I am not using Strassen for (Dyadic) Dense matrices. However, if the matrix
dimensions are not dyadic, then Strassen cannot be natively used. What folks
suggest is to zero pad the data and then employ Strassen's algorithm. The
second link that I shared, described an optimal way to using the Zero padded
data. What this would mean for regular matrices is that some of the
sub-matrices would become Dense matrix matrix products while others may become
sparse matrix/dense matrix products. Thanks and Regards
Sumit
From: Karl Rupp <[email protected]>
To: Sumit Kumar <[email protected]>
Cc: "[email protected]"
<[email protected]>
Sent: Wednesday, July 29, 2015 2:44 PM
Subject: Re: [ViennaCL-devel] ViennaCL reductions
Hi Sumit,
> ViennaCL: Sparse Matrix-Matrix Multiplication
> <http://www.iue.tuwien.ac.at/cse/index.php/gsoc/2014/ideas/167-viennacl-sparse-matrix-matrix-multiplication-2.html>
> Description
> View on www.iue.tuwien.ac.at
> <http://www.iue.tuwien.ac.at/cse/index.php/gsoc/2014/ideas/167-viennacl-sparse-matrix-matrix-multiplication-2.html>
>
> Do you have any example in ViennaCL that actually does this? Can you
> please point this out? I was rethinking my strategy and was wondering if
> I formulate my Dense matrix multiplication as a sparse matrix
> multiplication and,
1.7.0 will provide a sparse matrix-matrix product for
compressed_matrix<>. Some code for the nightly tests is in
tests/src/sparse_prod.cpp
The API is the same as for dense matrices:
A = viennacl::linalg::prod(B, C);
for A, B, and C being of type compressed_matrix<T>.
> a.) Use The modified Strassen algorithm to reduce the workload
> Strassen's Matrix Multiplication Relabeled
> <http://src.acm.org/loos/loos.html>
I don't understand what you are trying to achieve with Strassen for
sparse matrices. Strassen is (sometimes) useful for dense matrices, but
not for sparse matrices. The algorithmic complexities involved in sparse
and dense matrix-matrix multiplications are totally different.
Best regards,
Karli
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