PSyclone, the PSy code generator, is being developed for use in finite element, finite volume and finite difference codes. PSyclone development started with the aim to support the emerging API in the GungHo project for a finite element dynamical core.
The GungHo project was initiated in 2011 to address challenges of weather and climate prediction on the next generation of supercomputers. The project ran for 5 years as a collaboration between the Met Office, NERC (via NERC funded academics) and STFC. It laid a foundation for redesign of the heart of the Met Office’s Unified Model, known as the dynamical core, from the choices of numerical methods and model grids to the implementation of parallel algorithms that will scale to millions of cores.
The software infrastructure based on the GungHo project recommendations is now being developed in the LFRic project and is expected to is expected to start preparations for operational deployment in 2025. Its development is led by the requirements to support multiple meshes and element types, thus allowing for future model development.
GungHo also proposed a novel separation of concerns for the software implementation of the dynamical core. This approach distinguishes between three layers: the Algorithm layer, the Kernel layer and the Parallelisation System (PSy) layer. Together this separation is termed PSyKAl.
The Algorithm layer specifies the algorithm that the scientist would like to run (in terms of calls to kernel routines and built-in operations) and logically operates on full fields.
The Kernel layer provides the implementation of the code kernels as subroutines. These subroutines operate on local fields (a set of elements, a vertical column, or a set of vertical columns, depending on the kernel).
The PSy layer sits in-between the algorithm and kernel layers and its primary role is to provide node-based parallel performance for the target architecture. The PSy layer can be optimised for a particular hardware architecture, such as multi-core, many-core, GPGPUs, or some combination thereof with no change to the algorithm or kernel layer code. This approach therefore offers the potential for portable performance.
Rather than writing the PSy layer manually, the PSyclone code generation system can help a user to optimise the code for a particular architecture (by providing optimisations such as blocking, loop merging, inlining etc), or alternatively, generate the PSy layer automatically.
PSyclone is also being extended to support an API being developed in the GOcean project <https://www.hartree.stfc.ac.uk/publications/case-studies/2016/07/15/developing-next-generation-ocean-modelling-techniques> for two finite difference ocean model benchmarks, one of which is based on the NEMO ocean model.