Postgraduate Course: Programming Skills (INFR11177)
|School||School of Informatics
||College||College of Science and Engineering
|Credit level (Normal year taken)||SCQF Level 11 (Postgraduate)
||Availability||Available to all students
|Summary||This course is designed to help you to produce higher quality code; code that is readable, maintainable, usable, correct and efficient in less time and with less effort. These programming skills are applicable to programs in any language and the course is illustrated with examples from C, Python, Fortran 90, and Java.
Areas of specific relevance to parallel high-performance computing, including the use of batch systems, scientific libraries, profilers, and performance analysis, are also covered.
The course covers the following:
- Best practices for scientific computing.
- Introduction to operating systems, compilers and batch systems.
- Writing programs for people, programs that are readable, maintainable, and usable.
- Reviewing code to identify bugs and share expertise.
- Debugging using code browsers and debuggers.
- Managing versions and collaborating using revision control.
- Automating common tasks and building programs using a build tool.
- Automated regression and unit testing to help ensure and maintain program correctness.
- Profiling and performance analysis to identify and improve a program's use of processing and memory resources.
- Security, authentication and authorisation concepts.
- REST application programming interfaces.
- Open source software.
Information for Visiting Students
|Pre-requisites||You must have experience in a programming language e.g. C, C++, Python, FORTRAN or Java.
You must be familiar with programming concepts including conditionals, loops, arrays and functions.
A knowledge of bash shell is highly recommended.
No knowledge of parallel programming is required.
Some of the above relevant prerequisite material will be made available for self-study, but will not be taught in classes.
|High Demand Course?
Course Delivery Information
|Academic year 2020/21, Available to all students (SV1)
|Learning and Teaching activities (Further Info)
Lecture Hours 9,
Supervised Practical/Workshop/Studio Hours 18,
Programme Level Learning and Teaching Hours 2,
Directed Learning and Independent Learning Hours
|Assessment (Further Info)
|Additional Information (Assessment)
||Coursework 100 %.
This is a practical course. The course is assessed by two pieces of coursework. The assessments are:
1. Develop code and automated tests in Python (50%)
2. Measure and report performance of a C code (50%)
No Python or C experience is assumed or expected. Students will be given links to Python resources and a handout on C syntax and how to compile and run C programs.
||Provided on assessed work within 15 working days of deadlines and through practical sessions
|No Exam Information
On completion of this course, the student will be able to:
- Understand how to build, run and develop software in a Linux/Unix environment.
- Evaluate, and write, readable source code.
- Analyse code and create unit and regression tests.
- Design, implement, analyse and write-up software performance experiments.
- Evaluate and apply software development tools including version control systems, code browsers, automated test frameworks and profilers.
|Wilson G, Aruliah D.A., Brown C.T., Chue Hong N.P., Davis M., et al. 'Best Practices for Scientific Computing'. PLoS Biol 12(1): e1001745. doi:10.1371/journal.pbio.1001745.|
Wilson, G., Bryan, J., Cranston, K., Kitzes, J., Nederbragt, L. and Teal, T. 'Good enough practices in scientific computing'. PLoS Comput Biol 13(6): e1005510. doi:10.1371/journal.pcbi.1005510.
Sandve G.K., Nekrutenko A., Taylor J., Hovig E. 'Ten Simple Rules for Reproducible Computational Research'. PLoS Comput Biol 9(10): e1003285. doi:10.1371/journal.pcbi.1003285.
Barnes, N. and Jones, D. 'Clear Climate Code: Rewriting Legacy Science Software for Clarity'. IEEE Software 28(6), pp36-42, Nov-Dec 2011. doi:10.1109/MS.2011.113.
Bailey, D.H. 'Twelve Ways to Fool the Masses When Giving Performance Results on Parallel Computers', Supercomputing Review August 1991, pp54-55. Author copy: http://crd-legacy.lbl.gov/~dhbailey/dhbpapers/twelve-ways.pdf.
Bailey, D.H. 'Misleading performance in the supercomputing field', Supercomputing '92 Proceedings of the 1992 ACM/IEEE conference on Supercomputing, pp155-158, November 1992. doi:10.1109/SUPERC.1992.236699.
The following text books are not essential, but one or the other is recommended, if pursuing a career in software development:
McConnell, S. 'Code Complete: A Practical Handbook of Software Construction' (2nd ed.), Microsoft Press, 19 Jun 2004. ISBN-10: 0735619670. ISBN-13: 978-0735619678. Either this or The Pragmatic Programmer below is a good programming best practice book.
Hunt, A. 'The Pragmatic Programmer', Addison Wesley, 20 Oct 1999. ISBN-10: 020161622X, ISBN-13: 978-0201616224. Either this or Code Complete above is a good programming best practice book.
Additional links to other recommended papers, blog posts, articles and online resources are provided.
|Graduate Attributes and Skills
Integration of knowledge
Solution exploration, evaluation, and prioritisation
Ability to analyse and interpret results
||There are limited spaces on this course. Students not on the MSc in High Performance Computing or MSc High Performance Computing with Data Science should contact the course secretary to confirm availability and confirm that they have the required prerequisites before being enrolled on the course.
The course is available to PhD students for class-only study. PhD students requiring a form of assessment (e.g. SUPA/School of Physics and Astronomy CDT students) must contact the course secretary to confirm method of enrolment.
|Additional Class Delivery Information
||One two-hour practical/tutorial per week plus one lecture per week.
|Course organiser||Dr Michael Jackson
Tel: (0131 6)50 5141
|Course secretary||Miss Jemma Auns
Tel: (0131 6)51 3545