#include <deal.II/lac/petsc_sparse_matrix.h>

Inheritance diagram for PETScWrappers::MPI::SparseMatrix:

Classes
struct	Traits

Public Types
using	size_type = types::global_dof_index

Public Types inherited from PETScWrappers::MatrixBase
using	const_iterator = MatrixIterators::const_iterator

using	size_type = types::global_dof_index

using	value_type = PetscScalar

Public Member Functions
	SparseMatrix ()

	~SparseMatrix () override

	SparseMatrix (const MPI_Comm &communicator, const size_type m, const size_type n, const size_type local_rows, const size_type local_columns, const size_type n_nonzero_per_row, const bool is_symmetric=false, const size_type n_offdiag_nonzero_per_row=0)

	SparseMatrix (const MPI_Comm &communicator, const size_type m, const size_type n, const size_type local_rows, const size_type local_columns, const std::vector< size_type > &row_lengths, const bool is_symmetric=false, const std::vector< size_type > &offdiag_row_lengths=std::vector< size_type >())

template<typename SparsityPatternType >
	SparseMatrix (const MPI_Comm &communicator, const SparsityPatternType &sparsity_pattern, const std::vector< size_type > &local_rows_per_process, const std::vector< size_type > &local_columns_per_process, const unsigned int this_process, const bool preset_nonzero_locations=true)

SparseMatrix &	operator= (const value_type d)

void	copy_from (const SparseMatrix &other)

void	reinit (const MPI_Comm &communicator, const size_type m, const size_type n, const size_type local_rows, const size_type local_columns, const size_type n_nonzero_per_row, const bool is_symmetric=false, const size_type n_offdiag_nonzero_per_row=0)

void	reinit (const MPI_Comm &communicator, const size_type m, const size_type n, const size_type local_rows, const size_type local_columns, const std::vector< size_type > &row_lengths, const bool is_symmetric=false, const std::vector< size_type > &offdiag_row_lengths=std::vector< size_type >())

template<typename SparsityPatternType >
void	reinit (const MPI_Comm &communicator, const SparsityPatternType &sparsity_pattern, const std::vector< size_type > &local_rows_per_process, const std::vector< size_type > &local_columns_per_process, const unsigned int this_process, const bool preset_nonzero_locations=true)

template<typename SparsityPatternType >
void	reinit (const IndexSet &local_rows, const IndexSet &local_columns, const SparsityPatternType &sparsity_pattern, const MPI_Comm &communicator)

void	reinit (const SparseMatrix &other)

virtual const MPI_Comm &	get_mpi_communicator () const override

PetscScalar	matrix_norm_square (const Vector &v) const

PetscScalar	matrix_scalar_product (const Vector &u, const Vector &v) const

IndexSet	locally_owned_domain_indices () const

IndexSet	locally_owned_range_indices () const

void	mmult (SparseMatrix &C, const SparseMatrix &B, const MPI::Vector &V=MPI::Vector()) const

void	Tmmult (SparseMatrix &C, const SparseMatrix &B, const MPI::Vector &V=MPI::Vector()) const

Public Member Functions inherited from PETScWrappers::MatrixBase
	MatrixBase ()

	MatrixBase (const MatrixBase &)=delete

MatrixBase &	operator= (const MatrixBase &)=delete

virtual	~MatrixBase () override

MatrixBase &	operator= (const value_type d)

void	clear ()

void	set (const size_type i, const size_type j, const PetscScalar value)

void	set (const std::vector< size_type > &indices, const FullMatrix< PetscScalar > &full_matrix, const bool elide_zero_values=false)

void	set (const std::vector< size_type > &row_indices, const std::vector< size_type > &col_indices, const FullMatrix< PetscScalar > &full_matrix, const bool elide_zero_values=false)

void	set (const size_type row, const std::vector< size_type > &col_indices, const std::vector< PetscScalar > &values, const bool elide_zero_values=false)

void	set (const size_type row, const size_type n_cols, const size_type col_indices, const PetscScalar values, const bool elide_zero_values=false)

void	add (const size_type i, const size_type j, const PetscScalar value)

void	add (const std::vector< size_type > &indices, const FullMatrix< PetscScalar > &full_matrix, const bool elide_zero_values=true)

void	add (const std::vector< size_type > &row_indices, const std::vector< size_type > &col_indices, const FullMatrix< PetscScalar > &full_matrix, const bool elide_zero_values=true)

void	add (const size_type row, const std::vector< size_type > &col_indices, const std::vector< PetscScalar > &values, const bool elide_zero_values=true)

void	add (const size_type row, const size_type n_cols, const size_type col_indices, const PetscScalar values, const bool elide_zero_values=true, const bool col_indices_are_sorted=false)

void	clear_row (const size_type row, const PetscScalar new_diag_value=0)

void	clear_rows (const std::vector< size_type > &rows, const PetscScalar new_diag_value=0)

void	compress (const VectorOperation::values operation)

