Let us know the results! :) I think in the case of ALS, we can even use Solve.solveSymmetric()
Best, Sebastian On 18.04.2013 23:07, Sean Owen wrote: > Good lead -- from > https://github.com/mikiobraun/jblas/blob/master/src/main/java/org/jblas/Solve.java > it looks like it's an SVD. Definitely took a search to figure out what > 'gelsd' does in LAPACK! I'll see if I can test-drive this too to see > if it bumps performance. That would be great, JNI is a much smaller > requirement than a GPU! > > On Thu, Apr 18, 2013 at 10:01 PM, Sebastian Schelter <s...@apache.org> wrote: >> Hi Sean, >> >> I simply used the Solve.solve() method, I guess it uses a QR >> decomposition internally. I can provide a copy of the code if you want >> to have a look. >> >> Best, >> Sebastian >> >> On 18.04.2013 22:56, Sean Owen wrote: >>> I'm always interested in optimizing the bit where you solve Ax=B which >>> I so recently went on about. That's a dense-matrix problem. Is there a >>> QR decomposition available? >>> >>> I tried getting this part to run on a GPU, and it worked, but wasn't >>> faster. Still somehow it was slower to push the smalish dense matrix >>> onto the card so many times per second. Same issue is identified here >>> so I'm interested to hear if this is a win by using the direct buffer >>> approach. >>> >>> On Thu, Apr 18, 2013 at 9:51 PM, Dmitriy Lyubimov <dlie...@gmail.com> wrote: >>>> 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/ >>>>>>> >>>>>> >>>>>> >>>>> >>
