benchmarks
mypyc
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benchmarks | mypyc | |
---|---|---|
40 | 25 | |
2,730 | 1,661 | |
- | 2.0% | |
7.2 | 0.0 | |
2 months ago | 12 months ago | |
Makefile | ||
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.
benchmarks
- Building a high performance JSON parser
- Twitter (re)Releases Recommendation Algorithm on GitHub
- how to benchmark a programming language
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Ruby 3.2.0 Is from Another Dimension
In all the language comparisons I've found over the years, Python consistently comes out slightly slower, for example:
https://github.com/kostya/benchmarks
Bearing in mind these are probably not even using YJIT, which makes Ruby considerably faster in some scenarios.
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The original computer languages benchmark is back
Also, here is another benchmark: https://github.com/kostya/benchmarks
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Why does Scala seem to be slow at benchmark results?
Nowadays, I reached out for some benchmark results. Scala is slower than Java and Kotlin. Can you explain it? https://github.com/losvedir/transit-lang-cmp https://github.com/kostya/benchmarks
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New, fastest JSON library for C++20
https://github.com/kostya/benchmarks is the current ratings. Should be an easy PR to them too.
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What things would be awkward to do in a hypothetical "strict" Haskell variant, that are now not awkward to do?
I don't think that rule will get you to 1.5-2x of C speed though. This benchmark is the only one I could find that has both PyPy and C and it seems to still be around 5-35x.
They are at least trying to avoid measuring JIT compilation times. I don't know how effective that is, but I trust somebody would have complained if it wasn't fair.
mypyc
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Writing Python like it's Rust
That would be interesting! You might already be aware. But there's mypyc[0], which is an AOT compiler for Python code with type hints (that, IIRC, mypy uses to compile itself into a native extension).
Wanted to give you a head-start on the lit-review for your students I guess :)
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The different uses of Python type hints
https://github.com/mypyc/mypyc
> Mypyc compiles Python modules to C extensions. It uses standard Python type hints to generate fast code. Mypyc uses mypy to perform type checking and type inference.
> Mypyc can compile anything from one module to an entire codebase. The mypy project has been using mypyc to compile mypy since 2019, giving it a 4x performance boost over regular Python.
I have not experience a 4x boost, rather between 1.5x and 2x. I guess it depends on the code.
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The Python Paradox
Funny how emergence works with tools. Give a language too few tools but viral circumstances - the ecosystem diverges (Lisps, Javascript). Give it too long an iteration time but killer guarantees, you end up with committees. Python not falling into either of these traps should be understood as nothing short of magic in emergence.
I only recently discovered that python's reference typechecker, mypy, has a small side project for typed python to emit C [1], written entirely in python. Nowadays with python's rich specializer ecosystem (LLVM, CUDA, and just generally vectorized math), the value of writing a small program in anything else diminishes quickly.
Imagine reading the C++wg release notes in the same mood that you would the python release notes.
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Codon: A high-performance Python compiler
> Note that the mypyc issue tracker lives in this repository! Please don't file mypyc issues in the mypy issue tracker.
See https://github.com/mypyc/mypyc/blob/master/show_me_the_code....
What's the difference with mypyc [0] ? It also compiles Python to native code.
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Is it time for Python to have a statically-typed, compiled, fast superset?
More recent approaches include mypyc which is (on the tin) quite close to what you describe, and taichi that lives in between.
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Python 3.11 is 25% faster than 3.10 on average
https://github.com/mypyc/mypyc
> Mypyc compiles Python modules to C extensions. It uses standard Python type hints to generate fast code. Mypyc uses mypy to perform type checking and type inference.
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Comparing implementations of the Monkey language VIII: The Spectacular Interpreted Special (Ruby, Python and Lua)
Regarding the large execution time mentioned in your article, I discovered (mypyc)[https://github.com/mypyc/mypyc] on this subreddit in a post from the black formatter team https://www.reddit.com/r/Python/comments/v2009i/im_that_person_who_got_black_compiled_with_mypyc/?utm_medium=android_app&utm_source=share
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what a wonderful world
It's still alpha, but mypyc lets ordinary Python code be compiled to C extensions just by using type hints from the standard lib. It's currently used in the mypy project.
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Rust or C/C++ to learn as a secondary language?
you can check out [`mypyc`](https://github.com/mypyc/mypyc) . It is used by mypy, black for speedup
What are some alternatives?
Cython - The most widely used Python to C compiler
libuv - Cross-platform asynchronous I/O
mypy - Optional static typing for Python
beartype - Unbearably fast near-real-time hybrid runtime-static type-checking in pure Python.
CPython - The Python programming language
pex - A tool for generating .pex (Python EXecutable) files, lock files and venvs.
pyccel - Python extension language using accelerators
lua-languages - Languages that compile to Lua
typeguard - Run-time type checker for Python
julia - The Julia Programming Language
typed_python - An llvm-based framework for generating and calling into high-performance native code from Python.
mypyc-benchmark-results - Mypyc benchmark result data