PetscScalar	operator() (const size_type i, const size_type j) const

PetscScalar	el (const size_type i, const size_type j) const

PetscScalar	diag_element (const size_type i) const

size_type	m () const

size_type	n () const

size_type	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

PetscReal	l1_norm () const

PetscReal	linfty_norm () const

PetscReal	frobenius_norm () const

PetscScalar	matrix_norm_square (const VectorBase &v) const

PetscScalar	matrix_scalar_product (const VectorBase &u, const VectorBase &v) const

PetscScalar	trace () const

MatrixBase &	operator*= (const PetscScalar factor)

MatrixBase &	operator/= (const PetscScalar factor)

MatrixBase &	add (const PetscScalar factor, const MatrixBase &other)

MatrixBase &	add (const MatrixBase &other, const PetscScalar factor)

void	vmult (VectorBase &dst, const VectorBase &src) const

void	Tvmult (VectorBase &dst, const VectorBase &src) const

void	vmult_add (VectorBase &dst, const VectorBase &src) const

void	Tvmult_add (VectorBase &dst, const VectorBase &src) const

PetscScalar	residual (VectorBase &dst, const VectorBase &x, const VectorBase &b) const

const_iterator	begin () const

const_iterator	end () const

const_iterator	begin (const size_type r) const

const_iterator	end (const size_type r) const

	operator Mat () const

Mat &	petsc_matrix ()

void	transpose ()

PetscBool	is_symmetric (const double tolerance=1.e-12)

PetscBool	is_hermitian (const double tolerance=1.e-12)

void	write_ascii (const PetscViewerFormat format=PETSC_VIEWER_DEFAULT)

void	print (std::ostream &out, const bool alternative_output=false) const

std::size_t	memory_consumption () const

Public Member Functions inherited from Subscriptor
	Subscriptor ()

	Subscriptor (const Subscriptor &)

	Subscriptor (Subscriptor &&) noexcept

virtual	~Subscriptor ()

Subscriptor &	operator= (const Subscriptor &)

Subscriptor &	operator= (Subscriptor &&) noexcept

void	subscribe (std::atomic< bool > *const validity, const std::string &identifier="") const

void	unsubscribe (std::atomic< bool > *const validity, const std::string &identifier="") const

unsigned int	n_subscriptions () const

template<typename StreamType >
void	list_subscribers (StreamType &stream) const

void	list_subscribers () const

template<class Archive >
void	serialize (Archive &ar, const unsigned int version)

Static Public Member Functions
static ::ExceptionBase &	ExcLocalRowsTooLarge (int arg1, int arg2)

Static Public Member Functions inherited from PETScWrappers::MatrixBase
static ::ExceptionBase &	ExcSourceEqualsDestination ()

static ::ExceptionBase &	ExcWrongMode (int arg1, int arg2)

Static Public Member Functions inherited from Subscriptor
static ::ExceptionBase &	ExcInUse (int arg1, std::string arg2, std::string arg3)

static ::ExceptionBase &	ExcNoSubscriber (std::string arg1, std::string arg2)

Private Member Functions
void	do_reinit (const size_type m, const size_type n, const size_type local_rows, const size_type local_columns, const size_type n_nonzero_per_row, const bool is_symmetric=false, const size_type n_offdiag_nonzero_per_row=0)

void	do_reinit (const size_type m, const size_type n, const size_type local_rows, const size_type local_columns, const std::vector< size_type > &row_lengths, const bool is_symmetric=false, const std::vector< size_type > &offdiag_row_lengths=std::vector< size_type >())

template<typename SparsityPatternType >
void	do_reinit (const SparsityPatternType &sparsity_pattern, const std::vector< size_type > &local_rows_per_process, const std::vector< size_type > &local_columns_per_process, const unsigned int this_process, const bool preset_nonzero_locations)

template<typename SparsityPatternType >
void	do_reinit (const IndexSet &local_rows, const IndexSet &local_columns, const SparsityPatternType &sparsity_pattern)

