USQ Logo
The current and official versions of the course specifications are available on the web at
Please consult the web for updates that may occur during the year.

MAT2409 High Performance Numerical Computing

Semester 1, 2020 Online
Short Description: High Performance Num Computing
Units : 1
Faculty or Section : Faculty of Health, Engineering and Sciences
School or Department : School of Sciences
Student contribution band : Band 2
ASCED code : 010101 - Mathematics
Grading basis : Graded


Examiner: Harry Butler


Pre-requisite: (CSC2410 or CSC1401) and (MAT1102 or ENM1600) or Students must be enrolled in one of the following Programs: MPIT or MCOT or MCTE

Other requisites

Knowledge of basic programming structures is assumed.


Many areas of computing in engineering, science, technology and games require programmers to have insight and skills in the implementation of common numerical computations. Programming high performance computers to rapidly perform large scale tasks requires considerable skill. Modern vector and super-scalar computers are very fast - but to achieve anything remotely like the peak speed requires special programming styles sympathetic to the computer architecture. Using fundamental algorithmic tasks of science, this course develops the ability to design good algorithms for modern computer architectures.


This course develops skills in programming modern high performance computers. It examines some of the typical hardware architectures and how they affect performance and programming. Algorithms to illustrate the principles are chosen from a range of scientific tasks. The course includes the study of numerical solutions of linear and non-linear equations, numerical interpolation and curve fitting, the numerical solution of ordinary differential equations, and Monte Carlo simulation. Interaction utilising modern graphics is exploited.


Completion of this course will enable students to:

  1. discern the relationship between computer architecture and program performance
  2. understand the principles of high performance programming using vector operations
  3. demonstrate an understanding of a variety of computer-based numerical methods and their errors, used in the solution of numerical problems
  4. document, analyse and describe complex numerical code
  5. choose and implement appropriate numerical techniques (including graphics) for a range of real-world problems


Description Weighting(%)
1. Basics of numerical computation

Performance Measures and Computational Error Analysis
2. Solving Linear and Nonlinear Equations

Newton's method and other fixed point iteration; linear systems; condition numbers; Jacobi's iterative solution of linear systems; Newton's method for nonlinear systems.
3. Numerical Interpolation and Curve Fitting

Interpolation with polynomials, derivatives and integrals of interpolants; least squares approximations.
4. Solution of ordinary differential equations

Difference approximations; Euler's method; modified Euler's method; the Runge-Kutta RK4 method, systems of ODES,introduction to shooting and finite difference methods.
5. Simulation and Monte Carlo methods

Introduction to process simulation (e.g. random walks), Monte Carlo integration, Random Numbers

Text and materials required to be purchased or accessed

ALL textbooks and materials available to be purchased can be sourced from USQ's Online Bookshop (unless otherwise stated). (

Please contact us for alternative purchase options from USQ Bookshop. (

Access to computer and internet facilities for computer programming and assignment submission.
Anaconda Distribution Python 3.X for macOS, Linux or Windows. Available from Any version change will be notified on the Study Desk.
Study book, Course MAT2409 High Performance Numerical Computing
(Available on the StudyDesk in Electronic Form).

Reference materials

Reference materials are materials that, if accessed by students, may improve their knowledge and understanding of the material in the course and enrich their learning experience.
Austin, M & Chancogne, D 1999, Introduction to Engineering Programming: In C, Matlab and Java, Wiley, New York.
(ISBN: 0471001163.)
Burden, R and Faires, D 2012, Numerical Methods, 4th edn, Cengage Learning, US.
(ISBN 9780495114765.)
Etter, DM 1996, Engineering Problem Solving with MATLAB, 2nd edn, Prentice-Hall, Upper Saddle River, NJ.
(ISBN: 0133976882.)
Gerald, C and Wheatley, P 2004, Applied Numerical Analysis, 7th edn, Pearson.
(ISBN: 9780321133045.)
Gerber, Richard 2002, The Software Optimization Cookbook: high performance recipes for the Intel architecture, 1st edn, Intel Press, United States.
Kincaid, D & Cheney, W 2001, Numerical Analysis: Mathematics of Scientific Computing, 3rd edn, Brooks/Cole, Pacific Grove, Calif.
Kreyszig, E 2011, Advanced Engineering Mathematics, 10th edn, Wiley, New York.
Palm, WJ 2011, Introduction to Matlab for Engineers, 3rd edn, WCB McGraw-Hill, Boston.

