Numba
crystal
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Numba | crystal | |
---|---|---|
124 | 239 | |
9,432 | 19,110 | |
1.8% | 0.5% | |
9.9 | 9.8 | |
8 days ago | 5 days ago | |
Python | Crystal | |
BSD 3-clause "New" or "Revised" License | Apache License 2.0 |
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.
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
crystal
- A Language for Humans and Computers
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Top Paying Programming Technologies 2024
27. Crystal - $77,104
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Crystal 1.11.0 Is Released
I like the first code example on https://crystal-lang.org
# A very basic HTTP server
- Is Fortran "A Dead Language"?
- Choosing Go at American Express
- Odin Programming Language
- I Love Ruby
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Ruby 3.3's YJIT: Faster While Using Less Memory
Obviously as an interpreted language, it's never going to be as fast as something like C, Rust, or Go. Traditionally the ruby maintainers have not designed or optimized for pure speed, but that is changing, and the language is definitely faster these days compared to a decade ago.
If you like the ruby syntax/language but want the speed of a compiled language, it's also worth checking out Crystal[^1]. It's mostly ruby-like in syntax, style, and developer ergonomics.[^2] Although it's an entirely different language. Also a tiny community.
[1]: https://crystal-lang.org/
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What languages are useful for contribution to the GNOME project.
Crystal is a nice language that's not only simple to read and write but performs very well too. And the documentation is amazing as well.
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Jets: The Ruby Serverless Framework
Ruby is a super fun scripting language. I much prefer it to python when I need something with a little more "ooomph" than bash. It's just...nice...to write in. Ruby performance has come a long way in the last decade as well. There's libraries for pretty much everything.
My modern programming toolkit is basically golang + ruby + bash and I am never left wanting.
I do find Crystal (https://crystal-lang.org/) really interesting and am hoping it has its own "ruby on rails" moment that helps the language reach a tipping point in popularity. All the beauty of ruby with all of the speed of Go (and then some, it often compares favorably to languages like rust in benchmarks).
What are some alternatives?
NetworkX - Network Analysis in Python
zig - General-purpose programming language and toolchain for maintaining robust, optimal, and reusable software.
jax - Composable transformations of Python+NumPy programs: differentiate, vectorize, JIT to GPU/TPU, and more
Nim - Nim is a statically typed compiled systems programming language. It combines successful concepts from mature languages like Python, Ada and Modula. Its design focuses on efficiency, expressiveness, and elegance (in that order of priority).
Dask - Parallel computing with task scheduling
go - The Go programming language
cupy - NumPy & SciPy for GPU
Elixir - Elixir is a dynamic, functional language for building scalable and maintainable applications
Pyjion - Pyjion - A JIT for Python based upon CoreCLR
mint-lang - :leaves: A refreshing programming language for the front-end web
SymPy - A computer algebra system written in pure Python
Odin - Odin Programming Language