PyFastNoiseSIMD is a wrapper around Jordan Peck's synthetic noise library https://github.com/Auburns/FastNoise-SIMD which has been accelerated with SIMD instruction sets. It may be installed via pip:
pip install pyfastnoisesimd
Parallelism is further enhanced by the use of concurrent.futures
to multi-thread
the generation of noise for large arrays. Thread scaling is generally in the
range of 50-90 %, depending largely on the vectorized instruction set used.
The number of threads, defaults to the number of virtual cores on the system. The
ideal number of threads is typically the number of physical cores, irrespective
of Intel Hyperthreading®.
Here is a simple example to generate Perlin-style noise on a 3D rectilinear grid::
import pyfastnoisesimd as fns
import numpy as np
shape = [512, 512, 512]
seed = np.random.randint(2**31)
N_threads = 4
perlin = fns.Noise(seed=seed, numWorkers=N_threads)
perlin.frequency = 0.02
perlin.noiseType = fns.NoiseType.Perlin
perlin.fractal.octaves = 4
perlin.fractal.lacunarity = 2.1
perlin.fractal.gain = 0.45
perlin.perturb.perturbType = fns.PerturbType.NoPerturb
result = perlin.genAsGrid(shape)
where result
is a 3D numpy.ndarray
of dtype 'float32'
. Alternatively,
the user can provide coordinates, which is helpful for tasks such as
custom bump-mapping a tessellated surface, via Noise.getFromCoords(coords)
.
More extensive examples are found in the examples
folder on the Github repository.
Parallelism is further enhanced by the use of concurrent.futures
to multi-thread
the generation of noise for large arrays. Thread scaling is generally in the
range of 50-90 %, depending largely on the vectorized instruction set used.
The number of threads, defaults to the number of virtual cores on the system. The
ideal number of threads is typically the number of physical cores, irrespective
of Intel Hyperthreading®.
Check it out at:
http://pyfastnoisesimd.readthedocs.io
pyfastnoisesimd
is available on PyPI, and may be installed via pip
::
pip install --upgrade pip
pip install --upgrade setuptools
pip install -v pyfastnoisesimd
Wheels are provided for Windows, Mac OSX, and Linux via manywheels
.
Building from source on Windows will require the appropriate MS Visual Studio version for your version of Python, or MSVC Build Tools:
http://landinghub.visualstudio.com/visual-cpp-build-tools
On Linux or OSX, only a source distribution is provided and installation
requires gcc
or clang
. For AVX512 support with GCC, GCC7.2+ is required, lower
versions will compile with AVX2/SSE4.1/SSE2 support only. GCC earlier than
4.7 disables AVX2 as well. Note that pip
does not respect the $CC
environment
variable, so to clone and build from source with gcc-7
:
git clone https://github.com/robbmcleod/pyfastnoisesimd.git
alias gcc=gcc-7; alias g++=g++-7
pip install -v ./pyfastnoisesimd
Installing GCC7.2 on Ubuntu (with sudo
or as root)::
add-apt-repository ppa:ubuntu-toolchain-r/test
apt update
apt install gcc-7 g++-7
Benchmarks
Generally speaking thread scaling is higher on machines with SSE4 support only, as most CPUs throttle clock speed down to limit heat generation with AVX2. As such, AVX2 is only about 1.5x faster than SSE4 whereas on a pure SIMD instruction length basis (4 versus 8) you would expect it to be x2 faster.
The first test is used the default mode, a cubic grid, Noise.genAsGrid()
,
from examples\gridded_noise.py
:
Array shape: [8,1024,1024] CPU: Intel i7-7820X Skylake-X (8 cores, 3.6 GHz), Windows 7 SIMD level supported: AVX2 & FMA3
Single-threaded mode
Computed 8388608 voxels cellular noise in 0.298 s
35.5 ns/voxel
Computed 8388608 voxels Perlin noise in 0.054 s
6.4 ns/voxel
Multi-threaded (8 threads) mode
Computed 8388608 voxels cellular noise in 0.044 s 5.2 ns/voxel 685.0 % thread scaling Computed 8388608 voxels Perlin noise in 0.013 s 1.5 ns/voxel 431.3 % thread scaling
The alternative mode is Noise.getFromCoords()
where the user provides the
coordinates in Cartesian-space, from examples\GallPeters_projection.py
:
Single threaded mode
Generated noise from 2666000 coordinates with 1 workers in 1.766e-02 s
6.6 ns/pixel
Multi-threaded (4 threads) mode
Generated noise from 2666000 coordinates with 4 workers in 6.161e-03 s 2.3 ns/pixel 286.6 % thread scaling
0.4.3
0.4.2
0.4.1
0.4.0
emptyCoords
function to empty_coords
.0.3.2
genAsGrid
.start
parameter was not working
in multi-threading mode for calls to genAsGrid
.0.3.1
0.3.0
Noise.genFromCoords()
.orthographic_projection.py
to examples/
.sphinx.napoleon
formatting.doc
directory.PerturbType.NoPetrub
-> PerturbType.NoPerturb
fastnoisesimd
from freeing memory for coords
argument of
Noise.genFromCoords(coords)
. It should now be possible to reuse
coords without seg-faulting.0.2.1
RELEASING_GUIDE.txt
for notes.0.2.0
examples/projection.py
to demonstrate noise generation by supplied
coordinates as applied to a Gall-Peters cylindrical projection of a sphere
(i.e. a world map).Noise
object-oriented interface. Noise
uses Python properties to
expose the Set/Get
functions in FastNoiseSIMD
.unittest
support.pyfastnoisesimd.generate()
function.pyfastnoisesimd.generate()
that always set
the seed to 42.axisScales
-> axesScales
, indicies
-> indices
0.1.5
0.1.4
0.1.2
If you want a more direct interface with the underlying library you may use the
pyfastsimd._ext
module, which is a function-for-function mapping to the C++
code.
FastNoiseSIMD is implemented by Jordan Peck, and may be found at:
https://github.com/Auburns/FastNoiseSIMD
It aims to provide faster performance through the use of intrinsic(SIMD) CPU functions. Vectorisation of the code allows noise functions to process data in sets of 4/8/16 increasing performance by 700% in some cases (Simplex).
See the Wiki for usage information on the noise types:
https://github.com/Auburns/FastNoiseSIMD/wiki
Download links for a GUI-based reference noise generator may be found at:
https://github.com/Auburns/FastNoiseSIMD/releases
Robert A. McLeod wrote the Python wrapper, implemented multi-threading, and wrote the documentation.
Elliott Sales de Andrade contributed a number of fixes to allow building to succeed on many platforms.
Jordan Peck wrote the underlying library FastNoiseSIMD
.