• DocumentCode
    500919
  • Title

    GPU friendly Fast Poisson Solver for structured power grid network analysis

  • Author

    Shi, Jin ; Cai, Yici ; Hou, Wenting ; Ma, Liwei ; Tan, Sheldon X D ; Ho, Pei-Hsin ; Wang, Xiaoyi

  • Author_Institution
    Comput. Sci. Dept., Tsinghua Univ., Beijing, China
  • fYear
    2009
  • fDate
    26-31 July 2009
  • Firstpage
    178
  • Lastpage
    183
  • Abstract
    In this paper, we propose a novel simulation algorithm for large scale structured power grid networks. The new method formulates the traditional linear system as a special two-dimension Poisson equation and solves it using an analytical expressions based on FFT technique. The computation complexity of the new algorithm is O(NlgN), which is much smaller than the traditional solver´s complexity O(N1.5) for sparse matrices, such as the SuperLU solver and the PCG solver. Also, due to the special formulation, graphic process unit (GPU) can be explored to further speed up the algorithm. Experimental results show that the new algorithm is stable and can achieve 100X speed up on GPU over the widely used SuperLU solver with very little memory footprint.
  • Keywords
    Poisson equation; computational complexity; computer graphic equipment; distribution networks; fast Fourier transforms; sparse matrices; 2D Poisson equation; FFT technique; GPU friendly fast Poisson solver; PCG solver; SuperLU solver; computation complexity; graphic process unit; large scale structured power grid networks; linear system; memory footprint; simulation algorithm; sparse matrices; structured power grid network analysis; Acceleration; Algorithm design and analysis; Computational modeling; Large-scale systems; Linear systems; Packaging; Partitioning algorithms; Performance analysis; Poisson equations; Power grids; Fast Poisson Solver; GPU; P/G network;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Design Automation Conference, 2009. DAC '09. 46th ACM/IEEE
  • Conference_Location
    San Francisco, CA
  • ISSN
    0738-100X
  • Print_ISBN
    978-1-6055-8497-3
  • Type

    conf

  • Filename
    5227177