[SciPy-User] 64-bit matrix indices

[Nathaniel Smith]

On Tue, Nov 16, 2010 at 8:09 AM, Ioan-Alexandru Lazar <alexlz at lmn.pub.ro> wrote:
> I am trying to use SciPy for one of my HPC projects. A problem I am
> currently facing is that 32-bit indices are too small for the matrix sizes
> we require. Is there any way to use 64-bit ints for the indices?
[...]
> I only need a few sparse operations and .mat file reading from it.

You're asking specifically about the indices in scipy.sparse matrices, yes?

At a quick glance, all the core matrix manipulation code in
scipy.sparse seems to be templatized with respect to the type of the
index -- you *might* be able to get 64-bit index support for all the
core sparse matrix operations by editing
scipy/sparse/sparsetools/sparsetools.i and adding the obvious stuff at
around lines 145, 188, and 195, and then creating your matrices "by
hand" (i.e., building your own indices and indptr arrays of 64-bit
integers, and then passing them directly to the csc_matrix/csr_matrix
constructors). The .mat file reader is potentially more tricky, but it
sounds like you could read them in with 32-bit indices and then just
convert them to 64-bit:
  mymat.indptr = np.array(mymat.indptr, dtype=np.int64)
  mymat.indices = np.array(mymat.indices, dtype=np.int64)

> If anyone is interested on the background story: the matrices themselves
> aren't too big *at first*, but due to the peculiar structure they have,
> the fill-in is mind-blowing. UMFPACK complaints that it doesn't have
> enough memory for them; it does (our cluster's nodes have 24 GB of
> memory), but once the number of indices blows past the 32-bit limit, it
> hits the ceiling. Using a different solver is still not an option

You'd also need a way to call UMFPACK's 64-bit functions (the "zl/dl"
variants instead of the "zi/di" variants). It looks like
scikits.umfpack might let you do this easily, but I'm not sure.

-- Nathaniel