
Ublas : 
From: Gunter Winkler (guwi17_at_[hidden])
Date: 20080211 13:49:39
Am Montag, 11. Februar 2008 09:21 schrieb Michael Lehn:
> > BTW. Did you run the comparison in 32 or 64Bit mode?
>
> For the sparse matrixvector product I wrote a simple implementation
> crs_gemv as shown on
>
> http://flens.sourceforge.net/session2/tut3.html
>
> Therefore
> r = bAx is equivalent to copy and crs_gemv (4.7s)
> r = Axb is equivalent to crs_gemv and axpy (5.3s)
>
> As vectors are dense for copy and axpy BLAS gets used. In this case
> ATLAS which (compared to MKL) is not so good for axpy.
This means you used 32 Bit integers for the index arrays, right? uBLAS
by default uses std::size_t which is 64 Bit. Please try this type which
is the usual row major compressed matrix with 32 Bit indices.
// type of stiffnes matrix
typedef boost::numeric::ublas::compressed_matrix<
double,
boost::numeric::ublas::basic_row_major<unsigned int, int>,
0,
boost::numeric::ublas::unbounded_array<unsigned int>,
boost::numeric::ublas::unbounded_array<double>
> MY_STIMA;
> By the way: for small matrices and vectors the performance of uBLAS
> ist much better than that of ATLAS, MKL, ... So I think some hybrid
> approach might be interesting ...
for small fixed size matrices I suggest http://tvmet.sourceforge.net/ .
For medium size matrices ublas might be comparable to BLAS, but in my
last tests the limit was around 100x100 ...
>
> About
>
> http://flens.sourceforge.net/session2/tut7.html
>
> is there a better way to initialize a compressed sparse matrix in
> arbitrary order?
Personally, I use a coordinate matrix for assembly and then compress it.
Unfortunately the current implementation requires a copy of the data. I
did some experiments to compress the matrix 'in place' but the
performance gain was quite small compared to the additional complexity.
Alternatively one can use a vector of compressed or coordinate vectors.
The uBLAS type is
typedef boost::numeric::ublas::generalized_vector_of_vector<
double,
boost::numeric::ublas::row_major,
boost::numeric::ublas::vector<
boost::numeric::ublas::compressed_vector<double> >
> MY_STIMA_ASS;
which is quite efficient for random insertions.
mfg
Gunter