Installation

Installting the API

SPARC-X-API may be installed via the following approaches:

Using `conda` (recommended)

You can use any of anaconda, miniconda, or micromamba tools to install a conda package engine. The rest of the steps will be made in a conda environment to get the latest release of SPARC-X-API that comes with the pseudopotentials installed:

# Change 'sparc-env' to your desired name if needed
conda create -n sparc-env python=3.11 pip mamba
conda activate sparc-env
conda install -c conda-forge sparc-x-api

On Linux platforms (x86_64, aarch64), you can also install the pre-compiled SPARC DFT binaries alongside the API:

# Note the package name is sparc-x
conda install -c conda-forge sparc-x
# Re-activate to have the env variables effective
conda activate sparc-env

Install from PyPI 

If you prefer using pip to install SPARC-X-API, there is also a mirror package on PyPI:

python -m pip install sparc-x-api

The pseudopotential files will also be installed in this approach. If you wish to compile the SPARC C/C++ code, please refer to the manual installation.

Installing from latest source code

The latest version of the SPARC-X-API can also be installed using pip from the source code on Github.

python -m pip install git+https://github.com/SPARC-X/SPARC-X-API

Note

The pseudopotential files should be manually downloaded in this case.

You can download the latest SPMS pseudopotentials and unpacks the pseudopotential files into <python-lib-root>/site-packages/sparc/psp:

python -m sparc.download_data

For developers, please check the how to contribute page for setting up a dev-environment for SPARC-X-API.

Install the SPARC binary code

To utilize the API for drive SPARC calculations, please following the SPARC manual for compilation and installation of the SPARC DFT code itself.

We recommend using the conda-forge package to install the pre-compiled SPARC binary. If you want to compile the latest SPARC C/C++, it is also straightforward:

Use `conda` toolchains

In the previously configured sparc-env environment, install the build dependencies and compile.

The following process compilers SPARC with OpenMPI/OpenBLAS/Scalapack toolchains.

conda activate sparc-env
mamba install -c conda-forge \
                 make compilers \
				 fftw=*=mpi_openmpi_* \
				 openblas openmpi scalapack
git clone https://github.com/SPARC-X/SPARC.git
cd SPARC/src
make USE_MKL=0 USE_SCALAPACK=1 USE_FFTW=1

The compiled binary will be at SPARC/lib/sparc, and will run when sparc-env environment is activated.

TODO MKL available?

Compiling SPARC on HPC

High Performance Clusters (HPC) machines usually have some specific requirements about the parallel and numerical library setups. While conda installation works in most cases, it is often true to compile SPARC with existing MPI/MKL/BLAS libraries to ensure optimal performance. The following example shows the compilation with Intel MKL/MPICH on Georgia Tech’s Pheonix Cluster:

TODO make sure modules are correct.

module load git intel-one-api fftw
git clone https://github.com/SPARC-X/SPARC.git
cd SPARC/src
make USE_MKL=1 USE_SCALAPACK=0 USE_FFTW=1

TODO add module in script.

The compiled binary will be at SPARC/lib/sparc, and running it requires the dependent modules to be loaded at runtime.

Now head to the setup tutorial to finetune your settings.