Introduction
sfparticles is a simulation toolkit for particle tracking. Unklike particle-in-cell simulations, sfparticles does not solve field equations but instead tracks individual particles in pre-defined fields, without considering the inter-particle interactions. This allows for fast 3D simulations of large numbers of particles when plamsa denisty is low.
Features
- Numba Acceleration: sfparticles leverages Numba to accelerate simulations by compiling Python code to machine code.
- Cascade Simulation: Optimized for exponentially growing number of particles.
- Flexible Field Configuration: sfparticles allows you to define complex electromagnetic fields using python functions.
- Particle Tracking: Track the trajectories of particles. see example.
Installation
from github:
git clone https://github.com/xsgeng/sfparticles.git
cd sfparticles
pip install --user -e .
from pypi:
pip install sfparticles
Usage
Basic Simulation
To run a basic simulation, you can refer to the example scripts provided in the examples directory. For instance, to simulate a particle cascade, you can use the cascade.py script:
python examples/cascade.py
# set number of threads
NUMBA_NUM_THREADS=64 python examples/cascade.py
For trajectory simulations, you can use the trajectory.py script:
python examples/trajectory.py
Advanced Settings
You can set environment variables to configure the simulation. For example, to use an optical depth radiation model, set the SFPARTICLES_OPTICAL_DEPTH environment variable:
SFPARTICLES_OPTICAL_DEPTH=1 python examples/cascade.py
GPU Acceleration
For even faster simulations, sfparticles supports GPU acceleration. To enable this feature, you need to install CUDA and set SFPARTICLES_USE_GPU=1 environment variable.
Note: GPU is slow for small number of particles.
Installing CUDA
conda install -c conda-forge cupy cudatoolkit=11.2
Enabling GPU Acceleration
SFPARTICLES_USE_GPU=1 python examples/cascade.py
For Slurm job schedulers, you can use the following command:
SFPARTICLES_USE_GPU=1 srun -p gpu -G 1 -u python examples/cascade.py
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