The increasing need for higher resolution and greater accuracy in weather forecasts and climate simulations continues to drive software and hardware developments in high-performance computing (HPC) systems. To achieve increasingly faster simulations, the adoption of hardware accelerators has proven highly effective in recent years. However, these HPC codes exhibit, in parts, divergent memory access and computation patterns. Hence, their performance benefits strongly depend on how well the software's computational characteristics align with the underlying hardware architecture. While an accelerator may be well-suited for some code regions, other architectures may achieve better performance and energy efficiency elsewhere. We therefore propose a highly heterogeneous setup for a performant and energy-efficient execution of complex HPC codes such as those used in the weather and climate domain, incorporating a variety of processor and accelerator architectures.
Using the climate and weather model ICON, we explore the potential of mapping of components onto compute architectures. We describe the design of a highly heterogeneous test cluster and discuss its implications for middleware and software management. Furthermore, we present our comprehensive energy measurement infrastructure which allows for comparisons with water-cooled production systems. Leveraging this, we demonstrate that a heterogeneous cluster can achieve up to 40% higher energy efficiency compared to a traditional homogeneous configuration.