Skip to main content
Log in

Fast weighting method for plasma PIC simulation on GPU-accelerated heterogeneous systems

  • Published:
Journal of Central South University Aims and scope Submit manuscript

Abstract

Particle-in-cell (PIC) method has got much benefits from GPU-accelerated heterogeneous systems. However, the performance of PIC is constrained by the interpolation operations in the weighting process on GPU (graphic processing unit). Aiming at this problem, a fast weighting method for PIC simulation on GPU-accelerated systems was proposed to avoid the atomic memory operations during the weighting process. The method was implemented by taking advantage of GPU’s thread synchronization mechanism and dividing the problem space properly. Moreover, software managed shared memory on the GPU was employed to buffer the intermediate data. The experimental results show that the method achieves speedups up to 3.5 times compared to previous works, and runs 20.08 times faster on one NVIDIA Tesla M2090 GPU compared to a single core of Intel Xeon X5670 CPU.

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

Access this article

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Similar content being viewed by others

References

  1. BIRDSALL C, LANGDON A. Plasma physics via computer simulation [M]. New York: Adam Hilger, 1991: 23–24.

    Google Scholar 

  2. PRZEBINDA V, CARY J. Some improvements in PIC performance through sorting, caching, and dynamic load balancing [M]. Boulder, Colorado: University of Colorado, 2005: 1–14.

    Google Scholar 

  3. QIANG J, RYNE R, HABIB S, DECYK V. An object-oriented parallel particle-in-cell code for beam dynamics simulation in linear accelerators [J]. Journal of Computational Physics, 2000, 16(3): 434–451.

    Article  Google Scholar 

  4. GERMASCHEWSKI K, RUHL H, BHATTACHARJEE A. Dynamic load-balancing and GPU computing with the particle-in-cell code PSC [J]. Bulletin of the American Physical Society, 2011, 56(1): 13–23.

    Google Scholar 

  5. MADDURI K, IM E, IBRAHIM K, WILLIAMS S, ETHIER S, OLIKER L. Gyrokinetic particle-in-cell optimization on emerging multi-and manycore platforms [J]. Parallel Computing, 2011, 37(9): 501–520.

    MathSciNet  Google Scholar 

  6. FAN Z, QIU F, KAUFMAN A, YOAKUM-STOVER S. GPU cluster for high performance computing [C]// Proceedings of the 2004 ACM/IEEE Conference on Supercomputing. Washington DC, USA: IEEE Computer Society, 2004: 47–58.

    Google Scholar 

  7. NVIDIA C. Compute unified device architecture programming guide [M]. Santa Clara, CA: NVIDIA Coorperation, 2010: 3–5.

    Google Scholar 

  8. STANTCHEV G, DORLAND W, GUMEROV N. Fast parallel particle-to-grid interpolation for plasma PIC simulations on the GPU [J]. Journal of Parallel and Distributed Computing, 2008, 68(10): 1339–1349.

    Article  Google Scholar 

  9. BURAU H, WIDERA R, HONIG W, JUCKELAND G, DEBUS A, KLUGE T, SCHRAMM U, COWAN T, SAUERBREY R, BUSSMANN M. PIConGPU: A fully relativistic particle-in-cell code for a GPU cluster [J]. IEEE Transactions on Plasma Science, 2010, 38(10): 2831–2839.

    Article  Google Scholar 

  10. KONG X, HUANG M, REN C, DECYK V. Particle-in-cell simulations with charge-conserving current deposition on graphic processing units [J]. Journal of Computational Physics, 2011, 230(4): 1676–1685.

    Article  MATH  Google Scholar 

  11. COOKE S, LEVUSH B, CHERNYAVSKIY I, ANTONSEN T. GPU-accelerated 3d electromagnetic PIC simulations [C]// IEEE International Conference on Plasma Science (ICOPS). Washing DC, USA: IEEE Press, 2011: 1–2.

    Google Scholar 

  12. MERTMANN P, EREMIN D, MUSSENBROCK T, BRINKMANN R, AWAKOWICZ P. Fine-sorting one-dimensional particle-in-cell algorithm with montecarlo collisions on a graphics processing unit [J]. Computer Physics Communications, 2011, 18(2): 2161–2167.

