Details of Award
NERC Reference : NE/K008951/1
Abstracting the environment: automating geoscientific simulation
Fellowship Award
- Fellow:
- Dr DA Ham, Imperial College London, Computing
- Grant held at:
- Imperial College London, Computing
- Science Area:
- Atmospheric
- Earth
- Freshwater
- Marine
- Terrestrial
- Overall Classification:
- Atmospheric
- ENRIs:
- Environmental Risks and Hazards
- Global Change
- Natural Resource Management
- Pollution and Waste
- Science Topics:
- Large Scale Dynamics/Transport
- Climate & Climate Change
- Mantle & Core Processes
- Software Eng. Methods & Tools
- Software Engineering
- Ocean Circulation
- Abstract:
- This project will deliver a revolutionary increase in the ability of geoscientists to implement computer simulations, especially for emerging parallel hardware, and to work with the results of simulations. Computer simulation of processes in the Earth system has become one of the key tools if science. In the atmosphere and ocean and from frozen ice sheets to the molten rock of the Earth's mantle, simulations of fluids and solids are ubiquitous and essential tools. These are critical processes: many of the world's largest computers are engaged in simulating them. The numerical methods used to produce these models are becoming rapidly more sophisticated. At the same time the emergence of massively parallel computer hardware presents the opportunity for unprecedented levels of resolution. However, the complexity of the numerics and the new hardware is such that it is becoming very difficult for researchers to write computer code which is correct, sufficiently high performance and sufficiently usable. Conventional software development essentially requires superhuman developers who are simultaneously geoscientists, mathematicians and computer scientists. In essence these difficulties occur because conventional computer code mixes the numerics and the parallel implementation. Instead, models could be developed by specifying the numerical methods in a high-level computer language similar to the maths, and the parallel implementation could be generated automatically. This would enable experts in numerical modelling to specify their algorithm, and arrive at correct, parallel code at a tiny fraction of current development costs. This automatic generation of models is a reality today for scientists and engineers working on smaller-scale and simplified simulation problems. However the curvature of the earth, the extreme flatness of geophysical domains and the scale of the domains involved mean that geoscientists have additional needs which require deep changes in simulation code generation systems. I will extend code generation techniques to meet these special challenges, and therefore deliver automation to geoscientific model development. Much science does not just depend on simulating processes: it also depends on studying the sensitivity of systems, optimising inputs and parameters, stability analysis and error analysis. All of these processes require an adjoint model: essentially the gradient of the original simulation. Developing adjoint models is so complex that only the largest national centres can typically afford to develop them. Using code generation, I have already demonstrated that this can be made almost automatic for some types of model. I will extend this capability to other discretisations which are more common in the geosciences, and thereby put the powerful tool that adjoints are into the hands of the individual scientists and students who conduct much of the cutting edge geoscience. The largest simulations, particularly of the climate system, produce so much complex data that much important science occurs by studying the output of archived simulations. For large collections of data from many models, even the process of calculating statistics is labourious and error-prone. It is also currently impossible to verify if published data analyses are correctly calculated. I will extend the automated generation of simulation software to allow for an automated data query language. This make this form of data science far less labour-intensive, will allow data science with properly published methods, will reduce sources of error and will allow scientists to work effectively with the massive data sets of the future.
- NERC Reference:
- NE/K008951/1
- Grant Stage:
- Completed
- Scheme:
- Research Fellowship
- Grant Status:
- Closed
- Programme:
- IRF
This fellowship award has a total value of £492,313
FDAB - Financial Details (Award breakdown by headings)
| DI - Other Costs | Indirect - Indirect Costs | DA - Estate Costs | DI - Staff | DI - T&S | DA - Other Directly Allocated |
|---|---|---|---|---|---|
| £8,197 | £150,465 | £65,330 | £241,337 | £22,132 | £4,855 |
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