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//
// Copyright (C) 2008 - 2015 by the deal.II authors
//
// This file is part of the deal.II library.
//
// The deal.II library is free software; you can use it, redistribute
// it, and/or modify it under the terms of the GNU Lesser General
// Public License as published by the Free Software Foundation; either
// version 2.1 of the License, or (at your option) any later version.
// The full text of the license can be found in the file LICENSE at
// the top level of the deal.II distribution.
//
// ---------------------------------------------------------------------
#ifndef dealii__trilinos_sparsity_pattern_h
#define dealii__trilinos_sparsity_pattern_h
#include <deal.II/base/config.h>
#ifdef DEAL_II_WITH_TRILINOS
# include <deal.II/base/subscriptor.h>
# include <deal.II/base/index_set.h>
# include <deal.II/lac/exceptions.h>
# include <vector>
# include <cmath>
# include <memory>
# include <deal.II/base/std_cxx11/shared_ptr.h>
DEAL_II_DISABLE_EXTRA_DIAGNOSTICS
# include <Epetra_FECrsGraph.h>
# include <Epetra_Map.h>
# ifdef DEAL_II_WITH_MPI
# include <Epetra_MpiComm.h>
# include "mpi.h"
# else
# include "Epetra_SerialComm.h"
# endif
DEAL_II_ENABLE_EXTRA_DIAGNOSTICS
DEAL_II_NAMESPACE_OPEN
// forward declarations
class SparsityPattern;
class DynamicSparsityPattern;
namespace TrilinosWrappers
{
// forward declarations
class SparsityPattern;
namespace SparsityPatternIterators
{
// forward declaration
class Iterator;
/**
* Accessor class for iterators into sparsity patterns. This class is also
* the base class for both const and non-const accessor classes into
* sparse matrices.
*
* Note that this class only allows read access to elements, providing
* their row and column number. It does not allow modifying the sparsity
* pattern itself.
*
* @ingroup TrilinosWrappers
* @author Wolfgang Bangerth, Martin Kronbichler, Guido Kanschat
* @date 2004, 2008, 2012
*/
class Accessor
{
public:
/**
* Declare type for container size.
*/
typedef dealii::types::global_dof_index size_type;
/**
* Constructor.
*/
Accessor (const SparsityPattern *sparsity_pattern,
const size_type row,
const size_type index);
/**
* Copy constructor.
*/
Accessor (const Accessor &a);
/**
* Row number of the element represented by this object.
*/
size_type row() const;
/**
* Index in row of the element represented by this object.
*/
size_type index() const;
/**
* Column number of the element represented by this object.
*/
size_type column() const;
/**
* Exception
*/
DeclException0 (ExcBeyondEndOfSparsityPattern);
/**
* Exception
*/
DeclException3 (ExcAccessToNonlocalRow,
size_type, size_type, size_type,
<< "You tried to access row " << arg1
<< " of a distributed sparsity pattern, "
<< " but only rows " << arg2 << " through " << arg3
<< " are stored locally and can be accessed.");
private:
/**
* The matrix accessed.
*/
mutable SparsityPattern *sparsity_pattern;
/**
* Current row number.
*/
size_type a_row;
/**
* Current index in row.
*/
size_type a_index;
/**
* Cache where we store the column indices of the present row. This is
* necessary, since Trilinos makes access to the elements of its
* matrices rather hard, and it is much more efficient to copy all
* column entries of a row once when we enter it than repeatedly asking
* Trilinos for individual ones. This also makes some sense since it is
* likely that we will access them sequentially anyway.
*
* In order to make copying of iterators/accessor of acceptable
* performance, we keep a shared pointer to these entries so that more
* than one accessor can access this data if necessary.
*/
std_cxx11::shared_ptr<const std::vector<size_type> > colnum_cache;
/**
* Discard the old row caches (they may still be used by other
* accessors) and generate new ones for the row pointed to presently by
* this accessor.
*/
void visit_present_row ();
/**
* Make enclosing class a friend.
*/
friend class Iterator;
};
/**
* Iterator class for sparsity patterns of type
* TrilinosWrappers::SparsityPattern. Access to individual elements of the
* sparsity pattern is handled by the Accessor class in this namespace.
