Implementation and performance analysis of the massively parallel method of characteristics based on GPU

Abstract

The method of characteristics (MOC) is one of the most common methods for solving the neutron transport equation in practical application. Researches have been focused on the acceleration techniques and the parallel algorithm for improving the efficiency of MOC. The Graphics Processing Unit (GPU) provides an alternative method of parallelizing the MOC neutron transport sweep. In this work, a GPU-paralleled 2D MOC code is implemented, which employs the diamond difference (DD) scheme and the step characteristics (SC) scheme. Different parallel schemes which are ray-level, energy-group-level, and polar-angle-level, are analyzed to choose the proper parallel scheme. The C5G7 2D benchmark is calculated to verify the accuracy and efficiency of the code in different schemes with single precision and double precision. The bottlenecks of the GPU code are identified and the code is classified into three categories, which are compute-bound, memory-bound, and latency-bound, according to the performance analysis model introduced in this paper. In addition, corresponding optimization strategies are applied to improve the performance according to the analysis. Moreover, the speed, power efficiency, and hardware cost are compared for CPU and GPU based on a fictitious quarter core PWR problem. Numerical results demonstrate that the energy group-level parallelization can obtain the optimal performance on GPU. Optimization strategies are effective to improve the efficiency of the calculation on GPU, which indicates that the performance analysis model is useful and effective to locate the limitation of the code. Moreover, the GPU-version code is about 30 times faster than the CPU-version code with double precision and about 100 times faster with single precision, while the desired accuracy is kept. And the GPU delivers superior performance in both speed, energy efficiency, and hardware cost.