> when raw speed is required and the edit-compile-run cycle of binary extension modules is too heavy
This is a compelling pitch in my opinion, but can anybody explain to me where Lua would be better than, say, Numpy, or Numba for that matter, for computationally expensive tasks?
What are the use cases for this that well-known and really fast Python extensions do not already cater for?
Off the top of my head: any time you want to run a computational thing in another thread without doing something like an entire celery process or somesuch.
It's not about being THE solution for such things anyway. It's about being an option.
Numpy, is that, but Numba is a general purpose JIT:
> Numba works by generating optimized machine code using the LLVM compiler infrastructure at import time, runtime, or statically (using the included pycc tool). Numba supports compilation of Python to run on either CPU or GPU hardware
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[ 3.2 ms ] story [ 21.6 ms ] threadThis is a compelling pitch in my opinion, but can anybody explain to me where Lua would be better than, say, Numpy, or Numba for that matter, for computationally expensive tasks?
What are the use cases for this that well-known and really fast Python extensions do not already cater for?
It's not about being THE solution for such things anyway. It's about being an option.
> Numba works by generating optimized machine code using the LLVM compiler infrastructure at import time, runtime, or statically (using the included pycc tool). Numba supports compilation of Python to run on either CPU or GPU hardware
http://numba.pydata.org/