*/
class Iterator
{
public:
/**
* Declare type for container size.
*/
typedef dealii::types::global_dof_index size_type;
/**
* Constructor. Create an iterator into the matrix @p matrix for the
* given row and the index within it.
*/
Iterator (const SparsityPattern *sparsity_pattern,
const size_type row,
const size_type index);
/**
* Copy constructor.
*/
Iterator (const Iterator &i);
/**
* Prefix increment.
*/
Iterator &operator++ ();
/**
* Postfix increment.
*/
Iterator operator++ (int);
/**
* Dereferencing operator.
*/
const Accessor &operator* () const;
/**
* Dereferencing operator.
*/
const Accessor *operator-> () const;
/**
* Comparison. True, if both iterators point to the same matrix
* position.
*/
bool operator == (const Iterator &) const;
/**
* Inverse of <tt>==</tt>.
*/
bool operator != (const Iterator &) const;
/**
* Comparison operator. Result is true if either the first row number is
* smaller or if the row numbers are equal and the first index is
* smaller.
*/
bool operator < (const Iterator &) const;
/**
* Exception
*/
DeclException2 (ExcInvalidIndexWithinRow,
size_type, size_type,
<< "Attempt to access element " << arg2
<< " of row " << arg1
<< " which doesn't have that many elements.");
private:
/**
* Store an object of the accessor class.
*/
Accessor accessor;
friend class TrilinosWrappers::SparsityPattern;
};
}
/**
* 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 DynamicSparsityPattern, 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.
*
* @ingroup TrilinosWrappers
* @ingroup Sparsity
* @author Martin Kronbichler, 2008
*/
class SparsityPattern : public Subscriptor
{
public:
/**
* Declare type for container size.
*/
typedef dealii::types::global_dof_index size_type;
/**
* Declare a typedef for the iterator class.
*/
typedef SparsityPatternIterators::Iterator const_iterator;
/**
* @name Basic constructors and initialization.
*/
//@{
/**
* Default constructor. Generates an empty (zero-size) sparsity pattern.
*/
SparsityPattern ();
/**
* 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 @p 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 DynamicSparsityPattern classes. However, a good
* estimate will reduce the setup time of the sparsity pattern.
*/
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.
*
* The vector <tt>n_entries_per_row</tt> specifies the number of entries
* in each row (an information usually not available, though).
*/
SparsityPattern (const size_type m,
const size_type n,
const std::vector<size_type> &n_entries_per_row);
/**
* Copy constructor. Sets the calling sparsity pattern to be the same as
* the input sparsity pattern.
*/
SparsityPattern (const SparsityPattern &input_sparsity_pattern);
/**
* Destructor. Made virtual so that one can use pointers to this class.
*/
virtual ~SparsityPattern ();
/**
* 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 DynamicSparsityPattern classes, but a good estimate
* will reduce the setup time of the sparsity pattern.
*/
void
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 vector <tt>n_entries_per_row</tt> specifies the number of entries
* in each row.
*/
void
reinit (const size_type m,
const size_type n,
const std::vector<size_type> &n_entries_per_row);
/**
* Copy function. Sets the calling sparsity pattern to be the same as the
* input sparsity pattern.
*/
void
copy_from (const SparsityPattern &input_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.
*/
template<typename SparsityPatternType>
void
copy_from (const SparsityPatternType &nontrilinos_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.
*/
SparsityPattern &operator = (const SparsityPattern &input_sparsity_pattern);
/**
* 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 clear ();
/**
* 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 compress ();
//@}
/**
* @name Constructors and initialization using an Epetra_Map description
*/
//@{
/**
* 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.
*
* @deprecated Use the respective method with IndexSet argument instead.
*/
SparsityPattern (const Epetra_Map ¶llel_partitioning,
const size_type n_entries_per_row = 0) DEAL_II_DEPRECATED;
/**
* 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.
*
* @deprecated Use the respective method with IndexSet argument instead.
*/
SparsityPattern (const Epetra_Map ¶llel_partitioning,
const std::vector<size_type> &n_entries_per_row) DEAL_II_DEPRECATED;
/**
* 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.
*
* @deprecated Use the respective method with IndexSet argument instead.
*/
SparsityPattern (const Epetra_Map &row_parallel_partitioning,
const Epetra_Map &col_parallel_partitioning,
const size_type n_entries_per_row = 0) DEAL_II_DEPRECATED;
/**
* 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 <tt>n_entries_per_row</tt> specifies the
* number of entries in each row of the newly generated matrix.
*
* @deprecated Use the respective method with IndexSet argument instead.
*/
SparsityPattern (const Epetra_Map &row_parallel_partitioning,
const Epetra_Map &col_parallel_partitioning,
const std::vector<size_type> &n_entries_per_row) DEAL_II_DEPRECATED;
/**
* 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.
*
* @deprecated Use the respective method with IndexSet argument instead.
*/
void
reinit (const Epetra_Map ¶llel_partitioning,
const size_type n_entries_per_row = 0) DEAL_II_DEPRECATED;
/**
* 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.
*
* @deprecated Use the respective method with IndexSet argument instead.
*/
void
reinit (const Epetra_Map ¶llel_partitioning,
const std::vector<size_type> &n_entries_per_row) DEAL_II_DEPRECATED;
/**
* 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 arrangements when doing matrix-vector products with vectors
* based on that column map.
*
* The number of columns entries per row is specified by the argument
* <tt>n_entries_per_row</tt>.
*
* @deprecated Use the respective method with IndexSet argument instead.
*/
void
reinit (const Epetra_Map &row_parallel_partitioning,
const Epetra_Map &col_parallel_partitioning,
const size_type n_entries_per_row = 0) DEAL_II_DEPRECATED;
/**
* 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 <tt>n_entries_per_row</tt> specifies the
* number of entries in each row of the newly generated matrix.
*
* @deprecated Use the respective method with IndexSet argument instead.
*/
void
reinit (const Epetra_Map &row_parallel_partitioning,
const Epetra_Map &col_parallel_partitioning,
const std::vector<size_type> &n_entries_per_row) DEAL_II_DEPRECATED;
/**
* 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 @p
* 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 DynamicSparsityPattern.
*
* @deprecated Use the respective method with IndexSet argument instead.
*/
template<typename SparsityPatternType>
void
reinit (const Epetra_Map &row_parallel_partitioning,
const Epetra_Map &col_parallel_partitioning,
const SparsityPatternType &nontrilinos_sparsity_pattern,
const bool exchange_data = false) DEAL_II_DEPRECATED;
/**
* 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 @p
* 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 DynamicSparsityPattern.
*
* @deprecated Use the respective method with IndexSet argument instead.
*/
template<typename SparsityPatternType>
void
reinit (const Epetra_Map ¶llel_partitioning,
const SparsityPatternType &nontrilinos_sparsity_pattern,
const bool exchange_data = false) DEAL_II_DEPRECATED;
//@}
/**
* @name Constructors and initialization using an IndexSet description
*/
//@{
/**
* 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.
*/
SparsityPattern (const IndexSet ¶llel_partitioning,
const MPI_Comm &communicator = MPI_COMM_WORLD,
const size_type n_entries_per_row = 0);
/**
* 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.
*/
SparsityPattern (const IndexSet ¶llel_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 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 arrangements 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.
*/
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 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 <tt>n_entries_per_row</tt> specifies the
* number of entries in each row of the newly generated matrix.
*/
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 constructs general sparsity patterns, possible non-
* square ones. Constructing a sparsity pattern this way allows the user
* to explicitly specify the rows into which we are going to add elements.
* This set is required to be a superset of the first index set @p
* row_parallel_partitioning that includes also rows that are owned by
* another processor (ghost rows). Note that elements can only be added to
* rows specified by @p writable_rows.
*
* This method is beneficial when the rows to which a processor is going
* to write can be determined before actually inserting elements into the
* matrix. For the typical parallel::distributed::Triangulation class used
* in deal.II, we know that a processor only will add row elements for
* what we call the locally relevant dofs (see
* DoFTools::extract_locally_relevant_dofs). The other constructors
* methods use general Trilinos facilities that allow to add elements to
* arbitrary rows (as done by all the other reinit functions). However,
* this flexibility come at a cost, the most prominent being that adding
* elements into the same matrix from multiple threads in shared memory is
* not safe whenever MPI is used. For these settings, the current method
* is the one to choose: It will store the off-processor data as an
* additional sparsity pattern (that is then passed to the Trilinos matrix
* via the reinit mehtod) which can be organized in such a way that
* thread-safety can be ensured (as long as the user makes sure to never
* write into the same matrix row simultaneously, of course).
*/
SparsityPattern (const IndexSet &row_parallel_partitioning,
const IndexSet &col_parallel_partitioning,
const IndexSet &writable_rows,
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
reinit (const IndexSet ¶llel_partitioning,
const MPI_Comm &communicator = MPI_COMM_WORLD,
const size_type n_entries_per_row = 0);
/**
* 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
reinit (const IndexSet ¶llel_partitioning,
const MPI_Comm &communicator,
const std::vector<size_type> &n_entries_per_row);
/**
* 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
* arrangements 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
* <tt>n_entries_per_row</tt>.
*/
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);
/**
* This reinit function is used to specify general matrices, possibly non-
* square ones. In addition to the arguments of the other reinit method
* above, it allows the user to explicitly specify the rows into which we
* are going to add elements. This set is a superset of the first index
* set @p row_parallel_partitioning that includes also rows that are owned
* by another processor (ghost rows).
*
* This method is beneficial when the rows to which a processor is going
* to write can be determined before actually inserting elements into the
* matrix. For the typical parallel::distributed::Triangulation class used
* in deal.II, we know that a processor only will add row elements for
* what we call the locally relevant dofs (see
* DoFTools::extract_locally_relevant_dofs). Trilinos matrices allow to
* add elements to arbitrary rows (as done by all the other reinit
* functions) and this is what all the other reinit methods do, too.
* However, this flexibility come at a cost, the most prominent being that
* adding elements into the same matrix from multiple threads in shared
* memory is not safe whenever MPI is used. For these settings, the
* current method is the one to choose: It will store the off-processor
* data as an additional sparsity pattern (that is then passed to the
* Trilinos matrix via the reinit method) which can be organized in such a
* way that thread-safety can be ensured (as long as the user makes sure
* to never write into the same matrix row simultaneously, of course).
*/
void
reinit (const IndexSet &row_parallel_partitioning,
const IndexSet &col_parallel_partitioning,
const IndexSet &writeable_rows,
const MPI_Comm &communicator = MPI_COMM_WORLD,
const size_type n_entries_per_row = 0);
/**
* Same as before, but now using a vector <tt>n_entries_per_row</tt> for
* specifying the number of entries in each row of the sparsity pattern.
*/
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);
/**
* 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 @p 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 DynamicSparsityPattern.
*/
template<typename SparsityPatternType>
void
reinit (const IndexSet &row_parallel_partitioning,
const IndexSet &col_parallel_partitioning,
const SparsityPatternType &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 @p
* 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 DynamicSparsityPattern.
*/
template<typename SparsityPatternType>
void
reinit (const IndexSet ¶llel_partitioning,
const SparsityPatternType &nontrilinos_sparsity_pattern,
const MPI_Comm &communicator = MPI_COMM_WORLD,
const bool exchange_data = false);
//@}
/**
* @name Information on the sparsity pattern
*/
//@{
/**
* Returns the state of the sparsity pattern, i.e., whether compress()
* needs to be called after an operation requiring data exchange.
*/
bool is_compressed () const;
/**
* Gives the maximum number of entries per row on the current processor.
*/
unsigned int max_entries_per_row () const;
/**
* Return the number of rows in this sparsity pattern.
*/
size_type n_rows () const;
/**
* Return the number of columns in this sparsity pattern.
*/
size_type n_cols () 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().
*/
unsigned int local_size () 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
* <tt>n=local_size()</tt>.
*/
std::pair<size_type, size_type>
local_range () const;
/**
* Return whether @p index is in the local range or not, see also
* local_range().
*/
bool in_local_range (const size_type index) const;
/**
* Return the number of nonzero elements of this sparsity pattern.
*/
size_type n_nonzero_elements () const;
/**
* Number of entries in a specific row.
*/
size_type row_length (const size_type row) 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 $\max\{n-1,m-1\}$.
*/
size_type bandwidth () 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 empty () const;
/**
* Return whether the index (<i>i,j</i>) exists in the sparsity pattern
* (i.e., it may be non-zero) or not.
