# Compute unified device architecture (CUDA) based finite-difference time-domain (FDTD) implementation

@article{Demir2010ComputeUD, title={Compute unified device architecture (CUDA) based finite-difference time-domain (FDTD) implementation}, author={Veysel Demir and Atef Z. Elsherbeni}, journal={Applied Computational Electromagnetics Society Journal}, year={2010}, volume={25}, pages={303-314} }

Recent developments in the design of graphics processing units (GPUs) have made it possible to use these devices as alternatives to central processor units (CPUs) and perform high performance scientific computing on them. Though several implementations of finite- difference time-domain (FDTD) method have been reported, the unavailability of high level languages to program graphics cards had been a major obstacle for scientists and engineers who would want to develop codes for graphics cards…

## 53 Citations

CUDA Fortran acceleration for the finite-difference time-domain method

- Computer ScienceComput. Phys. Commun.
- 2013

The derived CUDA Fortran code is compared with an optimized CPU version that runs on a workstation-class CPU to present a realistic GPU to CPU run time comparison and thus help in making better informed investment decisions on FDTD code redesigns and equipment upgrades.

Improved performance of FDTD computation using a thread block constructed as a two-dimensional array with CUDA

- Computer Science
- 2010

The authors investigated the computational performance with respect to the size of a thread block constructed as a 2-D array, and improved the performance of the implementation.

Performance Optimization of Massively Parallel FDTD Computations

- Computer Science
- 2013

The NVIDIA’s Compute Unified Device Architecture CUDA on an NVIDIA GeForce processor will be used to solve a Finite Difference Time Domain FDTD computation, with the focus on optimizing the efficiency of the algorithm by maximizing the throughput through affective use of the fast on-chip shared memory, and avoid using the slow off-chip global memory.

Graphics processor unit (GPU) acceleration of finite-difference frequency-domain (FDFD) method

- Computer Science
- 2012

It is shown that FDFD can be solved with a speed-up factor of more than 20 on a GPU compared with the solution on a central processing unit (CPU), while memory usage can be reduced substantially with the presented algorithm.

A comparison of computing architectures and parallelization frameworks based on a two-dimensional FDTD

- Computer Science2013 International Conference on High Performance Computing & Simulation (HPCS)
- 2013

Assessment and compares the performance of different parallelization frameworks and different multicore architectures for exploiting parallelism when Maxwell's Equations have to be solved and results show that the recent GPU architecture, Kepler, did not outperform the older Fermi architecture.

FDTD Acceleration using MATLAB Parallel Computing Toolbox and GPU

- Computer Science
- 2017

A MATLAB based finite difference time domain (FDTD) method accelerated using the GPU functions in MATLAB’s parallel computing toolbox (PCT) is presented and several modifications to increase the efficiency on several different NVIDIA graphics cards are demonstrated.

FDTD on Distributed Heterogeneous Multi-GPU Systems

- Computer Science
- 2014

A new FDTD decomposition implementation that can be executed on a cluster of heterogeneous systems with a multi-core CPU, and one or several CUDA capable GPUs, and a performance increase of 66% when simulating large domains on two GPUs compared to a single GPU.

GPU‐based acceleration of computational electromagnetics codes

- Computer Science
- 2013

An overview of the main efforts of researchers to port computational electromagnetics codes to GPU is given and GPU implementation aspects of two well-known techniques, namely the finite-difference time domain (FDTD) and the method of moments (MoM), are investigated.

GPU accelerated finite-element computation for electromagnetic analysis

- Computer ScienceIEEE Antennas and Propagation Magazine
- 2014

This paper identifies the bottlenecks in the GPU parallelization of the Finite-Element Method for electromagnetic analysis, and proposes potential solutions to alleviate the bottlenecking, and shows that with a proper parallelization and implementation, GPUs are able to achieve significant speedups over OpenMP-enabled multi-core CPUs.

Acceleration of the Dual-Field Domain Decomposition Algorithm Using MPI–CUDA on Large-Scale Computing Systems

- Computer ScienceIEEE Transactions on Antennas and Propagation
- 2014

This paper is able to achieve a significant speedup by utilizing GPUs in a message-passing interface (MPI)-based cluster environment and the same acceleration strategy can be applied to the acceleration of the discontinuous Galerkin time-domain (DGTD) algorithms.

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