Q. of multi-componet system and data from input file
tsjb00
tsjb00 at hotmail.com
Tue May 13 10:14:38 CDT 2008
Many thanks for the reply! It really helps!
----------------------------------------
> Date: Mon, 12 May 2008 14:33:27 -0500
> From: knepley at gmail.com
> To: petsc-users at mcs.anl.gov
> Subject: Re: Q. of multi-componet system and data from input file
>
> 2008/5/12 tsjb00 :
>>
>> Hi, there! I am a beginner of PETSc and I have some questions about using PETSc to solve for a multi-componet system. The code is supposed to be applicable to different systems, where number of components, properties of components ,etc. would be input for the program.
>>
>> Say I define DA with dof=number of components = nc, number of grid in x,y,z = nx,ny,nz respectively. When I use DA related functions, it seems that by default the data objects (vectors, arrays, etc.) would be of nx*ny*nz*nc. However, some physical variables are independent of specific components, which means I need to handle data objects of nx*ny*nz*integral. My questions are:
>>
>> Does PETSc include tools or examples to deal with such problems?
>
> Make a new DA for those vectors. DA are extremely small since they
> store O(1) data.
>
>> If not, how can I make sure the 'nx*ny*nz*any integral' data objects are distributed over the nodes in a way defined by DA? I am using PETSc_Decide for partitioning right now. I would prefer that at least the number of processors be flexible.
>>
>> I need to read in a property f(x,y,z) from a data file and then distribute the data across different processors. Any suggestions on this would be appreciated. My concern is that if I use MPI_Send/Receive, the data to be transferred might correspond to discontinuous indices due to the partitioning.
>
> If you store that data in PETSc Vec format, you can just use VecLoad()
> and we will distribute everything for you. A
> simple way to do this, is to read it in on 1 process, put it in a Vec,
> and VecView(). Then you can read it back in on
> multiple processes after that.
>
> Matt
>
>> Many thanks in advance!
>>
>> BJ
>>
>> >
>>
>>
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>
>
>
> --
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