On 23.07.2014 20:54, Robert Kern wrote: > On Wed, Jul 23, 2014 at 6:19 PM, Julian Taylor > <jtaylor.deb...@googlemail.com> wrote: >> hi, >> it recently came to my attention that the default integer type in numpy >> on windows 64 bit is a 32 bit integers [0]. >> This seems like a quite serious problem as it means you can't use any >> integers created from python integers < 32 bit to index arrays larger >> than 2GB. >> For example np.product(array.shape) which will never overflow on linux >> and mac, can overflow on win64. > > Currently, on win64, we use Python long integer objects for `.shape` > and related attributes. I wonder if we could return numpy int64 > scalars instead. Then np.product() (or anything else that consumes > these via np.asarray()) would infer the correct dtype for the result.
this might be a less invasive alternative that might solve a lot of the incompatibilities, but it would probably also change np.arange(5) and similar functions to int64 which might change the dtype of a lot of arrays. The difference to just changing it everywhere might not be so large anymore. > >> I think this is a very dangerous platform difference and a quite large >> inconvenience for win64 users so I think it would be good to fix this. >> This would be a very large change of API and probably also ABI. > > Yes. Not only would it be a very large change from the status quo, I > think it introduces *much greater* platform difference than what we > have currently. The assumption that the default integer object > corresponds to the platform C long, whatever that is, is pretty > heavily ingrained. This should be only a concern for the ABI which can be solved by simply recompiling. In comparison that the API is different on win64 compared to all other platforms is something that needs source level changes. > >> But as we also never officially released win64 binaries we could change >> it for from source compilations and give win64 binary distributors the >> option to keep the old ABI/API at their discretion. > > That option would make the problem worse, not better. > maybe, I'm not familiar with the numpy win64 distribution landscape. Is it not like linux where you have one distributor per workstation setup that can update all its packages to a new ABI on one go? _______________________________________________ NumPy-Discussion mailing list NumPy-Discussion@scipy.org http://mail.scipy.org/mailman/listinfo/numpy-discussion
