ndarray_comparison
Benchmark of toy calculation on an n-dimensional array using python, numba, cython, pythran and rust (by synapticarbors)
OpticsPolynomials.jl
Polynomials used in optics. Zernike, Legendre, etc (by JuliaOptics)
| ndarray_comparison | OpticsPolynomials.jl | |
|---|---|---|
| 3 | 1 | |
| 24 | 6 | |
| - | - | |
| 0.0 | 10.0 | |
| over 2 years ago | over 3 years ago | |
| Jupyter Notebook | Julia | |
| MIT License | MIT License |
The number of mentions indicates the total number of mentions that we've tracked plus the number of user suggested alternatives.
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.
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.
ndarray_comparison
Posts with mentions or reviews of ndarray_comparison.
We have used some of these posts to build our list of alternatives
and similar projects. The last one was on 2022-02-18.
-
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
OpticsPolynomials.jl
Posts with mentions or reviews of OpticsPolynomials.jl.
We have used some of these posts to build our list of alternatives
and similar projects. The last one was on 2021-11-10.
-
Comparing a Rust extension to other methods of speeding up python
I gave Julia a rather serious try, converting the world's fastest numerical optics library to the language. It ended up being mildly faster (2-3x) for some things, but overall programs did not run meaningfully faster (and significantly slower when comparing jl to python on GPU -- I would have to write specializations of all the functions for GPU, which is a horrific prospect).
What are some alternatives?
When comparing ndarray_comparison and OpticsPolynomials.jl you can also consider the following projects:
nodevectors - Fastest network node embeddings in the west
scinim - The core types and functions of the SciNim ecosystem
nimpy - Nim - Python bridge
nimporter - Compile Nim Extensions for Python On Import!
PyCall.jl - Package to call Python functions from the Julia language
fbpic - Spectral, quasi-3D Particle-In-Cell code, for CPU and GPU
Numba - NumPy aware dynamic Python compiler using LLVM