blob: 2b6a81522850577f48f7e8016f69a349f69ebe98 (
plain)
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
|
[](https://discord.gg/sg6BNzXuc7)
# 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](https://discord.gg/sg6BNzXuc7). 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](https://vosen.github.io/ZLUDA/blog/zludas-third-life/)) 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.dll` and `nvml.dll` into 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](https://ninja-build.org/)
### Build steps
* Git clone the repo (make sure to use `--recursive` option to fetch submodules):
`git clone --recursive https://github.com/vosen/ZLUDA.git`
* Enter freshly cloned `ZLUDA` directory 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"](https://github.com/vosen/ZLUDA/issues?q=is%3Aissue+is%3Aopen+label%3A%22help+wanted%22) 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](https://discord.com/channels/1273316903783497778/1303329281409159270).
## License
This software is dual-licensed under either the Apache 2.0 license or the MIT license. See [LICENSE-APACHE](LICENSE-APACHE) or [LICENSE-MIT](LICENSE-MIT) for details
|
