Python is one of most widely used languages to do scientific data analysis, visualization, and even modelling and simulation. The popularity of Python is mainly due to the two pillars of a friendly syntax together with the availability of many high-quality libraries. The flexibility that Python offers comes with a few downsides though: code typically doesn’t perform as fast as lower-level implementations in C/C++ or Fortran, and it is not trivial to parallelize Python code to work efficiently on many-core architectures. This workshop addresses both these issues, with an emphasis on being able to run Python code efficiently (in parallel) on multiple cores. More information can be found on the workshop website.

We’ll start with learning to recognize problems that are suitable for parallel processing, looking at dependency diagrams and kitchen recipes. From then on, the workshop is highly interactive, diving straight into the first parallel programs. Participants will be coding along with the instructor in the style of teaching like Software Carpentry. This workshop teaches the principles of parallel programming in Python using Dask, Numba and Snakemake. More importantly, we try to give insight in how these different methods perform and when they should be used.

Who: the course is aimed at graduate students and other researchers and should be:

• familiar with basic Python: control flow, functions, NumPy
• comfortable working in Jupyter

Prerequired knowledge: understand how NumPy and/or Pandas work.

Requirements: Participants must have access to a computer with a Mac, Linux, or Windows operating system (not a tablet, Chromebook, etc.) that they have administrative privileges on. They should have a few specific software packages installed.

Where: this training will take place online. The instructors will provide you with the information you will need to connect to this meeting.

Syllabus

• Recognizing potential for parallelism
• Dependency diagrams
• Measuring performance
• Working with Dask arrays
• Working with Numba
• Parallel design patterns
• Delayed evaluation
• Dependency based programming using Snakemake