Parallel MLFMA on Heterogeneous CPU-GPU Architectures for Imaging and Inverse Scattering

Levent Gurel, University of Illinois at Urbana-Champaign

Usage Details

In general, our goal is to solve extremely large electromagnetics problems involving big-data issues. To accomplish that, one tool that we develop is the multilevel fast multipole algorithm (MLFMA) and another tool that we use is a parallel supercomputer.

In this project, our goals are to port and adapt our parallel MLFMA codes to the homogeneous CPU-only Blue Waters architecture; to further parallelize our MLFMA codes on the heterogeneous CPU-GPU Blue Waters architecture; and to get ready for (and perhaps attempt, depending on the preliminary results) the solution of unprecedentedly large problems.

GPU-incorporated implementation of MLFMA for the solution of very large-scale problems will be studied for the first time, since earlier attempts reported in the literature involve relatively small problems, with at most a few millions of unknowns. We will develop and employ novel parallelization techniques specific to the heterogeneous CPU-GPU architecture of Blue Waters.