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From Qifan Pu <qifan...@gmail.com>
Subject Re: distributed cholesky on systemml
Date Sun, 22 Apr 2018 01:28:41 GMT
Hi Matthias,

Thanks for the fast response and detailed information. This is really
helpful.

I just tried to run it, and was tracing down a indexing bug that can be
repeated by simply running the test script of triangle solve[1]
Caused by: org.apache.sysml.runtime.DMLRuntimeException: Invalid values for
matrix indexing: [1667:3333,1:1666] must be within matrix dimensions
[1000,1000]


Am I missing some configuration here?


[1]
https://github.com/apache/systemml/blob/master/scripts/staging/scalable_linalg/test/test_triangular_inv.dml


Best,
Qifan


On Sat, Apr 21, 2018 at 4:06 PM, Matthias Boehm <mboehm7@gmail.com> wrote:

> Hi Qifan,
>
> thanks for your feedback. You're right, the builtin functions
> cholesky, inverse, eigen, solve, svd, qr, and lu are currently only
> supported as single-node operations because they're still implemented
> via Apache commons.math.
>
> However, there is an experimental script for distributed cholesky [1]
> which uses a recursive approach (with operations that allow for
> automatic distributed computation) for matrices larger than a
> user-defined block size. Once blocks become small enough, we use again
> the builtin cholesky. Graduating this script would require a broader
> set of experiments (and potential improvements) but it simply did not
> have the highest priority so far. You might want to give it a try
> though.
>
> Thanks again for your feedback - we'll consider a higher priority for
> these distributed operations when discussing the roadmap for the next
> releases.
>
> [1] https://github.com/apache/systemml/blob/master/scripts/
> staging/scalable_linalg/cholesky.dml
>
> Regards,
> Matthias
>
> On Sat, Apr 21, 2018 at 2:15 PM, Qifan Pu <qifan.pu@gmail.com> wrote:
> > Hi,
> >
> > I would love to do distributed cholesky on large matrix with SystemML. I
> > found two related jiras (SYSTEMML-1213, SYSTEMML-1163), but AFAIK, this
> is
> > currently not implemented? I just wanted to check.
> >
> > Best,
> > Qifan
>

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