Please note that this is an Open Alpha release for developers and power users only.
Users should wait for our Open Beta release!
PIConGPU is a fully relativistic, many GPGPU, 3D3V particle-in-cell (PIC) code. The Particle-in-Cell algorithm is a central tool in plasma physics. It describes the dynamics of a plasma by computing the motion of electrons and ions in the plasma based on Maxwell's equations.
PIConGPU implements various numerical schemes to solve the PIC cycle. Its features include:
- a central or Yee-lattice for fields
- particle pushers that solve the equation of motion for charged and neutral particles, e.g., the Boris- and the Vay-Pusher
- Maxwell field solvers, e.g. Yee's and Lehe's scheme
- rigorously charge conserving current deposition schemes, such as Villasenor-Buneman, Esirkepov and ZigZag
- macro-particle form factors ranging from NGP (0th order), CIC (1st), TSC (2nd), PSQ (3rd) to P4S (4th)
Besides the electro-magnetic PIC algorithm, we developed a wide range of tools and diagnostics, e.g.:
- online, far-field radiation diagnostics for coherent and incoherent radiation emitted by charged particles
- full restart and output capabilities, including parallel HDF5 (via libSplash) and ADIOS, allowing for extreme I/O scalability and massively parallel online-analysis
- 2D and 3D live view and diagnostics tools
- a large selection of extensible online-plugins
Todays GPUs provide a computational performance of several TFLOP/s at considerable lower invest and maintenance costs compared to multi CPU-based compute architectures of similar performance. The latest high-performance systems (TOP500) are enhanced by accelerator hardware that boost their peak performance up to the multi-PFLOP/s level. With its outstanding performance and scalability to more than 18'000 GPUs, PIConGPU was one of the finalists of the 2013 Gordon Bell Prize.
PIConGPU is developed and maintained by the Computational Radiation Physics Group at the Institute for Radiation Physics at HZDR in close collaboration with the Center for Information Services and High Performance Computing (ZIH) of the Technical University Dresden (TUD). We are a member of the Dresden GPU Center of Excellence that cooperates on a broad range of scientific GPU and manycore applications, workshops and teaching efforts.
PIConGPU is a scientific project. If you present and/or publish scientific results that used PIConGPU, you should set a reference to show your support.
Our according up-to-date publication at the time of your publication should be inquired from:
The following slide should be part of oral presentations. It is intended to acknowledge the team maintaining PIConGPU and to support our community:
(coming soon) presentation_picongpu.pdf (svg version, key note version, png version: 1920x1080 and 1024x768)
PIConGPU is licensed under the GPLv3+. Furthermore, you can develop your own particle-mesh algorithms based on our general library libPMacc that is shipped alongside PIConGPU. libPMacc is dual licensed under both the GPLv3+ and LGPLv3+. For a detailed description, please refer to LICENSE.md
See our notes in INSTALL.md.
Dear User, please be aware that this is a developer and power user only release! We hereby emphasize that you should wait for our Beta release.
Having said that and assuming that you are either an enthusiast or scientist (or both/neither of them, the important point here is that you are willing to read and understand our documentation and change logs): you are very welcome!
For any questions regarding the usage of PIConGPU please do not contact the developers and maintainers directly.
Instead, please sign up to our PIConGPU-Users mailing list so we can distribute and archive user questions: Subscribe (Feed). You can subscribe by simply sending an e-mail to [[email protected]](mailto:[email protected]?subject=Subscribe me!) (and unsubscribe via [[email protected]](mailto:[email protected]?subject=Unsubscribe me!)).
Before you post a question, browse the PIConGPU documentation, wiki, issue tracker and the mailing list history to see if your question has been answered, already.
PIConGPU is a collaborative project. We thus encourage users to engage in answering questions of other users and post solutions to problems to the list. A problem you have encountered might be the future problem of another user.
In addition, please consider using the collaborative features of GitHub if you have questions or comments on code or documentation. This will allow other users to see the piece of code or documentation you are referring to.
Feel free to visit picongpu.hzdr.de to learn more
about the PIC algorithm. Further ressources are the
user section
of our wiki, documentation files in .md format in this repository and a
getting started video.
PIConGPU follows a
master - dev
development model. That means our latest stable release is shipped in a branch
called master while new and frequent changes to the code are incooporated
in the development branch dev.
Every time we update the master branch, we publish a new release
of PIConGPU. Before you pull the changes in, please read our
ChangeLog!
You may have to update some of your simulation .param and .cfg files by
hand since PIConGPU is an active project and new features often require changes
in input files. Additionally, a full description of new features and fixed bugs
in comparison to the previous release is provided in that file.
In case you decide to use new, potentially buggy and experimental features
from our dev branch, be aware that support is very limited and you must
participate or at least follow the development yourself. Syntax changes
and in-development bugs will not be announced outside of their according pull
requests and issues.
Before drafting a new release, we open a new release-* branch from dev with
the * being the version number of the upcoming release. This branch only
receives bug fixes (feature freeze) and users are welcome to try it out
(however, the change log and a detailed announcement might still be missing in
it).
See CONTRIBUTING.md
- Dr. Michael Bussmann
- Dr.-Ing. Guido Juckeland
- Heiko Burau*
- Dr. Alexander Debus
- Carlchristian Eckert
- Marco Garten
- Alexander Grund
- Axel Huebl*
- Richard Pausch*
- Stefan Tietze
- Rene Widera*
- Erik Zenker*
The PIConGPU Team expresses its thanks to:
- Florian Berninger
- Robert Dietrich
- Wen Fu, PhD
- Anton Helm
- Wolfgang Hoehnig
- Maximilian Knespel
- Dr. Remi Lehe
- Felix Schmitt
- Benjamin Schneider
- Joseph Schuchart
- Conrad Schumann
- Klaus Steiniger
- Benjamin Worpitz
Kudos to everyone who helped!