*/
bool exists (const size_type i,
const size_type j) const;
/**
* Determine an estimate for the memory consumption (in bytes) of this
* object. Currently not implemented for this class.
*/
std::size_t memory_consumption () const;
//@}
/**
* @name Adding entries
*/
//@{
/**
* Add the element (<i>i,j</i>) to the sparsity pattern.
*/
void add (const size_type i,
const size_type j);
/**
* Add several elements in one row to the sparsity pattern.
*/
template <typename ForwardIterator>
void add_entries (const size_type row,
ForwardIterator begin,
ForwardIterator end,
const bool indices_are_sorted = false);
//@}
/**
* @name Access of underlying Trilinos data
*/
//@{
/**
* Return a const reference to the underlying Trilinos Epetra_CrsGraph
* data that stores the sparsity pattern.
*/
const Epetra_FECrsGraph &trilinos_sparsity_pattern () 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.
*
* @deprecated Use locally_owned_domain_indices() instead.
*/
const Epetra_Map &domain_partitioner () const DEAL_II_DEPRECATED;
/**
* 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.
*
* @deprecated Use locally_owned_range_indices() instead.
*/
const Epetra_Map &range_partitioner () const DEAL_II_DEPRECATED;
/**
* 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.
*
* @deprecated Use locally_owned_range_indices() instead.
*/
const Epetra_Map &row_partitioner () const DEAL_II_DEPRECATED;
/**
* 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.
*
* @deprecated Usually not necessary. If desired, access via the
* Epetra_FECrsGraph.
*/
const Epetra_Map &col_partitioner () const DEAL_II_DEPRECATED;
/**
* Return a const reference to the communicator used for this object.
*
* @deprecated Use get_mpi_communicator instead.
*/
const Epetra_Comm &trilinos_communicator () const DEAL_II_DEPRECATED;
/**
* Return the MPI communicator object in use with this matrix.
*/
MPI_Comm get_mpi_communicator () const;
//@}
/**
* @name Partitioners
*/
//@{
/**
* Return the partitioning of the domain space of this pattern, i.e., the
* partitioning of the vectors a matrix based on this sparsity pattern has
* to be multiplied with.
*/
IndexSet locally_owned_domain_indices() const;
/**
* Return the partitioning of the range space of this pattern, i.e., the
* partitioning of the vectors that are the result from matrix-vector
* products from a matrix based on this pattern.
*/
IndexSet locally_owned_range_indices() const;
//@}
/**
* @name Iterators
*/
//@{
/**
* Iterator starting at the first entry.
*/
const_iterator begin () const;
/**
* Final iterator.
*/
const_iterator end () const;
/**
* Iterator starting at the first entry of row @p 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
* <tt>end(r)</tt>. Note also that the iterator may not be dereferencable
* in that case.
*/
const_iterator begin (const size_type r) const;
/**
* Final iterator of row <tt>r</tt>. It points to the first element past
* the end of line @p 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.
*/
const_iterator end (const size_type r) const;
//@}
/**
* @name Input/Output
*/
//@{
/**
* Abstract Trilinos object that helps view in ASCII other Trilinos
* objects. Currently this function is not implemented. TODO: Not
* implemented.
*/
void write_ascii ();
/**
* Print (the locally owned part of) the sparsity pattern to the given
* stream, using the format <tt>(line,col)</tt>. 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 print (std::ostream &out,
const bool write_extended_trilinos_info = false) const;
/**
* Print the sparsity of the matrix in a format that <tt>gnuplot</tt>
* understands and which can be used to plot the sparsity pattern in a
* graphical way. The format consists of pairs <tt>i j</tt> 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 <tt>(0,0)</tt> 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 <tt>plot</tt> command.
*/
void print_gnuplot (std::ostream &out) const;
//@}
/**
* @addtogroup Exceptions
* @{
*/
/**
* Exception
*/
DeclException1 (ExcTrilinosError,
int,
<< "An error with error number " << arg1
<< " occurred while calling a Trilinos function");
/**
* Exception
*/
DeclException2 (ExcInvalidIndex,
size_type, size_type,
<< "The entry with index <" << arg1 << ',' << arg2
<< "> does not exist.");
/**
* Exception
*/
DeclException0 (ExcSourceEqualsDestination);
/**
* Exception
*/
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.");
/**
* Exception
*/
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.");
//@}
private:
/**
* Pointer to the user-supplied Epetra Trilinos mapping of the matrix
* columns that assigns parts of the matrix to the individual processes.
