Performance and scaling of the LFRic weather and climate model on different generations of HPE Cray EX supercomputers

TL;DR

This paper evaluates the performance and scalability of the LFRic weather and climate model on various HPE Cray EX supercomputers, highlighting the impact of algorithms, threading, and I/O tuning.

Contribution

It provides detailed scaling results and performance analysis of LFRic across different supercomputers, emphasizing the effects of algorithm choices and parallelization strategies.

Findings

LFRic scales effectively to large node counts.

Algorithm choices like redundant computation influence performance.

Optimizations in I/O and threading improve overall efficiency.

Abstract

This study presents scaling results and a performance analysis across different supercomputers and compilers for the Met Office weather and climate model, LFRic. The model is shown to scale to large numbers of nodes which meets the design criteria, that of exploitation of parallelism to achieve good scaling. The model is written in a Domain-Specific Language, embedded in modern Fortran and uses a Domain-Specific Compiler, PSyclone, to generate the parallel code. The performance analysis shows the effect of choice of algorithm, such as redundant computation and scaling with OpenMP threads. The analysis can be used to motivate a discussion of future work to improve the OpenMP performance of other parts of the code. Finally, an analysis of the performance tuning of the I/O server, XIOS is presented.