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https://github.com/juliagpu/metal.jl
Metal programming in Julia
https://github.com/juliagpu/metal.jl
apple-gpu apple-silicon gpu hacktoberfest julia mac metal-framework
Last synced: 3 days ago
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Metal programming in Julia
- Host: GitHub
- URL: https://github.com/juliagpu/metal.jl
- Owner: JuliaGPU
- License: mit
- Created: 2020-05-08T09:17:44.000Z (over 4 years ago)
- Default Branch: main
- Last Pushed: 2024-03-26T00:41:36.000Z (6 months ago)
- Last Synced: 2024-03-26T15:39:37.111Z (6 months ago)
- Topics: apple-gpu, apple-silicon, gpu, hacktoberfest, julia, mac, metal-framework
- Language: Julia
- Homepage:
- Size: 4.25 MB
- Stars: 322
- Watchers: 15
- Forks: 28
- Open Issues: 26
-
Metadata Files:
- Readme: README.md
- License: LICENSE
- Citation: CITATION.cff
Awesome Lists containing this project
README
# Metal.jl
*Metal programming in Julia*
[![][doi-img]][doi-url] [![][docs-stable-img]][docs-stable-url] [![][docs-dev-img]][docs-dev-url] [![][buildkite-img]][buildkite-url] [![][codecov-img]][codecov-url]
[doi-img]: https://zenodo.org/badge/262279120.svg
[doi-url]: https://zenodo.org/badge/latestdoi/262279120
[docs-stable-img]: https://img.shields.io/badge/docs-stable-blue.svg
[docs-stable-url]: https://metal.juliagpu.org/stable/
[docs-dev-img]: https://img.shields.io/badge/docs-dev-blue.svg
[docs-dev-url]: https://metal.juliagpu.org/dev/
[buildkite-img]: https://badge.buildkite.com/a9b335b7d5d4d7ea90b031057728de9d1e9a73d5bcd9d89655.svg?branch=main
[buildkite-url]: https://buildkite.com/julialang/metal-dot-jl
[codecov-img]: https://codecov.io/gh/JuliaGPU/Metal.jl/branch/main/graph/badge.svg
[codecov-url]: https://codecov.io/gh/JuliaGPU/Metal.jl
With Metal.jl it's possible to program GPUs on macOS using the Metal programming
framework.
**The package is a work-in-progress.** There are bugs, functionality is missing,
and performance hasn't been optimized. Expect to have to make changes to this package
if you want to use it. PRs are very welcome!
## Requirements
- Mac device with M-series chip
- Julia 1.10-1.11
- macOS 13-15
These requirements are fairly strict, and are due to our limited development
resources (manpower, hardware). Technically, they can be relaxed. If you are
interested in contributing to this, see [this
issue](https://github.com/JuliaGPU/Metal.jl/issues/22) for more details.
In practice, Metal.jl will probably work on any macOS 10.15+, and other
GPUs that are supported by Metal might also function (if only partially),
but such combinations are unsupported for now.
## Quick start
Metal.jl can be installed with the Julia package manager. From the Julia REPL, type `]` to
enter the Pkg REPL mode and run:
```
pkg> add Metal
```
Or, equivalently, via the `Pkg` API:
```julia
julia> import Pkg; Pkg.add("Metal")
```
For an overview of the toolchain in use, you can run the following command after
importing the package:
```julia
julia> using Metal
julia> Metal.versioninfo()
macOS 14.5.0, Darwin 23.5.0
Toolchain:
- Julia: 1.10.4
- LLVM: 15.0.7
Julia packages:
- Metal.jl: 1.2.0
- LLVMDowngrader_jll: 0.3.0+1
1 device:
- Apple M2 Max (64.000 KiB allocated)
```
## Array abstraction
The easiest way to work with Metal.jl, is by using its array abstraction.
The `MtlArray` type is both meant to be a convenient container for device
memory, as well as provide a data-parallel abstraction for using the GPU
without writing your own kernels:
```julia
julia> a = MtlArray([1])
1-element MtlArray{Int64, 1}:
1
julia> a .+ 1
1-element MtlArray{Int64, 1}:
2
```
## Kernel programming
The above array abstractions are all implemented using Metal kernels written
in Julia. These kernels follow a similar programming style to Julia's other
GPU back-ends, and with that deviate from how kernels are implemented in Metal C
(i.e., indexing intrinsics are functions not arguments, arbitrary aggregate arguments
are supported, etc):
```julia
julia> function vadd(a, b, c)
i = thread_position_in_grid_1d()
c[i] = a[i] + b[i]
return
end
vadd (generic function with 1 method)
julia> a = MtlArray([1,1,1,1]); b = MtlArray([2,2,2,2]); c = similar(a);
julia> @metal threads=2 groups=2 vadd(a, b, c)
julia> Array(c)
4-element Vector{Int64}:
3
3
3
3
```
## Metal API wrapper
Finally, all of the above functionality is made possible by interfacing with the Metal
libraries through [ObjectiveC.jl](https://github.com/JuliaInterop/ObjectiveC.jl). We provide low-level objects and functions that map These
low-level API wrappers, along with some slightly higher-level Julia wrappers, are available
in the `MTL` submodule exported by Metal.jl:
```julia
julia> dev = MTLDevice(1)
name = Apple M1 Pro
julia> dev.name
NSString("Apple M1 Pro")
```
## Acknowledgements
This package builds upon the experience of several
Julia contributors to [CUDA.jl](https://github.com/JuliaGPU/CUDA.jl), [AMDGPU.jl](https://github.com/JuliaGPU/AMDGPU.jl) and [oneAPI.jl](https://github.com/JuliaGPU/oneAPI.jl).