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DEGREE REGULATIONS & PROGRAMMES OF STUDY 2012/2013
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DRPS : Course Catalogue : School of Informatics : Informatics

Postgraduate Course: Extreme Computing (INFR11088)

Course Outline
SchoolSchool of Informatics CollegeCollege of Science and Engineering
Course typeStandard AvailabilityAvailable to all students
Credit level (Normal year taken)SCQF Level 11 (Postgraduate) Credits10
Home subject areaInformatics Other subject areaNone
Course website http://www.inf.ed.ac.uk/teaching/courses/exc/ Taught in Gaelic?No
Course descriptionExtreme 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).
Entry Requirements (not applicable to Visiting Students)
Pre-requisites Co-requisites
Prohibited Combinations Other requirements Background in programming.
Additional Costs None
Information for Visiting Students
Pre-requisitesNone
Displayed in Visiting Students Prospectus?No
Course Delivery Information
Delivery period: 2012/13 Semester 1, Available to all students (SV1) Learn enabled:  No Quota:  None
Location Activity Description Weeks Monday Tuesday Wednesday Thursday Friday
CentralLecture1-11 16:10 - 17:00
CentralLecture1-11 16:10 - 17:00
First Class Week 1, Monday, 16:10 - 17:00, Zone: Central. HRB LT Robson Building
Exam Information
Exam Diet Paper Name Hours:Minutes
Main Exam Diet S2 (April/May)2:00
Delivery period: 2012/13 Semester 1, Part-year visiting students only (VV1) Learn enabled:  No Quota:  None
Location Activity Description Weeks Monday Tuesday Wednesday Thursday Friday
CentralLecture1-11 16:10 - 17:00
CentralLecture1-11 16:10 - 17:00
First Class Week 1, Monday, 16:10 - 17:00, Zone: Central. HRB LT Robson Building
Exam Information
Exam Diet Paper Name Hours:Minutes
Main Exam Diet S1 (December)2:00
Summary of Intended 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.
Assessment Information
Assessment Weightings (%)

Written Examination 50
Assessed Assignments 50
Oral Presentations 0
Special Arrangements
None
Additional Information
Academic description Not entered
Syllabus 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.
Transferable skills Not entered
Reading list Data Intensive Text Processing with MacReduce, Jimmy Linn & Chris Dyer
Hadoop: The Definitive Guide, Tom White, O'Reilly Media
Study Abroad Not entered
Study Pattern Lectures 20
Tutorials 0
Timetabled Laboratories 5
Non-timetabled assessed assignments 40
Private Study/Other 35
Total 100
KeywordsNot entered
Contacts
Course organiserDr Iain Murray
Tel: (0131 6)51 9078
Email: I.Murray@ed.ac.uk
Course secretaryMiss Kate Weston
Tel: (0131 6)50 2701
Email: Kate.Weston@ed.ac.uk
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