Question

I was working on drafting/testing a technique I devised for solving differential equations for speed and efficiency.

It would require a storing, manipulating, resizing, and (at some point) probably diagonalizing very large sparse matrices. I would like to be able to have rows consisting of zeros and a few (say <5) ones, and add them a few at a time (on the order of the number of cpus being used.)

I thought it would be useful to have gpu accelleration–so any suggestions as to the best way to take advange of that would be appreciated too (say pycuda, theano, etc.)

Answer 1

You can use a dictionary and tuples to access the data:

>>> size = (4,4)
>>> mat = {}
>>> mat[0,1] = 3
>>> mat[2,3] = 5
>>> for i in range(size[0]):
        for j in range(size[1]):
            print mat.get((i,j), 0) ,
        print

0 3 0 0
0 0 0 0
0 0 0 5
0 0 0 0

Of course you should make a class for that and add the methods you need:

class Sparse(dict):
    pass

BTW, You can also use scipy.sparse from the scipy lib