A Curious Cumulation of CUDA Cuisine
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CuArrays provides a fully-functional GPU array, which can give significant speedups over normal arrays without code changes. CuArrays are implemented fully in Julia, making the implementation elegant and extremely generic.


CuArrays should work out-of-the-box on Julia 1.0. You only need to have a proper set-up of CUDA, meaning the rest of the Julia CUDA stack should work (notably CUDAapi.jl, CUDAdrv.jl and CUDAnative.jl). If you encounter any issues with CuArrays.jl, please make sure those other packages are working as expected.

Some parts of CuArrays.jl depend on optional libraries, such as cuDNN. The build process should notify about missing dependencies, i.e. inspect the output of Pkg.build("CuArrays") to see whether your installation is complete.


xs = cu(rand(5, 5))
ys = cu[1, 2, 3]
xs_cpu = collect(xs)

Because CuArray is an AbstractArray, it doesn't have much of a learning curve; just use your favourite array ops as usual. The following are supported (on arbitrary numbers of arguments, dimensions etc):

  • Conversions and copy! with CPU arrays
  • General indexing (xs[1:2, 5, :])
  • permutedims
  • Concatenation (vcat(x, y), cat(3, xs, ys, zs))
  • map, fused broadcast (zs .= xs.^2 .+ ys .* 2)
  • fill!(xs, 0)
  • Reduction over dimensions (reducedim(+, xs, 3), sum(x -> x^2, xs, 1) etc)
  • Reduction to scalar (reduce(*, 1, xs), sum(xs) etc)
  • Various BLAS operations (matrix*matrix, matrix*vector)
  • FFTs, using the AbstractFFTs API

We welcome issues or PRs for functionality not on this list.

Note that some operations not on this list will work, but be slow, due to Base's generic implementations. This is intentional, to enable a "make it work, then make it fast" workflow. When you're ready you can disable slow fallback methods:

julia> CuArrays.allowscalar(false)
julia> xs[5]
ERROR: getindex is disabled