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# Detailed GPAW installation instructions on non-acclerated systems
These instructions are in addition to the brief instructions in [README.md](../README.md).
## Detailed dependency list
### Libraries and Python interpreter
GPAW needs (for the UEABS benchmarks)
* [Python](https://www.python.org/): GPAW 1.5.2 supports Python 2.7 and 3.4-3.7.
GPAW 19.8.1 needs Python 3.4-3.7 and GPAW 20.1.0 requires Python 3.5-3.8.
* [MPI library](https://www.mpi-forum.org/)
* [LibXC](https://www.tddft.org/programs/libxc/). GPAW 1.5.2 requires LibXC 1.5.2
or later. GPAW 19.8.1 and 20.1.0 need LibXC 3.x or 4.x.
* (Optimized) [BLAS](http://www.netlib.org/blas/) and
[LAPACK](http://www.netlib.org/lapack/) libraries.
There are both commercial and free and open source versions of these libraries.
Using the [reference implementation of BLAS from netlib](http://www.netlib.org/blas/)
will give very poor performance. Most optimized LAPACK libraries actually only
optimize a few critical routines while the remaining routines are compiled from
the reference version. Most processor vendors for HPC machines and system vendors
offer optmized versions of these libraries.
* [ScaLAPACK](http://www.netlib.org/scalapack/) and the underlying communication
layer [BLACS](http://www.netlib.org/blacs/).
* [FFTW](http://www.fftw.org/) or compatible FFT library.
For the UEABS benchmarks, the double precision, non-MPI version is sufficient.
GPAW also works with the
[Intel MKL](https://software.intel.com/content/www/us/en/develop/tools/math-kernel-library.html)
FFT routines when using the FFTW wrappers provided with that product.
For the GPU version, the following packages are needed in addition to the packages
above:
* CUDA toolkit
* [PyCUDA](https://pypi.org/project/pycuda/)
Optional components of GPAW that are not used by the UEABS benchmarks:
* [libvdwxc](https://gitlab.com/libvdwxc/libvdwxc), a portable C library
of density functionals with van der Waals interactions for density functional theory.
This library does not work with the MKL FFTW wrappers as it needs the MPI version
of the FFTW libraries too.
* [ELPA](https://elpa.mpcdf.mpg.de/),
which should improve performance for large systems when GPAW is used in
[LCAO mode](https://wiki.fysik.dtu.dk/gpaw/documentation/lcao/lcao.html)
### Python packages
GPAW needs
* [wheel](https://pypi.org/project/wheel/) is needed in most (if not all) ways of
installing the packages from source.
* [NumPy](https://pypi.org/project/numpy/) 1.9 or later (for GPAW 1.5.2/19.8.1/20.1.0)
* Installing NumPy from source will also require
[Cython](https://pypi.org/project/Cython/)
* [SciPy](https://pypi.org/project/scipy/) 0.14 or later (for GPAW 1.5.2/19.8.1/20.1.0)
* [ASE, Atomic Simulation Environment](https://wiki.fysik.dtu.dk/ase/), a Python package
from the same group that develops GPAW. Required versions are 3.17.0 or later for
GPAW 1.5.2 and 3.18.0 or later for GPAW 19.8.1 or 20.1.0.
ASE has a couple of dependendencies
that are not needed for running the UEABS benchmarks. However, several Python
package install methods will trigger the installation of those packages, and
with them may require a chain of system libraries.
* ASE does need NumPy and SciPy, but these are needed anyway for GPAW.
* [matplotlib](https://pypi.org/project/matplotlib/), at least version 2.0.0.
This package is optional and not really needed to run the benchmarks.
Matplotlib pulls in a lot of other dependencies. When installing ASE with pip,
it will try to pull in matplotlib and its dependencies
* [pillow](https://pypi.org/project/Pillow/) needs several exgternal
libraries. During the development of the benchmarks, we needed at least
zlib, libjpeg-turbo (or compatible libjpeg library) and freetype. Even
though the pillow documentation claimed that libjpeg was optional,
it refused to install without.