Private Attributes
MPI_Comm	communicator

Friends
class	BlockMatrixBase< SparseMatrix >

Additional Inherited Members
Protected Member Functions inherited from PETScWrappers::MatrixBase
void	prepare_action (const VectorOperation::values new_action)

void	assert_is_compressed ()

void	prepare_add ()

void	prepare_set ()

void	mmult (MatrixBase &C, const MatrixBase &B, const VectorBase &V) const

void	Tmmult (MatrixBase &C, const MatrixBase &B, const VectorBase &V) const

Protected Attributes inherited from PETScWrappers::MatrixBase
Mat	matrix

VectorOperation::values	last_action

Detailed Description

Implementation of a parallel sparse matrix class based on PETSc, with rows of the matrix distributed across an MPI network. All the functionality is actually in the base class, except for the calls to generate a parallel sparse matrix. This is possible since PETSc only works on an abstract matrix type and internally distributes to functions that do the actual work depending on the actual matrix type (much like using virtual functions). Only the functions creating a matrix of specific type differ, and are implemented in this particular class.

There are a number of comments on the communication model as well as access to individual elements in the documentation to the parallel vector class. These comments apply here as well.

Partitioning of matrices

PETSc partitions parallel matrices so that each MPI process "owns" a certain number of rows (i.e. only this process stores the respective entries in these rows). The number of rows each process owns has to be passed to the constructors and reinit() functions via the argument local_rows. The individual values passed as local_rows on all the MPI processes of course have to add up to the global number of rows of the matrix.

In addition to this, PETSc also partitions the rectangular chunk of the matrix it owns (i.e. the local_rows times n() elements in the matrix), so that matrix vector multiplications can be performed efficiently. This column-partitioning therefore has to match the partitioning of the vectors with which the matrix is multiplied, just as the row-partitioning has to match the partitioning of destination vectors. This partitioning is passed to the constructors and reinit() functions through the local_columns variable, which again has to add up to the global number of columns in the matrix. The name local_columns may be named inappropriately since it does not reflect that only these columns are stored locally, but it reflects the fact that these are the columns for which the elements of incoming vectors are stored locally.

To make things even more complicated, PETSc needs a very good estimate of the number of elements to be stored in each row to be efficient. Otherwise it spends most of the time with allocating small chunks of memory, a process that can slow down programs to a crawl if it happens to often. As if a good estimate of the number of entries per row isn't even, it even needs to split this as follows: for each row it owns, it needs an estimate for the number of elements in this row that fall into the columns that are set apart for this process (see above), and the number of elements that are in the rest of the columns.

Since in general this information is not readily available, most of the initializing functions of this class assume that all of the number of elements you give as an argument to n_nonzero_per_row or by row_lengths fall into the columns "owned" by this process, and none into the other ones. This is a fair guess for most of the rows, since in a good domain partitioning, nodes only interact with nodes that are within the same subdomain. It does not hold for nodes on the interfaces of subdomain, however, and for the rows corresponding to these nodes, PETSc will have to allocate additional memory, a costly process.

The only way to avoid this is to tell PETSc where the actual entries of the matrix will be. For this, there are constructors and reinit() functions of this class that take a DynamicSparsityPattern object containing all this information. While in the general case it is sufficient if the constructors and reinit() functions know the number of local rows and columns, the functions getting a sparsity pattern also need to know the number of local rows (local_rows_per_process) and columns (local_columns_per_process) for all other processes, in order to compute which parts of the matrix are which. Thus, it is not sufficient to just count the number of degrees of freedom that belong to a particular process, but you have to have the numbers for all processes available at all processes.

Author: Wolfgang Bangerth, 2004

Definition at line 367 of file petsc_sparse_matrix.h.

Member Typedef Documentation

◆ size_type

using PETScWrappers::MPI::SparseMatrix::size_type = types::global_dof_index

Declare type for container size.

Definition at line 373 of file petsc_sparse_matrix.h.

Constructor & Destructor Documentation

◆ SparseMatrix() [1/4]

SparseMatrix< number >::SparseMatrix ( )

Default constructor. Create an empty matrix.

Definition at line 34 of file petsc_parallel_sparse_matrix.cc.

◆ ~SparseMatrix()

SparseMatrix< number >::~SparseMatrix ( )

override

Destructor to free the PETSc object.

Definition at line 47 of file petsc_parallel_sparse_matrix.cc.

