Modern high-performance computing (HPC) systems are evolving towards greater heterogeneity and diversification. The heterogeneity is due to the use of specialized processing units for specific tasks, nowadays with a strong (commercial) focus on AI-specific algorithms. This strategy, led by companies like Nvidia with their (general-purpose) GPUs and tools like CUDA, is driven by the need to enhance computational performance while containing electrical-power consumption and total cost. Present-day exascale and pre-exascale systems commonly integrate GPUs with CPUs of different architectures and vendors. Additionally, alternative accelerators like tensor processing units (TPU), neural processing units (NPU), field programmable gate arrays (FPGA), and emerging technologies like neuromorphic and quantum processors add to the array of high-performance computing options. These will further contribute to the heterogeneity and diversification of high-performance computing but have not yet broken into scientific computing. Except for the quantum processor, the other accelerators adhere to classical architectures characterised by varying levels of parallelism.