Arc676 / ippl

IPPL is a C++ library to develop performance portable code for fully Eulerian, Lagrangian or hybrid Eulerian-Lagrangian methods.

Independent Parallel Particle Layer (IPPL)

Independent Parallel Particle Layer (IPPL) is a performance portable C++ library for Particle-Mesh methods. IPPL makes use of Kokkos (https://github.com/kokkos/kokkos), HeFFTe (https://github.com/icl-utk-edu/heffte), and MPI (Message Passing Interface) to deliver a portable, massively parallel toolkit for particle-mesh methods. IPPL supports simulations in one to six dimensions, mixed precision, and asynchronous execution in different execution spaces (e.g. CPUs and GPUs).

Installing IPPL and its dependencies

Requirements

The following libraries are required:

• MPI (GPU-aware if building for GPUs)
• Kokkos >= 4.1.00
• HeFFTe >= 2.2.0; only required if IPPL is built with FFTs enabled (ENABLE_FFT=ON)

To build IPPL and its dependencies, we recommend using the IPPL build scripts. See the documentation for more info on how to use the IPPL build script.

Contributions

We are open and welcome contributions from others. Please open an issue and a corresponding pull request in the main repository if it is a bug fix or a minor change.

For larger projects we recommend to fork the main repository and then submit a pull request from it. Please follow the coding guidelines as mentioned in this page

You can add an upstream to be able to get all the latest changes from the master. For example, if you are working with a fork of the main repository, you can add the upstream by:

$ git remote add upstream [e-mail]:IPPL-framework/ippl.git

You can then easily pull by typing

$ git pull upstream master

All the contributions (except for bug fixes) need to be accompanied with a unit test. For more information on unit tests in IPPL please take a look at this page.

Citing IPPL

@article{muralikrishnan2022scaling,
  title={Scaling and performance portability of the particle-in-cell scheme for plasma physics
         applications through mini-apps targeting exascale architectures},
  author={Muralikrishnan, Sriramkrishnan and Frey, Matthias and Vinciguerra, Alessandro
          and Ligotino, Michael and Cerfon, Antoine J and Stoyanov, Miroslav and
          Gayatri, Rahulkumar and Adelmann, Andreas},
  journal={arXiv preprint arXiv:2205.11052},
  year={2022}
}