Full tensor gravity gradiometry data inversion: Performance analysis of parallel computing algorithms

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 ar...

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Bibliographic Details
Published in:Applied geophysics 2015-09, Vol.12 (3), p.292-302
Main Authors: Hou, Zhen-Long, Wei, Xiao-Hui, Huang, Da-Nian, Sun, Xu
Format: Article
Language:English
Subjects:
Algorithms
Computation
Density
Earth and Environmental Science
Earth Sciences
Geophysics
Geophysics/Geodesy
Geotechnical Engineering & Applied Earth Sciences
Gravitation
Gravity
Inversions
Magnetic measurements
Mathematical analysis
Parallel processing
Tensors
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Summary: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.
ISSN:1672-7975
1993-0658
DOI:10.1007/s11770-015-0495-z