◆ SparseMatrix() [2/4]

SparseMatrix< number >::SparseMatrix	(	const MPI_Comm &	communicator,
		const size_type	m,
		const size_type	n,
		const size_type	local_rows,
		const size_type	local_columns,
		const size_type	n_nonzero_per_row,
		const bool	is_symmetric = `false`,
		const size_type	n_offdiag_nonzero_per_row = `0`
	)

Create a sparse matrix of dimensions m times n, with an initial guess of n_nonzero_per_row and n_offdiag_nonzero_per_row nonzero elements per row (see documentation of the MatCreateAIJ PETSc function for more information about these parameters). PETSc is able to cope with the situation that more than this number of elements are later allocated for a row, but this involves copying data, and is thus expensive.

For the meaning of the local_row and local_columns parameters, see the class documentation.

The is_symmetric flag determines whether we should tell PETSc that the matrix is going to be symmetric (as indicated by the call MatSetOption(mat, MAT_SYMMETRIC). Note that the PETSc documentation states that one cannot form an ILU decomposition of a matrix for which this flag has been set to true, only an ICC. The default value of this flag is false.

Definition at line 52 of file petsc_parallel_sparse_matrix.cc.

◆ SparseMatrix() [3/4]

SparseMatrix< number >::SparseMatrix	(	const MPI_Comm &	communicator,
		const size_type	m,
		const size_type	n,
		const size_type	local_rows,
		const size_type	local_columns,
		const std::vector< size_type > &	row_lengths,
		const bool	is_symmetric = `false`,
		const std::vector< size_type > &	offdiag_row_lengths = `std::vector<size_type>()`
	)

Initialize a rectangular matrix with m rows and n columns. The maximal number of nonzero entries for diagonal and off- diagonal blocks of each row is given by the row_lengths and offdiag_row_lengths arrays.

For the meaning of the local_row and local_columns parameters, see the class documentation.

Just as for the other constructors: PETSc is able to cope with the situation that more than this number of elements are later allocated for a row, but this involves copying data, and is thus expensive.

The is_symmetric flag determines whether we should tell PETSc that the matrix is going to be symmetric (as indicated by the call MatSetOption(mat, MAT_SYMMETRIC). Note that the PETSc documentation states that one cannot form an ILU decomposition of a matrix for which this flag has been set to true, only an ICC. The default value of this flag is false.

Definition at line 73 of file petsc_parallel_sparse_matrix.cc.

◆ SparseMatrix() [4/4]

template<typename SparsityPatternType >

SparseMatrix< SparsityPatternType >::SparseMatrix	(	const MPI_Comm &	communicator,
		const SparsityPatternType &	sparsity_pattern,
		const std::vector< size_type > &	local_rows_per_process,
		const std::vector< size_type > &	local_columns_per_process,
		const unsigned int	this_process,
		const bool	preset_nonzero_locations = `true`
	)

Initialize using the given sparsity pattern with communication happening over the provided communicator.

For the meaning of the local_rows_per_process and local_columns_per_process parameters, see the class documentation.

Note that PETSc can be very slow if you do not provide it with a good estimate of the lengths of rows. Using the present function is a very efficient way to do this, as it uses the exact number of nonzero entries for each row of the matrix by using the given sparsity pattern argument. If the preset_nonzero_locations flag is true, this function in addition not only sets the correct row sizes up front, but also pre-allocated the correct nonzero entries in the matrix.

PETsc allows to later add additional nonzero entries to a matrix, by simply writing to these elements. However, this will then lead to additional memory allocations which are very inefficient and will greatly slow down your program. It is therefore significantly more efficient to get memory allocation right from the start.

Definition at line 96 of file petsc_parallel_sparse_matrix.cc.

Member Function Documentation

◆ operator=()

SparseMatrix & SparseMatrix< number >::operator= ( const value_type d )

This operator assigns a scalar to a matrix. Since this does usually not make much sense (should we set all matrix entries to this value? Only the nonzero entries of the sparsity pattern?), this operation is only allowed if the actual value to be assigned is zero. This operator only exists to allow for the obvious notation matrix=0, which sets all elements of the matrix to zero, but keep the sparsity pattern previously used.

Definition at line 130 of file petsc_parallel_sparse_matrix.cc.

◆ copy_from()

void SparseMatrix< number >::copy_from ( const SparseMatrix & other )

Make a copy of the PETSc matrix other. It is assumed that both matrices have the same SparsityPattern.

