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Python in High-Performance Computing
Date: 23.01.2019 9:00 - 25.01.2019 17:00
Location details: The event is organised at the CSC Training Facilities located in the premises of CSC at Keilaranta 14, Espoo, Finland. The best way to reach us is by public transportation; more detailed travel tips are available.
Language: English
Lecturers: Jussi Enkovaara (CSC)
Martti Louhivuori (CSC)
  • Free for Finnish universities, universities of applied sciences and governmental research institutes.
  • Free for others.
The course materials, lunches as well as morning and afternoon coffees are free of charge.
THE COURSE IS FULLY BOOKED! If you have registered to this course and you are not able to attend, please CANCEL your registration in advance by sending an email to patc at! If you wish to be added to a limited waiting list (10 persons max) please contact to patc at
Additional information
This course is part of the PRACE Training Centres (PTCs) activity. Please visit the PRACE Training portal for further information about the course. For content please contact, for practicalities


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


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.


Day 1, Wednesday 23.1

  • Efficient use of NumPy

  • Performance analysis

Day 2, Thursday 24.1

  • Optimisation with Cython

  • Interfacing with external libraries

Day 3, Friday 25.1

  • Parallel computing with mpi4py

Projects and Collaboration Networks
Service Break
Projects and Collaboration Networks
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