*/
std_cxx11::shared_ptr<Epetra_Map> column_space_map;
/**
* A sparsity pattern object in Trilinos to be used for finite element
* based problems which allows for adding non-local elements to the
* pattern.
*/
std_cxx11::shared_ptr<Epetra_FECrsGraph> graph;
/**
* A sparsity pattern object for the non-local part of the sparsity
* pattern that is going to be sent to the owning processor. Only used
* when the particular constructor or reinit method with writable_rows
* argument is set
*/
std_cxx11::shared_ptr<Epetra_CrsGraph> nonlocal_graph;
friend class SparseMatrix;
friend class SparsityPatternIterators::Accessor;
friend class SparsityPatternIterators::Iterator;
};
// -------------------------- inline and template functions ----------------------
#ifndef DOXYGEN
namespace SparsityPatternIterators
{
inline
Accessor::Accessor (const SparsityPattern *sp,
const size_type row,
const size_type index)
:
sparsity_pattern(const_cast<SparsityPattern *>(sp)),
a_row(row),
a_index(index)
{
visit_present_row ();
}
inline
Accessor::Accessor (const Accessor &a)
:
sparsity_pattern(a.sparsity_pattern),
a_row(a.a_row),
a_index(a.a_index),
colnum_cache (a.colnum_cache)
{}
inline
Accessor::size_type
Accessor::row() const
{
Assert (a_row < sparsity_pattern->n_rows(), ExcBeyondEndOfSparsityPattern());
return a_row;
}
inline
Accessor::size_type
Accessor::column() const
{
Assert (a_row < sparsity_pattern->n_rows(), ExcBeyondEndOfSparsityPattern());
return (*colnum_cache)[a_index];
}
inline
Accessor::size_type
Accessor::index() const
{
Assert (a_row < sparsity_pattern->n_rows(), ExcBeyondEndOfSparsityPattern());
return a_index;
}
inline
Iterator::Iterator(const SparsityPattern *sp,
const size_type row,
const size_type index)
:
accessor(sp, row, index)
{}
inline
Iterator::Iterator(const Iterator &i)
:
accessor(i.accessor)
{}
inline
Iterator &
Iterator::operator++ ()
{
Assert (accessor.a_row < accessor.sparsity_pattern->n_rows(),
ExcIteratorPastEnd());
++accessor.a_index;
// If at end of line: do one
// step, then cycle until we
// find a row with a nonzero
// number of entries.
if (accessor.a_index >= accessor.colnum_cache->size())
{
accessor.a_index = 0;
++accessor.a_row;
while ((accessor.a_row < accessor.sparsity_pattern->n_rows())
&&
(accessor.sparsity_pattern->row_length(accessor.a_row) == 0))
++accessor.a_row;
accessor.visit_present_row();
}
return *this;
}
inline
Iterator
Iterator::operator++ (int)
{
const Iterator old_state = *this;
++(*this);
return old_state;
}
inline
const Accessor &
Iterator::operator* () const
{
return accessor;
}
inline
const Accessor *
Iterator::operator-> () const
{
return &accessor;
}
inline
bool
Iterator::operator == (const Iterator &other) const
{
return (accessor.a_row == other.accessor.a_row &&
accessor.a_index == other.accessor.a_index);
}
inline
bool
Iterator::operator != (const Iterator &other) const
{
return ! (*this == other);
}
inline
bool
Iterator::operator < (const Iterator &other) const
{
return (accessor.row() < other.accessor.row() ||
(accessor.row() == other.accessor.row() &&
accessor.index() < other.accessor.index()));
}
}
inline
SparsityPattern::const_iterator
SparsityPattern::begin() const
{
return const_iterator(this, 0, 0);
}
inline
SparsityPattern::const_iterator
SparsityPattern::end() const
{
return const_iterator(this, n_rows(), 0);
}
inline
SparsityPattern::const_iterator
SparsityPattern::begin(const size_type r) const
{
Assert (r < n_rows(), ExcIndexRangeType<size_type>(r, 0, n_rows()));
if (row_length(r) > 0)
return const_iterator(this, r, 0);
else
return end (r);
}
inline
