Accelerating Astrophysical Simulations with GPUs: A Case Study of Radiative Transfer in arepo-rt

Abstract

Radiative transfer (RT) is a crucial ingredient for self-consistent modelling of numerous astrophysical phenomena across cosmic history. However, on-the-fly integration into radiation-hydrodynamics (RHD) simulations is computationally demanding, particularly due to the stringent time-stepping conditions and increased dimensionality inherent in multifrequency collisionless Boltzmann physics. The recent emergence of exascale supercomputers, equipped with extensive CPU cores and GPU accelerators, offers new opportunities for enhancing RHD simulations. We present the first steps towards optimizing the RHD solver AREPO-RT for such high-performance computing environments. We implement a novel node-to-node communication strategy that utilizes shared memory to substitute intranode communication with direct memory access. Furthermore, combining multiple internode messages into a single message substantially enhances network bandwidth utilization and performance for large-scale simulations on modern supercomputers. The single-message node-to-node approach also improves performance on smaller-scale machines with less optimized networks. Additionally, by transitioning all RT-related calculations to GPUs, we achieve a significant computational speedup of around 15x for standard benchmarks compared to the original CPU implementation. As a case study, we perform cosmological RHD simulations of the Epoch of Reionization, employing a similar setup as the THESAN project. In this context, RT becomes sub-dominant such that even without modifying the core AREPO codebase, there is an overall threefold improvement in efficiency. The advancements presented here have broad implications, potentially transforming the complexity and scalability of future simulations for a wide variety of astrophysical studies. This work serves as a blueprint for porting similar simulation codes based on unstructured resolution elements to GPU-centric architectures.