fbpic
pure_numba_alias_sampling
fbpic | pure_numba_alias_sampling | |
---|---|---|
2 | 1 | |
165 | 3 | |
-0.6% | - | |
8.1 | 10.0 | |
9 days ago | about 6 years ago | |
Python | Python | |
GNU General Public License v3.0 or later | MIT License |
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.
fbpic
-
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
-
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
pure_numba_alias_sampling
-
Numba: A High Performance Python Compiler
It’s not suitable for all use cases.
But I highly highly recommend it if you need to do somewhat complex calculations iterating over numpy arrays for which standard numpy or scipy functions don’t exist. Even then, often we were surprised that we could speed up some of those calculations by placing them inside numba.
Edit: ex of a very small function I wrote with numba that speeds up an existing numpy function (note - written years ago and numba has undergone quite some amount of changes since!): https://github.com/grej/pure_numba_alias_sampling
Disclosure - I now work for Anaconda, the company that sponsors the numba project.
What are some alternatives?
WarpX - WarpX is an advanced, time-based electromagnetic & electrostatic Particle-In-Cell code.
codon - A high-performance, zero-overhead, extensible Python compiler using LLVM
simsopt - Simons Stellarator Optimizer Code
autograd - Efficiently computes derivatives of numpy code.
hn-search - Hacker News Search
ndarray_comparison - Benchmark of toy calculation on an n-dimensional array using python, numba, cython, pythran and rust
qha - A Python package for calculating thermodynamic properties under quasi-harmonic approximation, using data from ab-initio calculations
rust-numpy - PyO3-based Rust bindings of the NumPy C-API
nodevectors - Fastest network node embeddings in the west
ideas4 - An Additional 100 Ideas for Computing https://samsquire.github.io/ideas4/