An effective parallelization algorithm based on the compute-unified-device-architecture (CUDA) is developed for DEM generalization that is critical to multi-scale terrain analysis. It aims to improve the efficiency of DEM generalization by utilizing the parallel computing capabilities of modern graphics processing units (GPUs). The algorithm is designed to handle large datasets and can be easily integrated into existing DEM generalization workflows. By leveraging the power of parallel computing, the algorithm can significantly reduce the processing time required for DEM generalization, making it an essential tool for researchers and practitioners in the field of terrain analysis.