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In the QRendring, we present a novel graph cut algorithm that leverages a parallelized jump flooding technique and a heuristic pushing and relabeling scheme to enhance the components of the graph cut process, namely, multi-pass relabel, convergence detection and block-wise push-relabel. The entire process is parallelizable on GPU, and outperforms existing GPU-based implementations in terms of global convergence, information propagation, and performance. We design an intuitive user interface for specifying interested regions in cases of occlusions when handling volumetric data or video sequences. We conduct experiments on a variety of datasets, including images (up to 15000 10000), videos(up to 2560 1440 72), and volumetric data, and achieve high quality results and a maximum 66-fold speedup over conventional approaches.