Student workload expectations

Activity Hours
Assessments 42.00
Online Lectures 26.00
Online Tutorials or Workshops 26.00
Private Study 78.00

Assessment details

Description Marks out of Wtg (%) Due Date Notes
Problem Report 1 20 10 23 Mar 2020
Problem Report 2 20 15 20 Apr 2020
Problem Report 3 20 15 11 May 2020
Assignment 100 60 11 Jun 2020 (see note 1)

  1. The assignment date will be available via UConnect when the Alternative Assessment Schedule has been released. Students will be provided further instruction regarding the assignment by their course examiner via StudyDesk.

Important assessment information

  1. Attendance requirements:
    It is the students' responsibility to participate appropriately in all activities scheduled for them, and to study all material provided to them or required to be accessed by them to maximise their chance of meeting the objectives of the course and to be informed of course-related activities and administration.

  2. Requirements for students to complete each assessment item satisfactorily:
    Due to COVID-19 the requirements for S1 2020 are: To satisfactorily complete an individual assessment item a student must achieve at least 50% of the marks for that item.

    Requirements after S1 2020:
    To satisfactorily complete an assessment item a student must achieve at least 50% of the marks. Students do not have to satisfactorily complete each assessment item to be awarded a passing grade in this course. Refer to Statement 4 below for the requirements to receive a passing grade in this course.

  3. Penalties for late submission of required work:
    Students should refer to the Assessment Procedure (point 4.2.4)

  4. Requirements for student to be awarded a passing grade in the course:
    Due to COVID-19 the requirements for S1 2020 are: To be assured of receiving a passing grade a student must achieve at least 50% of the total weighted marks available for the course.

    Requirements after S1 2020:
    To be assured of receiving a passing grade a student must obtain at least 50% of the total weighted marks available for the course (i.e. the Primary Hurdle), and have satisfied the Secondary Hurdle (Supervised), i.e. the end of semester examination by achieving at least 40% of the weighted marks available for that assessment item.

    Supplementary assessment may be offered where a student has undertaken all of the required summative assessment items and has passed the Primary Hurdle but failed to satisfy the Secondary Hurdle (Supervised), or has satisfied the Secondary Hurdle (Supervised) but failed to achieve a passing Final Grade by 5% or less of the total weighted Marks.

    To be awarded a passing grade for a supplementary assessment item (if applicable), a student must achieve at least 50% of the available marks for the supplementary assessment item as per the Assessment Procedure (point 4.4.2).

  5. Method used to combine assessment results to attain final grade:
    The final grades for students will be assigned on the basis of the weighted aggregate of the marks obtained for each of the summative assessment items in the course.

  6. Examination information:
    Due to COVID-19 the requirements for S1 2020 are: There is no examination in this course.

    Requirements after S1 2020:
    An open examination is one in which candidates may have access to any printed or written material and a calculator during the examination.

  7. Examination period when Deferred/Supplementary examinations will be held:
    Due to COVID-19 the requirements for S1 2020 are: There is no examination in this course, there will be no deferred or supplementary examinations.

    Requirements after S1 2020:
    Any Deferred or Supplementary examinations for this course will be held during the next examination period.

  8. University Student Policies:
    Students should read the USQ policies: Definitions, Assessment and Student Academic Misconduct to avoid actions which might contravene University policies and practices. These policies can be found at

Assessment notes

  1. Reports and Python code must be submitted electronically by the due date and time in the manner prescribed in the Introductory Book or as modified on the Course Website. Late submissions will not normally be accepted.

  2. Exam paper presentation: All exam papers should be presented in accurate and clear writing by blue or black pen. Pencil writing is not acceptable. Assignments can be presented using any word processor such as Word or Latex, or can be neatly written by blue or black pen (but not by pencil).

Other requirements

  1. Computer, e-mail and Internet access:
    Students are required to have access to a personal computer, e-mail capabilities and Internet access to UConnect. Current details of computer requirements can be found at .

  2. Students can expect that questions in assessment items in this course may draw upon knowledge and skills that they can reasonably be expected to have acquired before enrolling in this course. This includes knowledge contained in pre-requisite courses and appropriate communication, information literacy, analytical, critical thinking, problem solving or numeracy skills. Students who do not possess such knowledge and skills should not expect the same grades as those students who do possess them.

Date printed 19 June 2020