Scaling Analysis of a Hierarchical Parallelization of Large Inverse Multiple-Scattering Solutions

We propose a hierarchical parallelization strategy to improve the scalability of inverse multiple-scattering solutions. The inverse solverparallelizes theindependent forward solutionscorresponding 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