Electrical capacitance tomography is an innovative method for visualization of industrial processes. One of its main advantages is it’s high time resolution that allows to the usage of ECT in systems with high volatility. In recent years there has been significant development of electrical capacitance tomography 3D, which however, has significantly reduced industrial it’s applications due to the complicated process of image reconstruction. The authors propose the use of multi-node, multi-GPU system to accelerate the process of image reconstruction in ECT 3D.

Electrical capacitance tomography (ECT) is one of non-invasive visualization techniques which can be used for industrial process monitoring. However, acquiring images trough 3D ECT often requires performing time consuming complex computations on large size matrices. Therefore, a new parallel approach for 3D ECT image reconstruction is proposed, which is based on application of multi-GPU, multi-node algorithms in heterogeneous distributed system. This solution allows to speed up the required data processing. Distributed measurement system with a new framework for parallel computing and a special plugin dedicated to ECT are presented in the paper. Computing system architecture and its main features are described. Both data distribution as well as transmission between the computing nodes are discussed. System performance was measured using LBP and the Landweber’s reconstruction algorithms which were implemented as a part of the ECT plugin. Application of the framework with a new network communication layer reduced data transfer times significantly and improved the overall system efficiency.

This paper presents the software for comprehensive processing and visualization of 2D and 3D electrical tomography data. The system name as TomoKIS Studio has been developed in the frame of DENIDIA international research project and has been improved in the frame of Polish Ministry of Science and Higher Education Project no 4664/B/T02/2010/38. This software is worldwide unique because it simultaneously integrates the process of tomographic data acquisition, numerical FEM modeling and tomographic images reconstruction. The software can be adapted to specific industrial applications, particularly to monitoring and diagnosis of two-phase flows. The software architecture is composed of independent modules. Their combination offers calibration, configuration and full-duplex communication with any tomographic acquisition system with known and open communication protocol. The other major features are: online data acquisition and processing, online and offline 2D/3D images linear and nonlinear reconstruction and visualization as well as raw data and tomograms processing. Another important ability is 2D/3D ECT sensor construction using FEM modeling. The presented software is supported with the multi-core GPU technology and parallel computing using Nvidia CUDA technology.

3D ECT provides a lot of challenging computational issues that have been reported in the past by many researchers. Image reconstruction using deterministic methods requires execution of many basic operations of linear algebra, such as matrix transposition, multiplication, addition and subtraction. In order to reach real-time reconstruction a 3D ECT computational subsystem has to be able to transform capacitance data into image in fractions of seconds. By assuming, that many of the computations can be performed in parallel using modern, fast graphics processor and by altering the algorithms time to achieve high quality image reconstruction will be shortened significantly. The research conducted while analysing ECT algorithms has also shown that, although dynamic development of GPU computational capabilities and its recent application for image reconstruction in ECT has significantly improved calculations time, in modern systems a single GPU is not enough to perform many tasks. Distributed Multi-GPU solutions can reduce reconstruction time to only a fraction of what was possible on pure CPU systems. Nevertheless performed tests clearly illustrate the need for developing a new distributed platform, which would be able to fully utilize the potential of the hardware. It has to take into account specific nature of computations in Multi-GPU systems.

Abstract-3D Electrical Capacitance Tomography provides a lot of challenging computational issues that have been reported in the past by many researchers. Image reconstruction using deterministic methods requires execution of many basic operations of linear algebra. Due to significant sizes of matrices used in ECT for image reconstruction and the fact that best image quality is achieved by using algorithms of which significant part is FEM and which are hard to parallelize or distribute. In order to solve these issues a new set of algorithms had to be developed.