From: Markus Weimer (markus.weimer_at_[hidden])
Date: 2007-06-29 06:53:29
we just did some experiments on the following case, which occurs often in
v = prod(u,M)
where v and u are dense vectors and M is a sparse matrix in compressed row
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,