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tokens: are required to grid the raw data generated by FAST in real-time with the existing methods, which could greatly increase the cost of computation for this survey. In the past decade, various studies on optimization of gridding performance have been carried out. For example, Winkel et al. ( 2016b ) have presented a versatile gridding module ( Cygrid : A fast Cython-powered convolution-based gridding module for python ) for radio astronomical data reduction, featuring a single-level lookup table based on the c ++ standard template library (STL) for gridding parallelization, as well as the Hierarchical Equal Area Latitude Pixelation ( healpix) -based spherical tessellation for fast neighbour searching. And the performance of the Cygrid code can be significantly improved when using multiple CPU cores. However, utilizing the STL vector, which features dynamical arrangements of storage allocations with a relatively small initial storage volume, to construct a lookup table for input raw data is not always practical. For example, if the raw samples are unevenly distributed, with more data points in the central regions and less on all sides, the required storage needs to be expanded in the middle part of each healpix -tessellated ring frequently. In this case, the STL vectors have to make massive data copies of the data frequently, which would consume broad bandwidth, thus resulting in degradation of gridding performance. In addition, the main workload of gridding can be considered as a combination of a large number of single-instruction, multidata stream operations. For such tasks, the high parallelization level, as well as throughput of GPUs brings a possibility for better performance. Thus, Romein ( 2012 ) has developed a work-distribution scheme for radio data gridding with GPUs, which could significantly reduce the memory access time of computing device, and map observed samples on to a grid with high efficiency. Merry ( 2016 ) has further optimized this algorithm with thread coarsening strategy, making one thread handle multiple samples simultaneously. However, the method proposed by Romein ( 2012 ) and Merry ( 2016 ) has been deeply customized for radio telescope arrays, strongly relying on the spatial coherence of the interference matrix, thus cannot be easily adapted for single-dish telescopes. Accordingly, in order to meet the requirements of large data volume from the latest single-dish radio telescopes, and to overcome the shortcomings of GPU-based gridding methods mainly designed for radio telescope array, inspired by Winkel et al. ( 2016b ), and based upon our previous work (Luo et al. 2018 ), here we present a convolutional gridding framework with hybrid computing environments, HCGrid , for radio astronomy, which can greatly improve the performance of the gridding process. Key features of our framework can be summarized as follows: We have designed a gridding module with high performance for large single-dish radio telescopes. We