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TrilinosWrappers::SparsityPattern Class Reference

#include <trilinos_sparsity_pattern.h>

Inheritance diagram for TrilinosWrappers::SparsityPattern:
[legend]

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)
 
SparsityPatternoperator= (const SparsityPattern &input_sparsity_pattern)
 
void clear ()
 
void compress ()
 
Constructors and initialization using an Epetra_Map description
 SparsityPattern (const Epetra_Map &parallel_partitioning, const size_type n_entries_per_row=0)
 
 SparsityPattern (const Epetra_Map &parallel_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 &parallel_partitioning, const size_type n_entries_per_row=0)
 
void reinit (const Epetra_Map &parallel_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 &parallel_partitioning, const SparsityType &nontrilinos_sparsity_pattern, const bool exchange_data=false)
 
Constructors and initialization using an IndexSet description
 SparsityPattern (const IndexSet &parallel_partitioning, const MPI_Comm &communicator=MPI_COMM_WORLD, const size_type n_entries_per_row=0)
 
 SparsityPattern (const IndexSet &parallel_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 &parallel_partitioning, const MPI_Comm &communicator=MPI_COMM_WORLD, const size_type n_entries_per_row=0)
 
void reinit (const IndexSet &parallel_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 &parallel_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_typelocal_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
 
- Public Member Functions inherited from Subscriptor
 Subscriptor ()
 
 Subscriptor (const Subscriptor &)
 
virtual ~Subscriptor ()
 
Subscriptoroperator= (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
 

Detailed Description

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.

Author
Martin Kronbichler, 2008

Definition at line 300 of file trilinos_sparsity_pattern.h.

Member Typedef Documentation

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.

Constructor & Destructor Documentation

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 $m$ rows and $n$ 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 $m$ rows and $n$ 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.

virtual TrilinosWrappers::SparsityPattern::~SparsityPattern ( )
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.

Member Function Documentation

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 $m$ rows and $n$ 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 $m$ rows and $n$ 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.

template<typename SparsityType >
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.

template<typename SparsityType >
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.

template<typename SparsityType >
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.

template<typename SparsityType >
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.

template<typename SparsityType >
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().

std::pair<size_type, size_type> TrilinosWrappers::SparsityPattern::local_range ( ) const

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().

bool TrilinosWrappers::SparsityPattern::in_local_range ( const size_type  index) const

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.

size_type TrilinosWrappers::SparsityPattern::row_length ( const size_type  row) const

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 $|i-j|$ for which the index pair $(i,j)$ represents a nonzero entry of the matrix. Consequently, the maximum bandwidth a $n\times m$ matrix can have is $\max\{n-1,m-1\}$.

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.

bool TrilinosWrappers::SparsityPattern::exists ( const size_type  i,
const size_type  j 
) const

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.

void TrilinosWrappers::SparsityPattern::add ( const size_type  i,
const size_type  j 
)

Add the element (i,j) to the sparsity pattern.

template<typename ForwardIterator >
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.

Member Data Documentation

std_cxx1x::shared_ptr<Epetra_Map> TrilinosWrappers::SparsityPattern::column_space_map
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.

bool TrilinosWrappers::SparsityPattern::compressed
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.

std_cxx1x::shared_ptr<Epetra_FECrsGraph> TrilinosWrappers::SparsityPattern::graph
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.


The documentation for this class was generated from the following file: