I do agree on the lack of documentation, especially on sparse matrices.

But... tada... as I am working on improving this documentation now, I would appreciate if you guys can send me examples of nice tricks when using sparse matrices of all sorts. Any examples are welcome and I will compile them into the new documentation.

If you have web pages and/or code that you can communicate, it will be greatly appreciated too.

I must say that I use dense matrices most of the time, so contributions are most welcome on sparse matrices.

In order not to bother the mailing list, you can also send all the materials to my personal email address directly.

Thanks everybody.

Cheers,

David

On Thu, Apr 22, 2010 at 02:10, Sunil Thomas <sgthomas27@gmail.com> wrote:

Hi Nasos,I see...thanks a lot for testing this out..really appreciate!I am using Boost 1.36.0 here (not 1.34 as I said before - sorry).And yes, I do have -DNDEBUG option on all the time.Cheers,Sunil.On Wed, Apr 21, 2010 at 4:11 PM, Nasos Iliopoulos <nasos_i@hotmail.com> wrote:

Thomas,

I tested your code and it looks that it runs for both compressed and mapped matrices (using -DNDEBUG, Boost v 1.40). ALTHOUGH for mapped matrices without -DNDEBUG it throws from an assertion in the iterator. It looks like a bug in uBlas (maybe wrong assertion), but I am not definite about it. I will file a bug report if I find out. The code I used to test is:

#define NDEBUG

#include <boost/numeric/ublas/matrix_sparse.hpp>

#include <boost/numeric/ublas/io.hpp>

//#define USE_MAPPED

using namespace boost::numeric::ublas;

#ifdef USE_MAPPED

typedef mapped_matrix<double> matrix_type;

#else

typedef compressed_matrix<double> matrix_type;

#endif

int main(int argc, char *argv[])

{

const std::size_t size = 10;

matrix_type P(size,size);

for (int ii=0; ii!=P.size1(); ii++)

for (int j=0; j!=P.size2(); j++)

P(ii,j)=ii*j+ii+j;

std::cout << P << std::endl;

typedef matrix_type::reverse_iterator1 itmr1;

typedef matrix_type::iterator2 itm2;

int i,j ;

for(itmr1 i1 = P.rbegin1(); i1 != P.rend1(); ++i1) {

itm2 i2 = i1.begin(); i = (int)i2.index1();

for(; i2 != i1.end(); ++i2) {

j = (int) i2.index2();

if(j <= i) continue;

//x(i) -= (*i2)*x(j);

}

std::cout << P(i,i) << " ";

//x(i) *= P(i,i);

}

return 0;

}

Best

Nasos

Date: Wed, 21 Apr 2010 15:11:48 -0700

From: sgthomas27@gmail.com

To: ublas@lists.boost.org

Subject: Re: [ublas] Question on ublas performance (resending as subscriber).

Thanks Nasos - I was going to try that next anyhow. Will let you know if I get a major improvement.In a related problem, I find that reverse iterator is not working as expected.. (for compressed_matrixand mapped_matrix). I really doubt that it is a problem of my usage (but that is possible). Here is asnippet of my usage from ILU back substitution (P is the matrix - compressed, or mapped):for(itmr1 i1 = P.rbegin1(); i1 != P.rend1(); ++i1) {

itm2 i2 = i1.begin(); i = (int)i2.index1();

for(; i2 != i1.end(); ++i2) {

j = (int) i2.index2();

if(j <= i) continue;

x(i) -= (*i2)*x(j);

}

x(i) *= P(i,i);

}

Greatly appreciate if anybody knows why this crashes as soon as it enters outer loop...let me know if you needany further info. Its been a while since I used it, I had given up on it and used a combination of a forward iteratorloop and a usual nested loop inside it. I have no such problem with forward iterator.Thanks in advance,Sunil.

On Wed, Apr 21, 2010 at 2:53 PM, Nasos Iliopoulos <nasos_i@hotmail.com> wrote:

Thomas,

replace sparse_matrix with compressed_matrix. Also try the following:

use the generalized vector of compressed_vector A as in the link to build the stiffness matrix, I think this is faster than every other sparse container (mapped, compressed, coordinate). If you need a storage type like compressed (most likely if you use a classical sparse solver), just assign it after the generalized is filled by your stiffness matrix construction algorithm like:

compressed_matrix<double> K=A;

(I am unsure that in earlier versions of uBlas the above is optimum, the latest svn though should be ok. What version are you using?)

The downside of this approach is that it requires twice the memory.

Let us know if it works for you.

Best

Nasos

Date: Wed, 21 Apr 2010 13:51:56 -0700

From: sgthomas27@gmail.com

To: ublas@lists.boost.org

Subject: Re: [ublas] Question on ublas performance (resending as subscriber).

Hi Jorn,Your answer correctly identified the problem. It appears for my problem, mapped_matrix is amuch better (by factor of 110), if not the best choice as far as assembling goes. It also hasn'thurt traversal that badly, it appearsIn the link you attached (thank you!), the author uses something called "sparse_matrix" inboost::numeric::ublas - does this even exist? Atleast in version 1.34, it gives me a compileerror, saying no sparse_matrix type exists in boost::numeric::ublas (it was the first thing Itested before going for mapped_matrix) and the documentation online doesn't mentionsparse_matrix as a type of sparse storage at all.Anyway, thanks a lot for all of your help!Regards,Sunil.2010/4/21 Jörn Ungermann <j.ungermann@fz-juelich.de>

Hi Sunil,

this is likely not a problem of uBLAS, but one of the principal problems

of using sparse matrices. Depending on the type of matrix either random

access or multiplication performance is efficient.

For the compressed_matrix, random access is rather costly *unless* you

can control the way in which elements are added to the matrix. If you

can assemble the (row_major) compressed_matrix row-by-row with ascending

column indices, this should take no time at all.

If you can't do this, use a different matrix type for assembly, e.g.

mapped_matrix (which offers efficient random access, but bad

computational performance) and construct the compressed_matrix from

there.

See Gunter Winkler's page for details:

http://www.guwi17.de/ublas/matrix_sparse_usage.html

Kind reagrds,

Jörn

------------------------------------------------------------------------------------------------

On Wed, 2010-04-21 at 03:09 +0200, Sunil Thomas wrote:

> Hi all,

>

> I've been using boost 1.34 ublas library, especially the

> compressed_matrix class for sparse matrices in

>

> compressed row storage form. But I noticed that simply accessing an

> element of the matrix (to assign

>

> it a value, for example) slows my application down to unusable levels,

> for problems of the order of just

>

> 80,000 unknowns. I've identified the program is there and yes, I am

> allocating the memory as I should

>

> be for the matrix, - for example here is a snippet (of important

> lines):

>

>

> ************************************************************************************

> matrix_A = compressed_matrix(nelem_a(), nelem_a(), nonzeros()); //

> allocation

>

> matrix_A(uic1, uic1) += -trans; // assignment

>

> matrix_A(uic2, uic2) += -trans; // assignment

>

> ************************************************************************************

>

> where all variables (and/or functions), e.g. uic1, uic2, trans,

> neleme_a(), nonzeros(), etc.. are all well-defined

>

> (this is all been checked thoroughly). Commenting out the two

> assignment statements for example reduced

>

> my overall run time from 110 seconds to 0 (practically zero), for

> 80000 runs. Has anyone encountered this

>

> problem and know of a solution? I've heard a lot of stories about how

> boost::ublas is just not up there in

>

> performance and I certainly hope I am missing something trivial. Do

> later versions of boost address this

>

> better?

>

>

>

> Greatly appreciate any help.

>

> Thanks,

>

> Sunil.

>

>

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