and everything before (e.g., I generate the matrix using another DML) was indeed run by Spark and shows up on the UI.
On Sun, Apr 22, 2018 at 5:05 PM, Qifan Pu <qifan...@gmail.com> wrote: > Thanks Jeremy and Matthias. When I run the script, the cholesky or the inv > is executed completely on the driver, and nothing shows up on Spark UI. > Is that the expected behavior? > > On Sun, Apr 22, 2018 at 3:34 PM, Jeremy Nilmeier <nilme...@us.ibm.com> > wrote: > >> Yes, I also spoke with Sasha about this some time last year. Thanks for >> following up. >> >> Cheers, J >> >> >> Jerome Nilmeier, PhD >> Data Scientist and Engineer >> IBM Spark Technology Center >> http://www.spark.tc/ >> >> >> >> ----- Original message ----- >> From: Matthias Boehm <mboe...@gmail.com> >> To: dev@systemml.apache.org >> Cc: Qifan Pu <qifan...@gmail.com>, Jeremy Nilmeier <nilme...@us.ibm.com> >> Subject: Re: distributed cholesky on systemml >> Date: Sun, Apr 22, 2018 2:41 PM >> >> thanks for the context Jeremy - that helps. I also had an offline >> conversion with Sasha and he pointed me to a script that does exactly >> that (iterative invert_lower_triangular) combined with a parfor over >> independent blocks. We'll merge these scripts soon and I'll reach out >> individually as necessary. Thanks everybody for now. >> >> Regards, >> Matthias >> >> On Sun, Apr 22, 2018 at 12:40 PM, Jeremy Nilmeier <nilme...@us.ibm.com> >> wrote: >> > This may be a duplicate...it was bounced from the dev list. >> > >> > I think that scalable triangular inverse will also have similar >> properties, >> > in that there is a sequential approach if it uses back substitution. >> > >> > For most of these algorithms (LU, Cholesky, QR), they are inherently >> > sequential, and the focus of the work is on minimizing interprocess >> > communication during the operations, which may explain why there was >> only >> > limited interest in pursuing this further. >> > >> > I had originally recommended that the recursive algorithms be rewritten >> as >> > iterative algorithms (and in fact provided an example of the LU in >> iterative >> > form), which would make the counting of operations more transparent, as >> well >> > as revealing possible parallelization points. >> > >> > Cheers, J >> > Jerome Nilmeier, PhD >> > Data Scientist and Engineer >> > IBM Spark Technology Center >> > https://urldefense.proofpoint.com/v2/url?u=http-3A__www.spar >> k.tc_&d=DwIFaQ&c=jf_iaSHvJObTbx-siA1ZOg&r=3mYOfURw_FSirAnoSv >> 2pWvLSi1psso4F9RdGjEWL6yc&m=VIdNVaIRvibBlaNVAOXLKmxXf7ma-EXr >> LWbjMd9Bmgo&s=YktpBBbqor3DKzS90Ah75BF6NBYtE4RauITF7QaL87g&e= >> >> > >> > >> > >> > ----- Original message ----- >> > From: Matthias Boehm <mboe...@gmail.com> >> > To: dev@systemml.apache.org >> > Cc: Qifan Pu <qifan...@gmail.com> >> > Subject: Re: distributed cholesky on systemml >> > Date: Sun, Apr 22, 2018 1:21 AM >> > >> > sure no problem - thanks again for catching this issue that was hidden >> > for a while. >> > >> > Yes, the same depth-first characteristic applies to the Cholesky >> > function as well. In contrast to U_triangular_inv, however, there are >> > data dependencies between the blocks per level (at least in the >> > current algorithm formulation), which means we cannot use the approach >> > I described for U_triangular_inv. >> > >> > L11 = Cholesky(A11, nb) >> > A22 = ... U_triangular_inv(t(L11)) >> > L22 = Cholesky(A22, nb) >> > >> > However, note that there are much fewer calls to Cholesky due to the >> > switch to the builtin cholesky according to the given min block size. >> > For example, in our new test for dimensions 