On Thu, Mar 12, 2015 at 3:05 PM, Jaonary Rabarisoa <jaon...@gmail.com> wrote:
> In fact, by activating netlib with native libraries it goes faster. > > Glad you got it work ! Better performance was one of the reasons we made the switch. > 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 >>>>> >>>> >>>> >>> >> >
