Application of GPU Parallel Computing for Acceleration of Finite Element Method Based 3D Reconstruction Algorithms in Electrical Capacitance Tomography

Abstract

Abstract With the increasing complexity and scale of industrial processes their visualization is becoming increasingly important. Especially popular are non-invasive methods, which do not interfere directly with the process. One of them is the 3D Electrical Capacitance Tomography. It possesses however a serious flaw - in order to obtain a fast and accurate visualization requires application of computationally intensive algorithms. Especially non-linear reconstruction using Finite Element Method is a multistage, complex numerical task, requiring many linear algebra transformations on very large data sets. Such process, using traditional CPUs can take, depending on the used meshes, up to several hours. Consequently it is necessary to develop new solutions utilizing GPGPU (General Purpose Computations on Graphics Processing Units) techniques to accelerate the reconstruction algorithm. With the developed hybrid parallel computing architecture, based on sparse matrices, it is possible to perform tomographic calculations much faster using GPU and CPU simultaneously, both with Nvidia CUDA and OpenCL.