Abstract: [Purposes] In response to the low efficiency of traditional serial processing in large-scale GNSS networks and the difficulty of meeting demands merely by upgrading single-node configurations,seeking rapid data processing has become a current research hotspot both domestically and internationally. [Methods] Based on the GAMIT software,a parallel computing engine was constructed on the basis of the spatio-temporal integrated two-layer data parallel algorithm； a distributed computing engine was built by using technologies such as remote procedure call,cluster computing,and message passing interface. Both were deeply integrated to propose a spatio-temporal integrated three-layer data parallel architecture for multi-core parallel and multi-node parallel in large-scale GNSS networks. [Findings] In the test environment,the maximum speedup ratio of this scheme reached 83.53,and the baseline solution cycle was significantly shortened from about 2.8 months in the traditional serial mode to about 1 d. [Conclusions] This scheme fully integrates the high performance of shared memory systems and the high scalability of distributed systems,and has significant advantages in terms of technological advancement and practical engineering value,providing strong technical support for the efficient processing of massive GNSS data．

Key words: large-scale GNSS network, double-difference positioning model, parallel computing, remote procedure call, cluster computing, message passing interface, speedup