GPU加速MOC输运计算性能分析研究

特征线方法（MOC）在求解堆芯规模中子输运方程时面临计算时间长的问题，加速和并行算法是目前研究的热点。基于MOC在特征线和能群层面的并行特性，采用统一计算设备构架（CUDA）编程规范，实现了基于图形处理器（GPU）的并行二维MOC算法。测试了菱形差分和步特征线法分别在双精度、混合精度及单精度浮点运算下的计算精度、效率及GPU加速效果。采用性能分析工具对GPU程序性能进行了分析，识别了程序性能瓶颈。结果表明：菱形差分和步特征线法在不同浮点运算精度下均表现出良好的计算精度；相比于CPU单线程计算，GPU加速效果在双精度和单精度情况下分别达到35倍和100倍以上。

Performance Analysis on Acceleration of Transport Calculation with Method of Characteristics Based on GPU

The method of characteristics (MOC) consumes more computing time when solving the neutron transport equation with the configuration of practical reactor cores. As a result, researches are focused on the acceleration techniques and the parallel algorithms. Based on the parallelism of characteristic rays and energy groups, the GPU accelerated parallel 2D MOC algorithm was implemented with the compute unified device architecture (CUDA). The code accuracy and efficiency were tested in the diamond difference scheme and the step characteristics scheme with single-precision, mixed precision and double-precision floating-point operation. Meanwhile, the performance bottleneck of GPU application was analyzed by utilizing the NVIDIA profiling tool. The numerical results demonstrate that the parallel algorithm maintains the desired accuracy for the diamond difference scheme and the step characteristics scheme in all selected floating-point precision conditions. In addition, the GPU-based code is 35 times and 100 times faster than the CPU-based code in double-precision and single-precision, respectively.