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Title: ACES Express Journal April 2016 Cover
File Type: Express Journal Paper
Issue:Volume: 1      Number: 4      Year: 2016
Download Link:Click here to download PDF     File Size: 1054 KB

Title: ACES Express Journal April 2016 Front/Back Matter
File Type: Express Journal Paper
Issue:Volume: 1      Number: 4      Year: 2016
Download Link:Click here to download PDF     File Size: 40 KB

Title: ACES Express Journal April 2016 Full
File Type: Express Journal Paper
Issue:Volume: 1      Number: 4      Year: 2016
Download Link:Click here to download PDF     File Size: 6752 KB

Title: The Success of GPU Computing in Applied Electromagnetics
Abstract: In the field of electromagnetic modeling, whether it is the complex designs for engineered materials or devices and components integrated within their natural environments, there is a big drive for highly efficient numerical techniques to model the performance of complex structures. This often cannot be achieved by conventional computer systems, but rather through using the so-called high performance computing (HPC) systems that utilize hardware acceleration. We review recent General Purpose Graphics Processing Units (GPGPU) computing strategies introduced in four fields of computational electromagnetics: Finite-Difference Time-Domain (FDTD), Finite Elements Method (FEM), Method of Moments (MoM) and ElectroMagnetic Ray Tracing (EMRT).
Author(s): A. Capozzoli, O. Kilic, C. Curcio, A. Liseno
File Type: Express Journal Paper
Issue:Volume: 1      Number: 4      Year: 2016
Download Link:Click here to download PDF     File Size: 1478 KB

Title: Benefits and Challenges of GPU Accelerated Electromagnetic Solvers from a Commercial Point of View
Abstract: This paper discusses the benefits but also challenges of GPU accelerated electromagnetic solvers from a commercial point of view, namely using FEKO as example. Specifically, the effects of some of the complex interdependencies between different components are presented. It is shown that despite the advances made in the field of GPGPU computing, and impressive speedups for parts of a program or simplified problems, there are a number of factors to consider before these techniques can be applied to a commercial product that is expected to be robust and, most importantly, to always give trustworthy results for a wide variety of problems.
Author(s): U. Jakobus
File Type: Express Journal Paper
Issue:Volume: 1      Number: 4      Year: 2016
Download Link:Click here to download PDF     File Size: 1572 KB

Title: GPU Acceleration of Nonlinear Modeling by the Discontinuous Galerkin Time-Domain Method
Abstract: A discontinuous Galerkin time-domain (DGTD) algorithm is formulated and implemented to model the third-order instantaneous nonlinear effect on electromagnetic fields due the field-dependent medium permittivity. The nonlinear DGTD computation is accelerated using graphics processing units (GPUs). Two nonlinear examples are presented to show the different Kerr effects observed through the third-order nonlinearity. With the acceleration using MPI + GPU under a large cluster environment, the solution times for nonlinear simulations are significantly reduced.
Author(s): H.-T. Meng, J.-M. Jin
File Type: Express Journal Paper
Issue:Volume: 1      Number: 4      Year: 2016
Download Link:Click here to download PDF     File Size: 1650 KB

Title: Multilevel Inverse-Based Factorization Preconditioner for Solving Sparse Linear Systems in Electromagnetics
Abstract: We introduce an algebraic recursive multilevel approximate inverse-based preconditioner, based on a distributed Schur complement formulation. The proposed preconditioner combines recursive combinatorial algorithms and multilevel mechanisms to maximize sparsity during the factorization.
Author(s): Y. Bu, B. Carpetieri, Z. Shen, T. Huang
File Type: Express Journal Paper
Issue:Volume: 1      Number: 4      Year: 2016
Download Link:Click here to download PDF     File Size: 1756 KB

