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

Wybrane pełne teksty z tego czasopisma
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Języki publikacji
EN
Abstrakty
EN
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.
Twórcy
autor
  • Institute of Applied Computer Science, Lodz University of Technology, Poland
  • Institute of Applied Computer Science, Lodz University of Technology, Poland
autor
  • Institute of Applied Computer Science, Lodz University of Technology, Poland
autor
  • Institute of Applied Computer Science, Lodz University of Technology, Poland
Bibliografia
  • [1] R. Wajman, R. Banasiak, Ł. Mazurkiewicz, T. Dyakowski, D. Sankowski, Spatial imaging with 3D capacitance measurements, Measurement Science and Technology, Vol. 17, No. 8, pp. 2113-2118, , August 2006
  • [2] M. Soleimani, Three-dimensional electrical capacitance tomography imaging, Insight, Non- Destructive Testing and Condition Monitoring, Vol. 48, No. 10, pp. 613-617, 2006
  • [3] W. Warsito, L-S. Fan, Development of 3- Dimensional Electrical Capacitance Tomography Based on Neural Network Multi-criterion Optimization Image Reconstruction, proc. of 3rd World Congress on Industrial Process Tomography (Banff), pp. 942-947, 2003
  • [4] M. Soleimani, C.N. Mitchell, R. Banasiak, R. Wajman, A. Adler, Four-dimensional electrical capacitance tomography imaging using experimental data, Progress In Electromagnetics Research-PIER, Vol. 90, pp. 171-186, 2009
  • [5] W.Q. Yang, L. Peng, Image reconstruction algorithms for electrical capacitance tomography, Institute of Physics Publishing, 2002
  • [6] R. Wajman, R. Banasiak, Ł. Mazurkiewicz, T. Dyakowski, D. Sankowski, Spatial imaging with 3D capacitance measurements, Measurement Science and Technology, Vol. 17, No. 8, pp. 2113-2118, 2006
  • [7] J. Sikora, Podstawy Metody Elementów Sko´nczonych: Zagadnienia potencjalne, Wydawnictwo IEL, 2008
  • [8] M. Geveler, D. Ribbrock, D. G oddeke, P. Zajac, S. Turek, Efficient Finite Element Geometric Multigrid Solvers for Unstructured Grids on GPUs, Proceedings of the The Second International Conference on Parallel, Distributed, Grid and Cloud Computing for Engineering (PARENG 2011), Ajaccio, Corsica, France, 2011
  • [9] J.W. Ruge, K. St uben, Algebraic Multigrid, Multigrid Methods (Frontier in Applied Mathematics), Society for industrial Mathematics, Ch. 4, pp. 73 -130, 1994
  • [10] W.H. Press, S.A. Teukolsky, W.T. Vetterling, B.P. Flannery, Numerical recipses in C++, Second Edition, The Press Syndicate of the University of Cambridge, 1992
  • [11] D. Michels, Sparse-Matrix-CG-Solver in CUDA, Proceedings of CESCG 2011: The 15th Central European Seminar on Computer Graphics, 2011
  • [12] B.D. Kirk, Programming Massively Parallel Processors: A Hands-on Approach, San Francisco, Morgan Kaufmann, 2010
  • [13] M. Herlihy, The Art of Multiprocessor Programming, San Francisco, Morgan Kaufmann, 2008
Typ dokumentu
Bibliografia
Identyfikator YADDA
bwmeta1.element.baztech-b9fdb072-f3f6-4c58-9dd0-115a31c3f668
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