GPU-based Parallel Acceleration of Magnetotelluric Tomography

• Main Authors: Po-Ta Tseng, 曾柏達
• Other Authors: Kai-Lung Hua
• Format: Others
• Language: zh-TW
• Published: 2016
• Online Access: http://ndltd.ncl.edu.tw/handle/77324148074943570090

碩士 === 國立臺灣科技大學 === 資訊工程系 === 104 === The 3D Magnetotelluric Method is a CPU-based finite difference method which uses Maxwell partial differential equations discretization to produce a linear system used in the iterative solution of quasi-static electromagnetic field distribution estimation and a Stratum Structure Inversion Algorithm to estimate the stratum's electric conductivity structure. But one of the drawbacks of the 3D Magnetotelluric Method is the solving process of linear systems iterative methods has a very high computation time cost. In order to solve the problem of computation time, this study proposes a graphics processor (GPU) based finite element method that estimates the distribution of the quasi-static electromagnetic field without relying on the iterative solving of linear systems for achieving a faster estimation process. This study can be separated into two parts: Simulation of Electromagnetic Diffusion Distribution Algorithm and Stratum Structure Inversion Algorithm. Simulation of Electromagnetic Diffusion Distribution Algorithm considers each cell in the grid of electromagnetic structure as an independent source of an electromagnetic field and uses this concept to estimate the electromagnetic field values detected at the observation points on the ground. Stratum Structure Inversion Algorithm calculates the grid’s Jacobian matrix based on the result of Simulation of Electromagnetic Diffusion Distribution Algorithm and find the next recursive search direction with Newton's Method. Because all electromagnetic field sources is independent in both algorithms, so our method is suitable for GPU's parallel computing capability. The results of our experiment are as follows: 1. For the Simulation of Electromagnetic Diffusion Distribution Algorithm, the GPU version is 50 ~ 71 times faster than the CPU version. 2. For a single iteration in the improved Stratum Structure Inversion Algorithm, the GPU version with simplified Jacobian matrix can speed 11,000 ~ 55,000 times faster than the CPU version with normal Jacobian matrix. 3. The improved Stratum Structure Inversion Algorithm on GPU completes the stratum condition estimation 68 ~ 82 times faster than the CPU version.