CUDA.jl 2.1
Tim Besard
CUDA.jl v2.1 is a bug-fix release, with one new feature: support for cubic texture interpolations. The release also partly reverts a change from v2.0: reshape
, reinterpret
and contiguous view
s now return a CuArray
again.
Generalized texture interpolations
CUDA's texture hardware only supports nearest-neighbour and linear interpolation, for other modes one is required to perform the interpolation by hand. In CUDA.jl v2.1 we are generalizing the texture interpolation API so that it is possible to use both hardware-backed and software-implemented interpolation modes in exactly the same way:
# N is the dimensionality (1, 2 or 3)
# T is the element type (needs to be supported by the texture hardware)
# source array
src = rand(T, fill(10, N)...)
# indices we want to interpolate
idx = [tuple(rand(1:0.1:10, N)...) for _ in 1:10]
# upload to the GPU
gpu_src = CuArray(src)
gpu_idx = CuArray(idx)
# create a texture array for optimized fetching
# this is required for N=1, optional for N=2 and N=3
gpu_src = CuTextureArray(gpu_src)
# interpolate using a texture
gpu_dst = CuArray{T}(undef, size(gpu_idx))
gpu_tex = CuTexture(gpu_src; interpolation=CUDA.NearestNeighbour())
broadcast!(gpu_dst, gpu_idx, Ref(gpu_tex)) do idx, tex
tex[idx...]
end
# back to the CPU
dst = Array(gpu_dst)
Here, we can change the interpolation
argument to CuTexture
to either NearestNeighbour
or LinearInterpolation
, both supported by the hardware, or CubicInterpolation
which is implemented in software (building on the hardware-supported linear interpolation).
Partial revert of array wrapper changes
In CUDA.jl v2.0, we changed the behavior of several important array operations to reuse available wrappers in Base: reshape
started returning a ReshapedArray
, view
now returned a SubArray
, and reinterpret
was reworked to use ReinterpretArray
. These changes were made to ensure maximal compatibility with Base's array type, and to simplify the implementation in CUDA.jl and GPUArrays.jl.
However, this change turned out to regress the time to precompile and load CUDA.jl. Consequently, the change has been reverted, and these wrappers are now implemented as part of the CuArray
type again. Note however that we intend to revisit this change in the future. It is therefore recommended to use the DenseCuArray
type alias for methods that need a CuArray
backed by contiguous GPU memory. For strided CuArray
s, i.e. non-contiguous views, you should use the StridedCuArray
alias.