Abstract: As one of the missions under China's Space Exploration Science Satellite Program, the Hongmeng project aims to deploy multiple small satellites in lunar orbit to conduct the first high-resolution ultra-long-wavelength sky survey on the lunar farside, opening the last observational window of the radio spectrum. To evaluate the feasibility of imaging under practical engineering conditions, an end-to-end parallel simulation framework capable of supporting tens of billions of baselines and processing hundreds of millions of pixels is required. An end-to-end simulation framework based on single-machine parallel computing is developed, covering available-baseline selection, visibility data generation, Earth-Moon occultation determination, and image reconstruction. Targeted data partitioning strategies are introduced for the generation and processing of large-scale observational data, and an engineering feasibility evaluation method is established for the full imaging chain. Imaging performance is further analyzed for both full-sky and partial-sky scenarios under nominal observations and multiple satellite failure conditions. Simulation experiments preliminarily show that low-resolution full-sky imaging achieves SSIM=0.9985 and PSNR=57.9568, while high-resolution partial-sky imaging achieves SSIM=0.8399 and PSNR=23.9138. As the number of available baselines increases, SSIM and PSNR improve steadily and become stable after sufficient observation time. When up to two low-frequency satellites fail during the later stages of observation, imaging quality doesn't degrade. The proposed data-partitioning and parallel strategies enable visibility generation and large-scale linear system solving without exceeding single-machine memory limits. The proposed end-to-end parallel simulation framework effectively supports imaging performance evaluation for the Hongmeng project under both nominal and partial sub-satellite failure conditions, demonstrating strong engineering applicability. The framework provides a solid technical foundation for future migration to high performance GPU clusters and is expected to be extendable to other large scale radio-interferometric arrays and deep space electromagnetic imaging missions.

Key words: radio astronomy, aperture synthesis, interferometric imaging, parallel computing, hierarchical Bayes