Configure Scheme of Mixed Computer Architecture for FMM Algorithm

Chapter
Part of the Lecture Notes in Electrical Engineering book series (LNEE, volume 125)

Abstract

Along with the scale expansion of high performance computing, accelerators are increasingly viewed as computer coprocessors that can provide significant computational performance at low price. Thus, research of mixed computer architecture is becoming popular. This paper presents a mixed configurable computer architecture which can run fast multipole method (FMM) algorithm of N-Body problem well. Each sub-procedure of FMM algorithm is implemented and tested on GPU, FPGA and CELL. FMM is optimized on the proposed configure scheme through decomposing its task flow. The probable solution for different task flow is also put forward. The conclusion is significant to the research on the mixed computer architecture of high performance computing.

Keywords

Graphical Processing Unit Field Programmable Gate Array High Performance Computing Computer Architecture Multipole Expansion 
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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References

  1. 1.
    Cruz, F.A., Barba, L.A.: Characterization of the accuracy of the fast multipole method in particle simulations. Journal of Numerical Methods in Engineer (79), 1577–1604 (2009)MathSciNetMATHCrossRefGoogle Scholar
  2. 2.
    Spurzem, R., Berczik, P., Marcus, G., et al.: Accelerating astrophysical particle simulations with programmable hardware (FPGA and GPU). Computer Science - Research and Development (23), 231–239 (2009)CrossRefGoogle Scholar
  3. 3.
    Che, S., Li, J., Sheaffer, J.W., et al.: Accelerating Compute -Intensive Applications with GPUs and FPGAs. In: Proc. of the IEEE Symposium on Application Specific Processors, SASP (June 2008)Google Scholar
  4. 4.
    Aubert, D., Amini, M., David, R.: A Particle-Mesh Integrator for Galactic Dynamics Powered by GPGPUs. In: Allen, G., Nabrzyski, J., Seidel, E., van Albada, G.D., Dongarra, J., Sloot, P.M.A. (eds.) ICCS 2009, Part I. LNCS, vol. 5544, pp. 874–883. Springer, Heidelberg (2009)CrossRefGoogle Scholar
  5. 5.
    Hamada, T., Nitadori, K., Benkrid, K., et al.: A novel multiple-walk parallel algorithm for the Barnes–Hut treecode on GPUs – towards cost effective, high performance N-body simulation. Computer Science - Research and Development (24), 21–31 (2009)CrossRefGoogle Scholar
  6. 6.
    Gumerov, N.A., Duraiswami, R.: Fast Multipole Methods on Graphics Processors. Journal of Computational Physics (227), 8290–8313 (2008)MathSciNetMATHCrossRefGoogle Scholar
  7. 7.
    Xu, K., Ding, D.Z., Fan, Z.H., et al.: Multilevel Fast Multipole Algorithm Enhanced by GPU parallel Technique for Electromagentic Scattering Problems. Microwave and Optical Technology Letters 52(3), 502–507 (2010)CrossRefGoogle Scholar
  8. 8.
    Stokes, M.L.: A Brief Look at FPGAs, GPUs and Cell Processors. ITEA Journal (7), 9–11 (2007)MathSciNetGoogle Scholar

Copyright information

© Springer-Verlag GmbH Berlin Heidelberg 2012

Authors and Affiliations

  1. 1.School of Computer Engineering & ScienceShanghai UniversityShanghaiChina

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