In addition to what Sebastian said about memory fragmentation and OS limits about memory allocations, I do think it will be hard to work with an array that close to the memory limit in NumPy regardless. Almost any operation will need to make a temporary array and exceed your memory limit. You might want to look at Dask Array for a NumPy-like API for working with chunked arrays that can be staged in and out of memory:

https://docs.dask.org/en/latest/array.html

As a bonus, Dask will also let you make better use of the large number of CPU cores that you likely have in your 1.9 TB RAM system. :)

On Tue, Mar 24, 2020 at 1:00 PM Keyvis Damptey <quantkeyvis@gmail.com> wrote:

Hi Numpy dev community,

I'm keyvis, a statistical data scientist.

I'm currently using numpy in python 3.8.2 64-bit for a clustering problem, on a machine with 1.9 TB RAM. When I try using np.zeros to create a 600,000 by 600,000 matrix of dtype=np.float32 it says
"Unable to allocate 1.31 TiB for an array with shape (600000, 600000) and data type float32"

I used psutils to determine how much RAM python thinks it has access to and it return with 1.8 TB approx.

Is there some way I can fix numpy to create these large arrays?
Thanks for your time and consideration
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