Hi,

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,

Markus