Background When using the Monte Carlo method for radiation shielding simulations, the efficiency is low. Employing specific variance reduction techniques is one of the methods to accelerate radiation shielding simulations, while another more universal approach is to use large-scale parallel technology to enhance the simulation speed from the hardware aspect. At present, due to the enormous demand for computing power triggered by the development of artificial intelligence technology, major supercomputing platforms have steadily improved their support for large-scale GPU parallel architectures. To adapt to the current and future GPU parallel architectures of supercomputing platforms, it is necessary to develop Monte Carlo transport algorithms suitable for GPU platforms.

Purpose This paper aims to accelerate fixed-source calculation of the NECP-MCX Monte Carlo particle transport code by utilizing GPU parallelism, thereby enhancing the efficiency of radiation shielding transport simulations.

Method This paper analyzes the characteristics of the GPU event-based parallel algorithm under the fixed-source mode. The GPU event-based parallel algorithm has been preliminarily implemented within the NECP-MCX code and was tested and analyzed using a simple fixed-source problem.

Results The results show that the maximum number of simultaneous simulated events is positively correlated with the simulation speed. Sorting particle information can accelerate the simulation by 28%, and the GPU parallel implementation is 25 times faster than the single-core CPU implementation.

Conclusions The initial implementation shows significant potential for acceleration; however, further research is essential to fully exploit its capabilities and optimize performance.