[SciPy-user] 64 bit Address Space Limitations
travis.brady at gmail.com
Thu Mar 23 09:51:53 CST 2006
Regarding the ability to do this with 2.4, what types of slicing would run
I might consider sticking with 2.4 for now if the slicing and buffer issues
mentioned can be coded around.
On 3/14/06, Travis Oliphant <oliphant.travis at ieee.org> wrote:
> Mark W. wrote:
> > Hi. We are converting our systems to a 64-bit platform to hopefully take
> > advantage of larger address spaces for arrays and such. Can anyone tell
> me -
> > or point me to documentation which tells - how much address space for an
> > array I could hope to get? We have a memory error on the 32-bit machines
> > when we try to load a large array and we're hoping this will get around
> > 2 Gig (or less) limit.
> This is finally possible using Python 2.5 and numpy. But, you need to
> use Python 2.5 which is only available as an SVN check-out and still has
> a few issues. Python 2.5 should be available as a release in the summer.
> NumPy allows creation of larger arrays even with Python 2.4 but there
> will be some errors in some uses of slicing, the buffer interface, and
> memory-mapped arrays because of inherent limitations to Python that were
> only recently removed.
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> SciPy-user at scipy.net
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