It appears I've run out of 32-bit address space. What are my options?

To be at your place, I would try to "pickle" (save) the matrix on my hard drive, close python , then in command line re-open the pickeled file and do my computation on a "fresh python" instance.

I would do that because maybe your problem is before computing the covariance.

import cPickleimport numpyM = numpy.array([[1,2],[3,4]]) # here it will be your matrixcPickle( M , open( "~/M.pic", "w") ) # here it's where you pickle the file

Here you close python. Your file should be saved in you home directory as "M.pic".

import cPickleimport numpyM = cPickle.load( open( "~/M.pic", "r") )M = numpy.coa( M )

If it still does not work, try setting a "good" dtype for your data. numpy seams to use dtype 'float64' of 'int64' by default. This is huge and if you do not need this precision, you might want to reduce it to 'int32' or 'float32'.

import numpyM = numpy.array([[1,2],[3,4]] , dtype.float32 )

Indeed, I can guarantee you that C/Fortran is not an option for you. Numpy is already written in C/Fortran and probably by people cleverer than you and me ;)

By curiosity, how big is your matrix? how big is your pickled file?