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Reference documentation for deal.II version 8.1.0
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#include <trilinos_sparsity_pattern.h>
Public Types | |
typedef ::types::global_dof_index | size_type |
typedef SparsityPatternIterators::Iterator | const_iterator |
Public Member Functions | |
DeclException1 (ExcTrilinosError, int,<< "An error with error number "<< arg1<< " occurred while calling a Trilinos function") | |
DeclException2 (ExcInvalidIndex, size_type, size_type,<< "The entry with index <"<< arg1<< ','<< arg2<< "> does not exist.") | |
DeclException0 (ExcSourceEqualsDestination) | |
DeclException4 (ExcAccessToNonLocalElement, size_type, size_type, size_type, size_type,<< "You tried to access element ("<< arg1<< "/"<< arg2<< ")"<< " of a distributed matrix, but only rows "<< arg3<< " through "<< arg4<< " are stored locally and can be accessed.") | |
DeclException2 (ExcAccessToNonPresentElement, size_type, size_type,<< "You tried to access element ("<< arg1<< "/"<< arg2<< ")"<< " of a sparse matrix, but it appears to not"<< " exist in the Trilinos sparsity pattern.") | |
Basic constructors and initalization. | |
SparsityPattern () | |
SparsityPattern (const size_type m, const size_type n, const size_type n_entries_per_row=0) | |
SparsityPattern (const size_type m, const size_type n, const std::vector< size_type > &n_entries_per_row) | |
SparsityPattern (const SparsityPattern &input_sparsity_pattern) | |
virtual | ~SparsityPattern () |
void | reinit (const size_type m, const size_type n, const size_type n_entries_per_row=0) |
void | reinit (const size_type m, const size_type n, const std::vector< size_type > &n_entries_per_row) |
void | copy_from (const SparsityPattern &input_sparsity_pattern) |
template<typename SparsityType > | |
void | copy_from (const SparsityType &nontrilinos_sparsity_pattern) |
SparsityPattern & | operator= (const SparsityPattern &input_sparsity_pattern) |
void | clear () |
void | compress () |
Constructors and initialization using an Epetra_Map description | |
SparsityPattern (const Epetra_Map ¶llel_partitioning, const size_type n_entries_per_row=0) | |
SparsityPattern (const Epetra_Map ¶llel_partitioning, const std::vector< size_type > &n_entries_per_row) | |
SparsityPattern (const Epetra_Map &row_parallel_partitioning, const Epetra_Map &col_parallel_partitioning, const size_type n_entries_per_row=0) | |
SparsityPattern (const Epetra_Map &row_parallel_partitioning, const Epetra_Map &col_parallel_partitioning, const std::vector< size_type > &n_entries_per_row) | |
void | reinit (const Epetra_Map ¶llel_partitioning, const size_type n_entries_per_row=0) |
void | reinit (const Epetra_Map ¶llel_partitioning, const std::vector< size_type > &n_entries_per_row) |
void | reinit (const Epetra_Map &row_parallel_partitioning, const Epetra_Map &col_parallel_partitioning, const size_type n_entries_per_row=0) |
void | reinit (const Epetra_Map &row_parallel_partitioning, const Epetra_Map &col_parallel_partitioning, const std::vector< size_type > &n_entries_per_row) |
template<typename SparsityType > | |
void | reinit (const Epetra_Map &row_parallel_partitioning, const Epetra_Map &col_parallel_partitioning, const SparsityType &nontrilinos_sparsity_pattern, const bool exchange_data=false) |
template<typename SparsityType > | |
void | reinit (const Epetra_Map ¶llel_partitioning, const SparsityType &nontrilinos_sparsity_pattern, const bool exchange_data=false) |
Constructors and initialization using an IndexSet description | |
SparsityPattern (const IndexSet ¶llel_partitioning, const MPI_Comm &communicator=MPI_COMM_WORLD, const size_type n_entries_per_row=0) | |
SparsityPattern (const IndexSet ¶llel_partitioning, const MPI_Comm &communicator, const std::vector< size_type > &n_entries_per_row) | |
SparsityPattern (const IndexSet &row_parallel_partitioning, const IndexSet &col_parallel_partitioning, const MPI_Comm &communicator=MPI_COMM_WORLD, const size_type n_entries_per_row=0) | |
SparsityPattern (const IndexSet &row_parallel_partitioning, const IndexSet &col_parallel_partitioning, const MPI_Comm &communicator, const std::vector< size_type > &n_entries_per_row) | |
