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From Matthias Boehm <mboe...@gmail.com>
Subject Re: distributed cholesky on systemml
Date Sat, 21 Apr 2018 23:06:57 GMT
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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