Hi KarlBased on all your suggestions,I came up with this implementation:
// Copy Sparse Eigen matrices to Dense viennacl matrices typedef
Eigen::Matrix<ScalarType, Eigen::Dynamic, Eigen::Dynamic, Eigen::RowMajor>
RMMatrix; viennacl::matrix<ScalarType, viennacl::row_major>
vcl_A(source->rows(), source->cols()); viennacl::copy(RMMatrix(*source),
vcl_A); viennacl::matrix<ScalarType, viennacl::row_major>
vcl_B(target->rows(), target->cols()); viennacl::copy(RMMatrix(*target),
vcl_B); viennacl::matrix<ScalarType, viennacl::row_major>
vcl_C(result->rows(), result->cols()); // Implement the matrix
multiplication on the GPU. vcl_C = viennacl::linalg::prod(vcl_A, vcl_B);
// Copy the matrix back to the host matrix RMMatrix temp =
RMMatrix(*result); viennacl::copy(vcl_C, temp); (*result) =
temp.sparseView();
Essentially, I have a 100K x 100k matrix that I have to generate. I split it up
into blocks with a max block size of 2048 x 2048Thus, source, target, and
result will all have dimensions of 2048 x 2048. I end getting a result.
However, I was wondering if I can do something differently? For example,can I
use fast_copy, async_copy anywhere? I would like to avoid this lineRMMatrix
temp = RMMatrix(*result);
but I am unable to convert to a dense matrix, implicitly.
If you notice, I am converting my sparse matrices to dense matrices and then
implementing a multiplication. Is this a good idea? I need to do this as I am
not sure when my matrix will become sparse or dense. Do we have any heuristics
that can help us (e.g., if source->nonZeros() > (source->rows() x
source->cols())/2, then switch to a dense multiplication, else use a
sparse?)....
If I do a naive sparse matrix multiplication, this is the code that I have:
viennacl::compressed_matrix<ScalarType> vcl_A(source->rows(), source->cols());
viennacl::copy((*source), vcl_A);
viennacl::compressed_matrix<ScalarType> vcl_B(target->rows(), target->cols());
viennacl::copy((*target), vcl_B);
viennacl::compressed_matrix<ScalarType> vcl_C(result->rows(), result->cols());
// Implement the matrix multiplication on the GPU. vcl_C =
viennacl::linalg::prod(vcl_A, vcl_B); // Copy the matrix back to the host
matrix viennacl::copy(vcl_C, (*result));
Thanks and Regards
Sumit
From: Karl Rupp <[email protected]>
To: Sumit Kumar <[email protected]>
Cc: "[email protected]"
<[email protected]>
Sent: Monday, August 3, 2015 1:23 AM
Subject: Re: [ViennaCL-devel] ViennaCL reductions
Hi Sumit,
> I was trying to run sparse matrix multiplication, but one of my explicit
> template typedefs had Int. After some digging, I found out that CSR only
> supported Float or double. Is there any reason for this? Can we also
> have support for other templates? (like int ?)
It is technically possible, but we haven't implemented it yet. After 5
years you are the first to even ask for it ;-)
> Another thing would be the alignment order. Suppose I have a Row-major
> Sparse Eigen matrix, then I can copy it to a (Row-Major ?) VCL
> compressed matrix. What about a column-major sparse Eigen matrix?
If you can point me to a fast, massively parallel column-major
matrix-vector multiplication routine, I look into it. However, as far as
I know, there is no such routine for general sparse matrices, hence it
does not make sense for us to support it.
Best regards,
Karli
> ------------------------------------------------------------------------
> *From:* Karl Rupp <[email protected]>
> *To:* Sumit Kumar <[email protected]>
> *Cc:* "[email protected]"
> <[email protected]>
> *Sent:* Friday, July 31, 2015 9:04 PM
> *Subject:* Re: [ViennaCL-devel] ViennaCL reductions
>
> Hi Sumit,
>
> > I am aware that Eigen can do it for its matrices and I am also aware
> > that VCL cannot do it natively. My question was this:
> > In your example of interfacing with Eigen, you have shown a VCL dense
> > matrix interfacing with an Eigen dense matrix. Do you have any example
> > of interfacing an Eigen Sparse matrix with a VCL dense matrix?
>
> No, we don't have this.
>
>
>
>
> Best regards,
> Karli
>
>
>
>
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