This Parallel Python workshop 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.

Python is one of the most used programming languages worldwide with applications in almost every data-oriented application domain. The Python data science ecosystem is a rich platform for scaling up workflows, enhancing scientific research and improving insight. However, Python can be performance limited when large datasets or challenging computations are required. Parallel computing and efficient data handling can overcome this barrier, enhancing research throughput.

If you have any questions regarding this training, please contact training.nci@anu.edu.au.

Prerequisites

Experience with Python.
Experience with bash or similar Unix shells.
Having a valid NCI account and vp91 membership
The training session is driven on the NCI ARE service. Attendees are encouraged to review the following page for background information: https://opus.nci.org.au/displa...

Learning Outcomes

After this training session, you will be able to

Learn the basic concepts of parallel programming.
Learn the different hardware components that make up an HPC machine.
Learn how an HPC machine is organized.
Learn how to submit a Job to a PBS batch scheduler.

Topics Covered

Topics	Material	Exercises	Duration
Basic of Parallelism	20 minutes	15 minutes	35 minutes
Requesting a Job	10 minutes	10 minutes	20 minutes
High-level HPC Architecture	20 minutes	15 minutes	35 minutes
Vector Parallelism	20 minutes	10 minutes	30 minutes
Multi-core Parallelism	20 minutes	10 minutes	30 minutes
GPU Parallelism	30 minutes	15 minutes	45 minutes
Multi-node Parallelism	20 minutes	15 minutes	35 minutes