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?  

Thanks,
James

----- Original Message -----
From: "Matthew Knepley" <[email protected]>
To: "James A Charles" <[email protected]>
Cc: [email protected]
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 < [email protected] > 
wrote: 




Hello, 

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. 


Matt 


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

Thanks, 
James 










-- 
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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