Solutions of large-scale electromagnetics problems involving dielectric objects with the parallel multilevel fast multipole algorithm

Fast and accurate solutions of large-scale electromagnetics problems involving homogeneous dielectric objects are considered. Problems are formulated with the electric and magnetic current combined-field integral equation and discretized with the Rao-Wilton-Glisson functions. Solutions are performed iteratively by using the multi-level fast multipole algorithm (MLFMA). For the solution of large-scale problems discretized with millions of unknowns, MLFMA is parallelized on distributed-memory architectures using a rigorous technique, namely, the hierarchical partitioning strategy. Efficiency and accuracy of the developed implementation are demonstrated on very large problems involving as many as 100 million unknowns

A Hierarchical Partitioning Strategy for an Efficient Parallelization of the Multilevel Fast Multipole Algorithm

Cataloged from PDF version of article.We present a novel hierarchical partitioning strategy for the efficient parallelization of the multilevel fast multipole algorithm (MLFMA) on distributed-memory architectures to solve large-scale problems in electromagnetics. Unlike previous parallelization techniques, the tree structure of MLFMA is distributed among processors by partitioning both clusters and samples of fields at each level. Due to the improved load-balancing, the hierarchical strategy offers a higher parallelization efficiency than previous approaches, especially when the number of processors is large. We demonstrate the improved efficiency on scattering problems discretized with millions of unknowns. In addition, we present the effectiveness of our algorithm by solving very large scattering problems involving a conducting sphere of radius 210 wavelengths and a complicated real-life target with a maximum dimension of 880 wavelengths. Both of the objects are discretized with more than 200 million unknowns

Towards a scalable parallel MLFMA in three dimensions

The development of a scalable parallel multilevel fast multipole algorithm (MLFMA) for three dimensional electromagnetic scattering problems is reported. In the context of this work, the term 'scalable' denotes the ability to handle larger simulations with a proportional increase in the number of parallel processes (CPU cores), without loss of parallel efficiency. The workload is divided among the different processes according to the hierarchical partitioning scheme. Crucial to ensure the parallel scalability of the algorithm, is that the radiation patterns - sampled on the sphere - are partitioned in two dimensions, i.e., both in azimuth and elevation directions

A parallel multilevel fast multipole algorithm for GRID computing allowing optical full-wave simulations

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Parallel hardware and software implementations for electromagnetic computations

Cataloged from PDF version of article.Multilevel fast multipole algorithm (MLFMA) is an accurate frequencydomain electromagnetics solver that reduces the computational complexity and memory requirement significantly. Despite the advantages of the MLFMA, the maximum size of an electromagnetic problem that can be solved on a single processor computer is still limited by the hardware resources of the system, i.e., memory and processor speed. In order to go beyond the hardware limitations of single processor systems, parallelization of the MLFMA, which is not a trivial task, is suggested. This process requires the parallel implementations of both hardware and software. For this purpose, we constructed our own parallel computer clusters and parallelized our MLFMA program by using message-passing paradigm to solve electromagnetics problems. In order to balance the work load and memory requirement over the processors of multiprocessors systems, efficient load balancing techniques and algorithms are included in this parallel code. As a result, we can solve large-scale electromagnetics problems accurately and rapidly with parallel MLFMA solver on parallel clusters.Bozbulut, Ali RızaM.S

Rigorous solutions of electromagnetic problems involving hundreds of millions of unknowns

Accurate simulations of real-life electromagnetic problems with integral equations require the solution of dense matrix equations involving millions of unknowns. Solutions of these extremely large problems cannot be easily achieved, even when using the most powerul computers with state-of-the-art technology. Hence, many electromagnetic problems in the literature have been solved by resoring to various approximation techniques, without controllable error. In this paper, we present full-wave solutions of scattering problems discretized with hundreds of millions of unknowns by employing a parallel implementation of the Multilevel Fast Multipole Algorithm. Various examples involving canonical and complicated objects, including scatterers larger than 1000λ, are presented, in order to demonstrate the feasibility of accurately solving large-scale problems on relatively inexpensive computing platforms

An efficient parallel implementation of the multilevel fast multipole algorithm for rigorous solutions of large-scale scattering problems

We present the solution of large-scale scattering problems discretized with hundreds of millions of unknowns. The multilevel fast multipole algorithm (MLFMA) is parallelized using the hierarchical partitioning strategy on distributed-memory architectures. Optimizations and load-balancing algorithms are extensively used to improve parallel MLFMA solutions. The resulting implementation is successfully employed on modest parallel computers to solve scattering problems involving metallic objects larger than 1000λ and discretized with more than 300 million unknowns. © 2010 IEEE

Accurate numerical modeling of the TARA reflector system

The radiation pattern of the large parabolic reflectors of the Transportable Atmospheric RAdar system (TARA), developed at Delft University of Technology, has been accurately simulated. The electric field integral equation (EFIE) formulation has been applied to a model of the reflectors including the feed housing and supporting struts, discretised using the method of moments. Because the problem is electrically large (the reflector has a diameter of 33λ) and nonsymmetrical, this lead to a badly conditioned linear system of approximately half a million unknowns. In order to solve this system, an iterative solver (generalized minimum residual method) was used, in combination with the multilevel fast multipole method. Because of the bad conditioning, the system could only be solved by using a huge preconditioner. A new block-incomplete LU preconditioner (ILU) algorithm has been employed to allow for efficient out-of-computer core memory preconditioning.Peer Reviewe