gputools 0.3.0

OpenCL accelerated volume processing

gputools - OpenCL accelerated volume processing in Python

This package aims to provide GPU accelerated implementations of common volume processing algorithms to the python ecosystem, such as

• convolutions
• denoising
• synthetic noise
• ffts (simple wrapper around reikna)
• affine transforms

via OpenCL and the excellent pyopencl bindings.

Some examples of processing tasks and their respective runtime (tests/benchmark/benchmark.py):

Task	Image Size/type	CPU[1]	GPU[2]	GPU (w/o transfer)[3]
Mean filter 7x7x7	(128, 1024, 1024) uint8	2627 ms	99 ms	24 ms
Median filter 3x3x3	(128, 1024, 1024) uint8	59750 ms	346 ms	252 ms
Gaussian filter 5x5x5	(128, 1024, 1024) float32	9594 ms	416 ms	101 ms
Zoom/Scale 2x2x2	(128, 1024, 1024) uint8	61829 ms	466 ms	-
NLM denoising	(64, 256, 256) float32	52736 ms	742 ms	-
FFT (pow2)	(128, 1024, 1024) complex64	13831 ms	615 ms	69 ms

[1] Xeon(R) CPU E5-2630 v4 using numpy/scipy functions
[2] NVidia Titan X using gputools
[3] as [2] but without CPU->GPU->CPU transfer

Requirements

• Python 3.9+
• a working OpenCL environment (check with clinfo).

Installation

pip install gputools

Or the development version:

pip install git+https://github.com/maweigert/gputools

Check if basic stuff is working:

python -m gputools

Troubleshooting

If you experience installation issues, this might be due to pyopencl not being properly installed. The easiest way to get a working pyopencl is via conda:

conda install -c conda-forge pyopencl

Usage

Docs are still to be done ;)

Most of the methods work on both numpy arrays or GPU memory objects (gputools.OCLArrays/OCLImage). The latter saving the memory transfer (which e.g. for simple convolutions accounts for the main run time)

Convolutions

• 2D-3D convolutions
• separable convolutions
• fft based convolution
• spatially varying convolutions

import gputools

d = np.zeros((128,128), np.float32)
d[64,64] = 1.
h = np.ones((17,17))
res = gputools.convolve(d,h)

d = np.zeros((128,128,128), np.float32)
d[64,64,64] = 1.
hx,hy,hz = np.ones(7),np.ones(9),np.ones(11)
res = gputools.convolve_sep3(d,hx,hy,hz)

Denoising

bilateral filter, non local means

...
d = np.zeros((128,128,128), np.float32)
d[50:78,50:78,50:78:2] = 4.
d = d+np.random.normal(0,1,d.shape)
res_nlm = gputools.denoise.nlm3(d,2.,2,3)
res_bilat = gputools.denoise.bilateral3(d,3,4.)

Perlin noise

fast 2d and 3d perlin noise calculations

gputools.perlin3(size = (256,256,256), scale = (10.,10.,10.))

Transforms

scaling, translate, rotate, affine...

gputools.transforms.scale(d,.2)
gputools.transforms.rotate(d,axis = (64,64,64),angle = .3)
gputools.transforms.shift(d,(10,20,30))
...

fft

wraps around reikna

gputools.fft(d)
gputools.fft(d, inverse = True)

Configuration

Some configuration data (e.g. the default OpenCL platform and devic) can be changed in the config file "~/.gputools" (create it if necessary)

#~/.gputools

id_platform = 0
id_device = 1

See

gputools.config.defaults

for available keys and their defaults.

Alternatively, the used OpenCL Device can be set via the environment variables gputools_id_device, gputools_id_platform, and gputools_use_gpu (variables present in the config file will take precendence, however).