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https://github.com/juliagpu/cuda.jl

CUDA programming in Julia.
https://github.com/juliagpu/cuda.jl

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CUDA programming in Julia.

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# CUDA.jl



*CUDA 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] [![][codespeed-trend-img]][codespeed-trend-url] [![][codespeed-chart-img]][codespeed-chart-url]



[doi-img]: https://zenodo.org/badge/doi/10.1109/TPDS.2018.2872064.svg

[doi-url]: https://ieeexplore.ieee.org/abstract/document/8471188



[docs-stable-img]: https://img.shields.io/badge/docs-stable-blue.svg

[docs-stable-url]: https://cuda.juliagpu.org/stable/



[docs-dev-img]: https://img.shields.io/badge/docs-dev-blue.svg

[docs-dev-url]: https://cuda.juliagpu.org/dev/



[buildkite-img]: https://badge.buildkite.com/27aaeb352a9420297ed2d30cb055ac383a399ea8f121599912.svg?branch=master

[buildkite-url]: https://buildkite.com/julialang/cuda-dot-jl



[codecov-img]: https://codecov.io/gh/JuliaGPU/CUDA.jl/branch/master/graph/badge.svg

[codecov-url]: https://codecov.io/gh/JuliaGPU/CUDA.jl



[codespeed-chart-img]: https://img.shields.io/badge/benchmarks-Chart-yellowgreen

[codespeed-chart-url]: https://speed.juliagpu.org/timeline/#/?exe=9,11&env=1&base=none&ben=grid&revs=50



[codespeed-trend-img]: https://img.shields.io/badge/benchmarks-Trend-yellowgreen

[codespeed-trend-url]: https://speed.juliagpu.org/changes/?exe=9&env=1&tre=50



The CUDA.jl package is the main programming interface for working with NVIDIA CUDA GPUs

using Julia. It features a user-friendly array abstraction, a compiler for writing CUDA

kernels in Julia, and wrappers for various CUDA libraries.



## Quick start



Before all, make sure you have a recent NVIDIA driver. On Windows, also make sure you have

the [Visual C++ redistributable](https://aka.ms/vs/16/release/vc_redist.x64.exe) installed.

You do not need to install the CUDA Toolkit.



CUDA.jl can be installed with the Julia package manager. From the Julia REPL, type `]` to

enter the Pkg REPL mode and run:



```

pkg> add CUDA

```



Or, equivalently, via the `Pkg` API:



```julia

julia> import Pkg; Pkg.add("CUDA")

```



For an overview of the CUDA toolchain in use, you can run the following command after

importing the package:



```julia

julia> using CUDA



julia> CUDA.versioninfo()

```



This may take a while, as it will precompile the package and download a suitable version of

the CUDA toolkit. If your GPU is not fully supported, the above command (or any other

command that initializes the toolkit) will issue a warning.



For more usage instructions and other information, please refer to [the

documentation](https://juliagpu.github.io/CUDA.jl/stable/).



## Requirements



The latest development version of CUDA.jl requires **Julia 1.10** or higher. If you are

using an older version of Julia, you need to use a previous version of CUDA.jl. This will

happen automatically when you install the package using Julia's package manager.



Note that CUDA.jl may not work with a custom build of Julia; it is recommended that you

install Julia using the [official binaries](https://julialang.org/downloads/) or

[juliaup](https://github.com/JuliaLang/juliaup).



The latest version of CUDA.jl also has certain requirements that cannot be enforced by the

package manager:



- Host platform: only 64-bit Linux and Windows are supported;

- Device hardware: only NVIDIA GPUs with **compute capability 3.5** (Kepler) or higher are

  supported;

- NVIDIA driver: a driver for **CUDA 11.0** or newer is required;

- CUDA toolkit (in case you need to use your own): only **CUDA toolkit 11.4** or newer are

  supported.



If you cannot meet these requirements, you may need to install an older version of CUDA.jl:



* CUDA.jl v5.3 is the last version with support for PowerPC (removed in v5.4)

* CUDA.jl v4.4 is the last version with support for CUDA 11.0-11.3 (deprecated in v5.0)

* CUDA.jl v4.0 is the last version to work with CUDA 10.2 (removed in v4.1)

* CUDA.jl v3.8 is the last version to work with CUDA 10.1 (removed in v3.9)

* CUDA.jl v1.3 is the last version to work with CUDA 9-10.0 (removed in v2.0)



## Supporting and Citing



Much of the software in this ecosystem was developed as part of academic research. If you

would like to help support it, please star the repository as such metrics may help us secure

funding in the future. If you use our software as part of your research, teaching, or other

activities, we would be grateful if you could cite our work. The

[CITATION.bib](https://github.com/JuliaGPU/CUDA.jl/blob/master/CITATION.bib) file in the

root of this repository lists the relevant papers.



## Project Status



The package is tested against, and being developed for, Julia 1.10 and above. Main

development and testing happens on x86 Linux, but the package is expected to work on

Windows and ARM and as well.



## Questions and Contributions



Usage questions can be posted on the [Julia Discourse

forum](https://discourse.julialang.org/c/domain/gpu) under the GPU domain and/or in the #gpu

channel of the [Julia Slack](https://julialang.org/community/).



Contributions are very welcome, as are feature requests and suggestions. Please open an

[issue](https://github.com/JuliaGPU/CUDA.jl/issues) if you encounter any problems.