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# DMPlex: Unstructured Grids

This chapter introduces the `DMPLEX` subclass of `DM`, which allows the user to handle unstructured grids (or meshes) using the generic `DM` interface for hierarchy and multi-physics. `DMPLEX` was created to remedy a huge problem in all current PDE simulation codes, namely that the discretization was so closely tied to the data layout and solver that switching discretizations in the same code was not possible. Not only does this preclude the kind of comparison that is necessary for scientific investigation, but it makes library (as opposed to monolithic application) development impossible.

## Representing Unstructured Grids

The main advantage of `DMPLEX` in representing topology is that it treats all the different pieces of a mesh, e.g. cells, faces, edges, and vertices, in the same way. This allows the interface to be small and simple, while remaining flexible and general. This also allows “dimension independent programming”, which means that the same algorithm can be used unchanged for meshes of different shapes and dimensions.

All pieces of the mesh (vertices, edges, faces, and cells) are treated as points (or mesh entities), which are each identified by a `PetscInt`. A mesh is built by relating points to other points, in particular specifying a “covering” relation among the points. For example, an edge is defined by being covered by two vertices, and a triangle can be defined by being covered by three edges (or even by three vertices). This structure is known as a Hasse Diagram, which is a Directed Acyclic Graph (DAG) representing a cell complex using the covering relation. The graph edges represent the relation, which also encodes a partially ordered set (poset).

For example, we can encode the doublet mesh as in Fig. 8,

Fig. 8 A 2D doublet mesh, two triangles sharing an edge.

which can also be represented as the DAG in Fig. 9.

Fig. 9 The Hasse diagram for our 2D doublet mesh, expressed as a DAG.

To use the PETSc API, we consecutively number the mesh pieces. The PETSc convention in 3 dimensions is to number first cells, then vertices, then faces, and then edges. In 2 dimensions the convention is to number faces, vertices, and then edges. In terms of the labels in Fig. 8, these numberings are

\[ f_0 \mapsto \mathtt{0}, f_1 \mapsto \mathtt{1}, \\ v_0 \mapsto \mathtt{2}, v_1 \mapsto \mathtt{3}, v_2 \mapsto \mathtt{4}, v_3 \mapsto \mathtt{5}, \\ e_0 \mapsto \mathtt{6}, e_1 \mapsto \mathtt{7}, e_2 \mapsto \mathtt{8}, e_3 \mapsto \mathtt{9}, e_4 \mapsto \mathtt{10} \]

First, we declare the set of points present in a mesh,

    DMPlexSetChart(dm, 0, 11);

Note that a chart here corresponds to a semi-closed interval (e.g \([0,11) = \{0,1,\ldots,10\}\)) specifying the range of indices we’d like to use to define points on the current rank. We then define the covering relation, which we call the cone, which are also the in-edges in the DAG. In order to preallocate correctly, we first provide sizes,

    /* DMPlexSetConeSize(dm, point, number of points that cover the point); */
    DMPlexSetConeSize(dm, 0, 3);
    DMPlexSetConeSize(dm, 1, 3);
    DMPlexSetConeSize(dm, 6, 2);
    DMPlexSetConeSize(dm, 7, 2);
    DMPlexSetConeSize(dm, 8, 2);
    DMPlexSetConeSize(dm, 9, 2);
    DMPlexSetConeSize(dm, 10, 2);
    DMSetUp(dm);

and then point values (recall each point is an integer that represents a single geometric entity, a cell, face, edge, or vertex),

    /* DMPlexSetCone(dm, point, [points that cover the point]); */
    DMPlexSetCone(dm, 0, [6, 7, 8]);
    DMPlexSetCone(dm, 1, [7, 9, 10]);
    DMPlexSetCone(dm, 6, [2, 3]);
    DMPlexSetCone(dm, 7, [3, 4]);
    DMPlexSetCone(dm, 8, [4, 2]);
    DMPlexSetCone(dm, 9, [4, 5]);
    DMPlexSetCone(dm, 10, [5, 3]);

There is also an API for providing the dual relation, using `DMPlexSetSupportSize()` and `DMPlexSetSupport()`, but this can be calculated automatically using the provided `DMPlexSetConeSize()` and `DMPlexSetCone()` information and then calling

    DMPlexSymmetrize(dm);

The “symmetrization” is in the sense of the DAG. Each point knows its covering (cone) and each point knows what it covers (support). Note that when using automatic symmetrization, cones will be ordered but supports will not. The user can enforce an ordering on supports by rewriting them after symmetrization using `DMPlexSetSupport()`.

