Hybrid parallel strategies for the neutron transport code PANDAS-MOC - Tao, Shunjiang; Xu, Yunlin (Frontiers in Nuclear Engineering, 2022)
PANDAS-MOC (Purdue Advanced Neutronics Design and Analysis System with Methods of Characteristics) is being developed to find the high fidelity 3D solutions for the steady state and transient neutron transport analysis. However, solving such transport problem in large reactor core could be extremely computational intensive and memory demanding. Knowing that parallel computing is capable to improve the computing efficiency and decrease memory requirement, three parallel models of PANDAS-MOC are designed using the distributed memory and share memory architectures in this paper: Pure MPI parallel model (PMPI), Segment OpenMP threading hybrid model(SGP), and Whole-code OpenMP threading hybrid model(WCP). Their parallel performances are examined by the C5G7 3D core. For the measured speedup, PMPI model > WCP model > SGP model. Yet, the memory consumed by the WCP model is about 60% of that by the PMPI model. This paper has also demonstrated that the performance of WCP parallelism is limited by the hybrid reduction in the CMFD calculation and omp atomic clause in the MOC sweep. Once they are optimized, the WCP model can outperform the PMPI model.