gpyfft

A Python wrapper for the OpenCL FFT library clFFT.

Introduction

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

gpyfft

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.

Status

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):

work done

• low level wrapper (mostly) completed
• high level wrapper

• complex-to-complex transform, in- and out-of-place
• real-to-complex transform (out-of-place)
• complex-to-real transform (out-of-place)
• single precision
• double precision
• interleaved data
• support injecting custom OpenCL code (pre and post callbacks)
• accept pyopencl arrays with non-zero offsets (Syam Gadde)

Basic usage

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. Note, for testing it is recommended to start simple_example.py from the command line, so you have the possibility to interactively choose an OpenCL context (otherwise, e.g. when using an IPython, you are not asked end might end up with a CPU device, which is prone to fail).

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()