On Sun, Feb 19, 2012 at 4:13 PM, xavier.gn...@gmail.com <xavier.gn...@gmail.com> wrote: > I'm no sure. If you want to be able to write A=B+C+D; with decent > performances, I think you have to use a lib based on expression templates. > It would be great if C++ compilers could automatically optimize out > spurious copies into temporaries. > However, I don't think the compilers are smart enough to do so...not yet.
But isn't this all irrelevant to numpy? Numpy is basically a large collection of bare inner loops, plus a bunch of dynamic dispatch machinery to make sure that the right one gets called at the right time. Since these are exposed directly to Python, there's really no way for the compiler to optimize out spurious copies or anything like that -- even a very smart fortran-esque static compiler can't optimize complex expressions like A=B+C+D if they simply aren't present at compile time. And I guess even less-fancy C compilers will still be able to optimize simple ufunc loops pretty well. IIUC the important thing for numpy speed is the code that works out at runtime whether this particular array would benefit from a column-based or row-based strategy, chooses the right buffer sizes, etc., which isn't really something a compiler can help with. -- Nathaniel _______________________________________________ NumPy-Discussion mailing list NumPy-Discussion@scipy.org http://mail.scipy.org/mailman/listinfo/numpy-discussion
