HyperLink   Scaling Analysis of a Hierarchical Parallelization of Large Inverse Multiple-Scattering Solutions
Publication Year:
  Mert Hidayetoglu, Carl Pearson, Izzat El Hajj, Weng Cho Chew, Levent Gurel, Wen-mei Hwu
  Poster at the International Conference for High Performance Computing, Networking, Storage and Analysis (SC), 2017.

We propose a hierarchical parallelization strategy to improve the scalability of inverse multiple-scattering solutions. The inverse solver parallelizes the independent forward solutions corresponding to different illuminations. For further scaling out on large numbers of computing nodes, each forward solver parallelizes the dense and large matrix-vector multiplications accelerated by the multilevel fast multipole algorithm. Numerical results on up to 1,024 of CPU nodes show that the former and latter parallelizations have 95% and 73% strong-scaling efficiencies, respectively.