Abstract: This review synthesizes recent advances in GPU (Graphics Processing Unit)——based acceleration for gamma-ray shielding and dose computation. Focusing on the computational kernels of deterministic and stochastic approaches, acceleration strategies for Monte Carlo transport, discrete ordinates (SN), and point-kernel integration are summarized. Building on these insights, we formalize a GPU-oriented framework for point-kernel methods that covers task granularity and thread mapping, cooperative use of shared/constant/texture memories, kernel refactoring, and host & device pipelining with data residency. We further survey CPU-GPU co-processing, real-time 3D radiation-field simulation with visualization feedback, and learning-based surrogates/denoisers with physics-informed constraints, identifying practical bottlenecks including load imbalance, unified memory page migration, unstructured-geometry memory access, and verification/validation of numerical consistency. Evidence indicates that GPUs can markedly shorten time-to-solution for thick-shield penetration, multiple scattering, and large voxelization, enabling field reconstruction and interactive assessment in near real-time.

Key words: gamma radiation, GPU acceleration, point-kernel integration, shielding calculation