This course introduces the capabilities of the Australian Research Environment and explains how to access and utilise ARE.

The AI/ML Applications on Gadi - Astronomy course takes recently published results from the Hydrogen Epoch of Reionizaton Array collaboration as a demonstration and uses Tensorflow/Keras to build a simple emulator for the 21cm FAST simulation. We also show how to write a simple Bayesian framework using EMCEE to perform inference with the emulator. At the end, you will take home 2 Jupyter notebooks which can be easily adapted for your own science projects.

Gadi is Australia’s most powerful supercomputer, a highly parallel cluster comprising more than 150,000 processor cores on ten different types of compute nodes. Gadi accommodates a wide range of tasks, from running climate models to genome sequencing, from designing molecules to astrophysical modelling. Introduction to Gadi is designed for new users, or users that want a refresher on the basics of Gadi.

This Parallel Python course is designed to teach cutting edge techniques to work with big data and process data in parallel using Python and is suitable for all participants who want to enhance their data science capabilities.

The Message Passing Interface (MPI) is arguably the primary programming model used for applications' internode parallelism. Developed by the NCI Training Team, the Introduction to MPI workshop demonstrates MPI procedures based on the latest MPI Standard - 4.0, with hands-on finite difference exercises.