Python: The Optimization Ladder
I've been in the pandas (and now polars world) for the past 15 years. Staying in the sandbox gets most folks good enough performance. (That's why Python is the language of data science and ML).
I generally teach my clients to reach for numba first. Potentially lots of bang for little buck.
One overlooked area in the article is running on GPUs. Some numpy and pandas (and polars) code can get a big speedup by using GPUs (same code with import change).
CPython 3.13 went further with an experimental copy-and-patch JIT compiler -- a lightweight JIT that stitches together pre-compiled machine code templates instead of generating code from scratch. It's not a full optimizing JIT like V8's TurboFan or a tracing JIT like PyPy's;
I can't speak for everyone on the team, but I did try the lazy basic block versioning in YJIT in a fork of CPython. The main problem is that the copy-and-patch backend we currently have in CPython is not too amenable to self-modifying machine code. This makes inter-block jumps/fallthroughs very inefficient. It can be done, it's just a little strange. Also for security reasons, we tried not to have self-modifying code in the original JIT and we're hoping to stick to that. Everything has their tradeoffs---design is hard! It's not too difficult to go from tracing to lazy basic blocks. Conceptually they're somewhat similar, as the original paper points out. The main thing we lack is the compact per-block type information that something like YJIT/Higgs has.
I guess while I'm here I might as well make the distinction:
- Tracing is the JIT frontend (region selection).
- Copy and Patch is the JIT backend (code generation).
We currently use both. PyPy uses meta-tracing. It traces the runtime itself rather than the user's code in CPython's tracing case. I did take a look at PyPy's code, and a lot of ideas in the improved JIT are actually imported from PyPy directly. So I have to thank them for their great ideas. I also talk to some of the PyPy devs.
Ending off: the team is extremely lean right now. Only 2 people were generously employed by ARM to work on this full time (thanks a lot to ARM too!). The rest of us are mostly volunteers, or have some bosses that like open source contributions and allow some free time. As for me, I'm unemployed at the moment and this is basically my passion project. I'm just happy the JIT is finally working now after spending 2-3 years of my life on it :). If you go to Savannah's website [1], the JIT is around 100% faster for toy programs like Richards, and even for big programs like tomli parsing, it's 28% faster on macOS AArch64. The JIT is very much a community effort right now.
[1]: https://doesjitgobrrr.com/?goals=5,10
PS: If you want to see how the work has progressed, click "all time" in that website, it's pretty cool to see (lower is faster). I have a blog explaining how we made the JIT faster here https://fidget-spinner.github.io/posts/faster-jit-plan.html.
> Missing @cython.cdivision(True) inserts a zero-division check before every floating-point divide in the inner loop. Millions of branches that are never taken.
I thought never taken branches were essentially free. Does this mean something in the loop is messing with the branch predictor?
This is the "two language problem" ( I would like to hear from people who extensively used Julia by the way, which claims to solve this problem, does it really ?)
Some nuance: try transpiling to a garbage collected rust like language with fast compilation until you have millions of users.
Also use a combination of neural and deterministic methods to transpile depending on the complexity.
I don't know what languages you might have in mind. "Rust-like" in what sense?
So what I do now (since Claude Code) is write really bare bones (and slow) pure python implementation (like I used to do for numba, pypy or cython ready code), with minimal dependencies. Then I use the REPL, notebooks and nice plotting tools to get a real understanding of the problem space and the intricacies of my algorithm/problem at hand. When done, I let Claude add tests and I ask it to transpile to equivalent Rust and boom! a flawless 1000x speed upgrade in a minutes.
The great thing is I don't need to do the mental gymnastics to vectorize code in a write only mode like I've had to do since my Matlab days. Instead I can write simple to read for loops that follow my intent much better, and result in much more legible code. So refreshing!
And with pyO3 i can still expose the Rust lib to python, and continue to use Python for glue and plotting
> 4 bytes of number, 24 bytes of machinery to support dynamism. a + b means: dereference two heap pointers, look up type slots, dispatch to int.__add__, allocate a new PyObject for the result (unless it hits the small-integer cache), update reference counts.
Would Python be a lot less useful without being maximally dynamic everywhere? Are there domains/frameworks/packages that benefit from this where this is a good trade-off?
I can't think of cases in strong statically typed languages where I've wanted something like monkey patching, and when I see monkey patching elsewhere there's often some reasonable alternative or it only needs to be used very rarely.
In python3.14 the support is there, but 2 years ago you could just import this library and it would just work normally.
Believe it or not, when you write a blog post in a different language, it really helps to use an LLM, even just to fix your grammar mistakes etc.
I assume that’s most likely what happened here too.
That said, I think this article demonstrates that focusing on whether or not an article used AI might be focusing on the wrong “problem.” I appreciate being sensitive to the "smell" (the number of low-effort, AI posts flying around these days has made me sensitive too), but personally, I found this article both (1) easy to read and (2) insightful. I think the number of AI-written content lacking (2) is the problem.
ok I guess the harder question is. Why isn't python as fast as javascript?
How can you suppose that this is not a good reason to object, especially days after https://news.ycombinator.com/item?id=47340079 ?
I find the style so reflexively grating that it's honestly hard for me to imagine others not being bothered by it, let alone being bothered by others being bothered.
Especially since I looked at previous posts on the blog and they didn't have the same problem.
There, FTFY :D
[0]: https://www.muna.ai/ [1]: https://docs.muna.ai/predictors/create
However if Rust with PyO3 is part of the alternatives, then Boost.Python, cppyy, and pybind11 should also be accounted for, given their use in HPC and HFT integrations.
I'm not just saying this to vent. I honestly wonder if we could eventually move to a norm where people publish two versions of their writing and allow the reader to choose between them. Even when the original is just a set of notes, I would personally choose to make my own way through them.
It's just somewhat unfortunate that I have to question every number and fact presented since the writing was clearly at least somewhat AI-assisted with the author seemingly not being upfront about that at all.
> looks inside
> the reference implementation of language is slow
Despite its content, this blogpost also pushes this exact "language slow" thinking in its preamble. I don't think nearly enough people read past introductions for that to be a responsible choice or a good idea.
The only thing worse than this is when Python specifically is outright taught (!) as an "interpeted language", as if an implementation-detail like that was somehow a language property. So grating.
But yes, the very terminology "interpreted language" was designed for a different era and is somewhere between misleading and incomprehensible in context. (Not unlike "pass by value".)