Hi Robin, It had your changes. In order to run the test on movielens1M, you have to apply this small patch: https://gist.github.com/sscdotopen/5416101
Furthermore, you have to download the movielens1M dataset here: http://www.grouplens.org/node/73 You have to convert the ratings.dat file like this: cat ratings.dat |sed -e s/::/,/g| cut -d, -f1,2,3 > movielens.csv Best, Sebastian On 18.04.2013 22:45, Robin Anil 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/ >>> >> >> >
