Acceleration of finite-difference time-domain (FDTD) using graphics processor units (GPU)

Gpu Approach for Hertzian Potential Formulation Tool Oriented on Electromagnetic Nanodevices

“…Moreover, given the wide integration in commercial off the shelf computers, they represent a powerful and cheap solution to computational science. GPU computing is being exploited in many scientific applications [6][7][8] with interesting results in the EM field and nanotechnology [9,10]. Much published work on EM computational simulations using the GPUs confirms the growing interest in this powerful technology of the computational EM community [11][12][13].…”

Auto-generation of Parallel Finite-Differencing Code for MPI, TBB and CUDA

“…Although finiteelement and other matrix-formulated methods are very popular for irregular mesh problems [2], finite-difference methods continue to find use in computational simulations and generally are straightforward to parallelise using geometric stencil methods of decomposition which attain good computational speedup [3]- [5]. Although finite-difference methods are quite feasible to hand-parallelise for low-order stencils when a small number of neighbouring cells is required for each calculation, in cases when higher-order calculus operations are employed [6] the codes become: very complex; hard to implement manually; and very difficult to verify since a small programming error concerning a data index may still lead to a numerically plausible solution that is hard to spot as being wrong.…”

GPU based numerical simulation of core shooting process

“…The discrete method of the core-shooting numerical simulation program is finite difference. Krakiwsky et al studied the finite difference time domain method with a GPU parallelization method, and good performance of GPU parallelization method has been proved in the improvement of computing efficiency and reliable simulation results [14,15] . In the casting field, simulation studies based on GPU have achieved some breakthroughs in the solidification process [16,17] , but the core shooting process is close to an isothermal process while the studies about the core shooting process are relatively limited.…”

“…A parallel program was developed to simulate a relatively simple test-piece, and the algorithm was improved in the process. In the GPU parallelization method, data delay and model capability of parallelization have a great influence on the improvement of computing efficiency [10][11][12][13][14][15] .…”