Hi,
Thanks for the hints! Right as you were writing, I have implemented
now a solution via matrix_range which already performs really well. I
guess there wouldn't be any difference in performance between
viennacl::column and some matrix_range solution, right?
viennacl::column() might be faster for large matrices because of the way
threads are assigned. I guess that most of your execution time is spent
elsewhere, so it's probably not worth optimizing further...
Best regards,
Karli
On Thu, Dec 12, 2013 at 2:49 PM, Karl Rupp r...@iue.tuwien.ac.at wrote:
Hi Albert,
I thought that a good way to get good performance is to formulate all
the calculations somehow vectorized but I'm not sure if I have chosen
the best way because the code performs badly. The matrices are big,
about 10k \times 10k in size.
This is correct, provided that the vectorization is not carried out via
adding a lot of operations by zeros.
My code is below. Maybe you can give me some suggestions about how to
do that fast. Basically, in `mat_column_mult`, I want to multiply a
certain column of a matrix. Currently, I multiply with 0, maybe that
is a special case where I can do even faster. In
`layerActivity_addBiasTerm`, I want to add a left scalar column to a
matrix.
The issue in the code are the matrix-matrix multiplications, even though you
only want to scale columns. For such cases you can use the new column()
function to extract a column from a matrix and scale that. In-place
operations are supposed to work, so you can directly write
viennacl::column(A, 7) *= factor;
Have a look here:
https://github.com/viennacl/viennacl-dev/blob/master/tests/src/matrix_vector.cpp#L235
on how to use it. As with most functionality in ViennaCL, it's basically the
same as in Boost.uBlas. For this to compile, however, you need to use the
developer version from GitHub (https://github.com/viennacl/viennacl-dev) or
wait a few more days until the 1.5.0 release is finally out. :-)
Best regards,
Karli
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