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 <mboe...@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...@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 >
