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I stumbled over this difference between CPU and GPU arrays:
julia>using JLArrays
julia>using CUDA
julia> a =rand(Float32, 1); b =rand(ComplexF32, 1);
julia>promote_type(typeof(a), typeof(b))
Vector{ComplexF32} (alias for Array{Complex{Float32}, 1})
julia>promote_type(typeof(JLArray(a)), typeof(JLArray(b)))
JLArray{T, 1} where T
julia>promote_type(typeof(CuArray(a)), typeof(CuArray(b)))
CuArray{T, 1, CUDA.DeviceMemory} where T
I've noticed this behaviour originally on CuArrays, but noticed it is also present for JLArrays, so I hope this is the correct repository. I have not been able to test it out on other GPU arrays (yet)
The text was updated successfully, but these errors were encountered:
nHackel
changed the title
promote_type work GPU arrays of Float32 and ComplexF32 promotes to UnionAllpromote_type on GPU arrays of Float32 and ComplexF32 promotes to UnionAll
Jun 24, 2024
Hello,
I stumbled over this difference between CPU and GPU arrays:
I've noticed this behaviour originally on CuArrays, but noticed it is also present for JLArrays, so I hope this is the correct repository. I have not been able to test it out on other GPU arrays (yet)
The text was updated successfully, but these errors were encountered: