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Stepping into Fully GPU Accelerated Biomedical Applications

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Large-Scale Scientific Computing (LSSC 2013)

Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 8353))

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Abstract

We present ideas and first results on a GPU acceleration of a non-linear solver embedded into the biomedical application code CARP. The linear system solvers have been transferred already in the past and so we concentrate on how to extend the GPU acceleration to larger portions of the code. The finite element assembling of stiffness and mass matrices takes at least 50 % of the CPU time and therefore we investigate this process for the bidomain equations but with focus on later use in non-linear and/or time-dependent problems. The CUDA code for matrix calculation and assembling is faster by a factor up to \(90\) compared to a single CPU core. The routines were integrated to CARP’s main code and they are already used to assemble the FE matrices of the bidomain model. Further performance studies are still required for the bidomain-mechanics model.

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References

  1. Bell, N., Dalton, S., Olson, L.N.: Exposing fine-grained parallelism in algebraic multigrid methods. SIAM J. Sci. Comput. 34(2), C123–C152 (2012)

    Article  MATH  MathSciNet  Google Scholar 

  2. Cecka, C., Lew, A.J., Darve, E.: Assembly of finite element methods on graphics processors. Int. J. Numer. Methods Eng. 85(5), 640–669 (2011)

    Article  MATH  Google Scholar 

  3. Geveler, M., Ribbrock, D., Göddeke, D., Zajac, P., Turek, S.: Towards a complete FEM-based simulation toolkit on GPUs: unstructured grid finite element geometric multigrid solvers with strong smoothers based on sparse approximate inverses. Comput. Fluids 80, 327–332 (2013)

    Article  MATH  MathSciNet  Google Scholar 

  4. Gockenbach, M.S.: Understanding and Implementing the Finite Element Method. SIAM, Philadelphia (2007)

    Google Scholar 

  5. Göddeke, D.: Fast and accurate finite-element multigrid solvers for PDE simulations on GPU clusters. Ph.D. thesis, Technische Universität Dortmund, Fakultät für Mathematik, May 2010. http://hdl.handle.net/2003/27243

  6. Göddeke, D., Strzodka, R., Mohd-Yusof, J., McCormick, P.S., Wobker, H., Becker, C., Turek, S.: Using GPUs to improve multigrid solver performance on a cluster. Int. J. Comput. Sci. Eng. 4(1), 36–55 (2008)

    Google Scholar 

  7. Haase, G., Liebmann, M., Douglas, C.C., Plank, G.: A parallel algebraic multigrid solver on graphics processing units. In: Zhang, W., Chen, Z., Douglas, C.C., Tong, W. (eds.) HPCA 2009. LNCS, vol. 5938, pp. 38–47. Springer, Heidelberg (2010)

    Chapter  Google Scholar 

  8. Jónasson, K. (ed.): PARA 2010, Part II. LNCS, vol. 7134. Springer, Heidelberg (2012)

    Google Scholar 

  9. Jung, M., Langer, U.: Methode der finiten Elemente für Ingenieure. Lehrbuch, 2nd edn. Springer Vieweg, Wiesbaden (2013)

    Book  Google Scholar 

  10. Larson, M.G., Bengzon, F.: The Finite Element Method: Theory, Implementations and Applications. Texts in Computational Science and Engineering, vol. 10, 1st edn. Springer, Heidelberg (2013)

    Google Scholar 

  11. Markall, G.R., Slemmer, A., Ham, D.A., Kelly, P.H.J., Cantwell, C.D., Sherwin, S.J.: Finite element assembly strategies on multi-core and many-core architectures. Int. J. Numer. Methods Fluids 71(1), 80–97 (2013)

    Article  MathSciNet  Google Scholar 

  12. Neic, A., Liebmann, M., Haase, G., Plank, G.: Algebraic multigrid solvers on clusters of CPUs and GPUs. In: Jónasson [8], pp. 389–398

    Google Scholar 

  13. Neic, A., Liebmann, M., Hötzl, E., Mitchell, L., Vigmond, E., Haase, G., Plank, G.: Accelerating cardiac bidomain simulations using graphics processing units. IEEE Trans. Biomed. Eng. 59(8), 2281–2290 (2012)

    Article  Google Scholar 

  14. NVIDIA Corporation. CUDA programming guide 5.0 (2012). http://docs.nvidia.com/cuda/cuda-c-programming-guide/index.html

  15. Pathmanathan, P., Whiteley, J.P.: A numerical method for cardiac mechanoelectric simulations. Ann. Biomed. Eng. 37(5), 860–873 (2009)

    Article  Google Scholar 

  16. Rocha, B., Campos, F., Plank, G., Weber dos Santos, R., Liebmann, M., Haase, G.: Simulations of the electrical activity in the heart with graphic processing units. Concur. Comput. Pract. Exp. 23, 708–720 (2011)

    Google Scholar 

  17. Tracy, F.T.: Optimizing finite element programs on the cray X1 using coloring schemes. In: Proceedings of the 2004 Users Group Conference, \(\text{ DOD }\!\!\_\!\!\text{ UGC } \text{'04 }\), pp. 329–333. IEEE Computer Society, Washington, DC, USA (2004)

    Google Scholar 

  18. Vigmond, E., Hughes, M., Plank, G., Leon, L.: Computational tools for modeling electrical activity in cardiac tissue. J. Electrocardiol. 36, 69–74 (2003)

    Article  Google Scholar 

  19. Vigmond, E., Plank, G.: Cardiac arrhythmia research package (2009). http://carp.meduni-graz.at

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Authors and Affiliations

  1. Institute for Mathematics and Scientific Computing, University of Graz, Graz, Austria

    Gundolf Haase, Manfred Liebmann & Aurel Neic

  2. Institute of Biophysics, Medical University of Graz, Graz, Austria

    Caroline Mendonca Costa & Gernot Plank

Authors
  1. Caroline Mendonca Costa
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  2. Gundolf Haase
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  3. Manfred Liebmann
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  4. Aurel Neic
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  5. Gernot Plank
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Corresponding author

Correspondence to Gundolf Haase .

Editor information

Editors and Affiliations

  1. Inst. of Info. & Comm. Technologies, Bulgarian Academy of Sciences, Sofia, Bulgaria

    Ivan Lirkov

  2. Bulgarian Academy of Sciences, Sofia, Bulgaria

    Svetozar Margenov

  3. Dept. of Informatics and Mathematical Modell, Technical University of Denmark, Kongens Lyngby, Denmark

    Jerzy Waśniewski

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Costa, C.M., Haase, G., Liebmann, M., Neic, A., Plank, G. (2014). Stepping into Fully GPU Accelerated Biomedical Applications. In: Lirkov, I., Margenov, S., Waśniewski, J. (eds) Large-Scale Scientific Computing. LSSC 2013. Lecture Notes in Computer Science(), vol 8353. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-662-43880-0_1

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  • DOI: https://doi.org/10.1007/978-3-662-43880-0_1

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  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-662-43879-4

  • Online ISBN: 978-3-662-43880-0

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