SC17 Denver, CO

P16: Scaling Analysis of a Hierarchical Parallelization of Large Inverse Multiple-Scattering Solutions

Authors: Mert Hidayetoglu (University of Illinois), Carl Pearson (University of Illinois), Izzat El-Hajj (University of Illinois), Weng Cho Chew (University of Illinois), Levent Gurel (University of Illinois), Wen-Mei Hwu (University of Illinois)

Abstract: 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. An inverse problem involving a large Shepp-Logan phantom is solved on up to 1,024 CPU nodes of the Blue Waters supercomputer in order to demonstrate the strong-scaling efficiency of the proposed parallelization scheme. The results show that parallelizing illuminations has almost perfect scaling efficiency of 95% because of the independent nature of forward-scattering solutions, however, parallelization of MLFMA has 73% efficiency due to MPI communications in MLFMA multiplications. Nevertheless, the proposed strategy improves granularity and allows spreading DBIM solutions on large numbers of nodes.
Award: Best Poster Finalist (BP): no

Poster: pdf
Two-page extended abstract: pdf

Poster Index