SUNVIZ: A Real-Time Visualization Environment for Space Physics Applications

  • S. Eliuk
  • P. Boulanger
  • K. Kabin
Part of the Lecture Notes in Computer Science book series (LNCS, volume 5359)


Real-time physically accurate simulations are difficult to create because of limited computational power available on a CPU. General purpose computing on the graphics processing unit (GPU) can provide a significant increase in performance. We are able to investigate the flow characteristics of a cloud of charged particles, which is one of the first steps in our goal of generating a real-time Coronal Mass Ejection (CME) simulator. Preliminary results show a sustained 60 Hz visual simulation with approximately four million particles and a non-visual simulation of 16 million particles at 30 Hz. The simulator provides a novel way to investigate a CME in real-time, and it has the potential to predict when a particular CME is geoeffective, i.e. an event that could damage electrical infrastructure such as satellites, space stations, power grids, etc...


Graphic Processing Unit Coronal Mass Ejection Interplanetary Coronal Mass Ejection Space Weather Prediction General Purpose Graphic Processing Unit 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


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  1. 1.
    Nyland, L., Harris, M., Prins, J.: GPUGEMS3. Addison-Wesley, Boston (2007); Fast n-body simulation with CUDAGoogle Scholar
  2. 2.
    Schive, H.Y., Chien, C.H., Wong, S.K., Tsai, Y.C., Chiueh, T.: Graphic-card cluster for astrophysics (gracca) – performance tests (2007)Google Scholar
  3. 3.
    Manchester, W.B., Gombosi, T.I., Roussev, I., Zeeuw, D.L.D., Sokolov, I.V., Powell, K.G., T’oth, G., Opher, M.: Three-dimensional mhd simulation of a ux rope driven cme. Journal of Geophysical Research (Space Physics) 109, 1102 (2004)CrossRefGoogle Scholar
  4. 4.
    Greengard, L., Rokhlin, V.: A Fast Algorithm for Particle Simulations. Journal of Computational Physics 73, 325–348 (1987)MathSciNetCrossRefzbMATHGoogle Scholar
  5. 5.
    Toth, G.: Space weather modeling framework: A new tool for the space. Journal of Geophysical Research 110, 1029–1091 (2005)CrossRefGoogle Scholar
  6. 6.
    Gombosi, T., DeZeeuw, D., Groth, C., Powell, K., Stout, Q.: Multiscale mhd simulation of coronal mass ejection and its interaction with the magnetosphere-ionsphere system. Journal of Atmospheric and Solar-Terrestial Physics 62, 1515–1525 (2000)CrossRefGoogle Scholar
  7. 7.
    Wu, C., Fry, C., Dryer, M., Liou, K.: Three-dimensional global simulation of interplanetary coronal mass ejection propagation from the sun to the heliosphere: Solar event of 12 may 1997. Journal of Geophysical Research 123, 1061–1078 (2007)Google Scholar
  8. 8.
    Drimmel, R., Abbott, B., Emmart, C., AMNH/Hayden, J.A., NCSA/UIUC, S.L: (Amnh star catalog, digital universe atlas version 3)Google Scholar
  9. 9.
    Treumann, R., Baumjohann, W.: Basic space plasma physics/advanced space plasma physics. Plasma Physics and Controlled Fusion 43, 371 (2001)CrossRefzbMATHGoogle Scholar
  10. 10.
    Roussev, I.I., Lugaz, N., Sokolov, I.V.: New physical insight on the changes in magnetic field topology during coronal mass ejections: Case study for the 2002 apr 21 event. In: American Astronomical Society Meeting Abstracts. American Astronomical Society Meeting Abstracts, vol. 210, p. 58.04 (2007)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2008

Authors and Affiliations

  • S. Eliuk
    • 1
  • P. Boulanger
    • 1
  • K. Kabin
    • 1
  1. 1.University of Alberta Computing Science, Advanced Man Machine Interface Laboratory, University of Alberta PhysicsCanada

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