>>>> Okay, I use 72x72 matrices and vectors of
the size 72. So it might be
>>>> beneficial to use atlas-blas then!
>>> Yes, unless your matrices are sparse.
"Standard" BLAS only support dense
>>> (and sometimes packed, banded) matrices.
>> Hmm... most of them are actually sparse (compressed
matrix).
>>
>> For future optimizations I better try these other
proposed methods out.
>
> For size 72, the matrices would have to be *extremely*
sparse to see a
> speedup over dense routines. Sparse operations lose a
lot of efficiency on
> mondern architectures from indirect pointer lookups,
and possibly poor
> cache performance, whereas vectorized dense operations
are getting
> relatively faster. If you try some performance
benchmarks of BLAS vs
> sparse, it would be interesting to see the results.
I have in mind that one of my matrices are more
"banded" than sparse,
but else I sometimes have only 3 or 6 elements in the sparse
matrices.
Some times it's quite more elements, but even though quite
sparse anyway.
To keep the software architecture I need to keep it sparse
for overall
better efficiency - it improves speed quite a bit actually.
Actually I found out that a product that was written like
this:
M(alpha, beta) = matrix(i,j)*v(i)*v(j)
actually was quite more efficient than the associated
product and inner
product function.
Don't know if this was in debug mode - I don't remember. But
I was quite
surprised. The matrix and v was both dense and NOT sparse.
If I get the time for it, I'll definately try more
benchmarking. And
I'll return the results here again!
Best regards,
Preben Holm
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