Investigation of the efficiency optimization for the improved subgroup resonance self-shielding treatment on the GPU platform

•The multigroup kernel for the subgroup fixed source and neutron slowing-down equation is developed.•The multigroup kernel and the conventional approach are both applied to CPU and GPU platforms.•The efficiency improvement of different approaches on different hardware platforms is analyzed. The improved subgroup method adopts a fine energy mesh to handle the resonance interference effect, then the neutron slowing-down equation is solved for group condensation to obtain the multigroup cross section. A large calculating burden is raised in this process. In this paper, an acceleration scheme by the multigroup kernel method is investigated on both GPU and CPU platform. First, the subgroup fixed source and the neutron slowing-down equations are both defined to multigroup kernels to prevent the repetitive MOC sweep, and the group batch is introduced to save the memory usage. Then, the resonance treatment code is developed on the CPU/GPU heterogeneous clusters. Finally, the efficiency between the multigroup kernel and conventional group-by-group method is compared. The numerical verification shows that the multigroup kernel method in the GPU platform could effectively accelerate the solutions of both subgroup fixed source and neutron slowing-down equations.