大规模GNSS网多进程并行解算研究

针对大规模GNSS网常规数据处理时效性差、效率低的问题，基于GAMIT/GLOBK软件，利用进程池技术从多时段与多子网2个维度分别设计共享内存模型数据并行算法，并融合实现大规模GNSS网在时间域和空间域上的时空一体化双层并行解决方案。该方案可突破软件传统串行处理GNSS数据时效性差、多核计算资源利用率低的限制，在测试环境下最大加速比高达19.39,可充分挖掘计算机算力，大幅提升大规模GNSS网数据处理的时效性。

Research on Multi-Process Parallel Solution of Large-Scale GNSS Network

Aiming at the problems of poor timeliness and low efficiency of conventional data processing in large-scale GNSS network, we design a shared memory model data parallel algorithm from two dimensions of multi-period and multi-subnet respectively based on GAMIT/GLOBK software. It further implements a two-layer parallel solution for spatio-temporal integration of large-scale GNSS network in the time and space domains. The results show that the solution breaks through the limitation of traditional serial processing of GNSS data by the software, which has poor timeliness and low utilization of multi-core computing resources. In the test environment, the maximum speedup ratio is up to 19.39, which fully exploits the computing power of the computer and greatly improves the timeliness of large-scale GNSS network data processing.