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: reinw...@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 <mboe...@googlemail.com> 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 >
