In fact, by activating netlib with native libraries it goes faster. Thanks
On Tue, Mar 10, 2015 at 7:03 PM, Shivaram Venkataraman < shiva...@eecs.berkeley.edu> wrote: > There are a couple of differences between the ml-matrix implementation and > the one used in AMPCamp > > - I think the AMPCamp one uses JBLAS which tends to ship native BLAS > libraries along with it. In ml-matrix we switched to using Breeze + Netlib > BLAS which is faster but needs some setup [1] to pick up native libraries. > If native libraries are not found it falls back to a JVM implementation, so > that might explain the slow down. > > - The other difference if you are comparing the whole image pipeline is > that I think the AMPCamp version used NormalEquations which is around 2-3x > faster (just in terms of number of flops) compared to TSQR. > > [1] > https://github.com/fommil/netlib-java#machine-optimised-system-libraries > > Thanks > Shivaram > > On Tue, Mar 10, 2015 at 9:57 AM, Jaonary Rabarisoa <jaon...@gmail.com> > wrote: > >> I'm trying to play with the implementation of least square solver (Ax = >> b) in mlmatrix.TSQR where A is a 50000*1024 matrix and b a 50000*10 >> matrix. It works but I notice >> that it's 8 times slower than the implementation given in the latest >> ampcamp : >> http://ampcamp.berkeley.edu/5/exercises/image-classification-with-pipelines.html >> . As far as I know these two implementations come from the same basis. >> What is the difference between these two codes ? >> >> >> >> >> >> On Tue, Mar 3, 2015 at 8:02 PM, Shivaram Venkataraman < >> shiva...@eecs.berkeley.edu> wrote: >> >>> There are couple of solvers that I've written that is part of the AMPLab >>> ml-matrix repo [1,2]. These aren't part of MLLib yet though and if you are >>> interested in porting them I'd be happy to review it >>> >>> Thanks >>> Shivaram >>> >>> >>> [1] >>> https://github.com/amplab/ml-matrix/blob/master/src/main/scala/edu/berkeley/cs/amplab/mlmatrix/TSQR.scala >>> [2] >>> https://github.com/amplab/ml-matrix/blob/master/src/main/scala/edu/berkeley/cs/amplab/mlmatrix/NormalEquations.scala >>> >>> On Tue, Mar 3, 2015 at 9:01 AM, Jaonary Rabarisoa <jaon...@gmail.com> >>> wrote: >>> >>>> Dear all, >>>> >>>> Is there a least square solver based on DistributedMatrix that we can >>>> use out of the box in the current (or the master) version of spark ? >>>> It seems that the only least square solver available in spark is >>>> private to recommender package. >>>> >>>> >>>> Cheers, >>>> >>>> Jao >>>> >>> >>> >> >
