On Jul 3, 2012, at 12:21 AM, Nathaniel Smith wrote: > Yes, but for a proper benchmark we need to compare this to the number > that we would get with some other implementation... I'm assuming you > aren't proposing we just delete the docstrings :-).
I suspect that we have a different meaning of the term 'benchmark'. A benchmark establishes first the baseline by which future implementations are measured. Which is why I did. Once there are changes, the benchmark, rerun, helps judge the usefulness of those changes. This I did not do. I do not believe that a benchmark requires the changed code as well before it can be considered a "proper benchmark" >> This says that 'add_newdocs', which is imported from >> numpy.core.multiarray (though there may be other importers) >> takes 0.038 seconds to go through __import__, including >> all of its children module imports. > > There are no "children modules", all these modules refer to each > other, and you're assuming that whichever module you happen to load > first is responsible for all the other modules it happens to > reference. While I believe there is an "import tree" analogous to a "call tree" and Python's import scheme helps ensure that it's a DAG (so that 'children modules' has a real meaning), you are correct in identifying that I was only pointing out the first parent, and not all of the parents. add_newdocs is the first module to import 'numpy.lib', but after further testing (I stubbed out the import and made a fake function), I see that other modules import numpy.lib and there's no measurable performance increase. I retract therefore my proposal to move the documentation which is currently in add_newdocs into the C code. >>>> With instrumentation I found that 0.083s of the 0.119s >>>> is spent loading numpy.core.multiarray. > > The number 0.083 doesn't appear anywhere in that profile you pasted, > so I don't know where this comes from... I did not save the output run which I used for my original email. It's easy to generate, so I just ran it again. Cheers, Andrew da...@dalkescientific.com _______________________________________________ NumPy-Discussion mailing list NumPy-Discussion@scipy.org http://mail.scipy.org/mailman/listinfo/numpy-discussion