Multi-GPU parallel algorithm design and analysis for improved inversion of probability tomography with gravity gradiometry data

- An hybrid parallel method combined CUDA and OpenMP is presented for the inversion of probability tomography.
- The parallel algorithm is able to process the large scale of data.
- The performance of different multi-GPU algorithms are compared and discussed.
- The algorithm design is described in detail by the false-code.
- The analysis specially for the multi-GPU parallel algorithm is presented and the new metrics are defined.

In this paper, we make a study on the inversion of probability tomography (IPT) with gravity gradiometry data at first. The space resolution of the results is improved by multi-tensor joint inversion, depth weighting matrix and the other methods. Aiming at solving the problems brought by the big data in the exploration, we present the parallel algorithm and the performance analysis combining Compute Unified Device Architecture (CUDA) with Open Multi-Processing (OpenMP) based on Graphics Processing Unit (GPU) accelerating. In the test of the synthetic model and real data from Vinton Dome, we get the improved results. It is also proved that the improved inversion algorithm is effective and feasible. The performance of parallel algorithm we designed is better than the other ones with CUDA. The maximum speedup could be more than 200. In the performance analysis, multi-GPU speedup and multi-GPU efficiency are applied to analyze the scalability of the multi-GPU programs. The designed parallel algorithm is demonstrated to be able to process larger scale of data and the new analysis method is practical.