
Ublas : 
From: Gunter Winkler (guwi17_at_[hidden])
Date: 20061103 07:26:27
Am Freitag, 3. November 2006 08:45 schrieb liu chang:
> dv = prod(sm, sv); // operation 1
> dv = prod(dm, sv); // operation 2
> So is this a limitation of ublas or am I doing something wrong? If
> ublas isn't able to handle such situation, can I code a routine
> myself to specifically take care of multiplication of dense matrix
> with sparse vector?
I had a closer look at the dispatcher. The current mechanism chooses the
wrong routine:
assignment of a sparse product (but dense*sparse yields dense)
I think this is a problem of the used priority: dense < packed < sparse.
(Which is the best choice for evaluating x+y, but the worst for the
result type of x*y)
The fastest solution would be a specialization of axpy_prod (see
operation.hpp). I attach my benchmark example. The sparse vector has a
nonzero entry at every 4th position.
$ ./mulv_bench 4000 2>errors
size of matrices  4000x4000
ublas axpy block
D+=RD 0.08 0.91 0.08
D+=CD 0.72 0.64 0.16
D+=RS 0.16 0.19 0.1
D+=CS 0.49 0.01 0.16
$ ./mulv_bench 8000 2>errors
size of matrices  8000x8000
ublas axpy block
D+=RD 0.22 2.67 0.35
D+=CD 4.31 2.61 0.59
D+=RS 0.68 1.06 0.36
D+=CS 1.97 0.06 0.6
(AMD Athlon(tm) 64 Processor 3200+, g++4.1 DNDEBUG O2)
You see: the layout of the matrix determines the speed of the product.
The best is column major * sparse (which gives the expected time of 1/4
of the dense*dense product)
mfg
Gunter