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Code Parallelization with Python

Seminar Room Ground Floor (CSBD)

Seminar Room Ground Floor



This tutorial will introduce you to parallalisation techniques in python. Having experience programming python is strongly suggested, but not a necessity. Many of the concepts apply to other programming languages. 

In the course, I will

  • provide you with a compute intensive problem
  • teach how to find the slowest parts of the code
  • calculate an expected improvement when parallelising the code
  • parallelize the code to use all cores on one machines
  • parallelize the code using cores on different machines
  • (if time permits) file-based high throughput computing

In case of questions or concerns, please approach me.

Peter Steinbach (Instructor/Organizer)

Feedback at the end of the course

red sticky notes

("something you liked, learned or had fun with")

  • was lost a bit during the installation (lost pace of the course)
  • would be good to touch on the fundamental differences in the approach for different types of parallelisation
  • wonderful to know, we can scale and how to scale but how I want to know when/it I should scale
  • can't really think of anything
  • could have been useful to have had a high level overview of different types of parallelization methods (distributed vs. memory-based)
  • struggled a bit with the parallelization exercise but more due to my lacking python knowledge

green sticky notes 

("something you liked, learned or had fun with")

  • very nice concise overview, +1
  • useful names introduced "multiprocessing module, MPI module, dask"
  • like the format
  • never were bored or tired
  • snippets in /tmp ... but not right away (can't fall behind with code)
  • nice content ... will be helpful for my project
  • good to have context
  • dask and how easy it is to use it on the cluster
  • all hands-on
  • helpfulness with questions and doubts
There are minutes attached to this event. Show them.
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