cij
codon
cij | codon | |
---|---|---|
1 | 34 | |
13 | 13,851 | |
- | 0.5% | |
4.0 | 7.9 | |
14 days ago | 6 days ago | |
Python | C++ | |
GNU General Public License v3.0 only | GNU General Public License v3.0 or later |
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cij
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Numba: A High Performance Python Compiler
Software from our group (cij[1], qha[2]) were developed when numba seems to be the best option for JIT. It generates more pain in the hindsight. It generates a lot of depreciated warning due to unstable API, locked numpy to a certain version (i remember 1.21) due to compatibility issues, and when M1 Mac comes out, there were for a long time lack of llvmlite porting to the new platform, so cannot run on these new Macs.
If I had to do it again I would just use plain numpy or use the JAX from Google if JIT is really necessary.
[1]: https://github.com/MineralsCloud/cij
[2]: https://github.com/MineralsCloud/qha
codon
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Should I Open Source my Company?
https://github.com/exaloop/codon/blob/develop/LICENSE
Here are some others: https://github.com/search?q=%22Business+Source+License%22+%2...
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Python running on the Dart VM?
I found at least one project that managed to compile python AOT to LLVM https://github.com/exaloop/codon. Even if LLVM is more expressive than Dart Kernel, that should at least be some evidence that this might not be too impractical.
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Codon: Python Compiler
Their fannkuch benchmark seems to be a bit dishonest. They claim an enormous perf delta on https://exaloop.io/benchmarks.html but fannkuch uses factorial a lot and they define factorial with a very small (n=20) table: https://github.com/exaloop/codon/blob/fb461371613049539654c1...
Disclaimer: I've worked on several Python runtimes and compilers, but I'm not by any means out to get Codon. Just happened across this by accident while looking at their inline LLVM, which is neat.
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The father of Swift made another baby: Mojo: looks to be based on Python using MLIR
If you literally want Python, but compiled ... Look at Codon: https://github.com/exaloop/codon
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Mojo – a new programming language for all AI developers
Another "Python with high-performance compiled builds" would be https://github.com/exaloop/codon.
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MIT Turbocharges Python’s Notoriously Slow Compiler
This is the project being discussed: https://github.com/exaloop/codon
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Is there a way to use turn a project into a single executable file that doesn't require anyone to do anything like install Python before using it?
Try Codon? https://github.com/exaloop/codon
- Since when did Python haters spread out everywhere? Maybe DNF5 would be faster because of ditched it, maybe.
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Budget HomeLab converted to endless money-pit
https://github.com/exaloop/codon might save you from the rewrite.
- What are your thoughts on Codon compiler having a paid licence?
What are some alternatives?
fbpic - Spectral, quasi-3D Particle-In-Cell code, for CPU and GPU
Nuitka - Nuitka 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. You feed it your Python app, it does a lot of clever things, and spits out an executable or extension module.
pure_numba_alias_sampling - Pure numba version of Alias sampling algorithm from L. Devroye's, "Non-Uniform Random Random Variate Generation"
Numba - NumPy aware dynamic Python compiler using LLVM
Cython - The most widely used Python to C compiler
taichi - Productive, portable, and performant GPU programming in Python.
julia - The Julia Programming Language
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).
Django - The Web framework for perfectionists with deadlines.
Coconut - Simple, elegant, Pythonic functional programming.
FrameworkBenchmarks - Source for the TechEmpower Framework Benchmarks project