WarpX
fbpic
Our great sponsors
WarpX | fbpic | |
---|---|---|
2 | 2 | |
256 | 165 | |
4.3% | -0.6% | |
9.8 | 8.1 | |
4 days ago | 3 days ago | |
C++ | Python | |
GNU General Public License v3.0 or later | GNU General Public License v3.0 or later |
Stars - the number of stars that a project has on GitHub. Growth - month over month growth in stars.
Activity is a relative number indicating how actively a project is being developed. Recent commits have higher weight than older ones.
For example, an activity of 9.0 indicates that a project is amongst the top 10% of the most actively developed projects that we are tracking.
WarpX
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Open source sofware/software contribution opportunities in Fusion
WarpX: an advanced, t-based electromagnetic & electrostatic Particle-In-Cell code
- Berkeley Lab-led team developing code for exascale that will explore laser-plasma interactions, perhaps leading to compact particle accelerators
fbpic
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Numba: A High Performance Python Compiler
When I wrote my bachelor thesis years back I worked on a particle-in-cell code [1] that makes heavy use of numba for GPU kernels. At the time it was the most convenient way to do that from python. I remember spending weeks to optimizing these kernels to eek out every last bit of performance I could (which interestingly enough did eventually involve using atomic operations and introducing a lot of variables[2] instead of using arrays everywhere to keep things in registers instead of slower caches).
I remember the team being really responsive to feature requests back then and I had a lot of fun working with it. IIRC compared to using numpy we managed to get speedups of up to 60x for the most critical pieces of code.
[1]: https://github.com/fbpic/fbpic
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Faster Python calculations with Numba: 2 lines of code, 13× speed-up
We used numba to accelerate the code and most importantly write GPU kernels for the heavy parts. I remember spending hours optimising my code to eek out the most performance possible (which eventually meant using atomics and manually unrolling many loops because somehow this was giving us the best performance) but honestly I was really happy that I didn't need to write cuda kernels in C and generally it was pretty easy to work with. I remember back then the documentation was sometimes a little rough around the edges but the numba team was incredibly helpful and responsive. Overall I had a great time.
[0] https://github.com/fbpic/fbpic
What are some alternatives?
pymcuprog - a Python utility for programming various Microchip MCU devices using Microchip CMSIS-DAP based debuggers
simsopt - Simons Stellarator Optimizer Code
BOUT-dev - BOUT++: Plasma fluid finite-difference simulation code in curvilinear coordinate systems
pure_numba_alias_sampling - Pure numba version of Alias sampling algorithm from L. Devroye's, "Non-Uniform Random Random Variate Generation"
cuda_memtest - Fork of CUDA GPU memtest :eyeglasses:
autograd - Efficiently computes derivatives of numpy code.
TLS_Pie - Software to run a DIY Terrestrial Laser Scanner using a Raspberry Pie 4 and a Velodyne Lidar unit
ndarray_comparison - Benchmark of toy calculation on an n-dimensional array using python, numba, cython, pythran and rust
GITR - Global Impurity Transport
rust-numpy - PyO3-based Rust bindings of the NumPy C-API
sofa - Real-time multi-physics simulation with an emphasis on medical simulation.
nodevectors - Fastest network node embeddings in the west