Full tensor gravity gradiometry data inversion: Performance analysis of parallel computing algorithms
Hou Zhen-Long1, Wei Xiao-Hui1, Huang Da-Nian2, and Sun Xu3
1. College of Computer Science and Technology, Jilin University, Changchun 130026, China.
2. College of Geo-exploration Science and Technology, Jilin University, Changchun 130026, China.
3. Engineering Training Center, Jilin University, Changchun 130026, China.
Abstract We apply reweighted inversion focusing to full tensor gravity gradiometry data using message-passing interface (MPI) and compute unified device architecture (CUDA) parallel computing algorithms, and then combine MPI with CUDA to formulate a hybrid algorithm. Parallel computing performance metrics are introduced to analyze and compare the performance of the algorithms. We summarize the rules for the performance evaluation of parallel algorithms. We use model and real data from the Vinton salt dome to test the algorithms. We find good match between model and real density data, and verify the high efficiency and feasibility of parallel computing algorithms in the inversion of full tensor gravity gradiometry data.
The research was supported by the Sino-Probe09 (No. 201011078) and National High-tech R&D Program (No. 863 and 2014AA06A613).
Cite this article:
Hou Zhen-Long,Wei Xiao-Hui,Huang Da-Nian et al. Full tensor gravity gradiometry data inversion: Performance analysis of parallel computing algorithms[J]. APPLIED GEOPHYSICS, 2015, 12(3): 292-302.
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