Skip to main content

BDDC Preconditioning on GPUs for Cardiac Simulations

  • Conference paper
  • First Online:
Euro-Par 2023: Parallel Processing Workshops (Euro-Par 2023)

Abstract

In order to understand cardiac arrhythmia, computer models for electrophysiology are essential. In the EuroHPC MicroCARD project, we adapt the current models and leverage modern computing resources to model diseased hearts and their microstructure accurately. Towards this objective, we develop a portable, highly efficient, and performing BDDC preconditioner and solver implementation, demonstrating scalability with over 90% efficiency on up to 100 GPUs.

This work was supported by the European High-Performance Computing Joint Undertaking EuroHPC under grant agreement No 955495 (MICROCARD).

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 44.99
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 59.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

References

  1. The MICROCARD Project. http://microcard.eu/. Accessed 23 Apr 2022

  2. Amestoy, P.R., Davis, T.A., Duff, I.S.: An approximate minimum degree ordering algorithm. SIAM J. Matrix Anal. Appl. 17(4), 886–905 (1996). https://doi.org/10.1137/S0895479894278952

    Article  MathSciNet  Google Scholar 

  3. Anzt, H., et al.: Ginkgo: a modern linear operator algebra framework for high performance computing. ACM Trans. Math. Softw. 48(1) (2022). https://doi.org/10.1145/3480935

  4. Dohrmann, C.R.: A preconditioner for substructuring based on constrained energy minimization. SIAM J. Sci. Comput. 25(1), 246–258 (2003). https://doi.org/10.1137/S1064827502412887

    Article  MathSciNet  Google Scholar 

  5. Duff, I.S., Koster, J.: On algorithms for permuting large entries to the diagonal of a sparse matrix. SIAM J. Matrix Anal. Appl. 22(4), 973–996 (2001). https://doi.org/10.1137/S0895479899358443

    Article  MathSciNet  Google Scholar 

  6. Huynh, N.M.M., Chegini, F., Pavarino, L.F., Weiser, M., Scacchi, S.: Convergence analysis of BDDC preconditioners for hybrid DG discretizations of the cardiac cell-by-cell model (2022)

    Google Scholar 

  7. Plank, G., et al.: The openCARP simulation environment for cardiac electrophysiology. Comput. Methods Programs Biomed. 208, 106223 (2021). https://doi.org/10.1016/j.cmpb.2021.106223

    Article  Google Scholar 

  8. Potse, M.: Microscale cardiac electrophysiology on exascale supercomputers. In: SIAM Conference on Parallel Processing (PP22). SIAM (2022)

    Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Terry Cojean .

Editor information

Editors and Affiliations

Rights and permissions

Reprints and permissions

Copyright information

© 2024 The Author(s), under exclusive license to Springer Nature Switzerland AG

About this paper

Check for updates. Verify currency and authenticity via CrossMark

Cite this paper

Göbel, F., Cojean, T., Anzt, H. (2024). BDDC Preconditioning on GPUs for Cardiac Simulations. In: Zeinalipour, D., et al. Euro-Par 2023: Parallel Processing Workshops. Euro-Par 2023. Lecture Notes in Computer Science, vol 14352. Springer, Cham. https://doi.org/10.1007/978-3-031-48803-0_30

Download citation

  • DOI: https://doi.org/10.1007/978-3-031-48803-0_30

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-031-48802-3

  • Online ISBN: 978-3-031-48803-0

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics