dfdx / cuda.jl

CUDA programming in Julia.

CUDA.jl

CUDA programming in Julia

Documentation	Build Status	Performance

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.

Requirements

The latest development version of CUDA.jl requires Julia 1.6 or higher. If you are using an older version of Julia, you need to use a released version of CUDA.jl. This will happen automatically when you install the package using Julia's package manager.

CUDA.jl currently also requires a CUDA-capable GPU with compute capability 5.0 (Maxwell) or higher, and an accompanying NVIDIA driver with support for CUDA 10.1 or newer. These requirements are not enforced by the Julia package manager when installing CUDA.jl. Depending on your system and GPU, you may need to install an older version of the package.

Quick start

The package 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> import Pkg; Pkg.add("CUDA")

For usage instructions and other information, please refer to the documentation.

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 file in the root of this repository lists the relevant papers.

Project Status

The package is tested against, and being developed for, Julia 1.3 and above. Main development and testing happens on Linux, but the package is expected to work on macOS and Windows as well.

Questions and Contributions

Usage questions can be posted on the Julia Discourse forum under the GPU domain and/or in the #gpu channel of the Julia Slack.

Contributions are very welcome, as are feature requests and suggestions. Please open an issue if you encounter any problems.