Using FPGAs to model the atmosphere

Author: Nick Brown
Posted: 11 Dec 2019 | 15:54

The Met Office relies on some of the world’s most computationally intensive codes and works to very tight time constraints. It is important to explore and understand any technology that can potentially accelerate its codes, ultimately modelling the atmosphere and forecasting the weather more rapidly.

Field Programmable Gate Arrays (FPGAs) provide a large number of configurable logic blocks sitting within a sea of configurable interconnect. It has recently become easier for developers to convert their algorithms to configure these fundamental components and so execute their HPC codes in hardware rather than software. This has significant potential benefits for both performance and energy usage, but as FPGAs are so different from CPUs or GPUs, a key challenge is how we design our algorithms to leverage them.

Accelerating cloud physics and atmospheric models using GPUs, KNLs and FPGAs

Author: Nick Brown
Posted: 24 Apr 2019 | 11:51

The blog post below is based on the abstract of a talk at the PASC mini-symposium 'Modelling Cloud Physics: Preparing for Exascale' (Zurich, 13 June 2019).

The Met Office NERC Cloud model (MONC) is an atmospheric model used throughout the weather and climate community to study clouds and turbulent flows. This is often coupled with the CASIM microphysics model, which provides the capability to investigate interactions at the millimetre scale and study the formation and development of moisture. One of the main targets of these models is the problem of fog, which is very hard to model due to the high resolution required – for context the main UK weather forecast resolves to 1km, whereas the fog problem requires 1metre or less.

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