Description
Python programming language has become popular in scientific computing due to many benefits it offers for fast code development. Unfortunately, the performance of pure Python programs is often sub-optimal, but fortunately this can be easily remedied. In this course we teach various ways to optimise and parallelise Python programs. Among the topics are performance analysis, efficient use of NumPy arrays, extending Python with more efficient languages (Cython), and parallel computing with message passing (mpi4py) approach.
Learning outcome
After the course participants are able to
- analyse performance of Python program and use NumPy more efficiently
- optimize Python programs with Cython
- utilize external libraries in Python programs
- write simple parallel programs with Python
Prerequisites
Participants need some experience in Python programming, but expertise is not required. One should be familiar with
- Python syntax
- Basic builtin datastructures (lists, tuples, dictionaries)
- Control structures (if-else, for, while)
- Writing functions and modules
Some previous experience on NumPy will be useful, but not strictly required.
Agenda
Day 1, Wednesday 23.1
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Efficient use of NumPy
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Performance analysis
Day 2, Thursday 24.1
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Optimisation with Cython
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Interfacing with external libraries
Day 3, Friday 25.1
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Parallel computing with mpi4py
Lecturers:
Jussi Enkovaara (CSC), Martti Louhivuori (CSC)
Language: English
Price: Free of charge
