在GPU上并行计算时域有限差分法_Cuda_下载.zip

# CUDA-FDTD-simulation Finite Difference Time Domain Simulation written in CUDA, with python Bindings. All of the CUDA Tensors for the Electric and Magnetic fields are exposed as numpy arrays in python. CUDA is used to calculate each point of the computational grid in parallel on a GPU. The FDTD uses a discretized form of Maxwell's equations to simulate propagation of a source through vacuum or a material described by a complex refractive index n(x,y,z). ## Maxwell's Equations discretized to 3D space <img src="images/fdtd_eqn1.png" width="600" height="200"> <img src="images/fdtd_eqn2.png" width="600" height="200"> The computational process is extremely demanding because the computational grid must be resolved on the order of the wavelength of the EM waves being simulated. <img src="images/cuda_fdtd.png"> ``` import matplotlib.pyplot as plt import numpy as np from Cfdtd import FDTD if __name__=="__main__": N_x=200 N_y=200 N_z=200 dx=50e-9 c=2.998e8 dt=dx/(2*c) freq=500e12; omega =2*np.pi*freq; tau=10*dt fdtd = FDTD(N_x,N_y,N_z,dx,dt,omega,tau) time_span=15e-15; tmax_steps=int(time_span/dt); # define source term _x=np.arange(N_x).reshape(-1,1,1) _y=np.arange(N_y).reshape(1,-1,1) _z=np.arange(N_z).reshape(1,1,-1) fdtd.J.z[:,:,:]=np.exp(-(_x-100)**2 / 5)*np.exp(-(_y-100)**2 / 5)*np.exp(-(_z-100)**2 / 5) fdtd.J.z[:,:,:]+=np.exp(-(_x-75)**2 / 5)*np.exp(-(_y-75)**2 / 5)*np.exp(-(_z-100)**2 / 5) plt.figure(1) for n in range(0,tmax_steps): fdtd.timestep(5) # run 5 timesteps plt.gca().cla() # show the x component of the magnetic field plt.imshow(fdtd.H.x[:,:,100]) plt.pause(0.01) fdtd.delete() ```