void | reinit (const IndexSet ¶llel_partitioning, const MPI_Comm &communicator=MPI_COMM_WORLD, const size_type n_entries_per_row=0) |
void | reinit (const IndexSet ¶llel_partitioning, const MPI_Comm &communicator, const std::vector< size_type > &n_entries_per_row) |
void | reinit (const IndexSet &row_parallel_partitioning, const IndexSet &col_parallel_partitioning, const MPI_Comm &communicator=MPI_COMM_WORLD, const size_type n_entries_per_row=0) |
void | reinit (const IndexSet &row_parallel_partitioning, const IndexSet &col_parallel_partitioning, const MPI_Comm &communicator, const std::vector< size_type > &n_entries_per_row) |
template<typename SparsityType > | |
void | reinit (const IndexSet &row_parallel_partitioning, const IndexSet &col_parallel_partitioning, const SparsityType &nontrilinos_sparsity_pattern, const MPI_Comm &communicator=MPI_COMM_WORLD, const bool exchange_data=false) |
template<typename SparsityType > | |
void | reinit (const IndexSet ¶llel_partitioning, const SparsityType &nontrilinos_sparsity_pattern, const MPI_Comm &communicator=MPI_COMM_WORLD, const bool exchange_data=false) |
Information on the sparsity pattern | |
bool | is_compressed () const |
unsigned int | max_entries_per_row () const |
size_type | n_rows () const |
size_type | n_cols () const |
unsigned int | local_size () const |
std::pair< size_type, size_type > | local_range () const |
bool | in_local_range (const size_type index) const |
size_type | n_nonzero_elements () const |
size_type | row_length (const size_type row) const |
size_type | bandwidth () const |
bool | empty () const |
bool | exists (const size_type i, const size_type j) const |
std::size_t | memory_consumption () const |
Adding entries | |
void | add (const size_type i, const size_type j) |
template<typename ForwardIterator > | |
void | add_entries (const size_type row, ForwardIterator begin, ForwardIterator end, const bool indices_are_sorted=false) |
Access of underlying Trilinos data | |
const Epetra_FECrsGraph & | trilinos_sparsity_pattern () const |
const Epetra_Map & | domain_partitioner () const |
const Epetra_Map & | range_partitioner () const |
const Epetra_Map & | row_partitioner () const |
const Epetra_Map & | col_partitioner () const |
const Epetra_Comm & | trilinos_communicator () const |
Iterators | |
const_iterator | begin () const |
const_iterator | end () const |
const_iterator | begin (const size_type r) const |
const_iterator | end (const size_type r) const |
Input/Output | |
void | write_ascii () |
void | print (std::ostream &out, const bool write_extended_trilinos_info=false) const |
void | print_gnuplot (std::ostream &out) const |
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Subscriptor () | |
Subscriptor (const Subscriptor &) | |
virtual | ~Subscriptor () |
Subscriptor & | operator= (const Subscriptor &) |
void | subscribe (const char *identifier=0) const |
void | unsubscribe (const char *identifier=0) const |
unsigned int | n_subscriptions () const |
void | list_subscribers () const |
DeclException3 (ExcInUse, int, char *, std::string &,<< "Object of class "<< arg2<< " is still used by "<< arg1<< " other objects.\n"<< "(Additional information: "<< arg3<< ")\n"<< "Note the entry in the Frequently Asked Questions of "<< "deal.II (linked to from http://www.dealii.org/) for "<< "more information on what this error means.") | |
DeclException2 (ExcNoSubscriber, char *, char *,<< "No subscriber with identifier \""<< arg2<< "\" did subscribe to this object of class "<< arg1) | |
template<class Archive > | |
void | serialize (Archive &ar, const unsigned int version) |
Private Attributes | |
std_cxx1x::shared_ptr< Epetra_Map > | column_space_map |
bool | compressed |
std_cxx1x::shared_ptr< Epetra_FECrsGraph > | graph |
Friends | |
class | SparsityPatternIterators::Accessor |
class | SparsityPatternIterators::Iterator |
This class implements a wrapper class to use the Trilinos distributed sparsity pattern class Epetra_FECrsGraph. This class is designed to be used for construction of parallel Trilinos matrices. The functionality of this class is modeled after the existing sparsity pattern classes, with the difference that this class can work fully in parallel according to a partitioning of the sparsity pattern rows.
This class has many similarities to the compressed sparsity pattern classes of deal.II (i.e., the classes CompressedSparsityPattern, CompressedSetSparsityPattern, and CompressedSimpleSparsityPattern), since it can dynamically add elements to the pattern without any memory being previously reserved for it. However, it also has a method SparsityPattern::compress(), that finalizes the pattern and enables its use with Trilinos sparse matrices.
Definition at line 300 of file trilinos_sparsity_pattern.h.
Declare type for container size.
Definition at line 307 of file trilinos_sparsity_pattern.h.
Declare a typedef for the iterator class.
Definition at line 313 of file trilinos_sparsity_pattern.h.
TrilinosWrappers::SparsityPattern::SparsityPattern | ( | ) |
Default constructor. Generates an empty (zero-size) sparsity pattern.
TrilinosWrappers::SparsityPattern::SparsityPattern | ( | const size_type | m, |
const size_type | n, | ||
const size_type | n_entries_per_row = 0 |
||
) |
Generate a sparsity pattern that is completely stored locally, having rows and
columns. The resulting matrix will be completely stored locally, too.
It is possible to specify the number of columns entries per row using the optional n_entries_per_row
argument. However, this value does not need to be accurate or even given at all, since one does usually not have this kind of information before building the sparsity pattern (the usual case when the function DoFTools::make_sparsity_pattern() is called). The entries are allocated dynamically in a similar manner as for the deal.II CompressedSparsityPattern classes. However, a good estimate will reduce the setup time of the sparsity pattern.
TrilinosWrappers::SparsityPattern::SparsityPattern | ( | const size_type | m, |
const size_type | n, | ||
const std::vector< size_type > & | n_entries_per_row | ||
) |
Generate a sparsity pattern that is completely stored locally, having rows and
columns. The resulting matrix will be completely stored locally, too.
The vector n_entries_per_row
specifies the number of entries in each row (an information usually not available, though).
TrilinosWrappers::SparsityPattern::SparsityPattern | ( | const SparsityPattern & | input_sparsity_pattern | ) |
Copy constructor. Sets the calling sparsity pattern to be the same as the input sparsity pattern.
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virtual |
Destructor. Made virtual so that one can use pointers to this class.
TrilinosWrappers::SparsityPattern::SparsityPattern | ( | const Epetra_Map & | parallel_partitioning, |
const size_type | n_entries_per_row = 0 |
||
) |
Constructor for a square sparsity pattern using an Epetra_map for the description of the parallel partitioning. Moreover, the number of nonzero entries in the rows of the sparsity pattern can be specified. Note that this number does not need to be exact, and it is allowed that the actual sparsity structure has more nonzero entries than specified in the constructor (the usual case when the function DoFTools::make_sparsity_pattern() is called). However it is still advantageous to provide good estimates here since a good value will avoid repeated allocation of memory, which considerably increases the performance when creating the sparsity pattern.
TrilinosWrappers::SparsityPattern::SparsityPattern | ( | const Epetra_Map & | parallel_partitioning, |
const std::vector< size_type > & | n_entries_per_row | ||
) |
Same as before, but now use the exact number of nonzeros in each m row. Since we know the number of elements in the sparsity pattern exactly in this case, we can already allocate the right amount of memory, which makes the creation process by the respective SparsityPattern::reinit call considerably faster. However, this is a rather unusual situation, since knowing the number of entries in each row is usually connected to knowing the indices of nonzero entries, which the sparsity pattern is designed to describe.
