Numerical simulation of physical phenomena by parallel computing

  • Gerhard Fritsch
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 253)


Beside experiment and theory, computational science is a new methodology, for the advance in research and development in natural and engineering sciences. In many fields numerical simulation becomes more and more important for industrial development and production (e.g. car builders, aircraft industry). By numerical simulation, the development of new products becomes less expensive, less time consuming and often more suitable physical properties can be obtained. Examples for compute-intensive numerical simulation such as from computational fluid dynamics, large scale integrated circuits, chemistry and other are being discussed. Furthermore, it is shown that appropriate numerical techniques together with suitably structured high performance multiprocessor systems represent powerful tools for large parallel computing.


Monte Carlo Shared Memory Multigrid Method User Problem Multiprocessor System 


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

5. References

  1. /1/.
    Brandt, A.; Multigrid Solvers on Parallel Computers. In: Elliptic Problem Solvers (M. Schultz, ed.), New York, 39–83 (1981)Google Scholar
  2. /2/.
    Stüben, K.; Trottenberg, U.; Multigrid methods: Fundamental algorithms, model problem analysis and applications. In: Multigrid Methods. Proc. Conf. held at Köln-Porz, Nov. 23–27, 1981 (W. Hackbusch, U. Trottenberg, eds.), Lecture Notes in Mathematics 960, Springer Verlag Berlin, 1–176 (1982)Google Scholar
  3. /3/.
    Bode, A.; Fritsch, G.; Händler, W.; Henning, W.; Hofmann, F.; Volkert, J.: Multi-Grid Oriented Computer Architecture. Proc. 1985 Int. Conf. on Parallel Processing, St. Charles 1985, 81–95. IEEE Comp. Soc. 1985Google Scholar
  4. /4/.
    Bode, A.; Fritsch, G.; Henning, W.; Volkert, J.: High performance multiprocessor systems for numerical applications. Proc. First Int. Conf. on Supercomputing Systems, St. Petersburg/Florida, Dec. 16–20, 1985, 460–467, IEEE Comp. Soc. Press 1985Google Scholar
  5. /5/.
    Hockney, R.W.; Eastwood, J.W.: Computer Simulation Using Particles. McGraw-Hill 1981Google Scholar
  6. /6/.
    Hockney, R.W.; Jesshope, J.R.: Parallel Computers. Adam Hilger Bristol 1981Google Scholar
  7. /7/.
    Monte Carlo Methods in Statistical Physics (Ed. K. Binder), Topics in Current Physics 7, Springer Verlag 1986Google Scholar
  8. /8/.
    Pearson, R.B.; Richardson, J.L.; Toussaint, D.: Special-purpose processors in theoretical physics. Comm. ACM 28,4 (1985),385–389Google Scholar
  9. /9/.
    Hoshino, T.: An invitation to the world of PAX. Computer, May 1986, 68–79Google Scholar
  10. /10/.
    Hoshino, T.; Takenouchi, K.: Processing of the molecular dynamics model by the parallel computer PAX. Computer Phys. Comm. 31 (1984), 287–296Google Scholar
  11. /11/.
    Clementi, E.; Corongiu, G.; Detrich, J.H.: Parallelism in computations in quantum and statistical mechanics. Computer Physics Comm. 37 (1985), 287–294Google Scholar
  12. /12/.
    Rapaport, D.C.: Many-body simulations using an array processor. Computer Physics Comm. 37 (1985), 343–349Google Scholar
  13. /13/.
    Kutler, P.: A perspective of theoretical and applied computational fluid dynamics. AIAA Journal, March 1985, 328–341Google Scholar
  14. /14/.
    Händler, W.; Hofmann, F.; Schneider, H.J.: A General Purpose Array with a Broad Spectrum of Applications. In: Händler, W.: Computer Architecture, Informatik Fachberichte, Springer Verlag Berlin Heidelberg New York, 4,311–35 (1976)Google Scholar
  15. /15/.
    Händler, W.; Herzog, U.; Hofmann, F.; Schneider, H.J.: Multiprozessoren für breite Anwendungsgebiete: Erlangen General Purpose Array. GI/NTG-Fachtagung "Architektur und Betrieb von Rechensystemen", Informatik-Fachberichte, Springer Verlag Berlin Heidelberg New York, 78, 195–208 (1984)Google Scholar
  16. /16/.
    Händler, W.; Rohrer, H.: Thoughts on a Computer Construction Kit. Elektronische Rechenanlagen 22, 1, 3–13;1980)Google Scholar
  17. /17/.
    Händler, W.; Schreiber, H.; Sigmund, V.: Computation Structure Reflected in General Purpose and Special Purpose Multi-Processor Systems. Proc. 1979 Int. Conf. on Parallel Processing, 95–102. IEEE Comp. Soc. 1979Google Scholar
  18. /18/.
    Händler, W.; Maehle, E.; Wirl, K.: DIRMU Multiprocessor Configurations, Proc. 1985 Int. Conf. on Parallel Processing, St. Charles 1985, 652–656. IEEE Comp. Soc. 1985Google Scholar
  19. /19/.
    Händler, W.: Dynamic computer structures for manifold utilization. Parallel Computing 2 (1985), 15–32.Google Scholar
  20. /20/.
    Regenspurg, G.: Entwicklung von Zentralprozessoren aus Einheitsbausteinen. Elektron. Rechenanlagen 21 (1979),61–64, 125–129Google Scholar
  21. /21/.
    Momoi, Sh.; Shimada, Sh.; Kobayaski, M.; Ishikawa, T.: Hierarchical array processor system (HAP). CONPAR 86, Aachen/F.R.Germany, Sept. 17–19 1986.Google Scholar
  22. /22/.
    Henning, W.; Volkert, J.: Programming EGPA systems. Proc. 5th Int. Conf. Distributed Computing Systems, Denver/Col., May 13–17, 1985, 552–559Google Scholar
  23. /23/.
    Suprenum, Vorhabensbeschreibung, Gesellschaft für Mathematik und Datenverarbeitung mbH, Inst. f. Math. Grundlagen, St. Augustin/F.R. Germany, Oct. 1985Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 1987

Authors and Affiliations

  • Gerhard Fritsch
    • 1
  1. 1.Department of Computer Science (IMMD)Universität Erlangen-NürnbergErlangenF.R. Germany

Personalised recommendations