In fact, by activating netlib with native libraries it goes faster.
Thanks
On Tue, Mar 10, 2015 at 7:03 PM, Shivaram Venkataraman <
shivaram@eecs.berkeley.edu> wrote:
> There are a couple of differences between the mlmatrix 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 mlmatrix 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 23x
> faster (just in terms of number of flops) compared to TSQR.
>
> [1]
> https://github.com/fommil/netlibjava#machineoptimisedsystemlibraries
>
> Thanks
> Shivaram
>
> On Tue, Mar 10, 2015 at 9:57 AM, Jaonary Rabarisoa <jaonary@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/imageclassificationwithpipelines.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 <
>> shivaram@eecs.berkeley.edu> wrote:
>>
>>> There are couple of solvers that I've written that is part of the AMPLab
>>> mlmatrix 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/mlmatrix/blob/master/src/main/scala/edu/berkeley/cs/amplab/mlmatrix/TSQR.scala
>>> [2]
>>> https://github.com/amplab/mlmatrix/blob/master/src/main/scala/edu/berkeley/cs/amplab/mlmatrix/NormalEquations.scala
>>>
>>> On Tue, Mar 3, 2015 at 9:01 AM, Jaonary Rabarisoa <jaonary@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
>>>>
>>>
>>>
>>
>
