[SciPy-user] Sparse int and float performance
Dinesh B Vadhia
Thu Nov 20 13:19:48 CST 2008
A question for Nathan Bell:
I use Scipy Sparse to solve y = Ax, where A is a MxN "binary" sparse matrix and x is a dense floating point vector, with M and N each >100,000
I use the following to create the CSR matrix:
row = numpy.empty(nnz, dtype='intc')
column = numpy.empty(nnz, dtype='intc')
<read i,j into row and column>
data = numpy.ones(nnz, dtype='intc')
A = sparse.csr_matrix((data, (row, column)), shape=(I,J))
Now, suppose that we change data to the float datatype ie.
data = numpy.ones(nnz, dtype=float)
I know I can test this but from the perspective of the scipy code, how would this impact the performance of the calculation of y = Ax ie.
- Same as data with dtype='intc'
- Slower than data with dtype = 'intc'
- Faster than data with dtype = 'intc'
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