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/ >>>>>> >>>>> >>>>> >>>> >