SparsityPattern::const_iterator
SparsityPattern::end(const size_type r) const
{
Assert (r < n_rows(), ExcIndexRangeType<size_type>(r, 0, n_rows()));
// place the iterator on the first entry
// past this line, or at the end of the
// matrix
for (size_type i=r+1; i<n_rows(); ++i)
if (row_length(i) > 0)
return const_iterator(this, i, 0);
// if there is no such line, then take the
// end iterator of the matrix
return end();
}
inline
bool
SparsityPattern::in_local_range (const size_type index) const
{
TrilinosWrappers::types::int_type begin, end;
#ifndef DEAL_II_WITH_64BIT_INDICES
begin = graph->RowMap().MinMyGID();
end = graph->RowMap().MaxMyGID()+1;
#else
begin = graph->RowMap().MinMyGID64();
end = graph->RowMap().MaxMyGID64()+1;
#endif
return ((index >= static_cast<size_type>(begin)) &&
(index < static_cast<size_type>(end)));
}
inline
bool
SparsityPattern::is_compressed () const
{
return graph->Filled();
}
inline
bool
SparsityPattern::empty () const
{
return ((n_rows() == 0) && (n_cols() == 0));
}
inline
void
SparsityPattern::add (const size_type i,
const size_type j)
{
add_entries (i, &j, &j+1);
}
template <typename ForwardIterator>
inline
void
SparsityPattern::add_entries (const size_type row,
ForwardIterator begin,
ForwardIterator end,
const bool /*indices_are_sorted*/)
{
if (begin == end)
return;
// verify that the size of the data type Trilinos expects matches that the
// iterator points to. we allow for some slippage between signed and
// unsigned and only compare that they are both either 32 or 64 bit. to
// write this test properly, not that we cannot compare the size of
// '*begin' because 'begin' may be an iterator and '*begin' may be an
// accessor class. consequently, we need to somehow get an actual value
// from it which we can by evaluating an expression such as when
// multiplying the value produced by 2
Assert (sizeof(TrilinosWrappers::types::int_type) ==
sizeof((*begin)*2),
ExcNotImplemented());
TrilinosWrappers::types::int_type *col_index_ptr =
(TrilinosWrappers::types::int_type *)(&*begin);
const int n_cols = static_cast<int>(end - begin);
int ierr;
if ( graph->RowMap().LID(static_cast<TrilinosWrappers::types::int_type>(row)) != -1)
ierr = graph->InsertGlobalIndices (row, n_cols, col_index_ptr);
else if (nonlocal_graph.get() != 0)
{
// this is the case when we have explicitly set the off-processor rows
// and want to create a separate matrix object for them (to retain
// thread-safety)
Assert (nonlocal_graph->RowMap().LID(static_cast<TrilinosWrappers::types::int_type>(row)) != -1,
ExcMessage("Attempted to write into off-processor matrix row "
"that has not be specified as being writable upon "
"initialization"));
ierr = nonlocal_graph->InsertGlobalIndices (row, n_cols, col_index_ptr);
}
else
ierr = graph->InsertGlobalIndices
(1, (TrilinosWrappers::types::int_type *)&row, n_cols, col_index_ptr);
AssertThrow (ierr >= 0, ExcTrilinosError(ierr));
}
inline
const Epetra_FECrsGraph &
SparsityPattern::trilinos_sparsity_pattern () const
{
return *graph;
}
inline
IndexSet
SparsityPattern::locally_owned_domain_indices () const
{
return IndexSet(static_cast<const Epetra_Map &>(graph->DomainMap()));
}
inline
IndexSet
SparsityPattern::locally_owned_range_indices () const
{
return IndexSet(static_cast<const Epetra_Map &>(graph->RangeMap()));
}
#endif // DOXYGEN
}
DEAL_II_NAMESPACE_CLOSE
#endif // DEAL_II_WITH_TRILINOS
/*-------------------- trilinos_sparsity_pattern.h --------------------*/
#endif
/*-------------------- trilinos_sparsity_pattern.h --------------------*/
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