Reference documentation for deal.II version Git 58efed1 20180220 08:43:45 0500

Namespaces  
ChunkSparsityPatternIterators  
DynamicSparsityPatternIterators  
SparsityPatternIterators  
SparsityTools  
Enumerations  
enum  SparsityTools::Partitioner { SparsityTools::Partitioner::metis = 0, SparsityTools::Partitioner::zoltan } 
Almost all finite element formulations lead to matrices that are "sparse", i.e., for which the number of nonzero elements per row is (i) relatively small compared to the overall size of the matrix, and (ii) bounded by a fixed number that does not grow if the mesh is refined. For such cases, it is more efficient to not store all elements of the matrix, but only those that are actually (or may be) nonzero. This requires storing, for each row, the column indices of the nonzero entries (we call this the "sparsity pattern") as well as the actual values of these nonzero entries. (In practice, it sometimes happens that some of the nonzero values are, in fact, zero. Sparsity patterns and sparse matrices only intend to provision space for entries that may be nonzero, and do so at a time when we don't know yet what values these entries will ultimately have; they may have a zero value if a coefficient or cell happens to have particular values.)
In deal.II, sparsity patterns are typically separated from the actual sparse matrices (with the exception of the SparseMatrixEZ class and some classes from interfaces to external libraries such as PETSc). The reason is that one often has several matrices that share the same sparsity pattern; examples include the stiffness and mass matrices necessary for time stepping schemes, or the left and right hand side matrix of generalized eigenvalue problems. It would therefore be wasteful if each of them had to store their sparsity pattern separately.
Consequently, deal.II has sparsity pattern classes that matrix classes build on. There are two main groups of sparsity pattern classes, as discussed below:
The main sparse matrix class in deal.II, SparseMatrix, only stores a value for each matrix entry, but not where these entries are located. For this, it relies on the information it gets from a sparsity pattern object associated with this matrix. This sparsity pattern object must be of type SparsityPattern.
Because matrices are large objects and because it is comparatively expensive to change them, SparsityPattern objects are built in two phases: first, in a "dynamic" phase, one allocates positions where one expects matrices built on it to have nonzero entries; in a second "static" phase, the representation of these nonzero locations is "compressed" into the usual Compressed Sparse Row (CSR) format. After this, no new nonzero locations may be added. Only after compression can a sparsity pattern be associated to a matrix, since the latter requires the efficient compressed data format of the former. Building a sparsity pattern during the dynamic phase often happens with the DoFTools::make_sparsity_pattern() function. Although this may appear a restriction, it is typically not a significant problem to first build a sparsity pattern and then to write into the matrix only in the previously allocated locations, since in finite element codes it is normally quite clear which elements of a matrix can possibly be nonzero and which are definitely zero.
The advantage of this twophase generation of a sparsity pattern is that when it is actually used with a matrix, a very efficient format is available. In particular, the locations of entries are stored in a linear array that allows for rapid access friendly to modern CPU types with deep hierarchies of caches. Consequently, the static SparsityPattern class is the only one on which deal.II's main SparseMatrix class can work.
The main drawback of static sparsity patterns is that their efficient construction requires a reasonably good guess how many entries each of the rows may maximally have. During the actual construction, for example in the DoFTools::make_sparsity_pattern() function, only at most as many entries can be allocated as previously stated. This is a problem because it is often difficult to estimate the maximal number of entries per row. Consequently, a common strategy is to first build and intermediate sparsity pattern that uses a less efficient storage scheme during construction of the sparsity pattern and later copy it directly into the static, compressed form. Most tutorial programs do this, starting at step2 (see also, for example the step11, step18, and step27 tutorial programs).
As explained above, it is often complicated to obtain good estimates for the maximal number of entries in each row of a sparsity pattern. Consequently, any attempts to allocate a regular SparsityPattern with bad estimates requires huge amounts of memory, almost all of which will not be used and be deallocated upon compression.
To avoid this, deal.II contains a "dynamic" or "compressed" sparsity pattern called DynamicSparsityPattern that only allocates as much memory as necessary to hold the currently added entries. While this saves much memory compared to the worstcase behavior mentioned above, it requires the use of less efficient storage schemes for insertion of elements, and the frequent allocation of memory often also takes significant compute time. The tradeoff to avoid excessive memory allocation cannot be avoided, however.
The class is typically used in the following way
* DynamicSparsityPattern dsp (dof_handler.n_dofs()); * DoFTools::make_sparsity_pattern (dof_handler, * dsp); * constraints.condense (dsp); * * SparsityPattern final_sparsity_pattern; * final_sparsity_pattern.copy_from (dsp); *
The intermediate, compressed sparsity pattern is directly copied into the "compressed" form of the final static pattern.
The class BlockDynamicSparsityPattern implements an array of dynamic sparsity patterns for constructing block matrices. See the documentation and step22 for more information.

strong 
Enumerator with options for partitioner
Enumerator  

metis 
Use METIS partitioner. 
zoltan 
Use ZOLTAN partitioner. 
Definition at line 53 of file sparsity_tools.h.