pytorch_dlprim
FluidX3D
| pytorch_dlprim | FluidX3D | |
|---|---|---|
| 3 | 53 | |
| 208 | 3,210 | |
| - | - | |
| 5.9 | 8.6 | |
| about 1 month ago | 5 days ago | |
| C++ | C++ | |
| MIT License | GNU General Public License v3.0 or later |
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pytorch_dlprim
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Linus Tech Tips: "China doesn't want me to have this GPU [Moore Threads MTT S80]" (Linus Tech Tips Reviews the Moore Threads MTT S80 GPU)
I know PyTorch supports OpenCL nows and you can do training with it as well. See here. Never try it myself.
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[P] OpenCL backend for PyTorch - progress works with mainstream pytorch
I'm working on PyTorch OpenCL backend based on dlprimitives core library. It exists for a while but until now it required building custom pytorch version.
- [P] Progress with OpenCL backend for pytorch
FluidX3D
- FluidX3D
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Earthquake in Japan yesterday may have shifted land 1.3 meters
Could even use this as a revers GLOBAL ORBITING SYSTEM [GoS] - Whereby a single satellite|probe dispels a lander to a planet with the Quantum Magnetic Cannister, and that QMC signals its global location to the satellite launcher, and the satellit can extrap its location based on the absolute location of the ground guys... (might need more than one ground magnet-moles?)
How can this be measured? Can fluidx3d do martian magnetics? [0]
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[0] https://github.com/ProjectPhysX/FluidX3D
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EDIT: "Mars does not have a magnetosphere"
ELI5: "how do electronics work when there is zero magnetic field around them? A complete antimagnetic environ?"
I've never heard any mention about making any electrical device work on a planet (such as mars) in a complete magnetically dark location?
How is there gravity on mars if there is no magnetic field for a planet, and how can mass, the size of a planet not produce magnetism/gravity if its not made of iron-sh (the RED of the planet)
ELI5, please.
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Challenging projects every programmer should try
See my post in this thread about dimples/barnacles...
But have you seen this guys package: https://github.com/ProjectPhysX/FluidX3D
- Fast and Memory efficient lattice Boltzmann CFD software, running on all GPUs
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What 8x AMD Instinct MI200 GPUs can do with a combined 512GB VRAM: Bell 222 Helicopter in FluidX3D CFD - 10 Billion Cells, 75k Time Steps, 71TB vizualized - 6.4 hours compute+rendering with OpenCL
Yes, I've made that super easy. You can change the VRAM capacity of your hardware as one number in the setup script and it will automatically scale the simulation up or down. See the documentation for details.
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Need help : I' using FluidX3D, and this model is taken from the game Assetto Corsa (.kn5 file) and converted in stl using blender. In blender and many stl file viewer it shows fine but when I try to use FluidX3D it shows weird lines and I don't know why. I tried using other methods to convert t
Also see this GitHub Issue on the problem: https://github.com/ProjectPhysX/FluidX3D/issues/59
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Real time CFD with FluidX3D - Cessna 172 - 20 million cells - Titan Xp GPU
If you want to play with the software yourself, FluidX3D is on GitHub: https://github.com/ProjectPhysX/FluidX3D
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GPUs in CFD
This is my opinion about what is happening. Production CFD solvers are really complicated and take a lot of time and energy to write. Engineers learning to use a CFD solver to get a job done is really time consuming and there are all sorts of issue that come up like actually trusting the new solver. Both of these things have really held back GPUs in this area. There are really only two ways out of this, either you write a solver from scratch and get people to adopt it (double hard) or you take an existing solver and modify it to run on GPUs (still pretty hard). The first option is very hard but ultimately the way to go in my opinion. The second option results in very poorly optimized GPU code and honestly just gives a bad name to GPU computing in my opinion. Take OpenLB for example, https://www.openlb.net/show-cases/highly-resolved-nozzle-simulation-performed-using-multi-gpu-support/. Terrible terrible performance compared to what you could get if you wrote the solver from scratch on the GPU, for example, https://github.com/ProjectPhysX/FluidX3D.
- Where to try/test (and learn) CFD models for free?
- FluidX3D: Fast, memory efficient lattice Boltzmann CFD software /w OpenCL
What are some alternatives?
dlprimitives - Deep Learning Primitives and Mini-Framework for OpenCL
HPX - The C++ Standard Library for Parallelism and Concurrency
oneDNN - oneAPI Deep Neural Network Library (oneDNN)
OpenCL-examples - Simple OpenCL examples for exploiting GPU computing
mace - MACE is a deep learning inference framework optimized for mobile heterogeneous computing platforms.
McCode - The home of the McStas (neutrons) and McXtrace (x-rays) Monte-Carlo ray-tracing instrument simulation codes.
Boost.Compute - A C++ GPU Computing Library for OpenCL
pysph - A framework for Smoothed Particle Hydrodynamics in Python
lbm - A simple full-python 2D lattice-boltzmann code
intel-extension-for-tensorflow - Intel® Extension for TensorFlow*
FsRayTracer - This project is an F# implementation of a raytracer based on the book "The Ray Tracer Challenge: A Test-Driven Guide to Your First 3D Renderer" by James Buck
OpenCL-Wrapper - OpenCL is the most powerful programming language ever created. Yet the OpenCL C++ bindings are cumbersome and the code overhead prevents many people from getting started. I created this lightweight OpenCL-Wrapper to greatly simplify OpenCL software development with C++ while keeping functionality and performance.