amd gpu give different results when nested loop is used #517
Description
Hi, I noticed that the following script produces different results depending on the backend. On my machine, the output is:
cpu: [18.0; 18.0; 18.0; 18.0; 18.0; 18.0; 18.0; 18.0; 18.0; 18.0;;;]
cuda: [18.0; 18.0; 18.0; 18.0; 18.0; 18.0; 18.0; 18.0; 18.0; 18.0;;;]
amd: [6.0; 6.0; 6.0; 6.0; 6.0; 6.0; 6.0; 6.0; 6.0; 6.0;;;]Is there a mistake in the kernel function?
using CUDA
using AMDGPU
using KernelAbstractions
function compute_tensors(tensor, kernel_fun, Nx, Ny, Nz)
kernel! = kernel_fun(get_backend(tensor), 512)
kernel!(tensor, Nx, Ny, Nz; ndrange=size(tensor))
KernelAbstractions.synchronize(get_backend(tensor))
return nothing
end
@kernel function kernel_xx!(tensor, Nx::Int64, Ny::Int64, Nz::Int64)
i, j, k = @index(Global, NTuple)
sum = zero(eltype(tensor))
for p in (-Nx):Nx, q in (-Ny):Ny
sum += 2.0
end
@inbounds tensor[i, j, k] = sum
end
nx, ny, nz = 10, 1, 1
Nx, Ny, Nz = 1, 1, 1
tensor = zeros(Float64, nx, ny, nz)
compute_tensors(tensor, kernel_xx!, Nx, Ny, Nz)
println("cpu:", tensor)
tensor = CUDA.zeros(Float64, nx, ny, nz)
compute_tensors(tensor, kernel_xx!, Nx, Ny, Nz)
println("cuda:", tensor)
tensor = AMDGPU.zeros(Float64, nx, ny, nz)
compute_tensors(tensor, kernel_xx!, Nx, Ny, Nz)
println("amd:", tensor)