[fxtentacle]

Based on your comment, I would guess that you never tried out numba. Of course, it can also do general python and loop optimizations. And in my experience, numba worked for every case where I couldn't get pypy to work.

And I stand by my opinion that that is something that the pypy developers should consider: is this actually usable as a solution to practical problems? Or is there something else that people use instead? If so, why? Analyzing your competition is usually a good way to learn about your own strengths and weaknesses.

[oefrha]

> Based on your comment, I would guess that you never tried out numba.

Well, you guessed wrong.

> it can also do general python and loop optimizations.

Yes, it can be used in general purpose workloads, with varying degrees of success. But its main purpose is made abundantly clear:

Accelerate Python Functions

Numba translates Python functions to optimized machine code at runtime using the industry-standard LLVM compiler library. Numba-compiled __numerical algorithms__ in Python can approach the speeds of C or FORTRAN.

Built for Scientific Computing

...

https://numba.pydata.org/

> ... Analyzing your competition is usually a good way to learn about your own strengths and weaknesses.

Except this is an announcement on their funding situation, so strengths and weaknesses are completely irrelevant, unless Numba has a particularly interesting funding model. (The funding model is government grants and corporate sponsorship, so, not particularly interesting.)

[wrmsr]

> Built for Scientific Computing

I mean the thing's called 'numba' lol.

I always liken Pypy to HotSpot in that to this day the numerical performance of the latter isn't spectacular and nobody really cares - it's built to handle the harder job of making vast tangled codebases of non-numerical application code run fast, not just tight math loops which are already handled perfectly well by other more specialized tools.