fbpic
ndarray_comparison
fbpic | ndarray_comparison | |
---|---|---|
2 | 3 | |
165 | 24 | |
-0.6% | - | |
8.1 | 0.0 | |
8 days ago | over 2 years ago | |
Python | Jupyter Notebook | |
GNU General Public License v3.0 or later | MIT License |
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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
ndarray_comparison
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Faster Python calculations with Numba: 2 lines of code, 13× speed-up
I use numba quite a bit at work and it's fantastic. I recently, however, did a comparison between numba, cython, pythran and rust (ndarray) for a toy problem, and it yielded some interesting results:
https://github.com/synapticarbors/ndarray_comparison/blob/ma...
Most surprising among them was how fast pythran was with little more effort than is required of numba (still required an aot compilation step with a setup.py, but minimal changes in the code). All of the usual caveats should be applied to a simple benchmark like this.
- Comparing a Rust extension to other methods of speeding up python
What are some alternatives?
WarpX - WarpX is an advanced, time-based electromagnetic & electrostatic Particle-In-Cell code.
nodevectors - Fastest network node embeddings in the west
simsopt - Simons Stellarator Optimizer Code
nimpy - Nim - Python bridge
pure_numba_alias_sampling - Pure numba version of Alias sampling algorithm from L. Devroye's, "Non-Uniform Random Random Variate Generation"
nimporter - Compile Nim Extensions for Python On Import!
autograd - Efficiently computes derivatives of numpy code.
scinim - The core types and functions of the SciNim ecosystem
rust-numpy - PyO3-based Rust bindings of the NumPy C-API
PyCall.jl - Package to call Python functions from the Julia language
OpticsPolynomials.jl - Polynomials used in optics. Zernike, Legendre, etc