Parallel NumPy with OpenBLAS

NumPy is a fantastic resource for doing numerical computations in Python, allowing significant speedups by allowing efficient C-implemented operations in the back end do the heavy lifting. However, standard shipped NumPy implementations from Linux package systems and through pip do not take advantage of the potential speed improvments available by executing appropriate array operations in parallel.

In order to do such operations in parallel, we must build it against a supported parallel implementation of BLAS/LAPACK, which are the libraries used to perform linear algebra in NumPy. Some of the supported implementations can be challenging to build themselves, however I found success with OpenBLAS.


Having installed OpenBLAS using the openblas-lapack package from the Arch Linux User Repository, I downloaded the latest version of NumPy from github and created a new build config from the template:

git clone git://
cd numpy
cp site.cfg.example site.cfg

I then uncommented the OpenBLAS section and changed the paths to the correct ones for my system (pacman -Ql openblas-lapack was of use to locate these paths on my Arch Linux system) and built and installed NumPy with:

python build
sudo python install

Opening an interactive Python shell, I was able to verify that the operations were running in parallel by executing the following instructions and observing CPU usage in htop:

import numpy as np
a = np.random.rand(10000, 10000)
b = np.random.rand(10000, 10000), b)


The SciPy package, an additional set of scientific tools built around NumPy, is compiled in much the same way. First, ensure that Cython is installed and retrieve the SciPy sources:

sudo pip install Cython
git clone git://

Then make a config file from the template, uncomment and update the OpenBLAS entries, build and install just as with NumPy.