Effectively using GPUs with Julia: video, slides.The following resources may also be of interest: For an overview of the available functionality, read the Usage section. It is highly recommended that new users start with the Introduction tutorial. To understand the toolchain in more detail, have a look at the tutorials in this manual. # the test suite takes command-line options that allow customization pass -help for details:įor more details on the installation process, consult the Installation section. Note that this test suite is fairly exhaustive, taking around an hour to complete when using a single thread (multiple processes are used automatically based on the number of threads Julia is started with), and requiring significant amounts of CPU and GPU memory. If you want to ensure everything works as expected, you can execute the test suite.
# smoke test (this will download the CUDA toolkit)
#Julia permute install#
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 If you have any questions, please feel free to use the #gpu channel on the Julia slack, or the GPU domain of the Julia Discourse.įor information on recent or upcoming changes, consult the NEWS.md document in the CUDA.jl repository. 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 CUDA.jl package is the main entrypoint for programming NVIDIA GPUs in Julia. Edit on GitHub CUDA programming in Julia
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