| .cargo | ||
| .devcontainer | ||
| comgr | ||
| cuda_base | ||
| cuda_types | ||
| detours-sys | ||
| ext | ||
| llvm_zluda | ||
| ptx | ||
| ptx_parser | ||
| ptx_parser_macros | ||
| ptx_parser_macros_impl | ||
| zluda | ||
| zluda_bindgen | ||
| zluda_dump | ||
| zluda_inject | ||
| zluda_ml | ||
| zluda_redirect | ||
| .gitattributes | ||
| .gitignore | ||
| .gitmodules | ||
| Cargo.toml | ||
| GeekBench_5_2_3.svg | ||
| LICENSE-APACHE | ||
| LICENSE-MIT | ||
| README.md | ||
ZLUDA
ZLUDA is a drop-in replacement for CUDA on non-NVIDIA GPU. ZLUDA allows to run unmodified CUDA applications using non-NVIDIA GPUs with near-native performance.
ZLUDA is work in progress. Follow development here and say hi on Discord. For more details see the announcement: https://vosen.github.io/ZLUDA/blog/zludas-third-life/
Usage
Warning: This version ZLUDA is under heavy development (more here) and right now only supports Geekbench. ZLUDA probably will not work with your application just yet.
Windows
You should have recent AMD GPU driver ("AMD Software: Adrenalin Edition") installed.
To run your application you should etiher:
- (Recommended approach) Copy ZLUDA-provided
nvcuda.dllandnvml.dllinto a path which your application uses to load CUDA. Paths vary application to application, but usually it's the directory where the .exe file is located - Use ZLUDA launcher like below. ZLUDA launcher is known to be buggy and unfinished
<ZLUDA_DIRECTORY>\zluda_with.exe -- <APPLICATION> <APPLICATIONS_ARGUMENTS>
Linux
Run your application like this:
LD_LIBRARY_PATH=<ZLUDA_DIRECTORY> <APPLICATION> <APPLICATIONS_ARGUMENTS>
where <ZLUDA_DIRECTORY> is the directory which contains ZLUDA-provided libcuda.so: target\release if you built from sources or zluda if you downloaded prebuilt package.
MacOS
Not supported
Building
Dependencies
- Git
- CMake
- Python 3
- Rust compiler (recent version)
- C++ compiler
- (Optional, but recommended) Ninja build system
Build steps
- Git clone the repo (make sure to use
--recursiveoption to fetch submodules):
git clone --recursive https://github.com/vosen/ZLUDA.git - Enter freshly cloned
ZLUDAdirectory and build with cargo (this takes a while):
cargo build --release
Linux
If you are building on Linux you must also symlink the ZLUDA output binaries after ZLUDA build finishes:
cd target/release
ln -s libnvcuda.so target/release/libcuda.so
ln -s libnvcuda.so target/release/libcuda.so.1
ln -s libnvml.so target/release/libnvidia-ml.so
Contributing
ZLUDA project has a commercial backing and does not accept donations. ZLUDA project accepts pull requests and other non-monetary contributions.
If you want to contribute a code fix or documentation update feel free to open a Pull Request.
Getting started
There's no architecture document (yet). Two most important crates in ZLUDA are ptx (PTX compiler) and zluda (AMD GPU runtime). A good starting point to tinkering the project is to run one of the ptx unit tests under a debugger and understand what it is doing. cargo test -p ptx -- ::add_hip is a simple test that adds two numbers.
Github issues tagged with "help wanted" are tasks that are self-containted. Their level of difficulty varies, they are not always great starting points, but they have a relatively clear definition of "done".
If you have questions feel free to ask on #devtalk channel on Discord.
License
This software is dual-licensed under either the Apache 2.0 license or the MIT license. See LICENSE-APACHE or LICENSE-MIT for details