ML/AI with accelerators at scale and Australia’s next generation of supercomputers
2:00pm - 3:30pm
The next Pawsey’s ‘Supercomputing Series’event will welcome industry representatives and researchers to share insight on projects spanning from astronomy to renewable energy and discussing the impact that Machine Learning (ML), Artificial Intelligence (AI) and accelerators have on their breakthroughs.
We have gathered a stellar group of experts discussing their experiences as part of a panel one more time.
Panellists include Alessandro Rigazzi, AI and Machine Learning researcher engineer at HPE/Cray and part of the team that created Smartsim. This library allows orchestration and integration of HPC simulations and AI. Joining Alessandro will be Dr Ivy Wong, a CSIRO Astronomy Science Leader in massive data challenges in the SKA era. Mathieu Cocho, Mechanical Engineer at Carnegie Clean Energy, using neural network to develop its wave predictor will also provide industry insight.
This session will be held before the deployment of Pawsey’s new system, Setonix. The 50 PFlops system has been designed to enable further research in artificial intelligence and machine learning, providing access to 750+ AMD Graphics Processing Unit (GPU) accelerators.
This event will also see the creation of a Community of Practice (CoP) where practitioners will have the opportunity to connect and discuss topics of interest, share best practices, and help answer each other’s questions on astronomy and HPC.
About the event
2:00pm AWST / 3:30pm ACST/ 4:00pm AEST
- Alessandro Rigazzi – HPE – AI-enhanced Simulations
- Ivy Wong – CSIRO – Astronomy
- Mathieu Cocho – Carnegie Clean Energy – Renewables
- Ugo Varetto – Pawsey CTO
- Cristian Di Pietrantonio – Pawsey Supercomputing Application Specialist supporting ML/AI research (see here for staff bios)
Hosted by Komathy Padmanabhan, Lead, Data Science AI and Sensitive Platforms at Monash University.
Topics covered during this webinar:
- Research and the impact of HPC
- Current limitations and the potential of supercomputers to overcome them
- The next generation of AI research
Alessandro is an AI and Machine Learning engineer at Hewlett Packard Enterprise and a member of Cray Labs.
Alessandro joined Cray (now part of HPE) after graduating with a PhD in Computational Science, with a thesis on fast solvers for non-linear continuum mechanics. He quickly shifted his focus to parallel algorithms for training and deploying deep learning models on supercomputers.
Alessandro is currently a developer of SmartSim and SmartRedis, two open-source libraries developed and maintained by Cray Labs at HPE. SmartSim and SmartRedis enable fast integration of AI and traditional HPC simulations.
Dr Ivy Wong
Ivy is a radio astronomer and a CSIRO Science Leader working on massive data challenges in the era of the Square Kilometre Array at CSIRO in Perth, WA. Using large all-sky radio surveys, Ivy studies how galaxies form stars; how central supermassive black holes grow (AGN) and how AGN affect the star formation history and evolution of a galaxy. Ivy’s research interests also include non-traditional data analysis methods such as the exploration of citizen science and the potential applications of deep learning algorithms. The next-generation radio telescopes begin to survey wider, deeper and further back in the Universe’s history, astronomers will enter the massive data era when traditional methods of analyses will be severely tested. Ivy obtained her PhD (Astrophysics) in 2008 from the University of Melbourne and has previously worked at Yale University, CSIRO and the International Centre for Radio Astronomy Research (University of Western Australia).
Mathieu currently works as a Data Analyst at Carnegie Clean Energy. With a Masters of Engineering from Ecole Centrale de Nantes and nearly 10 years’ experience in the Marine Renewables sector, he has gained extensive knowledge in simulation, design, and operation of Wave Energy Converters.
At Carnegie, Mathieu is the technical lead on the design and testing of the controller of the CETO technology. He is involved in the development of its various elements of Artificial Intelligence, which include supervised learning for wave prediction, and reinforcement learning. Through his work, Mathieu takes advantage of Topaz’s advanced GPU capabilities.