Skip to main content
SpringerLink
Log in

GPU Implementation of ConeTorre Algorithm for Fluid Dynamics Simulation

  • Conference paper
  • First Online:
Parallel Computing Technologies (PaCT 2019)

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

Included in the following conference series:

Abstract

LRnLA algorithms allow simulation of large problems with performance that exceeds the memory-bound limit of the traditional stepwise algorithms, that is, algorithms without any kind of temporal blocking. We show how the ConeTorre LRnLA algorithm that was successfully implemented for various CPU codes may be ported to work with CUDA framework and implemented the Lattice-Boltzmann Method (LBM) for fluid dynamics. As the standard tools and guidelines do not comply with the LRnLA paradigm, we have performed manual optimization of the communication between main memory levels of GPU and reduce overhead for data access patterns. We have made the performance estimate of the LRnLA implementation with the use of the Roofline model. The computation remains memory-bound, but with the ConeTorre algorithm the operational intensity is increased several times, and the maximum achievable performance for the chosen algorithm parameters is 9 billion cell updates per second on Tesla V100. We have achieved more than 66% of the estimate. As a result, we have developed a fluid simulation code based on the Lattice-Boltzmann method with a performance that surpasses state-of-the-art solutions.

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

Access this chapter

Log in via an institution
Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 59.99
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 79.99
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. Computer system K-60 (2018). http://kiam.ru/MVS/resourses/k60.html

  2. Bailey, P., Myre, J., Walsh, S.D., Lilja, D.J., Saar, M.O.: Accelerating lattice Boltzmann fluid flow simulations using graphics processors. In: International Conference on Parallel Processing, ICPP 2009, pp. 550–557. IEEE (2009)

    Google Scholar 

  3. Calore, E., Gabbana, A., Kraus, J., Pellegrini, E., Schifano, S.F., Tripiccione, R.: Massively parallel lattice-boltzmann codes on large GPU clusters. Parallel Comput. 58, 1–24 (2016)

    Article  MathSciNet  Google Scholar 

  4. Degenhardt, R.: Advanced Lattice Boltzmann Models for the Simulation of Additive Manufacturing Processes. doctoralthesis, Friedrich-Alexander-Universität Erlangen-Nürnberg (FAU) (2017)

    Google Scholar 

  5. Endo, T., Midorikawa, H., Sato, Y.: Software technology that deals with deeper memory hierarchy in post-petascale era. In: Sato, M. (ed.) Advanced Software Technologies for Post-Peta Scale Computing, pp. 227–248. Springer, Singapore (2019). https://doi.org/10.1007/978-981-13-1924-2_12

    Chapter  Google Scholar 

  6. Habich, J., Zeiser, T., Hager, G., Wellein, G.: Enabling temporal blocking for a lattice Boltzmann flow solver through multicore-aware wavefront parallelization. In: 21st International Conference on Parallel Computational Fluid Dynamics, pp. 178–182 (2009)

    Google Scholar 

  7. Levchenko, V., Perepelkina, A., Zakirov, A.: Diamondtorre algorithm for high-performance wave modeling. Computation 4(3), 29 (2016)

    Article  Google Scholar 

  8. Levchenko, V., Perepelkina, A.: Locally recursive non-locally asynchronous algorithms for stencil computation. Lobachevskii J. Math. 39(4), 552–561 (2018)

    Article  MathSciNet  Google Scholar 

  9. Malas, T., Hager, G., Ltaief, H., Stengel, H., Wellein, G., Keyes, D.: Multicore-optimized wavefront diamond blocking for optimizing stencil updates. SIAM J. Sci. Comput. 37(4), C439–C464 (2015)

    Article  MathSciNet  Google Scholar 

  10. Maruyama, N., Aoki, T.: Optimizing stencil computations for NVIDIA kepler GPUs. In: Proceedings of the 1st International Workshop on High-Performance Stencil Computations, Vienna, pp. 89–95 (2014)

    Google Scholar 

  11. Montessori, A., et al.: Chapter 20 - multicomponent lattice Boltzmann models for biological applications. In: Cerrolaza, M., Shefelbine, S.J., Garz-Alvarado, D. (eds.) Numerical Methods and Advanced Simulation in Biomechanics and Biological Processes, pp. 357–370. Academic Press (2018). https://doi.org/10.1016/B978-0-12-811718-7.00020-4, http://www.sciencedirect.com/science/article/pii/B9780128117187000204

    Chapter  Google Scholar 

  12. Morton, G.M.: A computer oriented geodetic data base and a new technique in file sequencing (1966)

    Google Scholar 

  13. Nguyen, A., Satish, N., Chhugani, J., Kim, C., Dubey, P.: 3.5-D blocking optimization for stencil computations on modern CPUs and GPUs. In: High Performance Computing, Networking, Storage and Analysis (SC), pp. 1–13. IEEE (2010)

