On 12 Nov, 18:33, J Kenneth King <ja...@agentultra.com> wrote: > Where Python might get hit *as a language* is that the Python programmer > has to drop into C to implement optimized data-structures for dealing > with the kind of IO that would slow down the Python interpreter. That's > why we have numpy, scipy, etc.
That's not a Python specific issue. We drop to SciPy/NumPy for certain compute-bound tasks that operates on vectors. If that does not help, we drop further down to Cython, C or Fortran. If that does not help, we can use assembly. In fact, if we use SciPy linked against GotoBLAS, a lot of compute-intensive work solving linear algebra is delegated to hand-optimized assembly. With Python we can stop at the level of abstraction that gives acceptable performance. When using C, we start out at a much lower level. The principle that premature optimization is the root of all evil applies here: Python code that is fast enough is fast enough. It does not matter that hand-tuned assembly will be 1000 times faster. We can direct our optimization effort to the parts of the code that needs it. -- http://mail.python.org/mailman/listinfo/python-list
