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MC316 Parallel and Distributed Computing
| Credits: 20 |
Convenor: Professor Iain A. Stewart |
Semester: 1 |
| Prerequisites: |
essential: MC103, MC104, MC111, MC116, MC117, MC204 |
desirable: MC211 |
| Assessment: |
Continual assessment: 30% |
Three hour exam in May/June: 70% |
| Lectures: |
36 |
Classes: |
none |
| Tutorials: |
12 |
Private Study: |
102 |
| Labs: |
none |
Seminars: |
none |
| Project: |
none |
Other: |
none |
| Total: |
150 |
|
|
Explanation of Pre-requisites
Typical of the material assumed for this module are the basics of: computer
architectures; computer networks; the design and analysis of algorithms; and
discrete mathematics especially graph theory.
Course Description
It has been well documented that one way of increasing the power of
computers is to utilise more than one processor, i.e., to compute in
parallel. As to how one organises ones processors is a matter for choice.
One can interconnect existing sequential machines (as is the
case in the Internet), to obtain a distributed system, or one can build
special purpose parallel machines, such as array processors,
multiprocessors (i.e., shared memory machines) and multicomputers (i.e.,
distributed memory machines). This course will look at the different ways
in which one can compute in parallel and at the different problems arising.
The course will be a fusion of the theoretical and the practical.
Aims
The course aims to introduce students to the technologically important area
of parallel and distributed computing, and to equip students with a
knowledge of a number of different ways in which one can compute using more
than one processor. Students will come to appreciate that the general topic
is an exciting mix of theory and practice, and that there is a number of
very difficult fundamental problems to overcome, both technological and
theoretical, in order to build efficient parallel and distributed machines.
Objectives
The course objectives are to ensure that students understand a number of
different models of parallel and distributed computing and how there are
different problems associated with the realisation of these models,
particularly with respect to communication and algorithm design.
Transferable Skills
Students will become more experienced in the application of
discrete mathematics, especially graph theory, in computing, and will
obtain a comprehensive grounding in the general area of parallel and
distributed computing, a direction in which technology will proceed in
future.
Syllabus
This course examines a variety of models for parallel and distributed
computing including multiprocessor computers (shared-memory and
distributed-memory) and computer networks. Architectural issues in the
realisation of these models are considered such as synchronisation, cache
consistency, network topologies, routing, flow control, switching and
communication standards. Algorithms for fundamental problems in
multiprocessor computers and computer networks are then developed, and new
advances in distributed and parallel computing, including mobile computing
and optical networks, are briefly studied.
Reading list
Essential:
H. El-Rewini and T.G. Lewis,
Distributed and Parallel Computing,
Manning, 1997.
Recommended:
M. Cosnard and D. Trystram,
Parallel Algorithms and Architectures,
International Thomsen Computer Press, 1995.
J.M. Crichlow,
An Introduction to Parallel and Distributed Computing,
Prentice-Hall, 1997.
J. Duato, S. Yalamanchili and L. Ni,
Interconnection Networks: an Engineering Approach,
IEEE Computer Society Press, 1997.
A.S. Tanenbaum,
Distributed Operating Systems,
Prentice Hall, 1995.
G. Tel,
Introduction to Distributed Algorithms,
Cambridge University Press, 1994.
ed. A.Y.H. Zomaya,
Parallel and Distributed Computing Handbook,
McGraw-Hill, 1996.
Background:
V.C. Barbosa,
An Introduction to Distributed Algorithms,
MIT Press, 1996.
G. Coulouris, J. Dollimore and T. Kindberg,
Distributed Systems: Concepts and Design,
Addison-Wesley, 1994.
A. Gibbons and W. Rytter,
Efficient Parallel Algorithms,
Cambridge University Press, 1988.
V. Kumar, A. Grama, A. Gupta and G. Karypis,
Introduction to Parallel Computing: Design and Analysis of
Algorithms,
Benjamin Cummings, 1994.
N.A. Lynch,
Distributed Algorithms,
Morgan Kaufmann, 1996.
M.J. Quinn,
Parallel Computing: Theory and Practice,
McGraw-Hill, 1994.
Details of Assessment
The coursework for the continual assessment consists of 5 assignments
staggered throughout the course at approximately one assignment every two
weeks.
Next: MC320 Modelling Biological Systems
Up: Year 3
Previous: MC315 Expert Systems and
S. J. Ambler
11/20/1999