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GPU-Aware AMR on Octree-Based Grids

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Book cover Parallel Computing Technologies (PaCT 2019)

Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 11657))

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Abstract

Algorithms for refinement/coarsening of octree-based grids entirely on GPU are proposed. Corresponding CUDA/OpenMP implementations demonstrate good performance results which are comparable with p4est library execution times. Proposed algorithms permit to perform all dynamic AMR procedures on octree-based grids entirely in GPU as well as solver kernels without exploiting CPU resourses and pci-e bus for grid data transfers.

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References

  1. Beckingsale, D., Gaudin, W., Herdman, A., Jarvis, S.: Resident block-structured adaptive mesh refinement on thousands of graphics processing units. In: Parallel Processing (ICPP), 2015 44th International Conference on Parallel Processing, pp. 61–70. IEEE (2015)

    Google Scholar 

  2. Lawlor, O.S., et al.: ParFUM: a parallel framework for unstructured meshes for scalable dynamic physics applications. Eng. Comput. 22(3–4), 215–235 (2006)

    Article  Google Scholar 

  3. Menshov, I.S., Pavlukhin, P.V.: Efficient parallel shock-capturing method for aerodynamics simulations on body-unfitted cartesian grids. Comput. Math. Math. Phys. 56(9), 1651–1664 (2016)

    Article  MathSciNet  Google Scholar 

  4. Pavlukhin, P., Menshov, I.: On implementation high-scalable CFD solvers for hybrid clusters with massively-parallel architectures. In: Malyshkin, V. (ed.) PaCT 2015. LNCS, vol. 9251, pp. 436–444. Springer, Cham (2015). https://doi.org/10.1007/978-3-319-21909-7_42

    Chapter  Google Scholar 

  5. Brown, A.: Towards achieving GPU-native adaptive mesh refinement. Oxford e-Research Centre (2017). https://www.oerc.ox.ac.uk/sites/default/files/uploads/ProjectFiles/CUDA//Presentations/2017/A_Brown_8th_March.pdf

  6. Sætra, M.L., Brodtkorb, A.R., Lie, K.A.: Efficient GPU-implementation of adaptive mesh refinement for the shallow-water equations. J. Sci. Comput. 63, 23 (2015). https://doi.org/10.1007/s10915-014-9883-4

    Article  MathSciNet  MATH  Google Scholar 

  7. Xinsheng, Q., Randall, L., Michael, R.M.: Accelerating wave-propagation algorithms with adaptive mesh refinement using the Graphics Processing Unit (GPU) (2018). https://arxiv.org/abs/1808.02638

  8. Burstedde, C., et al.: Extreme-scale AMR. In: Proceedings of the 2010 ACM/IEEE International Conference for High Performance Computing, Networking, Storage and Analysis, pp. 1–12. IEEE Computer Society (2010)

    Google Scholar 

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Acknowledgments

This research was supported by the Grant No 17-71-30014 from the Russian Science Foundation.

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Authors and Affiliations

  1. Keldysh Institute of Applied Mathematics, Moscow, 125047, Russia

    Pavel Pavlukhin & Igor Menshov

  2. Research and Development Institute “Kvant”, Moscow, 125438, Russia

    Pavel Pavlukhin

Authors
  1. Pavel Pavlukhin
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  2. Igor Menshov
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Corresponding author

Correspondence to Pavel Pavlukhin .

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Editors and Affiliations

  1. Institute of Computational Mathematics and Mathematical Geophysics SB RAS, Novosibirsk State University, Novosibirsk State Technical University, Novosibirsk, Russia

    Victor Malyshkin

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Pavlukhin, P., Menshov, I. (2019). GPU-Aware AMR on Octree-Based Grids. In: Malyshkin, V. (eds) Parallel Computing Technologies. PaCT 2019. Lecture Notes in Computer Science(), vol 11657. Springer, Cham. https://doi.org/10.1007/978-3-030-25636-4_17

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  • DOI: https://doi.org/10.1007/978-3-030-25636-4_17

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-25635-7

  • Online ISBN: 978-3-030-25636-4

  • eBook Packages: Computer ScienceComputer Science (R0)

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