MONASH DEEPNEURON

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MONASH DEEPNEURON ✦

HPC Projects.

Parallel Clip Training

A HPC x AI Collaboration

Create a CLIP (model which connects related images and text) from scratch, testing HPC techniques to make training it faster. Over the Summer, this involves parallelising the training of a CNN over multiple CPUs/GPUs and investigating whether this speeds up DL model training & execution.

Neural Cellular Automata

A HPC x AI Collaboration

Team members: Keren Collin, Alex Mai, Peter Cooper, Josh Riantoputra, Nyan Kyaw and Chloe Koe

Cellullar automata is a simulation technique that involves an n-dimensional (usually 2D) grid of cells, each with a state. Each cell’s state is updated iteratively based on a “ruleset”. The project will explore the different behaviours of cellular automata that emerge from different rulesets and eventually replace these rules with CNNs.

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Cluster Development

Project Lead: Jaspar Martin

Team members will design and implement a custom HPC cluster workload management and scheduling software on a mini-cluster made of 4 Raspberry Pi 4 SBCs. This will first involve setting up remote access (similar to MDN workstation) and performing other mini-cluster improvements (automated infra management using Ansible, etc...).

Ray Tracing

Project Lead: Paarth Sharma

This project looks at how to use HPC techniques to parallelise Ray Tracing and generate high quality 3D assets in a faster way.

DDoS Dissipation

Project Lead: Yuki Kume

This is an investigation into Distributed Denial of Service attacks and how HPC techniques can be used to defend a web service under such attacks. A research paper published in the HPC Asia 2018 conference introduces FlexProtect - A DDoS protection architecture for a distributed computing system. Our aim is to explore and investigate how FlexProtect performs against DDoS attacks using HPC simulations.

M3 Optimisation

Project Lead: Linton Charles

We will investigate ways of improving the performance of the M3 MASSIVE SUPERCOMPUTER, by implementing optimised job scheduling and resource allocation strategies. This involves exploring SLURM's parameter space and trying to find the best set of configuration variables for optimal throughput, fairness, turnaround time, etc... so that researchers using it have a better experience.

Astrophysical Simulation

Project Lead: Kevin Nguyen

This project aims to develop a sophisticated physics engine for astrophysical simulations, enabling in-depth investigations into celestial bodies like planets, black holes, and stars. This includes enhancing simulation efficiency through parallelisation of complex tasks like fluid dynamics and gravitational interactions across computing clusters.

Past HPC Projects

INDYscc-2022

DeepNeuron will be competing at IndySCC in November 2022 in order to win a place in the Student Cluster Competition (SCC), the world championship competition held at the International Supercomputing Conference.
Job Record analysis

In order to increase productivity of M3 MASSIVE users, we are devising a new way to view job records so that better, more informed decisions can be made when submitting their next job.
Horovod

Implementing Horovod on the Monash HPC cluster MASSIVE, and developing training and documentation to enable users to easily train their deep learning models.
Monitoring the cluster

Monitoring the cluster is a visualisation project to display interesting and important data about the Monash M3 Cluster.
ASC20-21 STUDENT SUPERCOMPUTER CHALLENGE

In ASC, students are required to work with hardware and assemble a HPC cluster that is capable of running a range of scientific applications.

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ASC21-22 STUDENT SUPERCOMPUTER CHALLENGE
DRONE PHOTOGRAMMETRY

Parallelising the photogrammetry pipeline and implementing a scalable, cloud based solution for point cloud analysis.