Note: This is an archived Handbook entry from 2016.
|Dates & Locations:|| |
This subject has the following teaching availabilities in 2016:Semester 2, Parkville - Taught on campus.
Timetable can be viewed here. For information about these dates, click here.
|Time Commitment:||Contact Hours: 3 hours per week; Non-contact time commitment: 84 hours |
Total Time Commitment:
Knowledge of Operating Systems and Networks, and C Programming.
|Recommended Background Knowledge:|| |
C programming and UNIX familiarity.
|Non Allowed Subjects:|| |
|Core Participation Requirements:||
For the purposes of considering request for Reasonable Adjustments under the Disability Standards for Education (Cwth 2005), and Student Support and Engagement Policy, academic requirements for this subject are articulated in the Subject Overview, Learning Outcomes, Assessment and Generic Skills sections of this entry.
It is University policy to take all reasonable steps to minimise the impact of disability upon academic study, and reasonable adjustments will be made to enhance a student's participation in the University's programs. Students who feel their disability may impact on meeting the requirements of this subject are encouraged to discuss this matter with a Faculty Student Adviser and Student Equity and Disability Support: http://services.unimelb.edu.au/disability
CoordinatorDr Aaron Harwood
Dr Aaron Harwood
The subject aims to introduce students to parallel algorithms and their analysis. Fundamental principles of parallel computing are discussed. Various parallel architectures and programming platforms are introduced. Parallel algorithms for different architectures, as well as parallel algorithms addressing specific scientific problems are critically analysed.
Topics include: principles of parallel computing, PRAM model, PRAM algorithms, parallel architectures, OpenMP, shared memory algorithms, systolic algorithms, parallel communication patterns, PVM/MPI, scientific applications, hypercube, graph embeddings and extended parallel computing models.
INTENDED LEARNING OUTCOMES (ILO)
On completion of this subject the student is expected to:
Hurdle requirement: To pass the subject, students must obtain at least:
|Prescribed Texts:|| |
|Breadth Options:|| |
This subject is not available as a breadth subject.
|Fees Information:||Subject EFTSL, Level, Discipline & Census Date|
On completion of this subject the student should have the following skills:
LEARNING AND TEACHING METHODS
The subject will be delivered through a combination of lectures, tutorials and project work. The project work involves developing parallel algorithms implemented on a variety of parallel architectures and report writing.
INDICATIVE KEY LEARNING RESOURCES
Students will have access to lecture notes and lecture slides. The subject LMS site also contains links to recommended literature and current survey papers of parallel computing. Students will make use of parallel computer systems.
CAREERS / INDUSTRY LINKS
The subject provides the fundamentals in parallel computing that support a career in areas such as HPC Systems Administrator, HPC Programmer, Specialist Programmer, Systems Administrator, Numerical Modelling and Analytics Developer.
Doctor of Philosophy - Engineering |
Master of Information Technology
Master of Information Technology
Master of Philosophy - Engineering
Master of Science (Computer Science)
MIT Distributed Computing Specialisation |
Master of Engineering (Software)
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