Advances in high-performance computing (HPC) have opened unprecedented possibilities to improve academic research and the industrial design process. With the rise of heterogeneous hardware architectures and the emergence of exa-scale class computing systems, scale-resolving simulation now supports previously unattainable fidelity enabling the modelling of complex geometries and highly accurate predictions of challenging turbulent flows. These technologies demand new numerical algorithms, scalable solvers and data-driven analysis methods such that these tools effectively advance the transport and energy sectors. State-of-the-art codes for scale-resolving simulations have transitioned to utilise heterogeneous hardware [1,2,3,4,5,6,7], however, the journey required major changes to established algorithms. Previously optimised, CPU-based algorithms require a different approach when integrating GPU-based accelerators. Scale-resolving simulations have specific challenges: extreme memory bandwidth requirements, communication bottlenecks with new CPU-GPU systems and efficient utilisation of accelerator hardware. These challenges set the stage for exchanging experience and discussing novel strategies for efficient usage of new and upcoming HPC systems. This minisymposium provides a forum for developers of numerical algorithms and application-oriented scientists to discuss HPC for scale-resolving simulations including DNS, wall-modelled and wall-resolved LES as well as hybrid approaches for complex geometries. Numerical algorithms tailored to achieve high throughput on multi-GPU, CPU-GPU coupled systems and novel hardware architectures. Scalability of solvers and preconditioners for Navier-Stokes type equations. Algorithmic design for load-balancing, communication reduction and mixed-precision strategies. In-situ data processing for handling parallel I/O at large-scale with on-the-fly analysis, data compression and feature extraction. Mesh generation for geometries with highly curved surfaces and complex shapes at large-scale and their adaptive optimisation. Further, multi-physics applications discussing coupling of cross-disciplinary solvers and integration of different code frameworks.