GPU-based multi-group discrete ordinates transport calculations: Parallel computing implementation in STRAUM

Discrete ordinates (S N ) method with unstructured meshes is highly appropriate for high-fidelity modeling and simulation of radiation shielding problems with complicated geometries. However, the large number of unknowns resulting from discretization of the transport equation on the spatial, angular, and energy variables, necessitates the use of parallel computing to achieve efficient solutions. In this work, a GPU-based S N transport sweep algorithm combined with a GPU-parallel multi-group Krylov subspace solver has been proposed and implemented in the STRAUM (S N Transport for Radiation Analysis with Unstructured Meshes) code. A group chunk decomposition method within the framework of the multi-group Krylov subspace solver has been applied to STRAUM to leverage multi-GPU parallel computing. For the Kobayashi-like and reactor pressure vessel problems, STRAUM typically runs faster by factors of 100 ∼ 200 on a single NVIDIA GeForce RTX 4090 GPU and by factors of 70 ∼ 120 on a single NVIDIA GeForce RTX 3080 Ti GPU than on a single AMD Ryzen 9 7900X CPU core. For the simulations on dual-GPU systems, the group chunk decomposition method achieves parallel computing efficiencies greater than 90% without degradation in convergence except for cases using very coarse angular divisions. Besides, this method reduces per-GPU memory usage by more than 40% and enables STRAUM to effectively simulate problems with up to ten billion unknowns using two RTX 4090 GPUs.