CPU and GPU (HSA)

The PyNUFFT ran originally on Numpy/Scipy. Unfortunately the default Numpy/Scipy is most efficient on a single CPU core.

Later it was ported to PyCUDA and PyOpenCL, which allows us to leverage the speed of multi-core CPU and GPU.

Mixing NUFFTs with CPU/GPU is possible but has no warranty.

The class methods are listed in Table 1

Table 1 Methods implemented in NUFFT

method name	NUFFT()	NUFFT(helper.device_list[0])	References
__init__()	✓	✓	Constructor
plan()	✓	✓	Planning the instance
forward()	✓	✓	Forward NUFFT
adjoint()	✓	✓	Adjoint NUFFT
offload()	×	✓	Offload the NUFFT_hsa() to device.
x2xx()	✓	✓	Apply the scaling factor
xx2k()	✓	✓	Oversampled FFT
k2y()	✓	✓	Interpolation
k2vec()	✓	×	Reshape the k-space to the vector
vec2y()	✓	×	Multiply the vector to generate the data
vec2k()	✓	×	Reshape the vector to k-space
y2vec()	✓	×	Multiply the data to get the vector
y2k()	✓	✓	Adjoint of k2y()
k2xx()	✓	✓	Inverse FFT (excessive parts are cropped)
xx2x()	✓	✓	Apply the scaling factor
_precompute	✓	✓	Apply the scaling factor

Parameters of PyNUFFT

Below we summarize the required variables in Table 2

Table 2 Parameters of the plan() method

Parameter	NUFFT	NUFFT(helper.device_list()[0])	References
om (Numpy Array)	✓	✓	Non-Cartesian coordinates (M, dim)
Nd (tuple)	✓	✓	Size of the image grid
Kd (tuple)	✓	✓	Size of the oversampled Fourier grid
Jd (tuple)	✓	✓	Size of the interpolator
ft_axes (tuple)	optional	optional	FFT on the given axes (default = None (all axes))
radix (int)	×	optional	radix (default = 1)