  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 multi-frequency collisionless Boltzmann
physics. The emergence of exascale supercomputers, equipped with extensive CPU
cores and GPU accelerators, offers new opportunities for enhancing RHD
simulations. We present a novel optimization of AREPO-RT explicitly tailored
for such high-performance computing environments. We implement a novel
node-to-node communication strategy that utilizes shared memory to substitute
intra-node communication with direct memory access. Furthermore, combining
multiple inter-node 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.
Furthermore, by transitioning all RT-related calculations to GPUs, we achieve a
significant computational speedup of around 15 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.
