[SciPy-user] Sparse int and float performance
Fri Nov 21 09:42:43 CST 2008
On Fri, Nov 21, 2008 at 8:53 AM, Dinesh B Vadhia
> Like I said, I haven't looked at the sparse solver code to know how it works
> but if x is a dense float vector, A a binary matrix with
> data = numpy.ones(nnz, dtype='intc')
> Then, if there were a mixed matrix-vector multiplication version of the
> sparse solver that didn't upcast it to float would there be a performance
> improvement in the calculation of y = Ax?
It depends which sparse solver we're talking about. In principle, the
iterative solvers could perform operations like y=A*x where x and y
are floats and A is an int. The direct solvers (i.e. SuperLU, a
sparse LU method) would always need floats.
Is there any reason not to use dtype='float32'? It should require no
upcast in the sparse solvers.
Nathan Bell email@example.com
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