This course provides an introduction to High Performance Computing with an orientation towards applications in science and engineering. Aspects of numerical computing and the design and construction of sophisticated scientific software will be considered. The focus will be on the C and C++ programming languages, although reflecting the reality of modern scientific computation this course will also touch on other languages such as Python, Java and FORTRAN95. The course will study high performance computer architectures, including modern parallel processors, and will describe how an algorithm interacts with these architectures. It will also look at practical methods of estimating and measuring algorithm/architecture performance.
The following topics will be addressed: the C++ programming language; basic numerical computing from aspects of floating point error analysis to algorithms for solving differential equations; the engineering of scientific software; general high performance computing concepts and architectural principles; modern scalar architectures and their memory structure; performance and programmability issues, and program analysis techniques for high performance computing; parallel computing paradigms and programming using the OpenMP standard; trends in HPC systems.
Learning Outcomes
Upon successful completion, students will have the knowledge and skills to:
- appreciate the building blocks of scientific and engineering software.
- demonstrate a basic knowledge of numerical computing using an appropriate programming language.
- be competent in experimental computing in a numerical context and of the optimisation of algorithms on high performance architectures.
- be able to reason about the accuracy of mathematical and numerical models of real physical phenomena.
- have an awareness of the modern field of computational science and engineering and of the impact of high performance computing on science and industry.
- have an understanding of the various paradigms of high performance computing and their potential for performance and programmability.
- be capable of writing algorithms that yield good performance on high-performance architectures, and to be able to estimate and evaluate their performance.
Other Information
Course offered Semester 1 in alternate, even-numbered years.
Indicative Assessment
- Assignment (40) [LO 1,2,3,4,5,6,7]
- Mid-semester exam (10) [LO 1,2,3,4,5,6,7]
- Final Exam (50) [LO 1,2,3,4,5,6,7]
In response to COVID-19: Please note that Semester 2 Class Summary information (available under the classes tab) is as up to date as possible. Changes to Class Summaries not captured by this publication will be available to enrolled students via Wattle.
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Workload
Thirty one-hour lectures and six two-hour tutorial/laboratory sessionsInherent Requirements
Information on inherent requirements are currently not available
Requisite and Incompatibility
Prescribed Texts
not available
Specialisations
Fees
Tuition fees are for the academic year indicated at the top of the page.
If you are a domestic graduate coursework or international student you will be required to pay tuition fees. Tuition fees are indexed annually. Further information for domestic and international students about tuition and other fees can be found at Fees.
- Student Contribution Band:
- 2
- Unit value:
- 6 units
If you are an undergraduate student and have been offered a Commonwealth supported place, your fees are set by the Australian Government for each course. At ANU 1 EFTSL is 48 units (normally 8 x 6-unit courses). You can find your student contribution amount for each course at Fees. Where there is a unit range displayed for this course, not all unit options below may be available.
| Units | EFTSL |
|---|---|
| 6.00 | 0.12500 |
Course fees
- Domestic fee paying students
| Year | Fee |
|---|---|
| 2020 | $4320 |
- International fee paying students
| Year | Fee |
|---|---|
| 2020 | $5760 |
Offerings, Dates and Class Summary Links
ANU utilises MyTimetable to enable students to view the timetable for their enrolled courses, browse, then self-allocate to small teaching activities / tutorials so they can better plan their time. Find out more on the Timetable webpage.
Class summaries, if available, can be accessed by clicking on the View link for the relevant class number.
First Semester
| Class number | Class start date | Last day to enrol | Census date | Class end date | Mode Of Delivery | Class Summary |
|---|---|---|---|---|---|---|
| 3083 | 24 Feb 2020 | 02 Mar 2020 | 31 Mar 2020 | 29 May 2020 | In Person | N/A |
Second Semester
| Class number | Class start date | Last day to enrol | Census date | Class end date | Mode Of Delivery | Class Summary |
|---|---|---|---|---|---|---|
| 9759 | 27 Jul 2020 | 03 Aug 2020 | 31 Aug 2020 | 30 Oct 2020 | In Person | N/A |