    Google Scholar 

  14. Niedermeier, C.A., Janßen, C.F., Indinger, T.: Massively-parallel multi-GPU simulations for fast and accurate automotive aerodynamics. In: 7th European Conference on Computational Fluid Dynamics (2018)

    Google Scholar 

  15. Perepelkina, A.Y., Levchenko, V.D., Goryachev, I.A.: Implementation of the kinetic plasma code with locally recursive non-locally asynchronous algorithms. J. Phys. Conf. Ser. 510, 012042 (2014)

    Article  Google Scholar 

  16. Perepelkina, A., Levchenko, V.: LRnLA algorithm ConeFold with non-local vectorization for LBM implementation. In: Voevodin, V., Sobolev, S. (eds.) RuSCDays 2018. CCIS, vol. 965, pp. 101–113. Springer, Cham (2019). https://doi.org/10.1007/978-3-030-05807-4_9

    Chapter  Google Scholar 

  17. Perepelkina, A., Levchenko, V., Khilkov, S.: The DiamondCandy LRnLA algorithm: raising efficiency of the 3D cross-stencil schemes. J. Supercomputing (2018). https://doi.org/10.1007/s11227-018-2461-z

  18. Perepelkina, A., Levchenko, V.: The DiamondCandy algorithm for maximum performance vectorized cross-stencil computation. Keldysh Institute Preprints (225) (2018)

    Google Scholar 

  19. Riesinger, C., Bakhtiari, A., Schreiber, M., Neumann, P., Bungartz, H.J.: A holistic scalable implementation approach of the lattice Boltzmann method for CPU/GPU heterogeneous clusters. Computation 5(4), 48 (2017)

    Article  Google Scholar 

  20. Shimokawabe, T., Endo, T., Onodera, N., Aoki, T.: A stencil framework to realize large-scale computations beyond device memory capacity on GPU supercomputers. In: 2017 IEEE International Conference on Cluster Computing (CLUSTER), pp. 525–529. IEEE (2017)

    Google Scholar 

  21. Succi, S.: The Lattice Boltzmann Equation: For Fluid Dynamics And Beyond. Oxford University Press, Oxford (2001)

    MATH  Google Scholar 

  22. Tomczak, T., Szafran, R.G.: A new GPU implementation for lattice-Boltzmann simulations on sparse geometries. arXiv preprint arXiv:1611.02445 (2016)

  23. Vizitiu, A., Itu, L., Niţă, C., Suciu, C.: Optimized three-dimensional stencil computation on Fermi and Kepler GPUs. In: 2014 IEEE High Performance Extreme Computing Conference (HPEC), pp. 1–6. IEEE (2014)

    Google Scholar 

  24. Williams, S., Waterman, A., Patterson, D.: Roofline: an insightful visual performance model for multicore architectures. Commun. ACM 52(4), 65–76 (2009)

    Article  Google Scholar 

  25. Wittmann, M.: Hardware-effiziente, hochparallele implementierungen von lattice-boltzmann-verfahren für komplexe geometrien (2016)

    Google Scholar 

  26. Zakirov, A., Levchenko, V., Perepelkina, A., Zempo, Y.: High performance FDTD algorithm for GPGPU supercomputers. J. Phys. Conf. Ser. 759, 012100 (2016)

    Article  Google Scholar 

Download references

Aknowledgement

The work is supported by Russian Science Foundation, grant # 18-71-10004.

Author information

Authors and Affiliations

  1. Keldysh Institute of Applied Mathematics, 4, Miusskaya Sq., Moscow, Russia

    Vadim Levchenko & Anastasia Perepelkina

  2. Kintech Lab Ltd., Moscow, Russia

    Andrey Zakirov & Anastasia Perepelkina

Authors
  1. Vadim Levchenko
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Andrey Zakirov
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Anastasia Perepelkina
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Anastasia Perepelkina .

Editor information

Editors and Affiliations

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

    Victor Malyshkin

Rights and permissions

Reprints and permissions

Copyright information

© 2019 Springer Nature Switzerland AG

About this paper

Check for updates. Verify currency and authenticity via CrossMark

Cite this paper

Levchenko, V., Zakirov, A., Perepelkina, A. (2019). GPU Implementation of ConeTorre Algorithm for Fluid Dynamics Simulation. 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_16

Download citation

  • DOI: https://doi.org/10.1007/978-3-030-25636-4_16

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-25635-7

  • Online ISBN: 978-3-030-25636-4

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics

Search

Navigation