Saturday 20th August, 2011
3:10pm to 3:40pm
This presentation will outline key lessons learnt in developing scientific software in Python. Methods of maintaining and assuring code quality will be discussed, in particular:
- designing effective unit tests;
- visualising output data to discover defects; and
- designing characterisation tests to test the actual system behaviour and to identify unintended system changes.
The challenges in optimising and parallelising Python code will also be presented, including:
- using NumPy to optimise numerical computations;
- using C code for intensive computational tasks; and
- parallelising software to run on high performance environments such as clusters.
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