In order to support efficient queries, we construct fast search structures and indices for the different types of points using

    DMPlexStratify(dm);

## Grid Point Orientations

TODO: fill this out with regard to orientations.

Should probably reference Hapla and summarize what’s there.

## Dealing with Periodicity

Plex allows you to represent periodic domains is two ways. Using the default scheme, periodic topology can be represented directly. This ensures that all topological queries can be satisfied, but then care must be taken in representing functions over the mesh, such as the coordinates. The second method is to use a non-periodic topology, but connect certain mesh points using the local-to-global map for that DM. This allows a more general set of mappings to be implemented, such as partial twists, but topological queries on the periodic boundary cease to function.

For the default scheme, a call to `DMLocalizeCoordinates()` (which usually happens automatically on mesh creation) creates a second, discontinuous coordinate field. These values can be accessed using `DMGetCellCoordinates()` and `DMGetCellCoordinatesLocal()`. Plex provides a convenience method, `DMPlexGetCellCoordinates()`, that extracts cell coordinates correctly, depending on the periodicity of the mesh. An example of its use is shown below:

    const PetscScalar *array;
    PetscScalar       *coords = NULL;
    PetscInt           numCoords;
    PetscBool          isDG;

    PetscCall(DMPlexGetCellCoordinates(dm, cell, &isDG, &numCoords, &array, &coords));
    for (PetscInt cc = 0; cc < numCoords/dim; ++cc) {
      if (cc > 0) PetscCall(PetscPrintf(PETSC_COMM_SELF, " -- "));
      PetscCall(PetscPrintf(PETSC_COMM_SELF, "("));
      for (PetscInt d = 0; d < dim; ++d) {
        if (d > 0) PetscCall(PetscPrintf(PETSC_COMM_SELF, ", "));
        PetscCall(PetscPrintf(PETSC_COMM_SELF, "%g", (double)PetscRealPart(coords[cc * dim + d])));
      }
      PetscCall(PetscPrintf(PETSC_COMM_SELF, ")"));
    }
    PetscCall(PetscPrintf(PETSC_COMM_SELF, "\n"));
    PetscCall(DMPlexRestoreCellCoordinates(dm, cell, &isDG, &numCoords, &array, &coords));

## Connecting Grids to Data Using PetscSection:

A `PetscSection` is used to describe the connection between the grid and data associated with the grid. Specifically, it assigns a number of dofs to each mesh entity on the DAG. See PetscSection: Connecting Grids to Data for more details. Using the mesh from Fig. 8, we provide an example of creating a `PetscSection` for a single field. We can lay out data for a continuous Galerkin \(P_3\) finite element method,

    PetscInt pStart, pEnd, cStart, cEnd, c, vStart, vEnd, v, eStart, eEnd, e;

    DMPlexGetChart(dm, &pStart, &pEnd);
    DMPlexGetHeightStratum(dm, 0, &cStart, &cEnd);   // cells
    DMPlexGetHeightStratum(dm, 1, &eStart, &eEnd);   // edges
    DMPlexGetHeightStratum(dm, 2, &vStart, &vEnd);   // vertices, equivalent to DMPlexGetDepthStratum(dm, 0, &vStart, &vEnd);
    PetscSectionSetChart(s, pStart, pEnd);
    for(c = cStart; c < cEnd; ++c)
        PetscSectionSetDof(s, c, 1);
    for(v = vStart; v < vEnd; ++v)
        PetscSectionSetDof(s, v, 1);
    for(e = eStart; e < eEnd; ++e)
        PetscSectionSetDof(s, e, 2); // two dof on each edge
    PetscSectionSetUp(s);

`DMPlexGetHeightStratum()` returns all the points of the requested height in the DAG. Since this problem is in two dimensions the edges are at height 1 and the vertices at height 2 (the cells are always at height 0). One can also use `DMPlexGetDepthStratum()` to use the depth in the DAG to select the points. `DMPlexGetDepth(dm,&depth)` returns the depth of the DAG, hence `DMPlexGetDepthStratum(dm,depth-1-h,)` returns the same values as `DMPlexGetHeightStratum(dm,h,)`.

For \(P_3\) elements there is one degree of freedom at each vertex, 2 along each edge (resulting in a total of 4 degrees of freedom along each edge including the vertices, thus being able to reproduce a cubic function) and 1 degree of freedom within the cell (the bubble function which is zero along all edges).

Now a PETSc local vector can be created manually using this layout,

    PetscSectionGetStorageSize(s, &n);
    VecSetSizes(localVec, n, PETSC_DETERMINE);
    VecSetFromOptions(localVec);

When working with `DMPLEX` and `PetscFE` (see below) one can simply get the sections (and related vectors) with

    DMSetLocalSection(dm, s);
    DMGetLocalVector(dm, &localVec);
    DMGetGlobalVector(dm, &globalVec);

### DMPlex-specific PetscSection Features:

The following features are built into `PetscSection`. However, their usage and purpose is best understood through `DMPLEX`.

#### Closure:

Closure information can be attached to a `PetscSection` to allow for more efficient closure information queries. This information can either be set directly with `DMPlexCreateClosureIndex()` or generated automatically for a `DMPLEX` via `DMPlexCreateClosureIndex()`.

#### Symmetries: Accessing data from different orientations

While mesh point orientation information specifies how one mesh point is oriented with respect to another, it does not describe how the dofs associated with that mesh point should be permuted for that orientation. This information is supplied via a `PetscSectionSym` object that is attached to the `PetscSection`. Generally the setup and usage of this information is handled automatically by PETSc during setup of a Plex using `PetscFE`.

#### Closure Permutation:

A permutation of the dof closure of a k-cell may be specified. This allows data to be returned in an order that might be more efficiently processed than the default (breadth-first search) ordering. For example, for tensor cells such as quadrilaterals, closure data can be permuted to lexicographic order (i.e. a tensor-product ordering). This is most commonly done via `DMPlexSetClosurePermutationTensor()`. Custom permutations can be set using `PetscSectionSetClosurePermutation()`.

## Data Layout using DMPLEX and PetscFE

A `DM` can automatically create the local section if given a description of the discretization, for example using a `PetscFE` object. We demonstrate this by creating a `PetscFE` that can be configured from the command line. It is a single, scalar field, and is added to the `DM` using `DMSetField()`. When a local or global vector is requested, the `DM` builds the local and global sections automatically.

    DMPlexIsSimplex(dm, &simplex);
    PetscFECreateDefault(PETSC_COMM_SELF, dim, 1, simplex, NULL, -1, &fe);
    DMSetField(dm, 0, NULL, (PetscObject) fe);
    DMCreateDS(dm);

Here the call to `DMSetField()` declares the discretization will have one field with the integer label 0 that has one degree of freedom at each point on the `DMPlex`. To get the \(P_3\) section above, we can either give the option `-petscspace_degree 3`, or call `PetscFECreateLagrange()` and set the degree directly.

## Partitioning and Ordering

In the same way as `MatPartitioning` or `MatGetOrdering()`, give the results of a partitioning or ordering of a graph defined by a sparse matrix, `PetscPartitionerDMPlexPartition` or `DMPlexPermute` are encoded in an `IS`. However, the graph is not the adjacency graph of the matrix but the mesh itself. Once the mesh is partitioned and reordered, the data layout from a `PetscSection` can be used to automatically derive a problem partitioning/ordering.

### Influence of Variables on One Another

The Jacobian of a problem represents the influence of some variable on other variables in the problem. Very often, this influence pattern is determined jointly by the computational mesh and discretization. `DMCreateMatrix()` must compute this pattern when it automatically creates the properly preallocated Jacobian matrix. In `DMDA` the influence pattern, or what we will call variable adjacency, depends only on the stencil since the topology is Cartesian and the discretization is implicitly finite difference.

In `DMPLEX`, we allow the user to specify the adjacency topologically, while maintaining good defaults. The pattern is controlled by two flags. The first flag, `useCone`, indicates whether variables couple first to their boundary [1] and then to neighboring entities, or the reverse. For example, in finite elements, the variables couple to the set of neighboring cells containing the mesh point, and we set the flag to `useCone = PETSC_FALSE`. By contrast, in finite volumes, cell variables first couple to the cell boundary, and then to the neighbors, so we set the flag to `useCone = PETSC_TRUE`. The second flag, `useClosure`, indicates whether we consider the transitive closure of the neighbor relation above, or just a single level. For example, in finite elements, the entire boundary of any cell couples to the interior, and we set the flag to `useClosure = PETSC_TRUE`. By contrast, in most finite volume methods, cells couple only across faces, and not through vertices, so we set the flag to `useClosure = PETSC_FALSE`. However, the power of this method is its flexibility. If we wanted a finite volume method that coupled all cells around a vertex, we could easily prescribe that by changing to `useClosure = PETSC_TRUE`.

## Evaluating Residuals

The evaluation of a residual or Jacobian, for most discretizations has the following general form:

- Traverse the mesh, picking out pieces (which in general overlap),

- Extract some values from the current solution vector, associated with this piece,

- Calculate some values for the piece, and

- Insert these values into the residual vector

DMPlex separates these different concerns by passing sets of points from mesh traversal routines to data extraction routines and back. In this way, the `PetscSection` which structures the data inside a `Vec` does not need to know anything about the mesh inside a `DMPLEX`.

The most common mesh traversal is the transitive closure of a point, which is exactly the transitive closure of a point in the DAG using the covering relation. In other words, the transitive closure consists of all points that cover the given point (generally a cell) plus all points that cover those points, etc. So in 2d the transitive closure for a cell consists of edges and vertices while in 3d it consists of faces, edges, and vertices. Note that this closure can be calculated orienting the arrows in either direction. For example, in a finite element calculation, we calculate an integral over each element, and then sum up the contributions to the basis function coefficients. The closure of the element can be expressed discretely as the transitive closure of the element point in the mesh DAG, where each point also has an orientation. Then we can retrieve the data using `PetscSection` methods,

    PetscScalar *a;
    PetscInt     numPoints, *points = NULL, p;

    VecGetArrayRead(u,&a);
    DMPlexGetTransitiveClosure(dm,cell,PETSC_TRUE,&numPoints,&points);
    for (p = 0; p <= numPoints*2; p += 2) {
      PetscInt dof, off, d;

      PetscSectionGetDof(section, points[p], &dof);
      PetscSectionGetOffset(section, points[p], &off);
      for (d = 0; d <= dof; ++d) {
        myfunc(a[off+d]);
      }
    }
    DMPlexRestoreTransitiveClosure(dm, cell, PETSC_TRUE, &numPoints, &points);
    VecRestoreArrayRead(u, &a);

This operation is so common that we have built a convenience method around it which returns the values in a contiguous array, correctly taking into account the orientations of various mesh points:

    const PetscScalar *values;
    PetscInt           csize;

    DMPlexVecGetClosure(dm, section, u, cell, &csize, &values);
    // Do integral in quadrature loop putting the result into r[]
    DMPlexVecRestoreClosure(dm, section, u, cell, &csize, &values);
    DMPlexVecSetClosure(dm, section, residual, cell, &r, ADD_VALUES);

A simple example of this kind of calculation is in `DMPlexComputeL2Diff_Plex()` (source). Note that there is no restriction on the type of cell or dimension of the mesh in the code above, so it will work for polyhedral cells, hybrid meshes, and meshes of any dimension, without change. We can also reverse the covering relation, so that the code works for finite volume methods where we want the data from neighboring cells for each face:

    PetscScalar *a;
    PetscInt     points[2*2], numPoints, p, dofA, offA, dofB, offB;

    VecGetArray(u,  &a);
    DMPlexGetTransitiveClosure(dm, cell, PETSC_FALSE, &numPoints, &points);
    assert(numPoints == 2);
    PetscSectionGetDof(section, points[0*2], &dofA);
    PetscSectionGetDof(section, points[1*2], &dofB);
    assert(dofA == dofB);
    PetscSectionGetOffset(section, points[0*2], &offA);
    PetscSectionGetOffset(section, points[1*2], &offB);
    myfunc(a[offA], a[offB]);
    VecRestoreArray(u, &a);

This kind of calculation is used in TS Tutorial ex11.

## Saving and Loading DMPlex Data with HDF5

PETSc allows users to save/load `DMPLEX`s representing meshes, `PetscSection`s representing data layouts on the meshes, and `Vec`s defined on the data layouts to/from an HDF5 file in parallel, where one can use different number of processes for saving and for loading.

### Saving

The simplest way to save `DM` data is to use options for configuration. This requires only the code

    DMViewFromOptions(dm, NULL, "-dm_view");
    VecViewFromOptions(vec, NULL, "-vec_view")

along with the command line options

    $ ./myprog -dm_view hdf5:myprog.h5 -vec_view hdf5:myprog.h5::append

Options prefixes can be used to separately control the saving and loading of various fields. However, the user can have finer grained control by explicitly creating the PETSc objects involved. To save data to “example.h5” file, we can first create a `PetscViewer` of type `PETSCVIEWERHDF5` in `FILE_MODE_WRITE` mode as:

    PetscViewer  viewer;

    PetscViewerHDF5Open(PETSC_COMM_WORLD, "example.h5", FILE_MODE_WRITE, &viewer);

As `dm` is a `DMPLEX` object representing a mesh, we first give it a mesh name, “plexA”, and save it as:

    PetscObjectSetName((PetscObject)dm, "plexA");
    PetscViewerPushFormat(viewer, PETSC_VIEWER_HDF5_PETSC);
    DMView(dm, viewer);
    PetscViewerPopFormat(viewer);

The `DMView()` call is shorthand for the following sequence

    DMPlexTopologyView(dm, viewer);
    DMPlexCoordinatesView(dm, viewer);
    DMPlexLabelsView(dm, viewer);

If the mesh name is not explicitly set, the default name is used. In the above `PETSC_VIEWER_HDF5_PETSC` format was used to save the entire representation of the mesh. This format also saves global point numbers attached to the mesh points. In this example the set of all global point numbers is \(X = [0, 11)\).

The data layout, `s`, needs to be wrapped in a `DM` object for it to be saved. Here, we create the wrapping `DM`, `sdm`, with `DMClone()`, give it a dm name, “dmA”, attach `s` to `sdm`, and save it as:

    DMClone(dm, &sdm);
    PetscObjectSetName((PetscObject)sdm, "dmA");
    DMSetLocalSection(sdm, s);
    DMPlexSectionView(dm, viewer, sdm);

If the dm name is not explicitly set, the default name is to be used. In the above, instead of using `DMClone()`, one could also create a new `DMSHELL` object to attach `s` to. The first argument of `DMPlexSectionView()` is a `DMPLEX` object that represents the mesh, and the third argument is a `DM` object that carries the data layout that we would like to save. They are, in general, two different objects, and the former carries a mesh name, while the latter carries a dm name. These names are used to construct a group structure in the HDF5 file. Note that the data layout points are associated with the mesh points, so each of them can also be tagged with a global point number in \(X\); `DMPlexSectionView()` saves these tags along with the data layout itself, so that, when the mesh and the data layout are loaded separately later, one can associate the points in the former with those in the latter by comparing their global point numbers.

We now create a local vector associated with `sdm`, e.g., as:

    Vec  vec;

    DMGetLocalVector(sdm, &vec);

After setting values of `vec`, we name it “vecA” and save it as:

    PetscObjectSetName((PetscObject)vec, "vecA");
    DMPlexLocalVectorView(dm, viewer, sdm, vec);

A global vector can be saved in the exact same way with trivial changes.

After saving, we destroy the `PetscViewer` with:

    PetscViewerDestroy(&viewer);

The output file “example.h5” now looks like the following:

    $ h5dump --contents example.h5
    HDF5 "example.h5" {
    FILE_CONTENTS {
     group      /
     group      /topologies
     group      /topologies/plexA
     group      /topologies/plexA/dms
     group      /topologies/plexA/dms/dmA
     dataset    /topologies/plexA/dms/dmA/order
     group      /topologies/plexA/dms/dmA/section
     dataset    /topologies/plexA/dms/dmA/section/atlasDof
     dataset    /topologies/plexA/dms/dmA/section/atlasOff
     group      /topologies/plexA/dms/dmA/vecs
     group      /topologies/plexA/dms/dmA/vecs/vecA
     dataset    /topologies/plexA/dms/dmA/vecs/vecA/vecA
     group      /topologies/plexA/labels
     group      /topologies/plexA/topology
     dataset    /topologies/plexA/topology/cells
     dataset    /topologies/plexA/topology/cones
     dataset    /topologies/plexA/topology/order
     dataset    /topologies/plexA/topology/orientation
     }
    }

### Saving in the new parallel HDF5 format

Since PETSc 3.19, we offer a new format which supports parallel loading. To write in this format, you currently need to specify it explicitly using the option

    -dm_plex_view_hdf5_storage_version 3.0.0

The storage version is stored in the file and set automatically when loading (described below). You can check the storage version of the HDF5 file with

    $ h5dump -a /dmplex_storage_version example.h5

To allow for simple and efficient implementation, and good load balancing, the 3.0.0 format changes the way the mesh topology is stored. Different strata (sets of mesh entities with an equal dimension; or vertices, edges, faces, and cells) are now stored separately. The new structure of `/topologies/<mesh_name>/topology` is following:

    $ h5dump --contents example.h5
    HDF5 "example.h5" {
    FILE_CONTENTS {
     ...
     group      /topologies/plexA/topology
     dataset    /topologies/plexA/topology/permutation
     group      /topologies/plexA/topology/strata
     group      /topologies/plexA/topology/strata/0
     dataset    /topologies/plexA/topology/strata/0/cone_sizes
     dataset    /topologies/plexA/topology/strata/0/cones
     dataset    /topologies/plexA/topology/strata/0/orientations
     group      /topologies/plexA/topology/strata/1
     dataset    /topologies/plexA/topology/strata/1/cone_sizes
     dataset    /topologies/plexA/topology/strata/1/cones
     dataset    /topologies/plexA/topology/strata/1/orientations
     group      /topologies/plexA/topology/strata/2
     dataset    /topologies/plexA/topology/strata/2/cone_sizes
     dataset    /topologies/plexA/topology/strata/2/cones
     dataset    /topologies/plexA/topology/strata/2/orientations
     group      /topologies/plexA/topology/strata/3
     dataset    /topologies/plexA/topology/strata/3/cone_sizes
     dataset    /topologies/plexA/topology/strata/3/cones
     dataset    /topologies/plexA/topology/strata/3/orientations
     }
    }

Group `/topologies/<mesh_name>/topology/strata` contains a subgroup for each stratum depth (dimension; 0 for vertices up to 3 for cells). DAG points (mesh entities) have an implicit global numbering, given by the position in `orientations` (or `cone_sizes`) plus the stratum offset. The stratum offset is given by a sum of lengths of all previous strata with respect to the order stored in `/topologies/<mesh_name>/topology/permutation`. This global numbering is compatible with the explicit numbering in dataset `topology/order` of previous versions.

For a DAG point with index `i` at depth `s`, `cone_sizes[i]` gives a size of this point’s cone (set of adjacent entities with depth `s-1`). Let `o = sum(cone_sizes[0:i]])` (in Python syntax). Points forming the cone are then given by `cones[o:o+cone_sizes[i]]` (in numbering relative to the depth `s-1`). The orientation of the cone with respect to point `i` is then stored in `orientations[i]`.

### Loading

To load data from “example.h5” file, we create a `PetscViewer` of type `PETSCVIEWERHDF5` in `FILE_MODE_READ` mode as:

    PetscViewerHDF5Open(PETSC_COMM_WORLD, "example.h5", FILE_MODE_READ, &viewer);

We then create a `DMPLEX` object, give it a mesh name, “plexA”, and load the mesh as:

    DMCreate(PETSC_COMM_WORLD, &dm);
    DMSetType(dm, DMPLEX);
    PetscObjectSetName((PetscObject)dm, "plexA");
    PetscViewerPushFormat(viewer, PETSC_VIEWER_HDF5_PETSC);
    DMLoad(dm, viewer);
    PetscViewerPopFormat(viewer);

where `PETSC_VIEWER_HDF5_PETSC` format was again used. The user can have more control by replace the single load call with

    PetscSF  sfO;

    DMCreate(PETSC_COMM_WORLD, &dm);
    DMSetType(dm, DMPLEX);
    PetscObjectSetName((PetscObject)dm, "plexA");
    PetscViewerPushFormat(viewer, PETSC_VIEWER_HDF5_PETSC);
    DMPlexTopologyLoad(dm, viewer, &sfO);
    DMPlexCoordinatesLoad(dm, viewer, sfO);
    PetscViewerPopFormat(viewer);

The object returned by `DMPlexTopologyLoad()`, `sfO`, is a `PetscSF` that pushes forward \(X\) to the loaded mesh, `dm`; this `PetscSF` is constructed with the global point number tags that we saved along with the mesh points.

As the `DMPLEX` mesh just loaded might not have a desired distribution, it is common to redistribute the mesh for a better distribution using `DMPlexDistribute()`, e.g., as:

    DM        distributedDM;
    PetscInt  overlap = 1;
    PetscSF   sfDist, sf;

    DMPlexDistribute(dm, overlap, &sfDist, &distributedDM);
    if (distributedDM) {
      DMDestroy(&dm);
      dm = distributedDM;
      PetscObjectSetName((PetscObject)dm, "plexA");
    }
    PetscSFCompose(sfO, sfDist, &sf);
    PetscSFDestroy(&sfO);
    PetscSFDestroy(&sfDist);

Note that the new `DMPLEX` does not automatically inherit the mesh name, so we need to name it “plexA” once again. `sfDist` is a `PetscSF` that pushes forward the loaded mesh to the redistributed mesh, so, composed with `sfO`, it makes the `PetscSF` that pushes forward \(X\) directly to the redistributed mesh, which we call `sf`.

We then create a new `DM`, `sdm`, with `DMClone()`, give it a dm name, “dmA”, and load the on-disk data layout into `sdm` as:

    PetscSF  globalDataSF, localDataSF;

    DMClone(dm, &sdm);
    PetscObjectSetName((PetscObject)sdm, "dmA");
    DMPlexSectionLoad(dm, viewer, sdm, sf, &globalDataSF, &localDataSF);

where we could also create a new `DMSHELL` object instead of using `DMClone()`. Each point in the on-disk data layout being tagged with a global point number in \(X\), `DMPlexSectionLoad()` internally constructs a `PetscSF` that pushes forward the on-disk data layout to \(X\). Composing this with `sf`, `DMPlexSectionLoad()` internally constructs another `PetscSF` that pushes forward the on-disk data layout directly to the redistributed mesh. It then reconstructs the data layout `s` on the redistributed mesh and attaches it to `sdm`. The objects returned by this function, `globalDataSF` and `localDataSF`, are `PetscSF`s that can be used to migrate the on-disk vector data into local and global `Vec`s defined on `sdm`.

We now create a local vector associated with `sdm`, e.g., as:

    Vec  vec;

    DMGetLocalVector(sdm, &vec);

We then name `vec` “vecA” and load the on-disk vector into `vec` as:

    PetscObjectSetName((PetscObject)vec, "vecA");
    DMPlexLocalVectorLoad(dm, viewer, sdm, localDataSF, localVec);

where `localDataSF` knows how to migrate the on-disk vector data into a local `Vec` defined on `sdm`. The on-disk vector can be loaded into a global vector associated with `sdm` in the exact same way with trivial changes.

After loading, we destroy the `PetscViewer` with:

    PetscViewerDestroy(&viewer);

The above infrastructure works seamlessly in distributed-memory parallel settings, in which one can even use different number of processes for saving and for loading; a more comprehensive example is found in DMPlex Tutorial ex12.

## Metric-based mesh adaptation

`DMPLEX` supports mesh adaptation using the Riemannian metric framework. The idea is to use a Riemannian metric space within the mesher. The metric space dictates how mesh resolution should be distributed across the domain. Using this information, the remesher transforms the mesh such that it is a unit mesh when viewed in the metric space. That is, the image of each of its elements under the mapping from Euclidean space into the metric space has edges of unit length.

One of the main advantages of metric-based mesh adaptation is that it allows for fully anisotropic remeshing. That is, it provides a means of controlling the shape and orientation of elements in the adapted mesh, as well as their size. This can be particularly useful for advection-dominated and directionally-dependent problems.

See [Ala10] for further details on metric-based anisotropic mesh adaptation.

The two main ingredients for metric-based mesh adaptation are an input mesh (i.e. the `DMPLEX`) and a Riemannian metric. The implementation in PETSc assumes that the metric is piecewise linear and continuous across elemental boundaries. Such an object can be created using the routine

    DMPlexMetricCreate(DM dm, PetscInt field, Vec *metric);

A metric must be symmetric positive-definite, so that distances may be properly defined. This can be checked using

    DMPlexMetricEnforceSPD(DM dm, Vec metricIn, PetscBool restrictSizes, PetscBool restrictAnisotropy, Vec metricOut, Vec determinant);

This routine may also be used to enforce minimum and maximum tolerated metric magnitudes (i.e. cell sizes), as well as maximum anisotropy. These quantities can be specified using

    DMPlexMetricSetMinimumMagnitude(DM dm, PetscReal h_min);
    DMPlexMetricSetMaximumMagnitude(DM dm, PetscReal h_max);
    DMPlexMetricSetMaximumAnisotropy(DM dm, PetscReal a_max);

or the command line arguments

    -dm_plex_metric_h_min <h_min>
    -dm_plex_metric_h_max <h_max>
    -dm_plex_metric_a_max <a_max>

One simple way to combine two metrics is by simply averaging them entry-by-entry. Another is to intersect them, which amounts to choosing the greatest level of refinement in each direction. These operations are available in PETSc through the routines

    DMPlexMetricAverage(DM dm, PetscInt numMetrics, PetscReal weights[], Vec metrics[], Vec metricAvg);
    DMPlexMetricIntersection(DM dm, PetscInt numMetrics, Vec metrics[], Vec metricInt);

However, before combining metrics, it is important that they are scaled in the same way. Scaling also allows the user to control the number of vertices in the adapted mesh (in an approximate sense). This is achieved using the \(L^p\) normalization framework, with the routine

    DMPlexMetricNormalize(DM dm, Vec metricIn, PetscBool restrictSizes, PetscBool restrictAnisotropy, Vec metricOut, Vec determinant);

There are two important parameters for the normalization: the normalization order \(p\) and the target metric complexity, which is analogous to the vertex count. They are controlled using

    DMPlexMetricSetNormalizationOrder(DM dm, PetscReal p);
    DMPlexMetricSetTargetComplexity(DM dm, PetscReal target);

or the command line arguments

    -dm_plex_metric_p <p>
    -dm_plex_metric_target_complexity <target>

Two different metric-based mesh adaptation tools are available in PETSc:

- Pragmatic;

- Mmg/ParMmg.

Mmg is a serial package, whereas ParMmg is the MPI version. Note that surface meshing is not currently supported and that ParMmg works only in three dimensions. Mmg can be used for both two and three dimensional problems. Pragmatic, Mmg and ParMmg may be specified by the command line arguments

    -dm_adaptor pragmatic
    -dm_adaptor mmg
    -dm_adaptor parmmg

Once a metric has been constructed, it can be used to perform metric-based mesh adaptation using the routine

    DMAdaptMetric(DM dm, Vec metric, DMLabel bdLabel, DMLabel rgLabel, DM dmAdapt);

where `bdLabel` and `rgLabel` are boundary and interior tags to be preserved under adaptation, respectively.

Footnotes

[Ala10]

Frédéric Alauzet. Metric-based anisotropic mesh adaptation. https://pages.saclay.inria.fr/frederic.alauzet/cours/cea2010_V3.pdf, 2010.

[1]

In three dimensions, the boundary of a cell (sometimes called an element) is its faces, the boundary of a face is its edges and the boundary of an edge is the two vertices.