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Sentinel-1 (S-1) TOPS data are widely applied in InSAR applications to monitor earthquakes and landslides. The large S-1 coverage however, leads to a high computational cost when executing InSAR techniques. Thus, we develop a GPU accelerated S-1 InSAR processing method implemented on a personal desktop. In the proposed method, computationally expensive modules including geometric coregistration, resampling, Enhanced Spectral Diversity, and coherence estimation, are implemented using CUDA. In addition, several optimizations are employed to enhance the efficiency of these modules. We select an efficient approximation method in the geometric coregistration module, and improve the GPU memory access efficiency in the resampling module through the GPU texture memory, temporary register array, and a configuration with more L1 cache. We develop a novel GPU-based parallel coherence estimation algorithm in ESD and coherence estimation modules, and use the asynchronous data transfer technology to hide the costs of CPU-GPU data transfer for resampling, ESD, and coherence estimation modules. After several optimizations, our GPU-accelerated modules (considering CPU-GPU transmission costs) achieves speedup ratios up to 157x, 166x, 145x, and 168x with respect to their single-threaded CPU counterparts. For a full frame S-1 image, our method reduces the computation time from 1415.32s to 8.59s. Moreover, our method is also validated in two case studies of the 2016 Mw6.2 Central Italy earthquake and 2018 Mw6.9 Leilani Estates earthquake caused by the Kilauea eruption in Hawaii. • GPU-based InSAR processing for Sentinel-1 (S-1) TOPS data is developed. • The computation time for full frame S-1 TOPS data is reduced into 8.59s. • Our GPU-based InSAR processing is used for detecting deformation in earthquakes. [ABSTRACT FROM AUTHOR]