Articles | Volume 10, issue 6
https://doi.org/10.5194/gmd-10-2221-2017
© Author(s) 2017. This work is distributed under
the Creative Commons Attribution 3.0 License.
the Creative Commons Attribution 3.0 License.
https://doi.org/10.5194/gmd-10-2221-2017
© Author(s) 2017. This work is distributed under
the Creative Commons Attribution 3.0 License.
the Creative Commons Attribution 3.0 License.
Development and technical paper
| 
16 Jun 2017
Development and technical paper |
| 16 Jun 2017
rpe v5: an emulator for reduced floating-point precision in large numerical simulations
Andrew Dawson
CORRESPONDING AUTHOR
Atmospheric, Oceanic & Planetary Physics, Department of Physics,
University of Oxford, Oxford, UK
Peter D. Düben
Atmospheric, Oceanic & Planetary Physics, Department of Physics,
University of Oxford, Oxford, UK
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Cited
29 citations as recorded by crossref.
- Climate‐change modelling at reduced floating‐point precision with stochastic rounding T. Kimpson et al. 10.1002/qj.4435
- Posit and floating-point based Izhikevich neuron: A Comparison of arithmetic T. Fernandez-Hart et al. 10.1016/j.neucom.2024.127903
- The Relationship between Numerical Precision and Forecast Lead Time in the Lorenz’95 System F. Cooper et al. 10.1175/MWR-D-18-0200.1
- Mixed‐Precision for Linear Solvers in Global Geophysical Flows J. Ackmann et al. 10.1029/2022MS003148
- Reduced-Precision Chemical Kinetics in Atmospheric Models K. Sophocleous & T. Christoudias 10.3390/atmos13091418
- Improving Weather Forecast Skill through Reduced-Precision Data Assimilation S. Hatfield et al. 10.1175/MWR-D-17-0132.1
- swNEMO_v4.0: an ocean model based on NEMO4 for the new-generation Sunway supercomputer Y. Ye et al. 10.5194/gmd-15-5739-2022
- Bounding the Round-Off Error of the Upwind Scheme for Advection L. Salem-Knapp et al. 10.1109/TETC.2022.3191472
- The physics of numerical analysis: a climate modelling case study T. Palmer 10.1098/rsta.2019.0058
- Choosing the Optimal Numerical Precision for Data Assimilation in the Presence of Model Error S. Hatfield et al. 10.1029/2018MS001341
- An Objective and Efficient Method for Assessing the Impact of Reduced‐Precision Calculations On Solution Correctness S. Zhang et al. 10.1029/2019MS001817
- How to use mixed precision in ocean models: exploring a potential reduction of numerical precision in NEMO 4.0 and ROMS 3.6 O. Tintó Prims et al. 10.5194/gmd-12-3135-2019
- Simulating Low Precision Floating-Point Arithmetic N. Higham & S. Pranesh 10.1137/19M1251308
- Number Formats, Error Mitigation, and Scope for 16‐Bit Arithmetics in Weather and Climate Modeling Analyzed With a Shallow Water Model M. Klöwer et al. 10.1029/2020MS002246
- Single-Precision in the Tangent-Linear and Adjoint Models of Incremental 4D-Var S. Hatfield et al. 10.1175/MWR-D-19-0291.1
- Single-precision arithmetic in ECHAM radiation reduces runtime and energy consumption A. Cotronei & T. Slawig 10.5194/gmd-13-2783-2020
- Acceleration of Nuclear Reactor Simulation and Uncertainty Quantification Using Low-Precision Arithmetic A. Cherezov et al. 10.3390/app13020896
- Low precision preconditioning for solving neutron diffusion eigenvalue problem by finite element method A. Cherezov et al. 10.1016/j.anucene.2024.110575
- A power law for reduced precision at small spatial scales: Experiments with an SQG model T. Thornes et al. 10.1002/qj.3303
- Reduced‐precision parametrization: lessons from an intermediate‐complexity atmospheric model L. Saffin et al. 10.1002/qj.3754
- CPFloat: A C Library for Simulating Low-precision Arithmetic M. Fasi & M. Mikaitis 10.1145/3585515
- Mixed precision algorithms in numerical linear algebra N. Higham & T. Mary 10.1017/S0962492922000022
- Scale-Selective Precision for Weather and Climate Forecasting M. Chantry et al. 10.1175/MWR-D-18-0308.1
- Climate Modeling in Low Precision: Effects of Both Deterministic and Stochastic Rounding E. Paxton et al. 10.1175/JCLI-D-21-0343.1
- Reliable low precision simulations in land surface models A. Dawson et al. 10.1007/s00382-017-4034-x
- A New Number Format for Ensemble Simulations P. Düben 10.1029/2018MS001420
- Quantifying and attributing time step sensitivities in present-day climate simulations conducted with EAMv1 H. Wan et al. 10.5194/gmd-14-1921-2021
- An approach to secure weather and climate models against hardware faults P. Düben & A. Dawson 10.1002/2016MS000816
- A study of reduced numerical precision to make superparameterization more competitive using a hardware emulator in the OpenIFS model P. Düben et al. 10.1002/2016MS000862
27 citations as recorded by crossref.
