pure_numba_alias_sampling
Numba
pure_numba_alias_sampling | Numba | |
---|---|---|
1 | 124 | |
3 | 9,452 | |
- | 1.1% | |
10.0 | 9.9 | |
about 6 years ago | 6 days ago | |
Python | Python | |
MIT License | BSD 3-clause "New" or "Revised" License |
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pure_numba_alias_sampling
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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.
Numba
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Mojo🔥: Head -to-Head with Python and Numba
Around the same time, I discovered Numba and was fascinated by how easily it could bring huge performance improvements to Python code.
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Is anyone using PyPy for real work?
Simulations are, at least in my experience, numba’s [0] wheelhouse.
[0]: https://numba.pydata.org/
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Any data folks coding C++ and Java? If so, why did you leave Python?
That's very cool. Numba introduces just-in-time compilation to Python via decorators and its sole reason for being is to turn everything it can into abstract syntax trees.
- Using Matplotlib with Numba to accelerate code
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Python Algotrading with Machine Learning
A super-fast backtesting engine built in NumPy and accelerated with Numba.
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PYTHON vs OCTAVE for Matlab alternative
Regarding speed, I don't agree this is a good argument against Python. For example, it seems no one here has yet mentioned numba, a Python JIT compiler. With a simple decorator you can compile a function to machine code with speeds on par with C. Numba also allows you to easily write cuda kernels for GPU computation. I've never had to drop down to writing C or C++ to write fast and performant Python code that does computationally demanding tasks thanks to numba.
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Codon: Python Compiler
Just for reference,
* Nuitka[0] "is a Python compiler written in Python. It's fully compatible with Python 2.6, 2.7, 3.4, 3.5, 3.6, 3.7, 3.8, 3.9, 3.10, and 3.11."
* Pypy[1] "is a replacement for CPython" with builtin optimizations such as on the fly JIT compiles.
* Cython[2] "is an optimising static compiler for both the Python programming language and the extended Cython programming language... makes writing C extensions for Python as easy as Python itself."
* Numba[3] "is an open source JIT compiler that translates a subset of Python and NumPy code into fast machine code."
* Pyston[4] "is a performance-optimizing JIT for Python, and is drop-in compatible with ... CPython 3.8.12"
[0] https://github.com/Nuitka/Nuitka
[1] https://www.pypy.org/
[2] https://cython.org/
[3] https://numba.pydata.org/
[4] https://github.com/pyston/pyston
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This new programming language has the potential to make python (the dominant language for AI) run 35,000X faster.
For the benefit of future readers: https://numba.pydata.org/
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Two-tier programming language
Taichi (similar to numba) is a python library that allows you to write high speed code within python. So your program consists of slow python that gets interpreted regularly, and fast python (fully type annotated and restricted to a subset of the language) that gets parallellized and jitted for CPU or GPU. And you can mix the two within the same source file.
- Numba Supports Python 3.11
What are some alternatives?
codon - A high-performance, zero-overhead, extensible Python compiler using LLVM
NetworkX - Network Analysis in Python
fbpic - Spectral, quasi-3D Particle-In-Cell code, for CPU and GPU
jax - Composable transformations of Python+NumPy programs: differentiate, vectorize, JIT to GPU/TPU, and more
autograd - Efficiently computes derivatives of numpy code.
Dask - Parallel computing with task scheduling
hn-search - Hacker News Search
cupy - NumPy & SciPy for GPU
qha - A Python package for calculating thermodynamic properties under quasi-harmonic approximation, using data from ab-initio calculations
Pyjion - Pyjion - A JIT for Python based upon CoreCLR
rust-numpy - PyO3-based Rust bindings of the NumPy C-API
SymPy - A computer algebra system written in pure Python