Abstract: Fast Fourier Transform (FFT) has better performance than Discrete Fourier Transform, which is the fundamental imaging algorithm of radio interferometry.However, because of the irregular sampling of antenna array, it is necessary to use gridding algorithms to resample visibilities to regular grids,so that FFT can be applied. The convolutional gridding in radio interferometric imaging is characterized by intensive and iterative computations. Especially in the case of massive visibility data processing, high-performance gridding computing is particularly important to accelerate the whole imaging process. In order to alleviate the pressure of data processing, the Dask parallel computing framework is extended and applied on the existing gridding algorithm which supports multi-core parallelism but processes whole blocks of data. Not only can the data be partitioned and distributed to multiple threads to improve the efficiency of numerical computation, but also the dynamic distributed task scheduling strategy can optimize the real-time workflow of gridding. The experimental results show that the multi-core utilization rate is significantly improved and the performance of gridding algorithm can be further enhanced even if the volume of visibility is increased. Distributed task scheduling can flexibly scale the gridding task of (single) multi-measurement set to (single) multi-machine system, which gives full play to the scale advantage of clustering.