Development of the integrated parallelism strategy for large scale depletion calculation in the Monte Carlo code RMC

Abstract

The Monte Carlo (MC) method is becoming more and more attractive for high fidelity simulations in reactor physics. The huge memory consumption is the bottleneck in the three dimensional (3D) full core detailed depletion calculations. Three strategies were proposed to overcome the memory problem, including domain decomposition, data decomposition and hybrid parallelism. These methods can solve this problem in some extent, but each of them has its own limitation. In this paper, the integrated parallelism platform which combines these three strategies was proposed and developed in the Reactor Monte Carlo (RMC) code. Based on this platform, the performances of different combinations of these three strategies were investigated on PWR assembly and PWR full-core models on the Tianhe-2 supercomputer. The results show that “domain decomposition + hybrid parallelism” performed better than “tally data decomposition + hybrid parallelism” at the scale of above 0.6 million burnup regions. Moreover, the batch method was adopted to reduce the frequency of collective communications and thus save computational time. Finally, the strategy of “domain decomposition + burnup data decomposition + hybrid parallelism” with the batch method was proposed and tested on Tianhe-2 supercomputer compute nodes, equipped with 64 Gigabytes (GB) of memory. The proposed strategy can handle about twenty millions of burnup regions with high efficiency, which is the important foundation of full core detailed depletion calculation and high fidelity simulation.