Significant progress has occurred in interferometric synthetic aperture radar (InSAR), emerging as a crucial technique for monitoring surface deformation. This evolution is attributed to expanded synthetic aperture radar (SAR) data availability and improved data quality. However, effectively managing and processing SAR big data presents substantial challenges for algorithms and pipelines, especially in large-scale contexts. In this article, we introduce a parallel time-series InSAR processing platform that leverages high-performance computing (HPC) clusters for efficiently managing and processing large-scale SAR data and incorporates graphics processing unit (GPU) acceleration to significantly enhance the speed and efficiency of specific InSAR processing algorithms. Our approach encompasses high-quality data compression, integration of classic InSAR models, and the introduction of a robust distributed scatterer InSAR method for time-series processing. The platform efficiently handles massive data, featuring a parallel optimization tool for acceleration. In addition, it provides web-based two-dimensional (2-D) result visualization and 3-D outcome representation for comprehensive user understanding. To illustrate our platform's capabilities, we applied it to 40 Sentinel-1 SAR data scenes from Tibet (2017–2019). Our data compression technique notably reduces data size, reducing mask data by 87.5% and coherence data to 25% of its original size. Leveraging HPC and GPU, we achieved a 50% reduction in registration computation time. This study offers valuable insights and a comprehensive platform for InSAR practitioners, facilitating calculations and enhancing comprehension of surface deformation processes. Our system's improved processing efficiency, coupled with a variety of InSAR methods, makes it an alternative choice for InSAR data handling and analysis.