Skip to main content

Particle-In-Cell Simulation Using Asynchronous Tasking

  • Conference paper
  • First Online:
Euro-Par 2021: Parallel Processing (Euro-Par 2021)

Abstract

Recently, task-based programming models have emerged as a prominent alternative among shared-memory parallel programming paradigms. Inherently asynchronous, these models provide native support for dynamic load balancing and incorporate data flow concepts to selectively synchronize the tasks. However, tasking models are yet to be widely adopted by the HPC community and their effective advantages when applied to non-trivial, real-world HPC applications are still not well comprehended. In this paper, we study the parallelization of a production electromagnetic particle-in-cell (EM-PIC) code for kinetic plasma simulations exploring different strategies using asynchronous task-based models. Our fully asynchronous implementation not only significantly outperforms a conventional, synchronous approach but also achieves near perfect scaling for 48 cores.

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 99.00
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 129.99
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

Notes

  1. 1.

    https://github.com/epeec/zpic-epeec.

References

  1. Intel\(\textregistered \)Threading Building Blocks. https://www.intel.com/content/www/us/en/develop/documentation/tbb-documentation/top.html

  2. Adcock, A.B., Sullivan, B.D., Hernandez, O.R., Mahoney, M.W.: Evaluating OpenMP tasking at scale for the computation of graph hyperbolicity. In: Rendell, A.P., Chapman, B.M., Müller, M.S. (eds.) IWOMP 2013. LNCS, vol. 8122, pp. 71–83. Springer, Heidelberg (2013). https://doi.org/10.1007/978-3-642-40698-0_6

    Chapter  Google Scholar 

  3. Akhmetova, D., Iakymchuk, R., Ekeberg, O., Laure, E.: Performance study of multithreaded MPI and OpenMP tasking in a large scientific code (2017)

    Google Scholar 

  4. Aliaga, J.I., Carratalá-Sáez, R., Kriemann, R., Quintana-Ortí, E.S.: Task-parallel LU factorization of hierarchical matrices using OmpSs. In: IEEE International Parallel and Distributed Processing Symposium Workshops (IPDPSW), pp. 1148–1157 (2017)

    Google Scholar 

  5. Anderson, M., Brodowicz, M., Kulkarni, A., Sterling, T.: Performance modeling of gyrokinetic toroidal simulations for a many-tasking runtime system. In: Jarvis, S.A., Wright, S.A., Hammond, S.D. (eds.) PMBS 2013. LNCS, vol. 8551, pp. 136–157. Springer, Cham (2014). https://doi.org/10.1007/978-3-319-10214-6_7

    Chapter  Google Scholar 

  6. Arber, T.D., et al.: Contemporary particle-in-cell approach to laser-plasma modelling. Plasma Phys. Controlled Fus. 57(11), 113001 (2015)

    Article  Google Scholar 

  7. Augonnet, C., Thibault, S., Namyst, R., Wacrenier, P.A.: StarPU: a unified platform for task scheduling on heterogeneous multicore architectures. Concurr. Comput. Pract. Exp. 23(2), 187–198 (2011)

    Article  Google Scholar 

  8. Ayguadé, E., Duran, A., Hoeflinger, J., Massaioli, F., Teruel, X.: An experimental evaluation of the new OpenMP tasking model. In: Adve, V., Garzarán, M.J., Petersen, P. (eds.) LCPC 2007. LNCS, vol. 5234, pp. 63–77. Springer, Heidelberg (2008). https://doi.org/10.1007/978-3-540-85261-2_5

    Chapter  Google Scholar 

  9. Blumofe, R.D., Joerg, C.F., Kuszmaul, B.C., Leiserson, C.E., Randall, K.H., Zhou, Y.: Cilk: an efficient multithreaded runtime system. J. Parallel Distrib. Comput. 37(1), 55–69 (1996)

    Article  Google Scholar 

  10. Bosch, J., Filgueras, A., Vidal, M., Jimenez-Gonzalez, D., Alvarez, C., Martorell, : X.: Exploiting parallelism on GPUs and FPGAs with OmpSs (2017)

    Google Scholar 

  11. Bueno, J., et al.: Productive cluster programming with OmpSs. In: Jeannot, E., Namyst, R., Roman, J. (eds.) Euro-Par 2011. LNCS, vol. 6852, pp. 555–566. Springer, Heidelberg (2011). https://doi.org/10.1007/978-3-642-23400-2_52

    Chapter  Google Scholar 

  12. Chasapis, D., et al.: PARSECSs: evaluating the impact of task parallelism in the PARSEC benchmark suite. ACM Trans. Arch. Code. Optim. 12(4), 41:1–41:22 (2015)

    Google Scholar 

  13. Ciesko, J., et al.: Task-parallel reductions in OpenMP and OmpSs. In: DeRose, L., de Supinski, B.R., Olivier, S.L., Chapman, B.M., Müller, M.S. (eds.) IWOMP 2014. LNCS, vol. 8766, pp. 1–15. Springer, Cham (2014). https://doi.org/10.1007/978-3-319-11454-5_1

    Chapter  Google Scholar 

  14. Derouillat, J., et al.: SMILEI: a collaborative, open-source, multi-purpose particle-in-cell code for plasma simulation. Comput. Phys. Commun. 222, 351–373 (2018)

