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 M...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 efficiency and feasibility of parallel computing gravity gradiometry data. and real density data, and verify the high algorithms in the inversion of full tensor展开更多
基金supported by the Sino-Probe09(No.201011078)National High-tech R&D Program(No.863 and2014AA06A613)
文摘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 efficiency and feasibility of parallel computing gravity gradiometry data. and real density data, and verify the high algorithms in the inversion of full tensor
