The same functionality is available in CuArrays.jl.

This is a wrapper of the CUFFT library. It works in conjunction with the CUDArt package.

Here's an example of taking a 2D real transform, and then it's inverse, and comparing against Julia's CPU-based

```
using CUDArt, CUFFT, Base.Test
CUDArt.devices(dev->capability(dev)[1] >= 2, nmax=1) do devlist
A = rand(7,6)
# Move data to GPU
G = CudaArray(A)
# Allocate space for the output (transformed array)
GFFT = CudaArray(Complex{eltype(A)}, div(size(G,1),2)+1, size(G,2))
# Compute the FFT
pl! = plan(GFFT, G)
pl!(GFFT, G, true)
# Copy the result to main memory
AFFTG = to_host(GFFT)
# Compare against Julia's rfft
AFFT = rfft(A)
@test_approx_eq AFFTG AFFT
# Now compute the inverse transform
pli! = plan(G,GFFT)
pli!(G, GFFT, false)
A2 = to_host(G)
@test_approx_eq A A2/length(A)
end
```

For those who dive into the internals, one potentially-confusing point is that C's (or FFTW's) convention for representing array dimensions is opposite that of Julia. C's convention stems from the static representation of arrays,

```
const NX = 3
const NY = 5
double *myarray[NX][NY] = {
{1.0, 2.0, 3.0, 4.0, 5.0},
{6.0, 7.0, 8.0, 9.0, 10.0},
{11.0, 12.0, 13.0, 14.0, 15.0}};
```

Consequently, `NX`

represents the number of rows, and `NY`

the number of columns (even though visually `x`

is the horizontal axis and `y`

the vertical axis). The first dimension therefore does *not* correspond to the "fast" dimension in linear-memory layout.

01/19/2014

3 months ago

29 commits