    Article  MathSciNet  Google Scholar 

  15. Ding, Y., Hu, K., Wu, K., Zhao, Z.: Performance monitoring and analysis of task-based OpenMP. PLOS ONE 8(10), 1–12 (2013)

    Google Scholar 

  16. Duran, A., Ferrer, R., Ayguadé, E., Badia, R.M., Labarta, J.: A proposal to extend the OpenMP tasking model with dependent tasks. Int. J. Parallel Program. 37(3), 292–305 (2009)

    Article  Google Scholar 

  17. Ethier, S., Tang, W.M., Lin, Z.: Gyrokinetic particle-in-cell simulations of plasma microturbulence on advanced computing platforms. J. Phys. Conf. Ser. 16, 1–15 (2005)

    Article  Google Scholar 

  18. Fonseca, R.A., Silva, L.O., Tsung, F.S., Decyk, V.K., Lu, W., Ren, C., Mori, W.B., Deng, S., Lee, S., Katsouleas, T., Adam, J.C.: OSIRIS: a three-dimensional, fully relativistic particle in cell code for modeling plasma based accelerators. In: Sloot, P.M.A., Hoekstra, A.G., Tan, C.J.K., Dongarra, J.J. (eds.) ICCS 2002. LNCS, vol. 2331, pp. 342–351. Springer, Heidelberg (2002). https://doi.org/10.1007/3-540-47789-6_36

    Chapter  Google Scholar 

  19. Fonseca, R.A., et al.: Exploiting multi-scale parallelism for large scale numerical modelling of laser wakefield accelerators. Plasma Phys. Controlled Fus. 55(12), 124011 (2013)

    Google Scholar 

  20. Fonseca, R.A., Silva, L.O., Tonge, J.W., Mori, W.B., Dawson, J.M.: Three-dimensional weibel instability in astrophysical scenarios. Phys. Plasmas 10(5), 1979–1984 (2003)

    Article  MathSciNet  Google Scholar 

  21. Germaschewski, K., et al.: The plasma simulation code: a modern particle-in-cell code with load-balancing and gpu support. 1310, 7866 (2015)

    Google Scholar 

  22. Kaiser, H., Heller, T., Adelstein-Lelbach, B., Serio, A., Fey, D.: HPX. In: A Task Based Programming Model in a Global Address Space, vol. 14, pp. 1–11. ACM Press, Eugene (2014)

    Google Scholar 

  23. Koniges, A., et al.: Application acceleration on current and future cray platforms. Proc. Cray User Group Meeting (2009)

    Google Scholar 

  24. OpenMP Specification. https://www.openmp.org/specifications/

  25. Prat, R., Colombet, L., Namyst, R.. : In: Combining task-based parallelism and adaptive mesh refinement techniques in molecular dynamics simulations, New York, NY, USA (2018)

    Google Scholar 

  26. Pukhov, A.: Three-dimensional electromagnetic relativistic particle-in-cell code VLPL. J. Plasma Phys. 61, 425–433 (1999)

    Article  Google Scholar 

  27. Rico, A., Sánchez Barrera, I., Joao, J.A., Randall, J., Casas, M., Moretó, M.: On the benefits of tasking with OpenMP. In: OpenMP: Conquering the Full Hardware Spectrum, pp. 217–230 (2019)

    Google Scholar 

  28. Tajima, T., Dawson, J.M.: Laser electron accelerator. Phys. Rev. Lett. 43, 267–270 (1979)

    Article  Google Scholar 

  29. Valero-Lara, P., Sirvent, R., Peña, A.J., Labarta, J.: MPI+OpenMP tasking scalability for multi-morphology simulations of the human brain. Parallel Comput. 84, 50–61 (2019)

    Article  Google Scholar 

  30. Verboncoeur, J.P.: Particle simulation of plasmas: review and advances. Plasma Phys. Controlled Fus. 47(5A), A231 (2005)

    Article  Google Scholar 

  31. Villasenor, J., Buneman, O.: Rigorous charge conservation for local electromagnetic field solvers. Comput. Phys. Commun. 69(2), 306–316 (1992)

    Article  Google Scholar 

  32. ZPIC documentation. https://github.com/zambzamb/zpic/blob/master/doc/Documentation.md, Accessed 05 Sept 2019

Download references

Acknowledgements

This work was partially supported by Fundação Ciência e Tecnologia (FCT) under grant UIDB /50021/2020 and by the EPEEC project, which has received funding from the European Union’s Horizon 2020 research and innovation programme under grant agreement No 801051.

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Nicolas Guidotti .

Editor information

Editors and Affiliations

Rights and permissions

Reprints and permissions

Copyright information

© 2021 Springer Nature Switzerland AG

About this paper

Check for updates. Verify currency and authenticity via CrossMark

Cite this paper

Guidotti, N. et al. (2021). Particle-In-Cell Simulation Using Asynchronous Tasking. In: Sousa, L., Roma, N., Tomás, P. (eds) Euro-Par 2021: Parallel Processing. Euro-Par 2021. Lecture Notes in Computer Science(), vol 12820. Springer, Cham. https://doi.org/10.1007/978-3-030-85665-6_30

Download citation

  • DOI: https://doi.org/10.1007/978-3-030-85665-6_30

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-85664-9

  • Online ISBN: 978-3-030-85665-6

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics