# Grid support

33.2 : Constructing a vector on a grid
33.2.1 : Create confirming vector
33.2.2 : Extract vector from DMDA
33.2.3 : Refinement
33.3 : Constructing a matrix on a grid
33.4 : Vectors of a distributed array
33.5 : Matrices of a distributed array

# 33 Grid support

PETSc's DM objects raise the abstraction level from the linear algebra problem to the physics problem: they allow for a more direct expression of operators in terms of their domain of definition. In this section we look at the DMDA distributed array' objects, which correspond to problems defined on Cartesian grids. Distributed arrays make it easier to construct the coefficient matrix of an operator that is defined as a stencil on a 1/2/3-dimensional Cartesian grid .

The main creation routine exists in three variants that mostly differ their number of parameters. For instance, DMDACreate2d has parameters along the \clstinline{x,y} axes. However, DMDACreate1d has no parameter for the stencil type, since in 1D those are all the same, or for the process distribution.

## 33.1 Grid definition

crumb trail: > petsc-dmda > Grid definition

A two-dimensional grid is created with

#include "petscdmda.h"
PetscErrorCode  DMDACreate2d(MPI_Comm comm,
DMBoundaryType bx,DMBoundaryType by,DMDAStencilType stencil_type,
PetscInt M,PetscInt N,PetscInt m,PetscInt n,PetscInt dof,
PetscInt s,const PetscInt lx[],const PetscInt ly[],
DM *da)

Input Parameters

comm - MPI communicator
bx,by - type of ghost nodes: DM_BOUNDARY_NONE, DM_BOUNDARY_GHOSTED, DM_BOUNDARY_PERIODIC.
stencil_type - stencil type: DMDA_STENCIL_BOX or DMDA_STENCIL_STAR.
M,N - global dimension in each direction of
m,n - corresponding number of processors in each dimension (or PETSC_DECIDE)
dof - number of degrees of freedom per node
s - stencil width
lx, ly - arrays containing the number of
nodes in each cell along the x and y coordinates, or NULL.

Output Parameter

da  -the resulting distributed array object

DMDACreate2d

DMDACreate2d( communicator,
x_boundary,y_boundary,
stenciltype,
gridx,gridy, procx,procy, dof,width,
partitionx,partitiony,
grid);

• Boundary type is a value of type DMBoundaryType . Values are:

• DM_BOUNDARY_NONE
• DM_BOUNDARY_GHOSTED ,
• DM_BOUNDARY_PERIODIC ,
• The stencil type is of type DMStencilType , with values

• DM_STENCIL_BOX ,
• DM_STENCIL_STAR .

(See figure  33.1 .)

• The gridx,gridy values are the global grid size. This can be set with commandline options da_grid_x /y/z .
• The procx,procy variables are an explicit specification of the processor grid. Failing this specification, PETSc will try to find a distribution similar to the domain grid.
• dof indicates the number of degrees of freedom', where 1 corresponds to a scalar problem.
• width indicates the extent of the stencil: 1 for a 5-point stencil or more general a 2nd order stencil for 2nd order PDEs , 2 for 2nd order discretizations of a 4th order PDE , et cetera.
• partitionx,partitiony are arrays giving explicit partitionings of the grid over the processors, or PETSC_NULL for default distributions.

After you define a DM object, each process has a contiguous subdomain out of the total grid. You can query its size and location with DMDAGetCorners , or query that and all other information with

#include "petscdmda.h"
PetscErrorCode  DMDAGetLocalInfo(DM da,DMDALocalInfo *info)

DMDAGetLocalInfo , which returns an
typedef struct {
PetscInt         dim,dof,sw;
PetscInt         mx,my,mz;    /* global number of grid points in each direction */
PetscInt         xs,ys,zs;    /* starting point of this processor, excluding ghosts */
PetscInt         xm,ym,zm;    /* number of grid points on this processor, excluding ghosts */
PetscInt         gxs,gys,gzs;    /* starting point of this processor including ghosts */
PetscInt         gxm,gym,gzm;    /* number of grid points on this processor including ghosts */
DMBoundaryType   bx,by,bz; /* type of ghost nodes at boundary */
DMDAStencilType  st;
DM               da;
} DMDALocalInfo;

Fortran Notes - This should be declared as

DMDALocalInfo :: info(DMDA_LOCAL_INFO_SIZE)

and the entries accessed via

info(DMDA_LOCAL_INFO_DIM)
info(DMDA_LOCAL_INFO_DOF) etc.

The entries bx,by,bz, st, and da are not accessible from Fortran.


DMDALocalInfo structure.

(A DMDALocalInfo struct is the same for 1/2/3 dimensions, so certain fields may not be applicable to your specific PDE .)

Using the fields in this structure, each process can now iterate over its own subdomain. For instance, the top left' corner of the owned subdomain is at xs,ys and the number of points is xm,ym (see figure  33.2 ), so we can iterate over the subdomain as:

for (int j=info.ys; j<info.ys+info.ym; j++) {
for (int i=info.xs; i<info.xs+info.xm; i++) {
// actions on point i,j
}
}


On each point of the domain, we describe the stencil at that point. First of all, we now have the information to compute the $x,y$ coordinates of the domain points:

PetscReal
hx = 1. / ( info.mx-1 ),
hy = 1. / ( info.my-1 );
for (int j=info.ys; j<info.ys+info.ym; j++) {
for (int i=info.xs; i<info.xs+info.xm; i++) {
PetscReal x = i*hx, y = j*hy;
...
}
}


## 33.2 Constructing a vector on a grid

crumb trail: > petsc-dmda > Constructing a vector on a grid

A DMDA object is a description of a grid, so we now need to concern how to construct a linear system defined on that grid.

We start with vectors: we need a solution vector and a right-hand side. Here we have two options:

1. we can build a vector from scratch that has the right structure; or
2. we can use the fact that a grid object has a vector that can be extracted.

### 33.2.1 Create confirming vector

crumb trail: > petsc-dmda > Constructing a vector on a grid > Create confirming vector

If we create a vector with VecCreate and VecSetSizes , it is easy to get the global size right, but the default partitioning will probably not be conformal to the grid distribution. Also, getting the indexing scheme right is not trivial.

First of all, the local size needs to be set explicitly, using information from the DMDALocalInfo object:

// dmrhs.c
Vec xy;
ierr = VecCreate(comm,&xy); CHKERRQ(ierr);
ierr = VecSetType(xy,VECMPI); CHKERRQ(ierr);
PetscInt nlocal = info.xm*info.ym, nglobal = info.mx*info.my;
ierr = VecSetSizes(xy,nlocal,nglobal); CHKERRQ(ierr);


After this, you don't use VecSetValues , but set elements directly in the raw array, obtained by DMDAVecGetArray :

PetscReal **xyarray;
DMDAVecGetArray(grid,xy,&xyarray); CHKERRQ(ierr);
for (int j=info.ys; j<info.ys+info.ym; j++) {
for (int i=info.xs; i<info.xs+info.xm; i++) {
PetscReal x = i*hx, y = j*hy;
xyarray[j][i] = x*y;
}
}
DMDAVecRestoreArray(grid,xy,&xyarray); CHKERRQ(ierr);


### 33.2.2 Extract vector from DMDA

crumb trail: > petsc-dmda > Constructing a vector on a grid > Extract vector from DMDA

### 33.2.3 Refinement

crumb trail: > petsc-dmda > Constructing a vector on a grid > Refinement

The routine DMDASetRefinementFactor can be activated with the options da_refine or separately da_refine_x /y/z for the directions.

## 33.3 Constructing a matrix on a grid

crumb trail: > petsc-dmda > Constructing a matrix on a grid

for (int j=info.ys; j<info.ys+info.ym; j++) {
for (int i=info.xs; i<info.xs+info.xm; i++) {
PetscReal x = i*hx, y = j*hy;
...
// set the row, col, v values
ierr = MatSetValuesStencil(A,1,&row,ncols,col,v,INSERT_VALUES);CHKERRQ(ierr);
}
}


Next, we express matrix row/column coordinates in terms of domain coordinates. The row number corresponds to the $(i,j)$ pair:

MatStencil  row;
row.i = i; row.j = j;


For a 5-point stencil we need five column numbers, as well as five element values:

MatStencil col[5];
PetscScalar v[5];
PetscInt    ncols = 0;
/**** diagonal element ****/
col[ncols].i = i; col[ncols].j = j;
v[ncols++] = 4.;


The other legs' of the stencil need to be set conditionally: the connection to $(i-1,j)$ is missing on the top row of the domain, and the connection to $(i,j-1)$ is missing on the left column.

/* if not top row */
if (i>0) {
col[ncols].j = j;   col[ncols].i = i-1;
v[ncols++] = -1.;
}
/* if not left column */
if (j>0) {
col[ncols].j = j-1; col[ncols].i = i;
v[ncols++] = -1.;
}


Ditto for the connections to $(i+1,j)$ and $(i,j+1)$.

## 33.4 Vectors of a distributed array

crumb trail: > petsc-dmda > Vectors of a distributed array

A distributed array is similar to a distributed vector, so there are routines of extracting the values of the array in the form of a vector. This can be done in two ways: of ways. (The routines here actually pertain to the more general DM Data Management' object, but we will for now discuss them in the context of DMDA .)

1. You can create a global' vector, defined on the same communicator as the array, and which is disjointly partitioned in the same manner. This is done with DMCreateGlobalVector :

PetscErrorCode DMCreateGlobalVector(DM dm,Vec *vec)

2. You can create a local' vector, which is sequential and defined on PETSC_COMM_SELF , that has not only the points local to the process, but also the halo' region with the extent specified in the definition of the \clstinline{DMDACreate} call. For this, use DMCreateLocalVector :

PetscErrorCode DMCreateLocalVector(DM dm,Vec *vec)


Values can be moved between local and global vectors by:

• DMGlobalToLocal : this establishes a local vector, including ghost/halo points from a disjointly distributed global vector. (For overlapping communication and computation, use DMGlobalToLocalBegin and DMGlobalToLocalEnd .)
• DMLocalToGlobal : this copies the disjoint parts of a local vector back into a global vector. (For overlapping communication and computation use DMLocalToGlobalBegin and DMLocalToGlobalEnd .)

## 33.5 Matrices of a distributed array

crumb trail: > petsc-dmda > Matrices of a distributed array

Once you have a grid, can create its associated matrix:

DMSetUp(grid);
DMCreateMatrix(grid,&A)


With this subdomain information you can then start to create the coefficient matrix:

DM grid;
PetscInt i_first,j_first,i_local,j_local;
DMDAGetCorners(grid,&i_first,&j_first,NULL,&i_local,&j_local,NULL);
for ( PetscInt i_index=i_first; i_index<i_first+i_local; i_index++) {
for ( PetscInt j_index=j_first; j_index<j_first+j_local; j_index++) {
// construct coefficients for domain point (i_index,j_index)
}
}


Note that indexing here is in terms of the grid, not in terms of the matrix.

For a simple example, consider 1-dimensional smoothing. From DMDAGetCorners we need only the parameters in $i$-direction: \cverbatimsnippet[examples/petsc/c/grid1d.c]{dmda1corners}

We then use a single loop to set elements for the local range in $i$-direction: \cverbatimsnippet[examples/petsc/c/grid1d.c]{dmda1stencil}