Postgraduate Course: Extreme Computing (INFR11088)
Course Outline
| School | School of Informatics |
College | College of Science and Engineering |
| Course type | Standard |
Availability | Available to all students |
| Credit level (Normal year taken) | SCQF Level 11 (Postgraduate) |
Credits | 10 |
| Home subject area | Informatics |
Other subject area | None |
| Course website |
http://www.inf.ed.ac.uk/teaching/courses/exc/ |
Taught in Gaelic? | No |
| Course description | 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). |
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-requisites | None |
| 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 |
| Central | Lecture | | 1-11 | 16:10 - 17:00 | | | | | | Central | Lecture | | 1-11 | | | | 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 |
| Central | Lecture | | 1-11 | 16:10 - 17:00 | | | | | | Central | Lecture | | 1-11 | | 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 |
| Keywords | Not entered |
Contacts
| Course organiser | Dr Iain Murray
Tel: (0131 6)51 9078
Email: I.Murray@ed.ac.uk |
Course secretary | Miss Kate Weston
Tel: (0131 6)50 2701
Email: Kate.Weston@ed.ac.uk |
|
© Copyright 2012 The University of Edinburgh - 31 August 2012 4:12 am
|
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