Overview of PyFastNoiseSIMD

PyFastNoiseSIMD (pyfastnoisesimd) is a Python wrapper around Jordan Peck’s FastNoiseSIMD (https://github.com/Auburns/FastNoise-SIMD) synthetic noise generation library, FastNoiseSIMD.

pyfastnoisesimd can generate noise in a 1-3D grid, via the Noise.genAsGrid() or the user can provide arbitrary coordinates in 3D Cartesian space with Noise.genFromCoords()

FastNoiseSIMD is also extremely fast due to its use of advanced x64 SIMD vectorized instruction sets, including SSE4.1, AVX2, and AVX512, depending on your CPU capabilities and the compiler used.

Parallelism in pyfastnoisesimd 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®.


pyfastnoisesimd is available on PyPI, and may be installed via pip:

pip install --upgrade pip
pip install --upgrade setuptools
pip install -v pyfastnoisesimd

On Windows, a wheel is provided for Python 3.6 only. Building from source or compiling the extension for 3.5 will require either MS Visual Studio 2015 or MSVC2015 Build Tools:


No Python versions compile with MSVC2017 yet, which is the newest version to support AVX512. Only Python 3.5/3.6 support AVX2 on Windows.

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


The combination of the optimized, SIMD-instruction level C library, and multi-threading, means that pyfastnoisesimd is very, very fast. 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.


  • CPU: Intel i7-7820X Skylake-X (8 cores, 3.6 GHz), Windows 7
  • SIMD level supported: AVX2 & FMA3

With Noise.genAsGrid()

The first test is used the default mode, a cubic grid, Noise.genAsGrid(), from examples\gridded_noise.py:

  • Array shape: [8,1024,1024]

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

With Noise.getFromCoords()

The alternative mode is Noise.getFromCoords() where the user provides the coordinates in Cartesian-space, from examples\GallPeters_projection.py: - noiseType = Simplex - peturbType = GradientFractal

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