Welcome to the WATGPU research cluster

Introduction

Welcome to WATGPU, a University of Waterloo School of Computer Science GPU cluster aiming to facilitate access to computing resources for research purposes. This documentation serves as a comprehensive guide to understanding and utilizing WATGPU, a cluster managed through the Slurm workload manager.

Download pdf presentation here: 2024/07/25 version.

View the recording of the seminar from the 2024/07/25.

Shared GPU Resources

The concept is simple:

• Faculties buy GPUs
• The school buys super-computers
• We plug the GPU in the machines and give access to everyone.
• When a GPU is not used by its owner, it is available for everyone to use.

This pipeline is flexible as the main is to share computing resources accros the school. For example, we have machines owned by faculies. If you would like to contribute hardware, you can contact Lori(lori.paniak@uwaterloo.ca).

Getting access

• Faculty (CS or cross-appointed): Please contact watgpu-admin@lists.uwaterloo.ca
• Student¹: With the agreement of the faculty you are working with, send an email to watgpu-admin@lists.uwaterloo.ca with them in CC.

Before making an account request, please load an SSH key at https://authman.uwaterloo.ca

Contact

If you require assistance while using WATGPU, you can contact the following:

• Admin support: watgpu-admin@lists.uwaterloo.ca

This address redirect your message to all WATGPU admins so we can answer faster.

Slurm: How it works

Slurm simplifies the user experience by allowing you to submit, monitor, and manage your computational jobs seamlessly. Through straightforward command-line interfaces, you can submit batch jobs, specify resource requirements, and monitor job progress.

Once your script is ready, launching a job is fairly simple:

1. Login: Access watgpu.cs using your credentials.
2. Submit a Job: Utilize the sbatch command to submit your script that you wish to run. Think of it as asking the server to perform specific computations for you using specific resources (how many GPUs, how much memory ...).
3. Monitor Progress: You can use squeue to view the job queue and monitor job details.
4. Enjoy: Your job will be run by the server as soon as the requested resources are available.

For more in-depth information, visit this page.

Thank you for choosing WATGPU. We're here to simplify your computational tasks and enhance your research.

Happy Computing!

1. _{WatGPU cluster is a research resource. Access will only be granted to students actively involved in a School of Computer Science or cross-appointed research group.} ↩