1362 x 1362 and min size >> > of 200, we call Cholesky 15 times but U_triangular_inv 2539 times. >> > >> > For sufficiently large min block size this might be ok for Cholesky, >> > because each level also does a number of matrix multiplies that will >> > exploit the available parallelism of your cluster. In that regard. you >> > might want to experiment with different block sizes and driver memory >> > budgets. If I get a chance, I will also run a number of experiments >> > and see if we can rewrite these scripts. >> > >> > Regards, >> > Matthias >> > >> > On Sun, Apr 22, 2018 at 12:48 AM, Qifan Pu <qifan...@gmail.com> wrote: >> >> Matthias, >> >> >> >> Thanks so much for taking time to fix. Really appreciated it. >> >> Does the same reasoning apply to the cholesky script? The recursive >> >> approach >> >> also looks inherently sequential. >> >> >> >> Best, >> >> Qifan >> >> >> >> On Sat, Apr 21, 2018 at 11:39 PM, Matthias Boehm <mboe...@gmail.com> >> >> wrote: >> >>> >> >>> just as a quick update: this issue has now been fixed in SystemML >> >>> master - it was essentially a missing guard for recursive functions >> >>> when checking for unary size-preserving functions during >> >>> inter-procedural analysis (IPA). >> >>> >> >>> However, while working with this recursive cholesky function I came to >> >>> the conclusion that it may need some rework. The current top-down, >> >>> depth-first, approach is inherently sequential. This is partially >> >>> unnecessary because for the used recursive function U_triangular_inv >> >>> (which is called many more times than cholesky), blocks per level are >> >>> independent. Therefore, we should look into a bottom-up, breadth-first >> >>> approach to parallelize over the blocks in each level, which could be >> >>> done via parfor at script level. >> >>> >> >>> Regards, >> >>> Matthias >> >>> >> >>> On Sat, Apr 21, 2018 at 6:59 PM, Matthias Boehm <mboe...@gmail.com> >> >>> wrote: >> >>> > thanks for catching this - I just ran a toy example and this seems >> to >> >>> > be a rewrite issue (there are specific right indexing rewrites that >> >>> > collapse U[1:k,1:k] and U[1:k,k+1:n] into a single access to U which >> >>> > helps for large distributed matrices). As a workaround, you can set >> >>> > "sysml.optlevel" to 1 (instead of default 2, where 1 disables all >> >>> > rewrites), which worked fine for me. I'll fix this later today. Also >> >>> > I'll fix the naming from "Choleskey" to "Cholesky". Thanks again. >> >>> > >> >>> > Regards, >> >>> > Matthias >> >>> > >> >>> > >> >>> > On Sat, Apr 21, 2018 at 6:28 PM, Qifan Pu <qifan...@gmail.com> >> wrote: >> >>> >> 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://urldefense.proofpoint.com/v2/url?u=https-3A__github. >> com_apache_systemml_blob_master_scripts_staging_scalable- >> 5Flinalg_test_test-5Ftriangular-5Finv.dml&d=DwIBaQ&c=jf_ >> iaSHvJObTbx-siA1ZOg&r=3mYOfURw_FSirAnoSv2pWvLSi1psso >> 4F9RdGjEWL6yc&m=FvqDr_AKzY5EAD_GAXIJoot0Z09NtMUt8kLS >> hXcJxqQ&s=zIEgt74yeZzCTqvLCgV_0J8ECApG541uUlbaGMcK8bs&e= >> >>> >> >> >>> >> >> >>> >> 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://urldefense.proofpoint.com/v2/url?u=https-3A__github. >> com_apache_systemml_blob_master_scripts_staging_scalable- >> 5Flinalg_cholesky.dml&d=DwIBaQ&c=jf_iaSHvJObTbx-siA1ZO >> g&r=3mYOfURw_FSirAnoSv2pWvLSi1psso4F9RdGjEWL6yc&m=FvqDr_ >> AKzY5EAD_GAXIJoot0Z09NtMUt8kLShXcJxqQ&s=Yrj4GGcTlpZGRw34RoON_oO6-xDUti >> IEUcO7-qIOyoc&e= >> >>> >>> >> >>> >>> 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 >> >>> >> >> >>> >> >> >> >> >> >> > >> > >> > >> > >> >> >> >> >> >> >
