The CUDA.jl package is the main entrypoint for programming NVIDIA GPUs in Julia. The package makes it possible to do so at various abstraction levels, from easy-to-use arrays down to hand-written kernels using low-level CUDA APIs.
The Julia CUDA stack only requires a working NVIDIA driver; you don't need to install the entire CUDA toolkit, as it will automatically be downloaded when you first use the package:
# install the package using Pkg Pkg.add("CUDA") # smoke test (this will download the CUDA toolkit) using CUDA CUDA.versioninfo()
If you want to ensure everything works as expected, you can execute the test suite:
using Pkg Pkg.test("CUDA") # takes ~40 minutes if using 1 thread
For more details on the installation process, consult the Installation section. To understand the toolchain in more detail, have a look at the tutorials in this manual. It is highly recommended that new users start with the Introduction tutorial. For an overview of the available functionality, read the Usage section. The following resources may also be of interest:
The Julia CUDA stack has been a collaborative effort by many individuals. Significant contributions have been made by the following individuals:
- Tim Besard (@maleadt) (lead developer)
- Valentin Churavy (@vchuravy)
- Mike Innes (@MikeInnes)
- Katharine Hyatt (@kshyatt)
- Simon Danisch (@SimonDanisch)
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.