Robert LaMarca wrote:
Hi,
I am using numpy and wish to create very large arrays. My system is AMD 64 x 2 Ubuntu 8.04. Ubuntu should be 64 bit. I have 3gb RAM and a 15 GB swap drive.
The command I have been trying to use is;
g=numpy.ones([1000,1000,1000],numpy.int32)
This returns a memory error.
A smaller array ([500,500,500]) worked fine..
Two smaller arrays again crashed the system.
So... I did the math. a 1000x1000x1000 array at 32 bits should be around 4gb
RAM... Obviously larger than RAM, but much smaller than the swap drive.
1. So... does Numpy have a really lot of overhead? Or is my system just not somehow getting to make use of the 15gb swap area.
2. Is there a way I can access the swap area, or direct numpy to do so? Or do I have to write out my own numpy cache system...
3. How difficult is it to use data compression internally on numpy arrays?
I do not know what numpy does, but constant arrays only need to store
the dimensions and the constant value and have a getitem method that
returns that constant value for any valid index. This is at most a few
hundred bytes regardless of the dimensions.
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