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Accelerating high-resolution weather models with deep-learning hardware

Abstract:

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 mult...

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Publication status:
Published
Peer review status:
Peer reviewed
Version:
Accepted Manuscript

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Publisher copy:
10.1145/3324989.3325711

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Institution:
University of Oxford
Division:
MPLS Division
Department:
Physics
Subgroup:
Physics - Central
Role:
Author
ORCID:
0000-0001-7235-6450
More by this author
Institution:
University of Oxford
Division:
MPLS Division
Department:
Physics
Subgroup:
Physics - Central
Role:
Author
More by this author
Institution:
University of Oxford
Division:
MPLS Division
Department:
Physics
Subgroup:
Physics - Central
Role:
Author
Publisher:
Association for Computing Machinery Publisher's website
Pages:
Article: 1
Publication date:
2019-06-12
Acceptance date:
2019-03-31
DOI:
Pubs id:
pubs:991671
URN:
uri:297b5229-485e-456f-b254-63be87406e74
UUID:
uuid:297b5229-485e-456f-b254-63be87406e74
Local pid:
pubs:991671

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