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Fun with compute shaders and fluid dynamics (kummerlaender.eu)
141 points by TsukiZombina 44 days ago | hide | past | web | favorite | 12 comments



The idea to put input energy via the mouse is nifty. Nice work!

I dont think vertex buffers are the ideal storage mechanism for lattice methods. I used Cuda to rasterize a plain buffer but I have two GPUs, the one doing the sim wasn't the one rendering. Its better this way if you are looking to run the sim 1000s of cycles per sec, but only render 60 fps. Theres a lot of extra data that could be eliminated by simply using a linear block of memory versus a vertex buffer. Depending on the goals for the sim, efficient rendering should be a lesser priority than efficient simulation speed.

Here are two very similar frameworks I made for exploring parallelizable lattice sims:

https://github.com/churchofthought/HexagonalComplexAutomata

https://github.com/churchofthought/ScatterLife (Note: the video is of a skewed version due to hexagonal lattice coords - this is fixed in the latest commits)

To OP, you may enjoy the study of transformation between Cellular Automata and Partial Differential Eqs.

The PDF below is a gem, a good introduction to the techniques that will allow you to take any reasonable PDEs and produce CAs that produce equivalent dynamics:

Cellular Automata, PDEs, and Pattern Formation https://drive.google.com/file/d/13cT_BU8LAaUK4KTYFfhVSpCVCmr...


(Author here) Thanks for the interesting links!

I agree that there is much that could be optimized. The choice to use vertex buffers was driven by the overarching goal of implementing the whole thing in GLSL. It really is just a small playground project to generate some nice visualizations. If we wanted to actually use the results beyond displaying them the tight coupling between simulation and rendering would certainly become a hindrance.

CUDA usage as you describe definitely seems to be the way to go for larger scale stuff. i.e. most GPU-based LBM codes that are actually used in research seem to be based on it.


Well it looks awesome, we all are just fooling around with this stuff :-)

If you did want to increase performance, you could try to use OpenGL for rendering but write your actual sim in CUDA C++.

The whole interop API is listed here: https://docs.nvidia.com/cuda/cuda-runtime-api/group__CUDART_...

Essentially it wouldn't involve any host/memory transfer, all would be processed on GPU.

Then you could limit your rendering thread to 60fps while running the CUDA kernel non-stop.

I haven't actually tested this out though because I had 2 GPUs for the sims above, and the beefy one running the sim was on TCC instead of WDDM mode (no attached display allowable.) [1] So I had the universe state buffer transferred to host memory, and then to the 2nd GPU for rendering to attached display.

I am not sure of the speed gains TCC vs WDDM really provides, but Nvidia says it makes "some difference."

[1] https://docs.nvidia.com/gameworks/content/developertools/des...


Love the use of compute shaders and interactivity! If anyone's interested, here's a very concise high-fidelity (3D with a volumetric renderer) fluid simulation I implemented in cuda. No frills (600 LOC), but pretty pictures! https://github.com/PWhiddy/Fat-Clouds/blob/master/README.md


Thank you for the kind words! Your simulation looks fantastic. I am frequently amazed how compact fluid simulations can be in practice. Definitely something I am going to aspire to reproduce on my own.


Here's another classic article about fluids on the GPU: http://developer.download.nvidia.com/books/HTML/gpugems/gpug...

It's significantly older, but it's still good and it goes into the details for those (like me) who don't have a background in fluid dynamics. I had a lot of fun implementing it on the side a while back.


The one I remember is the last reference there:

Jos Stam, Stable Fluids [1999]

http://www.dgp.toronto.edu/people/stam/reality/Research/pdf/...

More of his papers:

http://www.dgp.toronto.edu/people/stam/reality/Research/pub....


This really brought back memories and put a smile on my face. My Master's thesis was writing a fluid simulation on the GPU, also using OpenGL (with all work done in GLSL fragment shaders) and also restricting simulation to 2D (because I was specifically simulating fluid running across a surface).

The most interesting thing to me was how much this article resonated with my own experiences in GPU-based particle simulation. I instead went for a particle-based method and had to make do with the state of GPU hardware and drivers as they were back in 2011. But despite the difference in computational model and all the differences stemming from the evolution of GPUs and compute shaders, the main struggles are still the same: discretization and parallelization.


Is there a reason the article is linked to #fnref3? I'm not seeing anything in particular there that is being called out.


It's always a fun moment when you realize he's at the same university and might be sitting next to you right now. Especially because it's a rare occurence to see the KIT here. It makes you appreciate your education more (and also a bit humble...).


Wow, great to see this classic from my dynamics days,

(∂t​+ξ⋅∂x​+ρF​⋅∂ξ​)f=Ω(f)(=∂x​f⋅dtdx​+∂ξ​f⋅dtdξ​+∂t​f)

Brings back many great memories.


this is a really awsome article to get going with this stuff, thanks a lot!!




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