High Performance Computing
Imagine a large computer system that fails. One of the power supply units shuts down or a memory slip causes data to become corrupted. The computer might continue to operate, but it produces the wrong answer to an important question—this could be catastrophic in the case of tsunami prediction. Welcome to the realm of high-performance computing.
One of the main objectives of our research is to support large-scale scientific simulations, including highly accurate weather forecasting, flood modelling and nuclear fusion simulation. We write codes so that processors run efficiently both in terms of speed and energy consumption. We develop algorithms and run-time systems so these simulations are reliable even under system failures. We are also investigating innovative design processors for supercomputers.
Our work is motivated primarily by specific problems, and we work independently and collaboratively with other academic, industry and government organisations from Australia, the United States, China and Germany to solve them.
Explore our available student research projects below and if you’d like to discuss opportunities for collaboration or funding, please email us.
Academic staff
Student
Affiliates
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Technical staff
2010
Book Chapters
- Lloyd, J., (2010). Higher-order Logic. In Claude Sammut & Geoffrey I.Webb (eds.), Encyclopedia of Machine Learning, Springer, ISBN: 9780387307688.
Journal Articles
- Armstrong, W., Rendell, A., (2010). Runtime sparse matrix format selection. Procedia Computer Science,1(2010):1–10.
- Lloyd, J., Ng, K.S., (2010). Declarative programming for agent applications. Autonomous Agents and Multi-Agent Systems, pp. 1–49.
- Leung, G., Quadrianto, N., Smola, A., Tsioutsiouliklis, K., (2010). Optimal Web-scale Tiering as a Flow Problem. In International Conference on Neural Information Processing (ICONIP 2010), pp. 9, Sydney Australia.
Conference Papers
- Cai, J., Strazdins, P., Rendell, A., (2010). Region-Based Prefetch Techniques for Software Distributed Shared Memory Systems. In Manish Parashar and Rajkumar Buyya (eds.), IEEE/ACM International Conference on Cluster, Cloud & Grid Computing (CCGRID 2010), pp. 113–122, Melbourne Australia.
- El Zein, A., Rendell, A., (2010). From Sparse Matrix to Optimal GPU CUDA Sparse Matrix Vector Product Implementation. In Manish Parashar and Rajkumar Buyya (eds.), IEEE/ACM International Conference on Cluster, Cloud & Grid Computing (CCGRID 2010), pp. 808–813, Melbourne Australia.
- Mulerikkal, J., Strazdins, P., (2010). Service Oriented Approach to High Performance Scientific Computing. In Manish Parashar and Rajkumar Buyya (eds.), IEEE/ACM International Conference on Cluster, Cloud & Grid Computing (CCGRID 2010), pp. 820–823, Melbourne Australia.
