Solving mapping problems over NZ’s Wairarapa region

"They’ll fire it off to Mahuika and we’ll have a result back the next morning. In the past, this could have taken us a week or more."
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Overcoming data processing overload in scientific web mapping software

"To create the map caches for one of our tools would take about a month of continuous processing."
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Visualising ripple effects in riverbed sediment transport

“NeSI staff provided critical help at a critical time."
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NeSI Case Study Celine Cattoen Gilbert NIWA river forecast

New Zealand’s first national river flow forecasting system for flooding resilience

"Without NeSI’s input, we would have been much more limited. We wouldn’t have been able to develop a national-scale system running in real-time."
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NeSI Case Study Cyprien Bosserelle NIWA

A fast model for predicting floods and storm damage

"Rendering this grid in the traditional way is not trivial – it would take longer to render the result than run the model each time I wanted to find errors. I approached NeSI and we started talking about visualisation."
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NeSI Case Study Yoshihiro Kaneko GNS Science Hikurangi

How multithreading and vectorisation can speed up seismic simulations by 40%

GNS Science researchers are producing detailed images of the 3D structure and geometry of the Hikurangi mega-thrust region using a technique called seismic tomography.
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NeSI case study Giacomo Giorli NIWA

Machine learning for marine mammals

“By understanding the abundance and distribution of different marine mammals in New Zealand, we can inform conservation policy, management of marine resources, licensing for offshore activity and create better environmental impact assessments.”
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Joao Albuquerque NeSI Case Study University of Auckland

New Zealand 2100: The future through high-resolution wave modelling

"I’m lucky to have access to a cluster like NeSI’s. Without it, I’d be running this model for years, rather than about a month.”
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NeSI Case Study Alexis Marshal University of Waikato

Parallel processing for ocean life

"We contacted NeSI because we were going from trying to assemble 100,000 individual 150 nucleotide base sequences, to trying to assemble 1.4 billion. We were having computational issues with memory, but also time."
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