|
|
|
|
|
|
by ThePhysicist
2817 days ago
|
|
|
I wrote a CUDA/GPU based 2D electromagnetic simulation a while ago. The code is open-source and (hopefully) not too complicated: https://github.com/adewes/fdtd-ml Here's an example video of what the results look like (it shows how EM waves are reflected by a parabolic mirror): https://www.youtube.com/watch?v=ZPSzAaxkg5c I used mostly the PyCUDA documentation and examples as well as the official CUDA documentation (https://docs.nvidia.com/cuda/) to learn. I think what's most important is to first understand what blocks, grids and threads are and how they work (see e.g. here: https://docs.nvidia.com/cuda/cuda-c-programming-guide/index....). With that knowledge you can start thinking about how you can structure your problem to solve it efficiently on the GPU. For the simulation, I have basically two 2D blocks of memory for each variable of interest (e.g. electric and magnetic fields in X,Y direction, current density, material properties) that I transfer the to the GPU. There, I use the discretized differential equation for the electromagnetic field to update the field values using the values from the first buffer (and the material properties + currents). I store the result in the second buffer. I then swap the buffer references (without copying any memory) for the next step of the simulation. I perform this step e.g. n times and then transfer some of the buffers back to the main memory to e.g. plot them. That's mostly it! Of course there are many intricacies and ways to optimize code, but getting a basic program running inside your GPU isn't that hard actually. BTW, here's some really cool research work by Microsoft on GPU-based FDTD (finite difference time domain) simulations of wind instruments in two dimensions: https://www.youtube.com/watch?v=7Kf-rlUZAaU |
|
|