Enhancing Earth system models efficiency: Leveraging the Automatic Performance Profiling framework

The continuous increase in spatial and temporal resolution of Earth System Models (ESMs) is essential to better represent physical processes and extreme events. However, these advances come at a rapidly growing computational cost, pushing simulations towards unprecedented levels of parallelism on modern High Performance Computing (HPC) architectures. As a result, inefficiencies in load balance, communication, I/O, and memory usage increasingly limit scalability and scientific throughput.

Identifying and addressing parallel performance bottlenecks in large, multi-component climate models remains a complex and time-consuming task, often requiring specialized HPC expertise and manual profiling workflows. This represents a significant barrier for model developers aiming to efficiently exploit current and future exascale systems.

We present the Automatic Performance Profiling (APP) framework, an automated and extensible workflow designed to provide performance analysis of high-resolution ESMs. APP runs end-to-end profiling experiments and generates a comprehensive, multi-level performance report that combines high-level metrics (e.g., simulated years per day (SYPD) and scalability curves) with detailed insights into MPI communication patterns, cache behavior, and function profiling. This approach enables systematic identification of bottlenecks arising from extreme concurrency and fine spatial/temporal resolution demands.

Integrated with the Autosubmit workflow manager, APP facilitates reproducible performance studies, cross-platform and model configurations/resolutions comparisons. Its modular design supports multiple climate models (NEMO and ECE4) and HPC systems (BSC’s MN5 and ECMWF’s HPC2020) and allows straightforward extension to new HPC platforms and models.

By lowering the barrier to parallel performance analysis, APP empowers the climate modelling community to improve scalability and resource efficiency, supporting the sustainable development of next-generation high-resolution ESMs.