并行计算及其性能分析在重力全张量梯度数据反演中的应用（英文）

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

本文在实现重力全张量梯度数据重加权聚焦反演的基础上,利用MPI与CUDA实现了算法的并行运算,以及二者联合的混编并行程序,引入并行计算中的程序性能指标,对算法性能进行分析和综合对比,总结出一套评价规则用于并行算法程序的性能分析。此规则给出算法各自的性能评估方法,有助于客观全面地评价程序的性能,从程序开发和算法实现的角度帮助评估和提高算法的并行效率。同时我们将该套程序和评价规则用于理论模型和Vinton盐丘的实测梯度数据中,不仅反演出良好的密度结果,还获得了较高的运算效率,验证了所使用并行算法在重力全张量梯度数据反演中的可行性,根据评价规则认为程序均具有良好的运行效率。

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