Abstract: 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.