    Article  Google Scholar 

  13. HILL S, COLLIN D. Practical, dynamic visibility for games [J]. GPU Pro, 2011, 2(1): 329–330.

    Article  Google Scholar 

  14. OWENS J, HOUSTON M, LUEBKE D, GREEN S, STONE J, PHILLIPS J. GPU computing [J]// Proceedings of the IEEE. 2008, 96(5): 879–899.

    Article  Google Scholar 

  15. RYOO S, RODRIGUES C, BAGHSORKHI S, STONE S, KIRK D, HWU W. Optimization principles and application performance evaluation of a multithreaded GPU using CUDA [C]// Proceedings of the 13th ACM SIGPLAN Symposium on Principles and Practice of Parallel Programming. UT, USA: ACM Press, 2010: 73–82.

    Google Scholar 

  16. TANG Tao, YANG Xue-jun, LIN Yu-fei. Cache miss analysis for GPU programs based on stack distance profile [C]// Proceedings of the 31st International Conference on Distributed Computing Systems. Minneapolis, USA: ICDCS ICDCS’ 11, 2011: 623–634.

    Google Scholar 

  17. CALLAHAN D, KOBLENZ B. Register allocation via hierarchical graph coloring [C]// ACM SIGPLAN Notices. CA, USA: ACM Press, 1991: 182–203.

    Google Scholar 

  18. BRIGGS P, COOPER K, TORCZON L. Improvements to graph coloring register allocation [J]. ACM Transactions on Programming Languages and Systems, 1994, 16(3): 428–455.

    Article  Google Scholar 

  19. ZHANG Ai-qing, MO Ze-yao. Parallelization of LARED-P codes for simulation of laser plasma interactions [R]. GF Report, Technical Report, ZW-J-2002045, IAPCM, 2002.

    Google Scholar 

  20. MO Ze-yao, XU Lin-bao, ZHANG Bao-lin, SHEN Long-jun. parallel computing and performance analysis for 2-dimensional plasma simulations with particle clouds in cells method [J]. Chinese Journal of Computational Physics, 1999, 16(5): 496–504. (in Chinese)

    Google Scholar 

  21. ZHENG Chun-yang, ZHU Shao-ping, HE Xian-tu. Quasistatic magnetic field generation by an intense ultrashort laser pulse in underdense plasma [J]. Chinese Physics Letters. 2000, 17(10): 746–748.

    Article  Google Scholar 

  22. ZHENG Chun-yang, HE Xian-tu, ZHU Shao-ping. Magnetic field generation and relativistic electron dynamics in circularly polarized intense laser interaction with dense plasma [J]. Physics of plasmas. Physics of Plasmas, 2005, 12(4): 44–55.

    MathSciNet  Google Scholar 

  23. ZHENG Chun-yang, ZHANG Ai-qing, ZHU Shao-ping, HE Xian-tu. Simulation of electron beam instabilities in collisionless plasmas [J]. Journal of Plasma Physics, 2006, 72(2): 249–258.

    Article  Google Scholar 

  24. CHEN Min, SHENG Zheng-ming, ZHENG Jun, MA Yan-yun, ZHANG Jie. Development and application of multi-dimensional particle-in-cell codes for investigation of laser plasma interactions [J]. Chinese Journal of Computational Physics, 2008, 25(1): 50. (in Chinese)

    Google Scholar 

  25. GARLAND M, GRAND S, NICKOLLS J, ANDERSON J, HARDWICK J, MORTON S, PHILLIPS E, ZHANG Y, VOLKOV V. Parallel computing experiences with CUDA [J]. Micro, 2008, 28(4): 13–27.

    Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Qiang Wu  (吴强).

Additional information

Foundation item: Projects(61170049, 60903044) supported by National Natural Science Foundation of China; Project(2012AA010903) supported by National High Technology Research and Development Program of China

Rights and permissions

Reprints and permissions

About this article

Cite this article

Yang, Cq., Wu, Q., Hu, Hl. et al. Fast weighting method for plasma PIC simulation on GPU-accelerated heterogeneous systems. J. Cent. South Univ. 20, 1527–1535 (2013). https://doi.org/10.1007/s11771-013-1644-2

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11771-013-1644-2

Key words

Navigation