Details of Award
NERC Reference : NE/J013471/1
Can emerging general purpose graphics processing unit (GPGPU) technology be used to mitigate computational burdens in environmental models?
Grant Award
- Principal Investigator:
- Dr D Topping, The University of Manchester, Earth Atmospheric and Env Sciences
- Co-Investigator:
- Dr MK Bane, Manchester Metropolitan University, Ctr for Advanced Computational Science
- Grant held at:
- The University of Manchester, Earth Atmospheric and Env Sciences
- Science Area:
- Atmospheric
- Earth
- Overall Classification:
- Atmospheric
- ENRIs:
- Environmental Risks and Hazards
- Global Change
- Science Topics:
- Tropospheric Processes
- Climate & Climate Change
- Computer Architecture
- Computer Sys. & Architecture
- Parallel Computing
- Parallel Computing
- HPC Architecure
- High Performance Computing
- Abstract:
- Aerosol particles remain one of the most uncertain contributors to climate change and air quality. Gas-to-aerosol partitioning is key to determining the chemical composition and amount of aerosol particles, thus environmental impacts (e.g. the amount is critical to predicting air quality). Owing to the complexity and diversity of atmospheric aerosol components, quantification of the properties that determine their highly uncertain climatic and human health impacts requires the development of novel technological applications. The many thousands of individual aerosol components ensure that explicit manual calculation of these properties is laborious and time-consuming; the emergence of explicit automatic mechanism generation techniques predicting up to many millions of individual components. Due to heavy computational demands associated with this level of complexity, this presents two broad problems when trying to develop appropriate modeling frameworks to assess true environmental impacts: 1) It is impossible to include full complexity representations of aerosol processes within large-scale frameworks, such as regional climate models. As a result, reduced complexity representations are developed with the inevitable tradeoff between accuracy and improved performance. 2) To determine whether a parameterization of aerosol processes is suitable it is necessary to first perform sensitivity studies, comparing full representaions with parameterizations under a wide variety of conditions. This requires considerable computational power and time. Traditionally, computer processors have been single core. Recently this has evolved to several cores on a processor, typically 16 in an HPC server node. The graphics industry has been creating graphics cards (GPUs) with thousands of cores in order for games to have realistic effects. Recently, General Purpose GPUs (GPGPUs), although now commonly called just GPUs, have become available as "accelerators" for compute-intensive work. GPGPUs are available at a fraction of the cost of more traditional high performance computing (HPC) facilities and generally affordable (and thus accessible to) research groups. The advent of GPGPU computing is a new and exciting technological development. Some vendors and discipline areas have begun porting some codes to GPGPUs, yet the atmospheric chemistry field has little/zero work in this area. In this project we propose to quantify the performance of state-of-the-art models of gas-to-aerosol partitioning, as a first example, using the newly emerging GPGPU paradigm against the more traditional CPU implementations. This pump-priming activity is designed to act as a springboard for more generalized potential improvements in computational efficiency of chemistry schemes in environmental models. The successful outcome of this proposal will mean not only faster process models but that these could potentially be incorporated in to regional air quality & meteorological models, bringing higher accuracy and cost effectiveness to their solutions whilst improving their time-to-solution. As the emergence of GPU technology is relatively new, it is important lessons learned during this project will be shared by the broader research community, quantifying how the challenges of extracting near peak GPU performance were met. To this end we will use online facilities and informatics tools to ensure wider benefits are realised.
- NERC Reference:
- NE/J013471/1
- Grant Stage:
- Completed
- Scheme:
- Small Grants (FEC)
- Grant Status:
- Closed
- Programme:
- Small Grants
This grant award has a total value of £48,625
FDAB - Financial Details (Award breakdown by headings)
| DI - Other Costs | Indirect - Indirect Costs | DA - Estate Costs | DI - Equipment | DI - Staff | DA - Other Directly Allocated | DI - T&S |
|---|---|---|---|---|---|---|
| £1,200 | £15,558 | £3,356 | £4,896 | £22,142 | £32 | £1,440 |
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