* [kiwisolver](https://pypi.org/project/kiwisolver/): Contains C++-code
* [pyparsing](https://pypi.org/project/pyparsing/)
* [Cycler](https://pypi.org/project/Cycler/), which requires
* [six](https://pypi.org/project/six/)
* [python-dateutil](https://pypi.org/project/python-dateutil/), which also
requires
* [six](https://pypi.org/project/six/)
* [Flask](https://pypi.org/project/Flask/) is an optional dependency of ASE
that is not automatically pulled in by `pip` in versions of ASE tested during
the development of this version of the UEABS. It has a number of dependencies
too:
* [Jinja2](https://pypi.org/project/Jinja2/)
* [MarkupSafe](https://pypi.org/project/MarkupSafe/), contains some C
code
* [itsdangerous](https://pypi.org/project/itsdangerous/)
* [Werkzeug](https://pypi.org/project/Werkzeug/)
* [click]()
## Tested configurations
* Python
* Libraries used during the installation of Python:
* ncurses 6.2
* libreadline 8.0
* libffi 3.3
* zlib 1.2.11
* OpenSSL 1.1.1g, but only because EasyBuild was used and requires it.
* Python will of course pick up several other libraries that it might find on
the system. The benchmark installation was tested on a system with very few
development packages of libraries installed in the system image. Tcl/Tk
and SQLite3 development packages in particular where not installed, so the
standard Python library packages sqlite3 and tkinter were not fully functional.
* Python packages
* wheel
* Cython
* NumPy
* SciPy
* ASE
* GPAW
The table below give the combinations of major packages Python, NumPy, SciPy, ASE and
GPAW that were tested:
| Python | NumPy | SciPy | ASE | GPAW |
|:-------|:-------|:------|:-------|:--------|
| 3.7.9 | 1.18.5 | 1.4.1 | 3.17.0 | 1.5.2 |
| 3.7.9 | 1.18.5 | 1.4.1 | 3.18.0 | 19.8.1 |
| 3.8.5 | 1.18.5 | 1.4.1 | 3.19.1 | 20.1.0 |
## Installing all prerequisites
We do not include the optimized mathematical libraries in the instructions (BLAS, LAPACK,
FFT library, ...). Also, the instructions below will need to be adapted to the specific
libraries that are being used.
Other prerequisites:
* libxc
* Python interpreter
* Python package NumPy
* Python package SciPy
* Python package ase
### Installing libxc
* Installing libxc requires GNU automake and GNU buildtool besides GNU make and a
C compiler. The build process is the usual GNU configure - make - make install
cycle, but the `configure` script still needs to be generated with autoreconf.
### Installing Python from scratch
The easiest way to get Python on your system is to download an existing distribution
(one will likely already be installed on your system). Python itself does have a lot
of dependencies though, definitely in its Standard Python Library. Many of the
standard packages are never needed when executing the benchmark cases. Isolating them
to compile a Python with minimal dependencies is beyond the scope though. We did
compile Python without the necessary libraries for the standard libraries sqlite3
and tkinter (the latter needing Tcl/Tk).
Even though GPAW contains a lot of Python code, the Python interpreter is not the main
performance-determining factor in the GPAW benchmark. Having a properly optimized installation
of NumPy, SciPy and GPAW itself proves much more important.
## Configuring and installing GPAW
### GPAW 1.5.2
* GPAW 1.5.2 uses `distutils`. Customization of the installation process is possible
through the `customize.py` file.
* The FFT library: According to the documentation, the following strategy is used
* The compile process searches (in this order) for ``libmkl_rt.so``,
``libmkl_intel_lp64.so`` and ``libfftw3.so`. First one found will be
loaded.
* If none is found, the built-in FFT from NumPy will be used. This does not need
to be a problem if NumPy provides a properly optimized FFT library.
* The choice can also be overwritten using the GPAW_FFTWSO environment variable.
### GPAW 19.8.1
* GPAW 19.8.1 uses `distutils`. Customization of the installation process is possible
through a `customize.py` file.
* The selection process of the FFT library has changed from version 1.5.2. It is
now possible to specify the FFT library in `customize.py` or to simply select to
use the NumPy FFT routines.
### GPAW 30.1.0
* GPAW 20.1.0 uses `setuptools`. Customization of the installation process is possible
through the `siteconfig.py` file.
* The selection process of the FFT library is the same as in version 19.8.1, except
that the settings are now in `siteconfrig.py` rather than `customize.py`.