When i was a scientist, speed was getting the code written during my break, and if it took all afternoon to run that's fine because i was in the lab anyway.

Even as i moved more into the software engineer direction, and started profiling code more, most of the bottlenecks come from things like "creating objects on every incovation rather than pooling them", "blocking IO", "using a bad algorithm" or "using the wrong datasctructure for the task". problems that exist in every language, though "bad algorithm" or "using the wrong datasctructure" might matter less in a faster language you're still leaving performance on the table.

> "Python is so slow that we have to write any important code in C. And this is somehow a good thing."

The good thing is that python has a very vibrant ecosystem filled with great libraries, so we don't have to write it in C, because somebody else has. We can just benefit from that when the situation calls for it

I don't see an indication in the article that that's the case. Am I missing something?

https://www.pypy.org/posts/2011/05/numpy-follow-up-692862769...

https://doc.pypy.org/en/latest/faq.html#what-about-numpy-num...

i'm not sure what version they gave up at

Everybody obviously wants that. The question is are you willing to lose what you have in order to hopefully, eventually, get there. If Python 3 development stopped and Python 4 came out tomorrow and was 5x faster than python 3 and a promise of being 50-100x faster in the future, but you have to rewrite all the libraries that use the C API, it would probably be DOA and kill python. People who want a faster 'almost python' already have several options to choose from, none of which are popular. Or they use Julia.

That is the problem with all the fast python implementations that have come before. Yes, they're faster than 'normal' python in many benchmarks, but they don't support the entire current ecosystem. For example Instagram's python implementation is blazing fast for doing exactly what Instagram is using python for, but is probably completely useless for what I'm using python for.

That really depends.

To make the issue clear, let's think about a similar situation:

bash is nice because you can plug together inputs and outputs of different sub-executables (like grep, sed and so on) and have a big "functional" pipeline deliver the final result.

Your idea would be "wouldn't it be great if you could just write your libraries in bash and have them still be really fast?". Not if you make bash into C, tanking productivity. And definitely not if that new bash can't run the old grep anymore (which is what usually is implied by the proposal in the case of Python).

Also, I'm fine with not writing my search engines, databases and matrix multiplication algorithm implementations in bash, really. So are most other people, I suspect.

Also, many proposals would weaken Python-the-language so it's not as expressive anymore. But I want it to stay as dynamic as it is. It's nice as a scripting language about 30 levels above bash.

As always, there are tradeoffs. Also with this proposal there will be tradeoffs. Are the tradeoffs worth it or not?

For the record, rewriting BLAS in Python (or anything else), even if the result was faster (!), would be a phenomenally bad idea. It would just introduce bugs, waste everyone's time, essentially be a fork of BLAS. There's no upside I can see that justifies it.

But sure, I'm all for removing build steps and avoiding yet another layer.

While I’m all for making Python itself faster, it would be a shame to lose the glue language par excellence.

I think that's a pretty ignorant interpretation. Python has been built to have a giant ecosystem of useful, feature-complete, stable, well built code that has been used for decades and for which there is no need to reinvent the wheel. If that already describes the universe of libraries that you /need/ to be extremely fast and the rest of your code is IO limited and not CPU limited, why reinvent the wheel?

That makes your comment even more inaccurate because you likely don't need to write any "important" (which you are stretching to mean "fast") code in C -- you utilize existing off the shelf fast libraries that are written in Fortran, CUDA, C, Rust or any other language a pre-existing ecosystem was built in.

Try and think of a language that has mature capabilities for domains as far away as what Django solves for, what pandas solves for, what pytorch solves for, and still has fantastic tooling like jupyter and streamlit. I can't think of any other language that has the combined off the shelf breadth and depth of Python. I don't want to have to write fast code in any language unless forced to, because the vast majority of the time I can customize a great off the shelf package and only write the remaining 1% of glue. I can't see why a professional engineer would 99% of the time would need to take a remotely different approach.