i've looked at jblas some time year or two ago. It's a fast bridge to LAPack and LAPack by far is hard to beat. But, I think i convinced myself it lacks support for sparse stuff. Which will work nice though still for many blockified algorithms such as ALS-WR with try to avoid doing blas level 3 operations on sparse data.
On Thu, Apr 18, 2013 at 1:45 PM, Robin Anil <robin.a...@gmail.com> wrote: > BTW did this include the changes I made in the trunk recently? I would also > like to profile that code and see if we can squeeze out our Vectors and > Matrices more. Could you point me to how I can run the 1M example. > > Robin > > Robin Anil | Software Engineer | +1 312 869 2602 | Google Inc. > > > On Thu, Apr 18, 2013 at 3:43 PM, Robin Anil <robin.a...@gmail.com> wrote: > > > I was just emailing something similar on Mahout(See my email). I saw the > > TU Berlin name and I thought you would do something about it :) This is > > excellent. One of the next gen work on Vectors is maybe investigating > this. > > > > > > Robin Anil | Software Engineer | +1 312 869 2602 | Google Inc. > > > > > > On Thu, Apr 18, 2013 at 3:37 PM, Sebastian Schelter <s...@apache.org > >wrote: > > > >> Hi there, > >> > >> with regard to Robin mentioning JBlas [1] recently when we talked about > >> the performance of our vector operations, I ported the solving code for > >> ALS to JBlas today and got some awesome results. > >> > >> For the movielens 1M dataset and a factorization of rank 100, the > >> runtimes per iteration dropped from 50 seconds to less than 7 seconds. I > >> will run some tests with the distributed version and larger datasets in > >> the next days, but from what I've seen we should really take a closer > >> look at JBlas, at least for operations on dense matrices. > >> > >> Best, > >> Sebastian > >> > >> [1] http://mikiobraun.github.io/jblas/ > >> > > > > >