Title: Porting an Explicit Time-Domain Volume Integral Equation Solver onto Multiple GPUs Using MPI and OpenACC
Abstract: A scalable parallelization algorithm to port an explicit marching-on-in-time (MOT)-based time domain volume integral equation (TDVIE) solver onto multi-GPUs is described. The algorithm makes use of MPI and OpenACC for efficient implementation. The MPI processes are responsible for synchronizing and communicating the distributed compute kernels of the MOT-TDVIE solver between the GPUs, where one MPI task is assigned to one GPU. The compiler directives of the OpenACC are responsible for the data transfer and kernelsí offloading from the CPU to the GPU and their execution on the GPU. The speedups achieved against the MPI/OpenMP code execution on multiple CPUs and parallel efficiencies are presented.
Author(s): S. Feki, A. Al-Jarro, H. Bagci
File Type: Express Journal Paper
Issue:Volume: 1      Number: 4      Year: 2016
Download Link:Click here to download PDF     File Size: 2020 KB

Title: Parallel Realization of Element by Element Analysis of Eddy Current Field Based on Graphic Processing Unit
Abstract: The element by element parallel finite element method (EbE-PFEM) applied to engineering eddy current problem is presented in this paper. Unlike classical finite element method (FEM), only element matrix is needed to store for EbE method. Thereby more storage memory saved. Element by element conjugated gradient (EbE-CG) method is used to solve the equations which are discretized from elements level. Considering the ill-conditioned character of system equations, highly parallel Jacobi preconditioned (JP) method is used to accelerate the convergence. Besides, the process of dealing with boundary condition based on EbE theory is introduced. To validate the method, a 2D eddy current problem in complex frequency domain is used. The numerical analysis is carried out on the graphic processing units (GPU) with a compute unified device architecture (CUDA) parallel programming model to accelerate the convergence. And the results demonstrate that the JP method and GPU platform are effective in solving eddy current field with improved convergence.
Author(s): D. Wu, X. Yan, R. Tang, D. Xie, Z. Ren
File Type: Express Journal Paper
Issue:Volume: 1      Number: 4      Year: 2016
Download Link:Click here to download PDF     File Size: 1607 KB

Title: GPU-based Electromagnetic Optimization of MIMO Channels
Abstract: Strategies to accelerate MIMO channel capacity optimization on GPUs are outlined. The optimization scheme is dealt with by properly facing the main computational issues. In particular, the propagation environment is described by ultrafast Geometrical Optics (GO), singular values are computed by a very fast scheme and the optimizer is a parallel version of the differential evolutionary algorithm. The unknowns are given proper representations to reduce the number of optimization parameters.
Author(s): A. Breglia, A. Capozzoli, C. Curcio, S. Di Donna, A. Liseno
File Type: Express Journal Paper
Issue:Volume: 1      Number: 4      Year: 2016
Download Link:Click here to download PDF     File Size: 1652 KB

Title: Fast and Parallel Computational Techniques Applied to Numerical Modeling of RFX-mod Fusion Device
Abstract: This paper presents fast computational techniques applied to modelling the RFX-mod fusion device. An integral equation model is derived for the current distribution on the active coils of the conducting structures, and the input-output transfer functions are computed. Speed-up factors of about 200 can be obtained on hybrid CPU-GPU parallelization against uniprocessor computation.
Author(s): D. Abate, B. Carpentieri, A. G. Chiariello, G. Marchiori, N. Marconato, S. Mastrostefano, G. Rubinacci, S. Ventre, F. Villone
File Type: Express Journal Paper
Issue:Volume: 1      Number: 4      Year: 2016
Download Link:Click here to download PDF     File Size: 1506 KB

Title: Parallel Implementations of Multilevel Fast Multipole Algorithm on Graphical Processing Unit Cluster for Large-scale Electromagnetics Objects
Abstract: This paper investigates solving large-scale electromagnetic scattering problems by using the Multilevel Fast Multipole Algorithm (MLFMA). A parallel implementation for MLFMA is performed on a 12-node Graphics Processing Unit (GPU) cluster that populates NVidia Tesla M2090 GPUs. The details of the implementations and the performance achievements in terms of accuracy, speed up, and scalability are shown and analyzed. The experimental results demonstrate that our MLFMA implementation on GPUs is much faster than (up to 37x) that of the CPU implementation.
Author(s): N. Tran, O. Kilic
File Type: Express Journal Paper
Issue:Volume: 1      Number: 4      Year: 2016
Download Link:Click here to download PDF     File Size: 1806 KB