A Python wrapper for the OpenCL FFT library clFFT.
The open source library clFFT implements FFT for running on a GPU via OpenCL. Some highlights are:
- batched 1D, 2D, and 3D transforms
- supports many transform sizes (any combinatation of powers of 2,3,5,7,11, and 13)
- flexible memory layout
- single and double precisions
- complex and real-to-complex transforms
- supports injecting custom code for data pre- and post-processing
This python wrapper is designed to tightly integrate with PyOpenCL. It consists of a low-level Cython based wrapper with an interface similar to the underlying C library. On top of that it offers a high-level interface designed to work on data contained in instances of pyopencl.array.Array, a numpy work-alike array class for GPU computations. The high-level interface takes some inspiration from pyFFTW. For details of the high-level interface see fft.py.
The low lever interface is complete (more or less), the high-level interface is not yet settled and likely to change in future. Features to come (not yet implemented in the high-level interface):
- real-to-complex transforms (out-of-place coming soon, in-place?)
- double implemented in 0.4.1
Here we describe a simple example of performing a batch of 2D complex-to-complex FFT transforms on the GPU, using the high-level interface of gpyfft. The full source code of this example ist contained in simple_example.py, which is the essence of benchmark.py
imports:
import numpy as np
import pyopencl as cl
import pyopencl.array as cla
from gpyfft.fft import FFT```
initialize GPU:
``` python
context = cl.create_some_context()
queue = cl.CommandQueue(context)initialize memory (on host and GPU). In this example we want to perform in parallel four 2D FFTs for 1024x1024 single precision data.
data_host = np.zeros((4, 1024, 1024), dtype = np.complex64)
#data_host[:] = some_useful_data
data_gpu = cla.to_device(queue, data_host)```
create FFT transform plan for batched inline 2D transform along second two axes.
``` python
transform = FFT(context, queue, data_gpu, axes = (2, 1))If you want an out-of-place transform, provide the output array as additional argument after the input data.
Start the work and wait until it is finished (Note that enqueu() returns a tuple of events)
event, = transform.enqueue()
event.wait()Read back the data from the GPU to the host
result_host = data_gpu.get()- low level wrapper (mostly) completed
- high level wrapper
- complex-to-complex transform, in- and out-of-place
- real-to-complex transform
- single precision
- double precision
- interleaved data