PARALLEL COMPUTING - 2017/8
Module code: COM2039
KRAUSE PJ Prof (Computer Sci)
Number of Credits
FHEQ Level 5
Module cap (Maximum number of students)
Overall student workload
Independent Study Hours: 108
Lecture Hours: 24
Laboratory Hours: 20
|Assessment type||Unit of assessment||Weighting|
|Coursework||INDIVIDUAL COURSEWORK I||40%|
|Examination||2HR UNSEEN EXAMINATION||60%|
Prerequisites / Co-requisites
The course introduces concepts of parallel and distributed computing by considering different architectures that support this, and working through different categories of examples. The implementation of such solutions and their subsequent analysis gives practical experience and an understanding of the difficulties involved. Special consideration will be given to performance issues of resulting architectures, leading to a foundation for the design of high performance computing for distributed real-time control.
The module aims to develop the student's ability to think clearly about the relationship between a problem abstraction and architectural implementation details. We focus on the techniques for the development of solutions of parallel computing problems as leads to high-performance computing and distributed architectures. A number of case studies will be considered to illustrate facets of the subject. On completion of the module, the students will have a good understanding of methods for optimizing the performance of parallel, distributed, and concurrent architectures
|Explain the major benefits and limitations of parallel computing||KC|
|Identify and explain the differences between common current parallel architectures||KC|
|Develop parallel solutions for computationally intensive problems on distributed architectures||P|
|Analyse the performance of a parallel/distributed solutions||KCT|
C - Cognitive/analytical
K - Subject knowledge
T - Transferable skills
P - Professional/Practical skills
Scope of Parallel Computing:
From GPU to real time IoT;
Control Structure of Parallel Platforms
Communication Model of Parallel Platforms
Physical Organisation of Parallel Platforms
Communication Costs and Routing Mechanisms
Principles of Parallel Algorithm Design
Containing Interaction Overheads
Parallel Algorithm Models
Analytical Modelling of Parallel Algorithms
Response times; throughputs; queue lengths; utilizations
Basics of Control Theory:
Dynamics of resource management; stability;
Advanced examples from feedback control of biological systems
Methods of Teaching / Learning
The learning and teaching strategy is designed to:
Help students understand the distinctive features of a broad range of parallel programming techniques
Show the application of design techniques for solving distributed programming problems
Explain students how to analyse and optimise the performance characteristics of concurrent and distributed architectures
Equip students with necessary mathematical background to prepare them for exposure to more advanced analytical techniques
Enable students to apply taught techniques to solve concrete problems
The learning and teaching methods include:
Lectures (11 weeks at 2h) using detailed lecture slides to gauge the students’ understanding
Labs (10 weeks at 2h) using exercise sheets and their solutions.
Students will be expected to spend a minimum of 2 hours a week on self-study as part of preparation for the labs.
The assessment strategy is designed to provide students with the opportunity to demonstrate that they have achieved the module learning outcomes.
Thus, the summative assessment for this module consists of:
· An individual coursework on sets of problems that students are required to solve. This addresses LO1, LO2, LO3 and LO5.
· A 2h unseen examination on the whole course content. This addresses LO1, LO3, LO4 and LO5.
The individual courseworks will be due around week 8. The exam takes place at the end of the semester during the exam period.
Formative assessment and feedback
Lecture slides are used extensively in the lectures with each lecture consisting of a number of slides explaining the theory and showing the examples. Solutions to lab exercises are explained during the lab session and provided to the students as part of preparation for the exam.
Reading list for PARALLEL COMPUTING : http://aspire.surrey.ac.uk/modules/com2039
Please note that the information detailed within this record is accurate at the time of publishing and may be subject to change. This record contains information for the most up to date version of the programme / module for the 2017/8 academic year.