An efficient parallel computing strategy for the processing of large GNSS network datasets

The Global Navigation Satellite System (GNSS) has been an indispensable tool for geodetic surveying and geodynamics research and has rapidly developed over the past few years with abundant ground networks, modern constellations and multiple signal frequencies. However, due to increasing numbers of stations and satellites, the data processing burden has increased significantly. In this contribution, an improved parallel computing method is proposed for processing large GNSS network datasets. First, a parallelization strategy is introduced for the traditional GNSS processing model by analyzing the practicability of parallel integrated processing for GNSS network data. In addition, to maximize the advantages of modern microprocessors and local area network environments, we present the multi-core parallel computing of traditional GNSS data processing methods; then, the product is released as a service-oriented architecture that can be found and invoked through multiple nodes in the Internet. Obviously, this method combines the advantages of multi-core parallelism and network parallelism. Experiments show that the efficiency of the proposed method can be further increased with the accumulation of GNSS data and additional nodes. For example, in a network with 2000 stations, the efficiency of the parallel scheme with four quad-core nodes is at least 8 times faster than that of the traditional serial scheme. All the results demonstrate that the proposed strategy is an efficient and promising approach for processing large GNSS network datasets.