NESSi  v1.0.1
The NonEquilibrium Systems Simulation Library
Getting started with NESSi

Getting the code

The latest stable release of the NESSi package is NESSi-1.0.1 and can be cloned from the GitHub repository:

git clone

Get it here: tar.gz , zip


The NESSi repository nessi contains two folders: libcntr, which contains the library along with a collection of python tools (see Python tools), and examples, which includes example programs (see Example programs).

The library libcntr depends on the following two libraries:

eigen3 required
HDF5 optional, recommended

For compiling and installing the libcntr library, we use the CMake building environment to generate system specific make files. Several CMake variables need to be set to install libcntr; their are passed as -Dvar=[value]. CMake can be called directly from the terminal; however, it is more convenient to create a configure script with all variables and compile options. We suggest a script of the following structure:

CC=[C compiler] CXX=[C++ compiler]
cmake \
-DCMAKE_INSTALL_PREFIX=[install directory] \
-DCMAKE_BUILD_TYPE=[Debug|Release] \
-Domp=[ON|OFF] \
-Dhdf5=[ON|OFF] \
-Dmpi=[ON|OFF] \
-DCMAKE_INCLUDE_PATH=[include directory] \
-DCMAKE_LIBRARY_PATH=[library directory] \
-DCMAKE_CXX_FLAGS="[compiling flags]" \

, which in the following will be refered to as In the first line, the C and C++ compiler are set. We have tested the full library for both the GNU compilers (CC=gcc CXX=g++) as well as the Intel compilers (CC=icc CXX=icpc). The install directory (for instance /home/opt) is defined by the CMake variable CMAKE_INSTALL_PREFIX. Debugging tools are switched on by setting CMAKE_BUILD_TYPE=Debug; otherwise, all assertions and sanity checks are turned off. The code is significantly faster in release mode (CMAKE_BUILD_TYPE=Release) and thus recommended for a production run. The debug mode, on the other hand, turns on assertions (implemented as C++ standard assertions) checking a consistency of the input for all major routines. The following three lines trigger optional (but recommended) functionalities. Setting omp=ON turns on the compilation of routines parallelised with openMP, while setting mpi=ON is required for compiling distributed-memory routines based on MPI. In this case, MPI compilers have to be specified in the first line. Finally, hdf5=ON activates the usage of the hdf5 library. The path to the libraries that libcntr depends upon (eigen3 and, optionally, hdf5) are provided by specifying the include directory CMAKE_INCLUDE_PATH and the library path CMAKE_LIBRARY_PATH. Finally, the compilation flags are specified by CMAKE_CXX_FLAGS. To compile libcntr, the flags should include


As the next step, create a build directory (for instance cbuild). Navigate to this directory and run the configure script:

sh ../

After successful configuration (which generates the make files), compile the library by


and install it to the install directory by

make install

We have tested the installation procedure on the following platforms: MacOSX with MacPorts, MacOSX with homebrew, and Linux (Ubuntu, Debian, CentOS and Arch Linux) using the respective native package manager. Detailed installation instructions can be found below.

MacOSX with MacPorts

We recommend using the gcc compiler. As an example we show the installation with gcc9 and openmpi. The dependencies of libcntr are installed via

sudo port install gcc9 eigen3-devel hdf5
sudo port install openmpi-devel-gcc9

For installing doxygen to be able to build the documentation, run

sudo port install doxygen graphviz

The python packages numpy, scipy, matplotlib and h5py can be installed using the system's python package manager pip or using MacPorts.

Below we provide an example configure script based on default path (/opt/local/) of MacPorts:

CC=mpicc CXX=mpicxx
cmake \
-Domp=ON \
-Dhdf5=ON \
-Dmpi=ON \
-DCMAKE_INCLUDE_PATH=/opt/local/include \
-DCMAKE_LIBRARY_PATH=/opt/local/lib \
-DCMAKE_CXX_FLAGS="-std=c++11 -O3" \

Note that under MacOSX the installation directory ($HOME/opt) has to be provided explitly for other programs to find the shared library libcntr, which is accomplished by setting CMAKE_INSTALL_NAME_DIR=$HOME/opt/lib.

We recommend using MacPorts as it currently supports all compiling options directly.

MacOSX with homebrew

We have tested the compilation based Apple clang. Due to some legacy dependence, run

brew unlink python@2

first. The dependencies are installed by

brew install eigen hdf5 open-mpi


brew install doxygen graphviz

for building the documentation.

Using gcc is also possible, but openmpi has to be build from source in this case.

The following example configure script illustrates how to install libcntr:

CC=mpicc CXX=mpicxx
cmake \
-Domp=OFF \
-Dhdf5=ON \
-Dmpi=ON \
-DCMAKE_INCLUDE_PATH=/usr/local/include \
-DCMAKE_LIBRARY_PATH=/usr/local/lib \

Note that we disabled openMP support by omp=OFF. This is due to native Apple clang compiler not directly supporting openMP. Specific work-arounds exist, see for instance here.


The installation works similar under linux. For Debian or Ubuntu the dependencies can be installed by

apt-get install -y --allow-unauthenticated libhdf5-serial-dev libopenmpi-dev libeigen3-dev doxygen graphviz cmake


pacman -Sy --noconfirm hdf5 gcc openmpi eigen doxygen graphviz make cmake

on Arch Linux. For RedHat-based systems like CentOS use yum to install the required packages. The include and library path are usually automatically set, such that the following example configure script would work

CC=mpicc CXX=mpicxx
cmake \
-Domp=ON \
-Dhdf5=ON \
-Dmpi=ON \

Note that setting CMAKE_INSTALL_NAME_DIR is not required if the installation path (e. g. $HOME/opt/lib) can be found under LD_LIBRARY_PATH.

We have further compiled and installed the code on compute clusters with CentOS, where a module system provided the required libraries. We have also tested the compilation with Intel compilers by setting CC=mpiicc CXX=mpiicpc.

Creating a custom program

As a minimal working example, we provide a program which creates a Greens function and prints out its properties. First, we define Greens function G with given properties. After the Greens function object is created the program prints out corresponding attributes. Only one header (cntr.hpp) needs to be included to make all subroutines in namespace cntr:: available.

#include <sys/stat.h>
#include <iostream>
#include "cntr/cntr.hpp"
int Nt=100; // # of timesteps
int Ntau=100; // number of imaginary time steps
int size=3; // Size of Greens function
std::cout << "Number of timesteps: " << G.nt() << std::endl;
std::cout << "Number of Matsubara points: " << G.ntau() << std::endl;
std::cout << "Size of Greens function: " << G.size1() << std::endl;
return 0;

Test suite and documentation

We also provide a test suite for checking the functionality of every major routine in libcntr based on the Catch library. For running the tests, simply run

make test

After completing all test, the message All tests passed indicates that the compiled version of libcntr is fully functional. If compiled with MPI support, the MPI-based functions can be tested by running

make test_mpi

The C++ code is fully document using the automatic documentation tool doxygen. For generating the documentation, specify BUILD_DOC=ON in the configure script. The documentation is then generated automatically along the compilation. After a successful build, the html documentation can be found in doc/html/index.html.

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