Reducing MLFMA memory with out-of-core implementation and data-structure parallelization

We present two memory-reduction methods for the parallel multilevel fast multipole algorithm (MLFMA). The first method implements out-of-core techniques and the second method parallelizes the pre-processing data structures. Together, these methods decrease parallel MLFMA memory bottlenecks, and hence fast and accurate solutions can be achieved for large-scale electromagnetics problems.