TrilinosWrappers::SparsityPattern::SparsityPattern | ( | const Epetra_Map & | row_parallel_partitioning, |
const Epetra_Map & | col_parallel_partitioning, | ||
const size_type | n_entries_per_row = 0 |
||
) |
This constructor is similar to the one above, but it now takes two different Epetra maps for rows and columns. This interface is meant to be used for generating rectangular sparsity pattern, where one map describes the parallel partitioning of the dofs associated with the sparsity pattern rows and the other one of the sparsity pattern columns. Note that there is no real parallelism along the columns – the processor that owns a certain row always owns all the column elements, no matter how far they might be spread out. The second Epetra_Map is only used to specify the number of columns and for specifying the correct domain space when performing matrix-vector products with vectors based on the same column map.
The number of columns entries per row is specified as the maximum number of entries argument.
TrilinosWrappers::SparsityPattern::SparsityPattern | ( | const Epetra_Map & | row_parallel_partitioning, |
const Epetra_Map & | col_parallel_partitioning, | ||
const std::vector< size_type > & | n_entries_per_row | ||
) |
This constructor is similar to the one above, but it now takes two different Epetra maps for rows and columns. This interface is meant to be used for generating rectangular matrices, where one map specifies the parallel distribution of rows and the second one specifies the distribution of degrees of freedom associated with matrix columns. This second map is however not used for the distribution of the columns themselves – rather, all column elements of a row are stored on the same processor. The vector n_entries_per_row
specifies the number of entries in each row of the newly generated matrix.
TrilinosWrappers::SparsityPattern::SparsityPattern | ( | const IndexSet & | parallel_partitioning, |
const MPI_Comm & | communicator = MPI_COMM_WORLD , |
||
const size_type | n_entries_per_row = 0 |
||
) |
Constructor for a square sparsity pattern using an IndexSet and an MPI communicator for the description of the parallel partitioning. Moreover, the number of nonzero entries in the rows of the sparsity pattern can be specified. Note that this number does not need to be exact, and it is even allowed that the actual sparsity structure has more nonzero entries than specified in the constructor. However it is still advantageous to provide good estimates here since a good value will avoid repeated allocation of memory, which considerably increases the performance when creating the sparsity pattern.
TrilinosWrappers::SparsityPattern::SparsityPattern | ( | const IndexSet & | parallel_partitioning, |
const MPI_Comm & | communicator, | ||
const std::vector< size_type > & | n_entries_per_row | ||
) |
Same as before, but now use the exact number of nonzeros in each m row. Since we know the number of elements in the sparsity pattern exactly in this case, we can already allocate the right amount of memory, which makes the creation process by the respective SparsityPattern::reinit call considerably faster. However, this is a rather unusual situation, since knowing the number of entries in each row is usually connected to knowing the indices of nonzero entries, which the sparsity pattern is designed to describe.
TrilinosWrappers::SparsityPattern::SparsityPattern | ( | const IndexSet & | row_parallel_partitioning, |
const IndexSet & | col_parallel_partitioning, | ||
const MPI_Comm & | communicator = MPI_COMM_WORLD , |
||
const size_type | n_entries_per_row = 0 |
||
) |
This constructor is similar to the one above, but it now takes two different index sets to describe the parallel partitioning of rows and columns. This interface is meant to be used for generating rectangular sparsity pattern. Note that there is no real parallelism along the columns – the processor that owns a certain row always owns all the column elements, no matter how far they might be spread out. The second Epetra_Map is only used to specify the number of columns and for internal arragements when doing matrix-vector products with vectors based on that column map.
The number of columns entries per row is specified as the maximum number of entries argument.
TrilinosWrappers::SparsityPattern::SparsityPattern | ( | const IndexSet & | row_parallel_partitioning, |
const IndexSet & | col_parallel_partitioning, | ||
const MPI_Comm & | communicator, | ||
const std::vector< size_type > & | n_entries_per_row | ||
) |
This constructor is similar to the one above, but it now takes two different index sets for rows and columns. This interface is meant to be used for generating rectangular matrices, where one map specifies the parallel distribution of rows and the second one specifies the distribution of degrees of freedom associated with matrix columns. This second map is however not used for the distribution of the columns themselves – rather, all column elements of a row are stored on the same processor. The vector n_entries_per_row
specifies the number of entries in each row of the newly generated matrix.
void TrilinosWrappers::SparsityPattern::reinit | ( | const size_type | m, |
const size_type | n, | ||
const size_type | n_entries_per_row = 0 |
||
) |
Initialize a sparsity pattern that is completely stored locally, having rows and
columns. The resulting matrix will be completely stored locally.
The number of columns entries per row is specified as the maximum number of entries argument. This does not need to be an accurate number since the entries are allocated dynamically in a similar manner as for the deal.II CompressedSparsityPattern classes, but a good estimate will reduce the setup time of the sparsity pattern.
void TrilinosWrappers::SparsityPattern::reinit | ( | const size_type | m, |
const size_type | n, | ||
const std::vector< size_type > & | n_entries_per_row | ||
) |
Initialize a sparsity pattern that is completely stored locally, having rows and
columns. The resulting matrix will be completely stored locally.
The vector n_entries_per_row
specifies the number of entries in each row.
void TrilinosWrappers::SparsityPattern::copy_from | ( | const SparsityPattern & | input_sparsity_pattern | ) |
Copy function. Sets the calling sparsity pattern to be the same as the input sparsity pattern.
void TrilinosWrappers::SparsityPattern::copy_from | ( | const SparsityType & | nontrilinos_sparsity_pattern | ) |
Copy function from one of the deal.II sparsity patterns. If used in parallel, this function uses an ad-hoc partitioning of the rows and columns.
SparsityPattern& TrilinosWrappers::SparsityPattern::operator= | ( | const SparsityPattern & | input_sparsity_pattern | ) |
Copy operator. This operation is only allowed for empty objects, to avoid potentially very costly operations automatically synthesized by the compiler. Use copy_from() instead if you know that you really want to copy a sparsity pattern with non-trivial content.
void TrilinosWrappers::SparsityPattern::clear | ( | ) |
Release all memory and return to a state just like after having called the default constructor.
This is a collective operation that needs to be called on all processors in order to avoid a dead lock.
void TrilinosWrappers::SparsityPattern::compress | ( | ) |
In analogy to our own SparsityPattern class, this function compresses the sparsity pattern and allows the resulting pattern to be used for actually generating a (Trilinos-based) matrix. This function also exchanges non-local data that might have accumulated during the addition of new elements. This function must therefore be called once the structure is fixed. This is a collective operation, i.e., it needs to be run on all processors when used in parallel.
void TrilinosWrappers::SparsityPattern::reinit | ( | const Epetra_Map & | parallel_partitioning, |
const size_type | n_entries_per_row = 0 |
||
) |
Reinitialization function for generating a square sparsity pattern using an Epetra_Map for the description of the parallel partitioning and the number of nonzero entries in the rows of the sparsity pattern. Note that this number does not need to be exact, and it is even allowed that the actual sparsity structure has more nonzero entries than specified in the constructor. However it is still advantageous to provide good estimates here since this will considerably increase the performance when creating the sparsity pattern.
This function does not create any entries by itself, but provides the correct data structures that can be used by the respective add() function.
void TrilinosWrappers::SparsityPattern::reinit | ( | const Epetra_Map & | parallel_partitioning, |
const std::vector< size_type > & | n_entries_per_row | ||
) |
Same as before, but now use the exact number of nonzeros in each m row. Since we know the number of elements in the sparsity pattern exactly in this case, we can already allocate the right amount of memory, which makes process of adding entries to the sparsity pattern considerably faster. However, this is a rather unusual situation, since knowing the number of entries in each row is usually connected to knowing the indices of nonzero entries, which the sparsity pattern is designed to describe.
void TrilinosWrappers::SparsityPattern::reinit | ( | const Epetra_Map & | row_parallel_partitioning, |
const Epetra_Map & | col_parallel_partitioning, | ||
const size_type | n_entries_per_row = 0 |
||
) |
This reinit function is similar to the one above, but it now takes two different Epetra maps for rows and columns. This interface is meant to be used for generating rectangular sparsity pattern, where one map describes the parallel partitioning of the dofs associated with the sparsity pattern rows and the other one of the sparsity pattern columns. Note that there is no real parallelism along the columns – the processor that owns a certain row always owns all the column elements, no matter how far they might be spread out. The second Epetra_Map is only used to specify the number of columns and for internal arragements when doing matrix-vector products with vectors based on that column map.
The number of columns entries per row is specified by the argument n_entries_per_row
.
void TrilinosWrappers::SparsityPattern::reinit | ( | const Epetra_Map & | row_parallel_partitioning, |
const Epetra_Map & | col_parallel_partitioning, | ||
const std::vector< size_type > & | n_entries_per_row | ||
) |
This reinit function is similar to the one above, but it now takes two different Epetra maps for rows and columns. This interface is meant to be used for generating rectangular matrices, where one map specifies the parallel distribution of rows and the second one specifies the distribution of degrees of freedom associated with matrix columns. This second map is however not used for the distribution of the columns themselves – rather, all column elements of a row are stored on the same processor. The vector n_entries_per_row
specifies the number of entries in each row of the newly generated matrix.
void TrilinosWrappers::SparsityPattern::reinit | ( | const Epetra_Map & | row_parallel_partitioning, |
const Epetra_Map & | col_parallel_partitioning, | ||
const SparsityType & | nontrilinos_sparsity_pattern, | ||
const bool | exchange_data = false |
||
) |
Reinit function. Takes one of the deal.II sparsity patterns and a parallel partitioning of the rows and columns for initializing the current Trilinos sparsity pattern. The optional argument exchange_data
can be used for reinitialization with a sparsity pattern that is not fully constructed. This feature is only implemented for input sparsity patterns of type CompressedSimpleSparsityPattern.
void TrilinosWrappers::SparsityPattern::reinit | ( | const Epetra_Map & | parallel_partitioning, |
const SparsityType & | nontrilinos_sparsity_pattern, | ||
const bool | exchange_data = false |
||
) |
Reinit function. Takes one of the deal.II sparsity patterns and a parallel partitioning of the rows and columns for initializing the current Trilinos sparsity pattern. The optional argument exchange_data
can be used for reinitialization with a sparsity pattern that is not fully constructed. This feature is only implemented for input sparsity patterns of type CompressedSimpleSparsityPattern.
void TrilinosWrappers::SparsityPattern::reinit | ( | const IndexSet & | parallel_partitioning, |
const MPI_Comm & | communicator = MPI_COMM_WORLD , |
||
const size_type | n_entries_per_row = 0 |
||
) |
Reinitialization function for generating a square sparsity pattern using an IndexSet and an MPI communicator for the description of the parallel partitioning and the number of nonzero entries in the rows of the sparsity pattern. Note that this number does not need to be exact, and it is even allowed that the actual sparsity structure has more nonzero entries than specified in the constructor. However it is still advantageous to provide good estimates here since this will considerably increase the performance when creating the sparsity pattern.
This function does not create any entries by itself, but provides the correct data structures that can be used by the respective add() function.
void TrilinosWrappers::SparsityPattern::reinit | ( | const IndexSet & | parallel_partitioning, |
const MPI_Comm & | communicator, | ||
const std::vector< size_type > & | n_entries_per_row | ||
) |
Same as before, but now use the exact number of nonzeros in each m row. Since we know the number of elements in the sparsity pattern exactly in this case, we can already allocate the right amount of memory, which makes process of adding entries to the sparsity pattern considerably faster. However, this is a rather unusual situation, since knowing the number of entries in each row is usually connected to knowing the indices of nonzero entries, which the sparsity pattern is designed to describe.
void TrilinosWrappers::SparsityPattern::reinit | ( | const IndexSet & | row_parallel_partitioning, |
const IndexSet & | col_parallel_partitioning, | ||
const MPI_Comm & | communicator = MPI_COMM_WORLD , |
||
const size_type | n_entries_per_row = 0 |
||
) |
This reinit function is similar to the one above, but it now takes two different index sets for rows and columns. This interface is meant to be used for generating rectangular sparsity pattern, where one index set describes the parallel partitioning of the dofs associated with the sparsity pattern rows and the other one of the sparsity pattern columns. Note that there is no real parallelism along the columns – the processor that owns a certain row always owns all the column elements, no matter how far they might be spread out. The second IndexSet is only used to specify the number of columns and for internal arragements when doing matrix-vector products with vectors based on an EpetraMap based on that IndexSet.
The number of columns entries per row is specified by the argument n_entries_per_row
.
void TrilinosWrappers::SparsityPattern::reinit | ( | const IndexSet & | row_parallel_partitioning, |
const IndexSet & | col_parallel_partitioning, | ||
const MPI_Comm & | communicator, | ||
const std::vector< size_type > & | n_entries_per_row | ||
) |
Same as before, but now using a vector n_entries_per_row
for specifying the number of entries in each row of the sparsity pattern.
void TrilinosWrappers::SparsityPattern::reinit | ( | const IndexSet & | row_parallel_partitioning, |
const IndexSet & | col_parallel_partitioning, | ||
const SparsityType & | nontrilinos_sparsity_pattern, | ||
const MPI_Comm & | communicator = MPI_COMM_WORLD , |
||
const bool | exchange_data = false |
||
) |
Reinit function. Takes one of the deal.II sparsity patterns and the parallel partitioning of the rows and columns specified by two index sets and a parallel communicator for initializing the current Trilinos sparsity pattern. The optional argument exchange_data
can be used for reinitialization with a sparsity pattern that is not fully constructed. This feature is only implemented for input sparsity patterns of type CompressedSimpleSparsityPattern.
void TrilinosWrappers::SparsityPattern::reinit | ( | const IndexSet & | parallel_partitioning, |
const SparsityType & | nontrilinos_sparsity_pattern, | ||
const MPI_Comm & | communicator = MPI_COMM_WORLD , |
||
const bool | exchange_data = false |
||
) |
Reinit function. Takes one of the deal.II sparsity patterns and a parallel partitioning of the rows and columns for initializing the current Trilinos sparsity pattern. The optional argument exchange_data
can be used for reinitialization with a sparsity pattern that is not fully constructed. This feature is only implemented for input sparsity patterns of type CompressedSimpleSparsityPattern.
bool TrilinosWrappers::SparsityPattern::is_compressed | ( | ) | const |
Returns the state of the sparsity pattern, i.e., whether compress() needs to be called after an operation requiring data exchange.
unsigned int TrilinosWrappers::SparsityPattern::max_entries_per_row | ( | ) | const |
Gives the maximum number of entries per row on the current processor.
size_type TrilinosWrappers::SparsityPattern::n_rows | ( | ) | const |
Return the number of rows in this sparsity pattern.
size_type TrilinosWrappers::SparsityPattern::n_cols | ( | ) | const |
Return the number of columns in this sparsity pattern.
unsigned int TrilinosWrappers::SparsityPattern::local_size | ( | ) | const |
Return the local dimension of the sparsity pattern, i.e. the number of rows stored on the present MPI process. In the sequential case, this number is the same as n_rows(), but for parallel matrices it may be smaller.
To figure out which elements exactly are stored locally, use local_range().
Return a pair of indices indicating which rows of this sparsity pattern are stored locally. The first number is the index of the first row stored, the second the index of the one past the last one that is stored locally. If this is a sequential matrix, then the result will be the pair (0,n_rows()), otherwise it will be a pair (i,i+n), where n=local_size()
.
Return whether index
is in the local range or not, see also local_range().
size_type TrilinosWrappers::SparsityPattern::n_nonzero_elements | ( | ) | const |
Return the number of nonzero elements of this sparsity pattern.
Number of entries in a specific row.
size_type TrilinosWrappers::SparsityPattern::bandwidth | ( | ) | const |
Compute the bandwidth of the matrix represented by this structure. The bandwidth is the maximum of for which the index pair
represents a nonzero entry of the matrix. Consequently, the maximum bandwidth a
matrix can have is
.
bool TrilinosWrappers::SparsityPattern::empty | ( | ) | const |
Return whether the object is empty. It is empty if no memory is allocated, which is the same as when both dimensions are zero.
Return whether the index (i,j) exists in the sparsity pattern (i.e., it may be non-zero) or not.
std::size_t TrilinosWrappers::SparsityPattern::memory_consumption | ( | ) | const |
Determine an estimate for the memory consumption (in bytes) of this object. Currently not implemented for this class.
Add the element (i,j) to the sparsity pattern.
void TrilinosWrappers::SparsityPattern::add_entries | ( | const size_type | row, |
ForwardIterator | begin, | ||
ForwardIterator | end, | ||
const bool | indices_are_sorted = false |
||
) |
Add several elements in one row to the sparsity pattern.
const Epetra_FECrsGraph& TrilinosWrappers::SparsityPattern::trilinos_sparsity_pattern | ( | ) | const |
Return a const reference to the underlying Trilinos Epetra_CrsGraph data that stores the sparsity pattern.
const Epetra_Map& TrilinosWrappers::SparsityPattern::domain_partitioner | ( | ) | const |
Return a const reference to the underlying Trilinos Epetra_Map that sets the parallel partitioning of the domain space of this sparsity pattern, i.e., the partitioning of the vectors matrices based on this sparsity pattern are multiplied with.
const Epetra_Map& TrilinosWrappers::SparsityPattern::range_partitioner | ( | ) | const |
Return a const reference to the underlying Trilinos Epetra_Map that sets the partitioning of the range space of this sparsity pattern, i.e., the partitioning of the vectors that are result from matrix-vector products.
const Epetra_Map& TrilinosWrappers::SparsityPattern::row_partitioner | ( | ) | const |
Return a const reference to the underlying Trilinos Epetra_Map that sets the partitioning of the sparsity pattern rows. Equal to the partitioning of the range.
const Epetra_Map& TrilinosWrappers::SparsityPattern::col_partitioner | ( | ) | const |
Return a const reference to the underlying Trilinos Epetra_Map that sets the partitioning of the sparsity pattern columns. This is in general not equal to the partitioner Epetra_Map for the domain because of overlap in the matrix.
const Epetra_Comm& TrilinosWrappers::SparsityPattern::trilinos_communicator | ( | ) | const |
Return a const reference to the communicator used for this object.
const_iterator TrilinosWrappers::SparsityPattern::begin | ( | ) | const |
STL-like iterator with the first entry.
const_iterator TrilinosWrappers::SparsityPattern::end | ( | ) | const |
Final iterator.
const_iterator TrilinosWrappers::SparsityPattern::begin | ( | const size_type | r | ) | const |
STL-like iterator with the first entry of row r
.
Note that if the given row is empty, i.e. does not contain any nonzero entries, then the iterator returned by this function equals end(r)
. Note also that the iterator may not be dereferencable in that case.
const_iterator TrilinosWrappers::SparsityPattern::end | ( | const size_type | r | ) | const |
Final iterator of row r
. It points to the first element past the end of line r
, or past the end of the entire sparsity pattern.
Note that the end iterator is not necessarily dereferencable. This is in particular the case if it is the end iterator for the last row of a matrix.
void TrilinosWrappers::SparsityPattern::write_ascii | ( | ) |
Abstract Trilinos object that helps view in ASCII other Trilinos objects. Currently this function is not implemented. TODO: Not implemented.
void TrilinosWrappers::SparsityPattern::print | ( | std::ostream & | out, |
const bool | write_extended_trilinos_info = false |
||
) | const |
Print (the locally owned part of) the sparsity pattern to the given stream, using the format (line,col)
. The optional flag outputs the sparsity pattern in Trilinos style, where even the according processor number is printed to the stream, as well as a summary before actually writing the entries.
void TrilinosWrappers::SparsityPattern::print_gnuplot | ( | std::ostream & | out | ) | const |
Print the sparsity of the matrix in a format that gnuplot
understands and which can be used to plot the sparsity pattern in a graphical way. The format consists of pairs i j
of nonzero elements, each representing one entry of this matrix, one per line of the output file. Indices are counted from zero on, as usual. Since sparsity patterns are printed in the same way as matrices are displayed, we print the negative of the column index, which means that the (0,0)
element is in the top left rather than in the bottom left corner.
Print the sparsity pattern in gnuplot by setting the data style to dots or points and use the plot
command.
|
private |
Pointer to the user-supplied Epetra Trilinos mapping of the matrix columns that assigns parts of the matrix to the individual processes.
Definition at line 1380 of file trilinos_sparsity_pattern.h.
|
private |
A boolean variable to hold information on whether the vector is compressed or not.
Definition at line 1387 of file trilinos_sparsity_pattern.h.
|
private |
A sparsity pattern object in Trilinos to be used for finite element based problems which allows for adding non-local elements to the pattern.
Definition at line 1396 of file trilinos_sparsity_pattern.h.