		Hours & Minutes
Main Exam Diet S1 (December)			2:00

Learning Outcomes
- Demonstrate knowledge of the need for extreme computing by providing motivating examples of the scale of problems only extreme computing can solve. - Demonstrate knowledge of the infrastructure necessary for extreme computing through enumerating different file system designs, virtualisation techniques, and fault-tolerance paradigms. In particular, the first essay coursework will present the students with a large-scale computing problem and ask them to present a design using the knowledge acquired from the first part of the course (see also detailed syllabus). - Demonstrate knowledge of cluster-based algorithms for natural language processing, database query evaluation, machine learning, and data stream processing through the use of the Map/Reduce programming paradigm. Specifically, the second assessed piece of coursework will test the students' ability to implement large-scale data analytics using Map/Reduce.

Reading List
Data Intensive Text Processing with MacReduce, Jimmy Linn & Chris Dyer Hadoop: The Definitive Guide, Tom White, O'Reilly Media

Additional Information
Course URL	http://course.inf.ed.ac.uk/exc
Graduate Attributes and Skills	Not entered
Keywords	Not entered

Contacts
Course organiser	Dr Iain Murray Tel: (0131 6)51 9078 Email: I.Murray@ed.ac.uk	Course secretary	Miss Claire Edminson Tel: (0131 6)51 4164 Email: C.Edminson@ed.ac.uk