Intensity model with blur effect is widely employed to accurately simulate the imaging process of star simulator used for attitude determination and guiding feedback. It imposes great demands of computing power for realistic domains, and modern Graphics Processing Units (GPUs) have demonstrated to be a powerful accelerator for this kind of computationally intensive simulations. This paper presents a parallel design and implementation of the intensity model applied to large-scale star simulators on GPUs using the compute unified device architecture (CUDA) programming model. The study analyzes the double parallel nature inherent in this model and use the CUDA framework to efficiently exploit the potential fine-grain data parallelism. Two versions of simulator are designed and studied: One is sequential simulator used as the baseline simulator, and another is parallel simulator using CUDA. In parallel strategy, model, and GPU implementation level, the study employs specific optimized strategies to efficiently improve the parallel performance. Finally, two benchmarks corresponding with the double parallelism are developed to fully evaluate the performance behavior of our simulators. The result analysis demonstrates the efficiency of the CUDA simulators and also illustrates the restriction and bottlenecks presented in this simulator.