[petsc-users] Dense Matrix setting local rows / local columns Question

James A Charles charlesj at purdue.edu
Fri Oct 18 12:35:53 CDT 2013

Thank you all for the responses. I was able to figure out the issue with the dense matrix. I had another question related to dense matrices. Does there exist support for multiplication of a sparse A and dense B matrix matrix multiplication in parallel? I'm using Petsc Release Version 3.4.2 and I am getting an error "No support for this operation for this object type! MatMatMult not supported for B of type mpidense" This makes me think that this is not supported. If it's not, are there plans to support this?  


----- Original Message -----
From: "Matthew Knepley" <knepley at gmail.com>
To: "James A Charles" <charlesj at purdue.edu>
Cc: petsc-users at mcs.anl.gov
Sent: Thursday, October 17, 2013 1:22:36 PM
Subject: Re: [petsc-users] Dense Matrix setting local rows / local columns Question

On Thu, Oct 17, 2013 at 10:02 AM, James A Charles < charlesj at purdue.edu > wrote: 


I'm trying to use a Dense Parallel matrix where there are no nonzeros that is rectangular of size Nxp where N >> p. P is anywhere from 2-6 typically and N can be of size 1E6 or more. 

For this I would like to only distribute the rows along the MPI processes and not have the columns distributed at all (local columns size = global column size). What is the best way to do this in Petsc? If it matters I am using Petsc3.4. 

We do not distribute columns. You could if you used the Elemental implementation, but you do not need that. 

The operations performed after the Matrix allocation are: 

direct call to Lapack for QR factorization via pointer to array. 

You really want Tall-Skinny QR here (TSQR). We have not implemented it, but it is not hard, so if 
you would like to contribute it, that would be great. 

Matrix multiply with an NxN matrix. 

This works. 


I have this working in serial but when I try to distribute my matrices I get columns with size p*(number of processes). 


What most experimenters take for granted before they begin their experiments is infinitely more interesting than any results to which their experiments lead. 
-- Norbert Wiener 

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