- Climate‐change modelling at reduced floating‐point precision with stochastic rounding T. Kimpson et al. 10.1002/qj.4435
- Posit and floating-point based Izhikevich neuron: A Comparison of arithmetic T. Fernandez-Hart et al. 10.1016/j.neucom.2024.127903
- The Relationship between Numerical Precision and Forecast Lead Time in the Lorenz’95 System F. Cooper et al. 10.1175/MWR-D-18-0200.1
- Mixed‐Precision for Linear Solvers in Global Geophysical Flows J. Ackmann et al. 10.1029/2022MS003148
- Reduced-Precision Chemical Kinetics in Atmospheric Models K. Sophocleous & T. Christoudias 10.3390/atmos13091418
- Improving Weather Forecast Skill through Reduced-Precision Data Assimilation S. Hatfield et al. 10.1175/MWR-D-17-0132.1
- swNEMO_v4.0: an ocean model based on NEMO4 for the new-generation Sunway supercomputer Y. Ye et al. 10.5194/gmd-15-5739-2022
- Bounding the Round-Off Error of the Upwind Scheme for Advection L. Salem-Knapp et al. 10.1109/TETC.2022.3191472
- The physics of numerical analysis: a climate modelling case study T. Palmer 10.1098/rsta.2019.0058
- Choosing the Optimal Numerical Precision for Data Assimilation in the Presence of Model Error S. Hatfield et al. 10.1029/2018MS001341
- An Objective and Efficient Method for Assessing the Impact of Reduced‐Precision Calculations On Solution Correctness S. Zhang et al. 10.1029/2019MS001817
- How to use mixed precision in ocean models: exploring a potential reduction of numerical precision in NEMO 4.0 and ROMS 3.6 O. Tintó Prims et al. 10.5194/gmd-12-3135-2019
- Simulating Low Precision Floating-Point Arithmetic N. Higham & S. Pranesh 10.1137/19M1251308
- Number Formats, Error Mitigation, and Scope for 16‐Bit Arithmetics in Weather and Climate Modeling Analyzed With a Shallow Water Model M. Klöwer et al. 10.1029/2020MS002246
- Single-Precision in the Tangent-Linear and Adjoint Models of Incremental 4D-Var S. Hatfield et al. 10.1175/MWR-D-19-0291.1
- Single-precision arithmetic in ECHAM radiation reduces runtime and energy consumption A. Cotronei & T. Slawig 10.5194/gmd-13-2783-2020
- Acceleration of Nuclear Reactor Simulation and Uncertainty Quantification Using Low-Precision Arithmetic A. Cherezov et al. 10.3390/app13020896
- Low precision preconditioning for solving neutron diffusion eigenvalue problem by finite element method A. Cherezov et al. 10.1016/j.anucene.2024.110575
- A power law for reduced precision at small spatial scales: Experiments with an SQG model T. Thornes et al. 10.1002/qj.3303
- Reduced‐precision parametrization: lessons from an intermediate‐complexity atmospheric model L. Saffin et al. 10.1002/qj.3754
- CPFloat: A C Library for Simulating Low-precision Arithmetic M. Fasi & M. Mikaitis 10.1145/3585515
- Mixed precision algorithms in numerical linear algebra N. Higham & T. Mary 10.1017/S0962492922000022
- Scale-Selective Precision for Weather and Climate Forecasting M. Chantry et al. 10.1175/MWR-D-18-0308.1
- Climate Modeling in Low Precision: Effects of Both Deterministic and Stochastic Rounding E. Paxton et al. 10.1175/JCLI-D-21-0343.1
- Reliable low precision simulations in land surface models A. Dawson et al. 10.1007/s00382-017-4034-x
- A New Number Format for Ensemble Simulations P. Düben 10.1029/2018MS001420
- Quantifying and attributing time step sensitivities in present-day climate simulations conducted with EAMv1 H. Wan et al. 10.5194/gmd-14-1921-2021
2 citations as recorded by crossref.
Saved (final revised paper)
Latest update: 14 Dec 2024
Short summary
Weather and climate models must become more efficient if they continue growing in complexity. One option for reducing computational cost is to reduce numerical precision. We present a tool that allows users to study how models perform with reduced numerical precision. The tool is applied to a geophysical use case where precision is heavily reduced while maintaining suitable accuracy. The tool can be applied to other models to determine whether they can be made more computationally efficient.
Weather and climate models must become more efficient if they continue growing in complexity....
