HPC NEWS

Scientific computing specialists aid New Zealand research

29 July 2013



New Zealand’s national high-performance computing (HPC) platform, NeSi, has expanded its team with the addition of international computing experts. Its director, Nick Jones says NeSi’s nine-person computational science team brings together the largest group of scientific computing specialists ever to support New Zealand research.

The enhanced NeSI computational science team is led by Mark Cheeseman. He brings experience and knowledge to the New Zealand research sector from a background in international HPC centres in Switzerland, Saudi Arabia, Canada, the US and UK. Cheeseman is joined by team members with scientific programming experience, knowledge of numerical and statistical methods, optimisation techniques for scaling computations and a working knowledge of specialised HPC computing systems.

NeSI’s HPC services now include a more focused computational science capability, which international scientists say is essential to researchers. Prof Peter Hunter, a globally recognised leader in computational physiology, anticipates significant benefits to the NZ science and engineering research and development community. He said NeSI’s computational science initiative is a critical discipline for an innovation-led economy based on fundamental scientific research. ‘Our ability to predict the properties of the complex, composite materials used in the manufacture of many new products depends very much on multi-scale modelling and high-performance computing,’ said Hunter.

Cheeseman and the NeSI team are optimising the performance of specific research tasks, contributing their understanding of specialised computing algorithms and methods. ‘Programming to scale across large numbers of CPUs is hard, yet is quickly becoming required across many fields of research,’ said Cheeseman. ‘Programming with distributed memory is harder. Understanding the challenges within each separate research community requires years of exposure and collaboration. Why would we bother doing this? Because solving this complexity enables us to tackle problems with increased accuracy and significance.’

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