Installing a matrix backend

Nutils currently supports three matrix backends: Numpy, Scipy and MKL. Since Numpy is a primary dependency this backend is always available. Unfortunately it is also the least performant of the three because of its inability to exploit sparsity. It is therefore strongly recommended to install one of the other two backends via the instructions below.

By default, Nutils automatically activates the best available matrix backend: MKL, Scipy or Numpy, in that order. A consequence of this is that a faulty installation may easily go unnoticed as Nutils will silently fall back on a lesser backend. As such, to make sure that the installation was successful it is recommended to force the backend at least once by setting the NUTILS_MATRIX environment variable. In Linux:



The Scipy matrix backend becomes available when Scipy is installed, either using the platform's package manager or via pip:

python -m pip install --user scipy

In addition to a sparse direct solver, the Scipy backend provides many iterative solvers such as CG, CGS and GMRES, as well as preconditioners. The direct solver can optionally be made more performant by additionally installing the scikit-umfpack module.


Intel's oneAPI Math Kernel Library provides the Pardiso sparse direct solver, which is easily the most powerful direct solver that is currently supported. It is installed via the official instructions, or, if applicable, by any of the steps below.

On a Debian based Linux system (such as Ubuntu) the libraries can be directly installed via the package manager:

sudo apt install libmkl-rt

For Fedora or Centos Linux, Intel maintains its own repository that can be added with the following steps:

sudo dnf config-manager --add-repo
sudo rpm --import
sudo dnf install intel-mkl
sudo tee /etc/ << EOF > /dev/null
sudo ldconfig -v