Accelerating high-resolution weather models with deep-learning hardware
The next generation of weather and climate models will have an unprecedented level of resolution and model complexity, and running these models efficiently will require taking advantage of future supercomputers and heterogeneous hardware. In this paper, we investigate the use of mixed-precision har...
| Main Authors: | , , , |
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| Formato: | Conference item |
| Publicado em: |
Association for Computing Machinery
2019
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| _version_ | 1826264435953500160 |
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| author | Hatfield, S Chantry, M Duben, P Palmer, T |
| author_facet | Hatfield, S Chantry, M Duben, P Palmer, T |
| author_sort | Hatfield, S |
| collection | OXFORD |
| description | The next generation of weather and climate models will have an unprecedented level of resolution and model complexity, and running these models efficiently will require taking advantage of future supercomputers and heterogeneous hardware. In this paper, we investigate the use of mixed-precision hardware that supports floating-point operations at double-, single- and half-precision. In particular, we investigate the potential use of the NVIDIA Tensor Core, a mixed-precision matrix-matrix multiplier mainly developed for use in deep learning, to accelerate the calculation of the Legendre transforms in the Integrated Forecasting System (IFS), one of the leading global weather forecast models. In the IFS, the Legendre transform is one of the most expensive model components and dominates the computational cost for simulations at a very high resolution. We investigate the impact of mixed-precision arithmetic in IFS simulations of operational complexity through software emulation. Through a targeted but minimal use of double-precision arithmetic we are able to use either half-precision arithmetic or mixed half/single-precision arithmetic for almost all of the calculations in the Legendre transform without affecting forecast skill. |
| first_indexed | 2024-03-06T20:07:45Z |
| format | Conference item |
| id | oxford-uuid:297b5229-485e-456f-b254-63be87406e74 |
| institution | University of Oxford |
| last_indexed | 2024-03-06T20:07:45Z |
| publishDate | 2019 |
| publisher | Association for Computing Machinery |
| record_format | dspace |
| spelling | oxford-uuid:297b5229-485e-456f-b254-63be87406e742022-03-26T12:19:23ZAccelerating high-resolution weather models with deep-learning hardwareConference itemhttp://purl.org/coar/resource_type/c_5794uuid:297b5229-485e-456f-b254-63be87406e74Symplectic Elements at OxfordAssociation for Computing Machinery2019Hatfield, SChantry, MDuben, PPalmer, TThe next generation of weather and climate models will have an unprecedented level of resolution and model complexity, and running these models efficiently will require taking advantage of future supercomputers and heterogeneous hardware. In this paper, we investigate the use of mixed-precision hardware that supports floating-point operations at double-, single- and half-precision. In particular, we investigate the potential use of the NVIDIA Tensor Core, a mixed-precision matrix-matrix multiplier mainly developed for use in deep learning, to accelerate the calculation of the Legendre transforms in the Integrated Forecasting System (IFS), one of the leading global weather forecast models. In the IFS, the Legendre transform is one of the most expensive model components and dominates the computational cost for simulations at a very high resolution. We investigate the impact of mixed-precision arithmetic in IFS simulations of operational complexity through software emulation. Through a targeted but minimal use of double-precision arithmetic we are able to use either half-precision arithmetic or mixed half/single-precision arithmetic for almost all of the calculations in the Legendre transform without affecting forecast skill. |
| spellingShingle | Hatfield, S Chantry, M Duben, P Palmer, T Accelerating high-resolution weather models with deep-learning hardware |
| title | Accelerating high-resolution weather models with deep-learning hardware |
| title_full | Accelerating high-resolution weather models with deep-learning hardware |
| title_fullStr | Accelerating high-resolution weather models with deep-learning hardware |
| title_full_unstemmed | Accelerating high-resolution weather models with deep-learning hardware |
| title_short | Accelerating high-resolution weather models with deep-learning hardware |
| title_sort | accelerating high resolution weather models with deep learning hardware |
| work_keys_str_mv | AT hatfields acceleratinghighresolutionweathermodelswithdeeplearninghardware AT chantrym acceleratinghighresolutionweathermodelswithdeeplearninghardware AT dubenp acceleratinghighresolutionweathermodelswithdeeplearninghardware AT palmert acceleratinghighresolutionweathermodelswithdeeplearninghardware |