if I remember correctly then it is not trivial to accurately estimate the
memory foot print for these commons math functions at compile time
depending on what intermediates they produce ... Meaning you may still end
up with java heap space OOM at runtime.
Regards,
Berthold Reinwald
IBM Almaden Research Center
office: (408) 927 2208; T/L: 457 2208
e-mail: reinwald@us.ibm.com
From: Matthias Boehm
To: dev@systemml.incubator.apache.org
Date: 10/24/2016 11:54 AM
Subject: Re: Local versions of Linear Algebra Operators in DML
well, we still compute memory estimates for these operations. So I
guess, a good compromise would be to raise a warning whenever the memory
estimate is known to exceed the local memory budget.
Regards,
Matthias
On 10/24/2016 8:29 PM, Deron Eriksson wrote:
> Would it be acceptable for a user to receive a log warning if the user
uses
> an operation that is currently only implemented for single node? My
concern
> is that there is an expectation for operations to be distributed with
> SystemML, and if an operation is not currently distributed, the user
needs
> to made aware of this.
>
> Thoughts?
>
> Deron
>
>
> On Mon, Oct 24, 2016 at 10:38 AM, Nakul Jindal
wrote:
>
>> Hi,
>>
>> There is an initial implementation and PR.
>> https://github.com/apache/incubator-systemml/pull/273
>>
>> -Nakul
>>
>>
>>> On Oct 24, 2016, at 12:59 AM, Berthold Reinwald
>> wrote:
>>>
>>> Thanks, Imran. I think it is a good idea to start off with the
DML-bodied
>>> function implementation. This will hold until we can have a built in
>>> implementation.
>>>
>>> We prototyped an implementation of distributed Cholesky as a DML
bodied
>>> function as well. For performance optimization, as the matrix becomes
>>> "small" enough, we switched over and exploit a single node
>> implementation.
>>>
>>> Adding a new svd() built in function that initially routes to a local
>>> library is fine. I don't know whether Apache commons math has an
>>> implementation that can be re-used.
>>>
>>> I object renaming the functions or changing the externals. Eventually
>>> distributed instructions need to be added to these implementations,
and
>>> there are open jiras for it.
>>>
>>> Regards,
>>> Berthold Reinwald
>>> IBM Almaden Research Center
>>> office: (408) 927 2208; T/L: 457 2208
>>> e-mail: reinwald@us.ibm.com
>>>
>>>
>>>
>>> From: Niketan Pansare/Almaden/IBM@IBMUS
>>> To: dev@systemml.incubator.apache.org
>>> Date: 10/21/2016 01:14 PM
>>> Subject: Re: Local versions of Linear Algebra Operators in DML
>>>
>>>
>>>
>>> I am also comfortable with option (2) ... "with a plan to implement
its
>>> distributed version"
>>>
>>> Thanks,
>>>
>>> Niketan Pansare
>>> IBM Almaden Research Center
>>> E-mail: npansar At us.ibm.com
>>> http://researcher.watson.ibm.com/researcher/view.php?person=us-npansar
>>>
>>> Matthias Boehm ---10/21/2016 01:00:51 PM---thanks Nakul for reaching
out
>>> before starting work on this. Actually, the introduction of these CP-
>>>
>>> From: Matthias Boehm
>>> To: dev@systemml.incubator.apache.org
>>> Date: 10/21/2016 01:00 PM
>>> Subject: Re: Local versions of Linear Algebra Operators in DML
>>>
>>>
>>>
>>> thanks Nakul for reaching out before starting work on this. Actually,
>>> the introduction of these CP-only builtin functions was a big mistake
>>> because (as you already mentioned) they mistakenly suggest that we
>>> provide distributed operations for them too. The intend was to support
>>> them in later versions with our own local and distributed
>>> implementations. So far, this had low priority though because these
>>> O(n^3) operations are seldom used over large data. However, a while
>>> back, we lost potential users who were specifically interested in
>>> distributed eigen - so there are still use cases.
>>>
>>> Despite the good intentions behind the renaming, I would strongly
argue
>>> against it. First, it would unnecessarily lose compatibility with R
>>> syntax. Second, it would defeat our clean abstraction by exposing
>>> explicit local operations.
>>>
>>> This leaves us with two options here: (1) you could use an external
>>> (java-implemented) function, which gives you virtually the same
runtime
>>> behavior but a clear separation via an explicit registration, or (2)
add
>>> it to the list of CP-only operations (with a plan to implement its
>>> distributed version) but name it 'svd' as in R.
>>>
>>>
>>> Regards,
>>> Matthias
>>>
>>>
>>>> On 10/21/2016 9:34 PM, Nakul Jindal wrote:
>>>> Hi,
>>>>
>>>> Imran was planning on implementing a distributed SVD as a DML bodied
>>>> function.
>>>> The algorithm is described in the paper titled "A Distributed and
>>>> Incremental SVD Algorithm for Agglomerative Data Analysis on Large
>>>> Networks" available at https://arxiv.org/abs/1601.07010.
>>>>
>>>> This algorithm requires the availability of a local SVD function,
which
>>> we
>>>> currently do not have in SystemML.
>>>> Seeing as how there are other linear algebra functions (eigen, lu,
qr,
>>>> cholesky) in DML that reroute to Apache Common Math and only operate
in
>>>> standalone/CP mode, would it be ok to add "svd" to this set?
>>>>
>>>> Also, since these operations are local and not distributed and the
>>>> documentation doesn't make it clear that these operations wont
operate
>>> in
>>>> distributed mode, would it make sense to rename them to
"local_eigen",
>>>> "local_qr", "local_cholesky", etc?
>>>> Obviously, this change would go into the version after 0.11.
>>>>
>>>> I understand that the ideal solution to this problem is to have a
>>>> distributed version of the aforementioned linear algebra routines,
but
>>> for
>>>> the time being, would it be ok to go ahead do the rename, while also
>>>> introducing a "local_svd" ?
>>>>
>>>>
>>>> Niketan, Berthold, Matthias, Sasha - Any thoughts?
>>>>
>>>> Thanks,
>>>> Nakul Jindal
>>>>
>>>
>>>
>>>
>>>
>>>
>>>
>>>
>>
>