| > 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.) |
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.