Smilei : A collaborative, open-source, multi-purpose particle-in-cell code for plasma simulation

By J. Derouillat, A. Beck, F. Pérez, T. Vinci, M. Chiaramello, A. Grassi, M. Flé, G. Bouchard, I. Plotnikov, N. Aunai and 3 more. Computer Physics Communications, 2017

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Abstract Smilei is a collaborative, open-source, object-oriented (C++) particle-in-cell code. To benefit from the latest advances in high-performance computing (HPC), Smilei is co-developed by both physicists and HPC experts. The code’s structures, capabilities, parallelization strategy and performances are discussed. Additional modules (e.g. to treat ionization or collisions), benchmarks and physics highlights are also presented. Multi-purpose and evolutive, Smilei is applied today to a wide range of physics studies, from relativistic laser–plasma interaction to astrophysical plasmas. Program summary Program title: Smilei (version 3.2) Program Files doi: http://dx.doi.org/10.17632/gsn4x6mbrg.1 Licensing provisions: This version of the code is distributed under the GNU General Public License v3 Programming language: C++11, Python 2.7 Nature of the problem: The kinetic simulation of plasmas is at the center of various physics studies, from laser–plasma interaction to astrophysics. To address today’s challenges, a versatile simulation tool requires high-performance computing on massively parallel super-computers. Solution method: The Vlasov–Maxwell system describing the self-consistent evolution of a collisionless plasma is solved using the Particle-In-Cell (PIC) method. Additional physics modules allow to account for additional effects such as collisions and/or ionization. A hybrid MPI-OpenMP strategy, based on a patch-based super-decomposition, allows for efficient cache-use, dynamic load balancing and high-performance on massively parallel super-computers. Additional comments: Repository https://github.com/SmileiPIC/Smilei References: http://www.maisondelasimulation.fr/smilei

Subfields: Geophysics, Mechanics of Materials, Nuclear and High Energy Physics

View Paper: https://www.semanticscholar.org/paper/6b9185cc963eef628a361c3e880301bd5d9f68ff

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