3D inversion of vertical gravity gradient with multiple graphics processing units based on matrix compression

• DOI: 10.1190/geo2021-0472.1
• Publication Date: 2022-09-07T13:23:13Z
• Authors: Zhenlong Hou; Jikang Wei; Tianxiao Mao; Yujun Zheng; Yuchen Ding
• Keywords: Speedup; Python

Among single-component inversions, vertical gravity gradient data have been proven to be sufficient obtain reasonable density distribution. However, the increasing numbers of observations and model parameters inevitably produce a large-scale sensitivity matrix, causing problems such as large memory occupation slow computations in inversion. Aiming at solving these long-standing problems, we introduced matrix compression technique reduce constructed hybrid parallel algorithm for accelerating focusing We develop source codes design pattern proposed using C language with open multiprocessing compute unified device architecture further improving feasibility reusability. A graphical user interface visualization tools also are developed Python. In tests synthetic produced by numerical simulation real from Vinton Dome, it is verified that inversion can an accurate result values object location. The performance analysis conducted different scales data. speedup increases number graphics processing units (GPUs) parameters, proving scalable. compare this others without demonstrate how compressed storing accessing method influences performance. More specifically, 100 × 20 cells, average speed approximately 6 s per iteration, maximum 36.94. Our has short running time high multiple-GPU efficiency. Hence, more powerful implementing calculations could used computing mass inversions.