This lesson is in the early stages of development (Alpha version)

HPC Parallelisation For Novices: Instructor Notes

Instructor’s Briefing

This document tries to describe what preparations are required of instructors to make the workshop work as smooth as possible. In order to build the materials for the workshop at your site, consult the _config.yaml file before you generate the site with make site. It contains the ‘environment variables’ that are specific to your workshop, such as the name of the login node, the root folder of the shared file system etc.


The lineup of lessons is meant to be as flexible as possible. Skip lessons if needed.

Recap: Changing the Environment

We start with environment variables as a recap and to get all learners on board. Feel free to skip this if you know from the pre-workshop survey that your learners are capable of this.

Estimation of Pi for Pedestrians

This lessons introduces the computational working hours for all of this material. We use this way to calculate pi as it is compute intensive and typically doesn’t need any I/O. As it is compute intensive it renders itself as a good example to illustrate data parallelism. Other types of parallelism (task parallelism, latency hiding, …) are excluded from the material on purpose in order to keep the cognitive load on the learners low.

In addition, this lesson does nothing more than profile the code to find hot spots with respect to performance, i.e. runtime.

Parallel Estimation of Pi for Pedestrians

This lesson uses multiprocessing to parallelise the code in a shared memory environment.

Higher levels of parallelism

This lesson introduces dask as a representative candidate for cloud-native parallelisation approaches. This offers multiple things to the learners which often induce trouble in understanding.

It shows that there are libraries out there that perform parallelization automatically. It also tries to demo the infrastructure required for this, i.e. setting up a server process that dispatches work to worker processes etc.

Searching for Pi

This introduces concepts from high throughput computing by illustrating map-reduce style searches through files.

Use the file to produce 16 files that comply to the files used in this section, e.g.:

This could be done like so:

for i in `seq -f "%02.0f" 1 16`
    python3 ./ pi_estimate_${i}.data

At best, create directories for the each learner so that they don’t get into each other’s way.

Note, that this lesson is currently quite fragile as the i/o caching can easily get into the way of the learners.

Bonus session: Distributing computations among computers

A guided tour through a MPI program with all it’s nuances and brain quirks in program flow.