Welcome to Scientific Computing Using Python - Advanced Python.

Many research projects involve scientific computing for analyzing [big] data and/or simulating complex systems. This makes it necessary have a systematic approach to obtaining well-tested and documented code. Further, we see an increased interest in reproducible research, which allows other researchers the opportunity to dig further into others research results as well as easy access to results and improving productivity by reusing code and software.

This is an introductory course in scientific computing using the increasingly popular programming language Python. Python is gaining popularity in science due to a number of advantages such as having a rich set of libraries for computing and data visualization, excellent performance optimizing possibilities, standard tools for simple parallel computing, fast development cycle and high productivity – just to name a few. Python is open source and as such an asset for any researcher following the reproducible research paradigm.

This course covers the main area: High performance computing.

This specific course part contents as follows:

High performance computing:

1. Profiling:a.  Memory profiling. b.  Time profiling (function based / line based). 2. Performance optimization: a.  Numba (just in time compilation). b.  Cython (compiled Python via C-extensions). c.  SWIG (C integration with Python). d.  Fortran wrapping using f2py 3. Parallel computing. a.  Theoretical aspects (Amdahl's law, Gustafson-Barsis' law etc.). b.  Parallel computing methodologies. c.  Distributed computing and shared memory computing. 4. Parallel computing in Python.

You can enroll in this part May 26-27 on this page.

It is expected that the participant has the necessary competences from the May 19-21 part if only the May 26-27 part is followed.

You can enroll in the part - Introduction - May 19-20-21 here:

You can also enroll in the whole course here: https://phd.moodle.aau.dk/course/view.php?id=232

Audience: The targeted audience is mainly engineers or similar with an interest in developing robust, portable and high quality code for various scientific computing purposes. By this we mean code to solve actual problems where [lots of] floating-point computations are needed. It is not a course in object-oriented programming and we apply a procedural approach to programming in the course.

Prerequisites: Participants must have some basic experience in code development in e.g. MATLAB, C or FORTRAN. Further, some basic skills in general use of a computer are expected. The tools applied work best using Linux or Mac OSX – Microsoft Windows may experience challenges when using parallel computing. We have USB memory sticks, from which you can boot Ubuntu Linux and run Python directly from the memory stick. These can be borrowed if you like.

Learning objectives: After completing the course the participants will:

1. have fundamental knowledge of important aspects of scientific computing. 2. be able to map a mathematically formulated algorithm to Python code. 3. know how to document, debug, test and profile the developed code. 4. know when and how to optimize Python code. 5. know when and how to apply parallel computing.

Teaching methods: A combination of lectures, demonstrating examples using iPython notebooks, smaller exercises and a mini-project is used to facilitate learning. The course is rich in examples and active user participation is expected to facilitate learning – the topics covered demand a “learning by doing” approach.

Criteria for assessment: Solutions to exercises must be delivered individually and a mini-project (preferably a smaller computational task relevant to the participant) must be delivered (5-10 pages) in addition to the developed code. The code must include testing/validation, and performance evaluation. Active participation and completion of assignments must be fulfilled to pass the course.

Key literature: We expect to use a combination of the following:

1. A good book on Python of which there are several possibilities and none selected yet. 2. References to Python and all relevant packages (freely available via http://python.org). 3. A number of scientific papers relevant for specific parts of the course.

Organizer: Professor Torben Larsen, Department of Electronic Systems

Lecturers: Assistant Professor Thomas Arildsen, Department of Electronic Systems. Post-doc Tobias Lindstrøm Jensen, Department of Electronic Systems. Ian Ozsvald, Mor Consulting, UK. [Professor Torben Larsen, Department of Electronic Systems.]


Time: May 26-27, 2014

Place: Aalborg University, Niels Jernes Vej 12A/5-006 

Zip code:

Aalborg Ø

Number of seats: 25

Deadline: May 5, 2014