Abstract:In the process of gravity recovery using satellite-satellite tracking data, there are some problems caused by processing massive observation data and solving large equations, such as low computational efficiency and high requirements for hardware level of computing platform. In view of the above problems, we propose a fast heterogeneous parallel algorithm for gravity recovery using energy conservation method. CUDA is used to realize parallel computation of design matrix on GPU, MKL library is used in partition adjustment method and preconditioned conjugate gradient method for constructing and solving normal equation on CPU fast, thus realizing heterogeneous parallel computation of gravity recovery using satellite data. A 120×120 gravity field model, named GM-GraceFO2020h, is obtained by processing the observation data of GRACE-FO satellite from January 1, 2020 to June 30, 2020 using this algorithm. Compared with the existing models and algorithms, the result shows that the accuracy of the model derived by the proposed algorithm is equivalent to that of existing GRACE gravity field models, and compared with the traditional serial algorithm, the inversion time is reduced by 98.479%, and the memory consumption is an order of magnitude smaller.
TAN Xuli,WANG Qingbin,FAN Diao et al. Heterogeneous Parallel Fast Gravity Recovery Algorithm Based on Energy Conservation Method[J]. jgg, 2021, 41(9): 954-960.