Performance-portable GPU acceleration of the hybrid particle-in-cell code dHybridR
Hybrid particle-in-cell simulations are widely used to study kinetic processes in collisionless astrophysical and space plasmas, yet the high computational cost of large-scale three-dimensional runs has largely confined production studies to two dimensions or restricted domains. To address this challenge, we present a performance-portable GPU implementation of the hybrid particle-in-cell code dHybridR. The implementation combines OpenMP target offloading with specialized SYCL kernels for the most computationally expensive operations, while preserving a unified CPU-GPU codebase that supports Intel, AMD, and NVIDIA GPUs. On the exascale supercomputers Aurora and Frontier, dHybridR achieves weak scaling efficiencies of $86\%$ to $97\%$ across 49,152 accelerators, and at 256 particles per cell, its full-node GPU throughput is up to $32\times$ that of the vectorized CPU implementation at approximately $90\%$ less energy per particle-update. To our knowledge, no other hybrid particle-in-cell code has reported GPU performance at this scale, leaving dHybridR uniquely positioned to exploit exascale systems. These advances substantially lower the computational barrier to large-scale three-dimensional hybrid-kinetic simulations of collisionless plasmas.