Definition at line 137 of file petsc_parallel_sparse_matrix.cc.

◆ reinit() [1/5]

void SparseMatrix< number >::reinit	(	const MPI_Comm &	communicator,
		const size_type	m,
		const size_type	n,
		const size_type	local_rows,
		const size_type	local_columns,
		const size_type	n_nonzero_per_row,
		const bool	is_symmetric = `false`,
		const size_type	n_offdiag_nonzero_per_row = `0`
	)

Throw away the present matrix and generate one that has the same properties as if it were created by the constructor of this class with the same argument list as the present function.

Definition at line 150 of file petsc_parallel_sparse_matrix.cc.

◆ reinit() [2/5]

void SparseMatrix< number >::reinit	(	const MPI_Comm &	communicator,
		const size_type	m,
		const size_type	n,
		const size_type	local_rows,
		const size_type	local_columns,
		const std::vector< size_type > &	row_lengths,
		const bool	is_symmetric = `false`,
		const std::vector< size_type > &	offdiag_row_lengths = `std::vector<size_type>()`
	)

Throw away the present matrix and generate one that has the same properties as if it were created by the constructor of this class with the same argument list as the present function.

Definition at line 177 of file petsc_parallel_sparse_matrix.cc.

◆ reinit() [3/5]

template<typename SparsityPatternType >

void SparseMatrix< SparsityPatternType >::reinit	(	const MPI_Comm &	communicator,
		const SparsityPatternType &	sparsity_pattern,
		const std::vector< size_type > &	local_rows_per_process,
		const std::vector< size_type > &	local_columns_per_process,
		const unsigned int	this_process,
		const bool	preset_nonzero_locations = `true`
	)

Initialize using the given sparsity pattern with communication happening over the provided communicator.

Note that PETSc can be very slow if you do not provide it with a good estimate of the lengths of rows. Using the present function is a very efficient way to do this, as it uses the exact number of nonzero entries for each row of the matrix by using the given sparsity pattern argument. If the preset_nonzero_locations flag is true, this function in addition not only sets the correct row sizes up front, but also pre-allocated the correct nonzero entries in the matrix.

PETsc allows to later add additional nonzero entries to a matrix, by simply writing to these elements. However, this will then lead to additional memory allocations which are very inefficient and will greatly slow down your program. It is therefore significantly more efficient to get memory allocation right from the start.

Definition at line 206 of file petsc_parallel_sparse_matrix.cc.

◆ reinit() [4/5]

template<typename SparsityPatternType >

void SparseMatrix< SparsityPatternType >::reinit	(	const IndexSet &	local_rows,
		const IndexSet &	local_columns,
		const SparsityPatternType &	sparsity_pattern,
		const MPI_Comm &	communicator
	)

Create a matrix where the size() of the IndexSets determine the global number of rows and columns and the entries of the IndexSet give the rows and columns for the calling processor. Note that only ascending, 1:1 IndexSets are supported.

Definition at line 230 of file petsc_parallel_sparse_matrix.cc.

◆ reinit() [5/5]

void SparseMatrix< number >::reinit ( const SparseMatrix & other )

Initialize this matrix to have the same structure as other. This will not copy the values of the other matrix, but you can use copy_from() for this.

Definition at line 114 of file petsc_parallel_sparse_matrix.cc.

◆ get_mpi_communicator()

const MPI_Comm & SparseMatrix< number >::get_mpi_communicator ( ) const

inlineoverridevirtual

Return a reference to the MPI communicator object in use with this matrix.

Implements PETScWrappers::MatrixBase.

Definition at line 767 of file petsc_sparse_matrix.h.

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

include/deal.II/lac/petsc_sparse_matrix.h
source/lac/petsc_parallel_sparse_matrix.cc

Classes

Public Types

Public Member Functions

Static Public Member Functions

Private Member Functions

Private Attributes

Friends

Additional Inherited Members

Detailed Description

Partitioning of matrices

Member Typedef Documentation

◆ size_type

Constructor & Destructor Documentation

◆ SparseMatrix() [1/4]

◆ ~SparseMatrix()

◆ SparseMatrix() [2/4]

◆ SparseMatrix() [3/4]

◆ SparseMatrix() [4/4]

Member Function Documentation

◆ operator=()

◆ copy_from()

◆ reinit() [1/5]

◆ reinit() [2/5]

◆ reinit() [3/5]

◆ reinit() [4/5]

◆ reinit() [5/5]

◆ get_mpi_communicator()