RITS Case Studies
- Centre for Innovation expertise benefits tsunami modelling at UCL
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- Crime scene DNA analysis: assessing the evidence
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- Restructuring and extending DCPROGS, an established suite for the analysis of single ion channel data
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Restructuring and extending DCPROGS, an established suite for the analysis of single ion channel data
Published: Dec 9, 2013 11:08:00 AM
Published: Oct 16, 2013 11:48:00 AM
Published: Oct 2, 2013 4:33:00 PM
Published: Jul 24, 2013 4:47:00 PM
Published: Jul 3, 2013 11:48:34 AM
Centre for Innovation expertise benefits tsunami modelling at UCL
16 October 2013
Dr Serge Guillas from UCL’s Department of Statistical Science is researching uncertainties in models for geophysics. Via UCL’s membership of the Centre for Innovation (CfI), he has worked with GPU specialists in Oxford to greatly improve the performance of a tsunami simulation code. The team used the CfI GPU-based supercomputer, Emerald, to achieve its results.
High-performance computing is an integral part of research aimed at modelling real-life phenomena. Recently, major tsunamis in Indonesia and Japan have caused the insurance industry and civil authorities around the world to demand better models to assess tsunami risk.
Dr Serge Guillas received proof-of-concept funding from UCL Business in 2012 to start quantifying tsunami risk in the Cascadia Region on the pacific coast of North America. This area is of major interest to the insurance industry.
Dr Guillas is collaborating on a project to improve and efficiently use a tsunami modelling application called VOLNA. This code can produce high-quality results, but the high precision and scale of these models meant that simulations could take weeks to perform on a standard desktop computer. So, Dr Guillas worked with CfI staff at the Oxford e-Research Centre to adapt the simulation code to run on cutting edge high-performance computers.
Dr Serge Guillas
What we did
Via UCL’s membership of the CfI, Dr Guillas contacted Mike Giles and Istvan Reguly at Oxford. The Oxford team took on the task of porting VOLNA into the new framework: OP2. This allows VOLNA to run on a wide variety of CPU-GPU supercomputers (GPUs are Graphical Processor Units, a high-end version of the graphics card in a desktop PC). This work enables the simulation code to be parallelised so that the software can take advantage of faster computer systems. The software remains as readable and maintainable as the original but can speed up the time taken to complete simulations by a factor of 3000 to 8000.
Additionally, by making use of supercomputers, such as Emerald, to run simulations across multiple GPUs, the calculations can be further accelerated by a factor close to the number of GPUs used. For example, a simulation that previously took 1 week to run in serial on a desktop PC can now be completed in around 5 minutes on Emerald using just two GPUs.
VOLNA uses a mesh of triangular areas to model the characteristics of the waves as they travel. Smaller triangles are used as tsunami waves approach the shore where greater precision is required
Results / impact of the work
This work opens up new horizons for tsunami modelling and catastrophe prevention. It is now possible to simulate how tsunamis begin, grow and impact faster than real-time. This helps with early warnings to reduce loss of life and damage to property. The tsunami models currently in use for this purpose tend to either be too slow to simulate or too simple to be useful.
One of the key issues with prediction systems is the uncertainty about the exact characteristics of the earthquake that caused the tsunami. It helps to be able to run a number of simulations in parallel on a supercomputer such as Emerald because one can more accurately determine the likely range of outcomes.
Following the successful completion of the proof-of-concept stage of his work, Dr Guillas has recently been awarded £100k from the follow-on funding scheme from the Natural Environment Research Council (NERC) to make the system commercially viable. He is currently collaborating with partners at Oasis Loss Modelling Framework as part of a wider initiative to create a framework for catastrophe loss modelling.
- To learn more about the Centre for Innovation and to find out about partnership opportunities, visit the CfI website
- To find out about help available to researchers at UCL, visit our Help for UCL researchers page