Acceleration of image reconstruction in 3D Electrical Capacitance Tomography in heterogeneous, multi-GPU system using sparse matrix computations and Finite Element Method
Paweł Kapusta, Michał Majchrowicz, Dominik Sankowski, Lidia Jackowska-Strumiłło
Citation: Proceedings of the 2016 Federated Conference on Computer Science and Information Systems, M. Ganzha, L. Maciaszek, M. Paprzycki (eds). ACSIS, Vol. 8, pages 679–683 (2016)
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, such as matrix transposition, multiplication, addition and subtraction. Due to significant sizes of matrices used in ECT for image reconstruction and the fact that only small number of elements in these matrices are non-zero means, that often some kind of sparse format is used. However both parallel and distributed algorithms are not optimized for sparse formats. Moreover best image reconstruction 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, which are specifically optimized for the purpose of image reconstruction in ECT.
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