From: Ian Fellows (ifellows_at_[hidden])
Date: 2007-06-29 11:23:40
First off, I think it might be a good idea to turn off debugging by defining:
next, if you can assume no shared memory between the left and right side of the equation, then you can use noalias
noalias(v) = prod(u,M);
What are the time results with these changes?
From: ublas-bounces_at_[hidden] [mailto:ublas-bounces_at_[hidden]]On Behalf Of Markus Weimer
Sent: Friday, June 29, 2007 3:53 AM
To: ublas mailing list
Subject: [ublas] vector * Matrix is slow compared to hand written code
we just did some experiments on the following case, which occurs often in our code:
v = prod(u,M)
where v and u are dense vectors and M is a sparse matrix in compressed row major format.
We also did an alternative implementatio of prod called ourProd in the attached code. It follows the following algorithm:
for i in M.size1():
v += u[i] * M[i,*]
where M[i,*] equals row i of M.
This version often is 10-20 times faster than the prod in uBLAS on sparse M. While this is all good, the implementation in test_fast() in the attached source is sometimes 1000 times faster on very sparse matrices, for example when running the code with the following parameters:
executable 100000 10000 1 0.001
The parameters are as follows:
executable ROWS COLS HOWOFTEN NONZEROPROBABILITY
where HOWOFTEN controls how often the experiments are run.
Do you see any way to achieve better performance within uBLAS for prod(u,M) with M being very sparse, as in 1 out of 1000 entries are set.
Thanks in advance,