ParticleInCell.jl is a Julia package for kinetic plasma physics simulation. Specifically, it focuses on the simulation of kinetic (non-thermal) plasmas using particle-in-cell (PIC) algorithms. Currently, this package is in in a pre-1.0.0 state, and thus breaking changes should be expected. However, this also means that I am willing to entertain radical suggestions to improve the functionality of the package. If you are interested in using ParticleInCell for your plasma research, and you find that it does not meet you needs, please reach out on either GitHub, or over email, so that we can discuss how the package can be modified to suite your needs.

Getting Started

ParticleInCell is currently not registered in the Julia package registry. Thus, to install this package, you should use Pkg.develop:

using Pkg
Pkg.develop(url="https://github.com/JuliaPlasma/ParticleInCell.jl")

Documentation

You can view the latest documentation here.

Goals

• Fast: aim to have core time of less than 1 microsecond per particle per step without collisions.
• Flexible: it should be possible to implement essentially any kinetic plasma simulation in ParticleInCell.jl. For common types of simulations, this might mean just piecing together components that are already included. More esoteric problems might require writing custom types that implement the desired algorithms. The advantage of writing this package in Julia is that these custom types will be just as performant as native components that are included in the package.
• Scalable: the eventual goal is to enable scaling across an essentially unlimited number of cores using Julia's native multithreading for parallelization on a single node, and MPI.jl for communication across nodes. The goal is to support two different modes of parallelization:
  • Each core is responsible for a single rectangular subdomain. The domain assigned to an entire node is also rectangular, which imposes constraints on how the node domain can be subdivided into subdomains for each core. Load balancing is achieved by varying the relative sizes of the domains such that each core has a similar amount of work per step.
  • The simulation domain is subdivided into subdomains called 'patches', and every node is assigned a list of patches that it is responsible for updating. The cores on each node work collaboratively on the list, each choosing one patch to work on, and then selecting another when they are finished. Load balancing is achieved by swapping patches between nodes to balance the workload while also seeking to minimize communication time by keeping the surface area of each node's responsibilities minimized. In order for this scheme to effectively load balance, it must be the case that the total number of patches is larger (ideally much larger) that the total number of cores.