Mixed-Precision Computing in the GRIST Dynamical Core for Weather and Climate Modelling
arxiv(2024)
Abstract
Atmosphere modelling applications become increasingly memory-bound due to the
inconsistent development rates between processor speeds and memory bandwidth.
In this study, we mitigate memory bottlenecks and reduce the computational load
of the GRIST dynamical core by adopting the mixed-precision computing strategy.
Guided by a limited-degree of iterative development principle, we identify the
equation terms that are precision insensitive and modify them from double- to
single-precision. The results show that most precision-sensitive terms are
predominantly linked to pressure-gradient and gravity terms, while most
precision-insensitive terms are advective terms. The computational cost is
reduced without compromising the solver accuracy. The runtime of the model's
hydrostatic solver, non-hydrostatic solver, and tracer transport solver is
reduced by 24
real-world weather and climate modelling tests, has been performed to assess
the optimized model performance qualitatively and quantitatively. In
particular, in the high-resolution weather forecast simulation, the model
sensitivity to the precision level is mainly dominated by the small-scale
features. While in long-term climate simulation, the precision-induced
sensitivity can form at the large scale.
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