MLFMA optimal decomposing strategy

To decompose the far-neighborhood and near-neighborhood data domains of Multi-Level Fast Multi-Pole Algorithm (MLFMA), an optimal parallel implementation is discussed in this paper. To solve this problem, the characteristics of the two fields are respectively considered. Especially in the near-neighborhood decomposing, the clustering property of data is applied. The effectiveness of the optimized strategy is demonstrated by experimental results. Comparing with the former strategy, a higher parallel efficiency is obtained.