CSR is a matrix format which stores only the nonzero coefficients by compressing each row of the matrix (compressed sparse row format). More...

#include <ginkgo/core/matrix/csr.hpp>

Inheritance diagram for gko::matrix::Csr< ValueType, IndexType >:

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Collaboration diagram for gko::matrix::Csr< ValueType, IndexType >:

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Classes
class	strategy_type
	strategy_type is to decide how to set the csr algorithm. More...
class	classical
	classical is a strategy_type which uses the same number of threads on each row. More...
class	merge_path
	merge_path is a strategy_type which uses the merge_path algorithm. More...
class	cusparse
	cusparse is a strategy_type which uses the sparselib csr. More...
class	sparselib
	sparselib is a strategy_type which uses the sparselib csr. More...
class	load_balance
	load_balance is a strategy_type which uses the load balance algorithm. More...
class	automatical
class	multiply_reuse_info
	Class describing the internal lookup structures created by multiply_reuse(const Csr*) to recompute a sparse matrix-matrix product with updated values. More...
class	multiply_add_reuse_info
	Class describing the internal lookup structures created by multiply_add_reuse to recompute a sparse matrix-matrix product with updated values. More...
class	scale_add_reuse_info
	Class describing the internal lookup structures created by scale_add_reuse to recompute a sparse matrix-matrix sum with updated values. More...
struct	permuting_reuse_info
	A struct describing a transformation of the matrix that reorders the values of the matrix into the transformed matrix. More...

Public Types
using	value_type = ValueType
using	index_type = IndexType
using	transposed_type = Csr<ValueType, IndexType>
using	mat_data = matrix_data<ValueType, IndexType>
using	device_mat_data = device_matrix_data<ValueType, IndexType>
using	absolute_type = remove_complex<Csr>
Public Types inherited from gko::EnablePolymorphicAssignment< Csr< default_precision, int32 > >
using	result_type
Public Types inherited from gko::DiagonalExtractable< default_precision >
using	value_type
Public Types inherited from gko::ReadableFromMatrixData< default_precision, int32 >
using	value_type
using	index_type
Public Types inherited from gko::WritableToMatrixData< default_precision, int32 >
using	value_type
using	index_type
Public Types inherited from gko::EnableAbsoluteComputation< remove_complex< Csr< default_precision, int32 > > >
using	absolute_type

Public Member Functions
void	convert_to (Csr< next_precision< ValueType >, IndexType > *result) const override
void	move_to (Csr< next_precision< ValueType >, IndexType > *result) override
void	convert_to (Dense< ValueType > *other) const override
void	move_to (Dense< ValueType > *other) override
void	convert_to (Coo< ValueType, IndexType > *result) const override
void	move_to (Coo< ValueType, IndexType > *result) override
void	convert_to (Ell< ValueType, IndexType > *result) const override
void	move_to (Ell< ValueType, IndexType > *result) override
void	convert_to (Fbcsr< ValueType, IndexType > *result) const override
void	move_to (Fbcsr< ValueType, IndexType > *result) override
void	convert_to (Hybrid< ValueType, IndexType > *result) const override
void	move_to (Hybrid< ValueType, IndexType > *result) override
void	convert_to (Sellp< ValueType, IndexType > *result) const override
void	move_to (Sellp< ValueType, IndexType > *result) override
void	convert_to (SparsityCsr< ValueType, IndexType > *result) const override
void	move_to (SparsityCsr< ValueType, IndexType > *result) override
void	read (const mat_data &data) override
void	read (const device_mat_data &data) override
void	read (device_mat_data &&data) override
void	write (mat_data &data) const override
std::unique_ptr< LinOp >	transpose () const override
	Returns a LinOp representing the transpose of the Transposable object.
std::unique_ptr< LinOp >	conj_transpose () const override
	Returns a LinOp representing the conjugate transpose of the Transposable object.
std::unique_ptr< Csr >	multiply (ptr_param< const Csr > other) const
	Computes the sparse matrix product this * other on the executor of this matrix.
std::pair< std::unique_ptr< Csr >, multiply_reuse_info >	multiply_reuse (ptr_param< const Csr > other) const
	Computes the sparse matrix product this * other on the executor of this matrix, and necessary data for value updates:
std::unique_ptr< Csr >	multiply_add (ptr_param< const Dense< value_type > > scale_mult, ptr_param< const Csr > mtx_mult, ptr_param< const Dense< value_type > > scale_add, ptr_param< const Csr > mtx_add) const
	Computes the sparse matrix product scale_mult * this * mtx_mult + scale_add * mtx_add on the executor of this matrix.
std::pair< std::unique_ptr< Csr >, multiply_add_reuse_info >	multiply_add_reuse (ptr_param< const Dense< value_type > > scale_mult, ptr_param< const Csr > mtx_mult, ptr_param< const Dense< value_type > > scale_add, ptr_param< const Csr > mtx_add) const
	Computes the sparse matrix product scale_mult * this * mtx_mult + scale_add * mtx_add on the executor of this matrix, and necessary data for value updates:
std::unique_ptr< Csr >	scale_add (ptr_param< const Dense< value_type > > scale_this, ptr_param< const Dense< value_type > > scale_other, ptr_param< const Csr > mtx_other) const
	Computes the sparse matrix sum scale_this * this + scale_other * mtx_add on the executor of this matrix.
std::pair< std::unique_ptr< Csr >, scale_add_reuse_info >	add_scale_reuse (ptr_param< const Dense< value_type > > scale_this, ptr_param< const Dense< value_type > > scale_other, ptr_param< const Csr > mtx_other) const
	Computes the sparse matrix sum scale_this * this + scale_other * mtx_add on the executor of this matrix, and necessary data for value updates:
std::pair< std::unique_ptr< Csr >, permuting_reuse_info >	transpose_reuse () const
	Computes the necessary data to update a transposed matrix from its original matrix.
std::unique_ptr< Csr >	permute (ptr_param< const Permutation< index_type > > permutation, permute_mode mode=permute_mode::symmetric) const
	Creates a permuted copy of this matrix with the given permutation .
std::unique_ptr< Csr >	permute (ptr_param< const Permutation< index_type > > row_permutation, ptr_param< const Permutation< index_type > > column_permutation, bool invert=false) const
	Creates a non-symmetrically permuted copy of this matrix with the given row and column permutations and .
std::pair< std::unique_ptr< Csr >, permuting_reuse_info >	permute_reuse (ptr_param< const Permutation< index_type > > permutation, permute_mode mode=permute_mode::symmetric) const
	Computes the operations necessary to propagate changed values from a matrix A to a permuted matrix.
std::pair< std::unique_ptr< Csr >, permuting_reuse_info >	permute_reuse (ptr_param< const Permutation< index_type > > row_permutation, ptr_param< const Permutation< index_type > > column_permutation, bool invert=false) const
	Computes the operations necessary to propagate changed values from a matrix A to a permuted matrix.
std::unique_ptr< Csr >	scale_permute (ptr_param< const ScaledPermutation< value_type, index_type > > permutation, permute_mode=permute_mode::symmetric) const
	Creates a scaled and permuted copy of this matrix.
std::unique_ptr< Csr >	scale_permute (ptr_param< const ScaledPermutation< value_type, index_type > > row_permutation, ptr_param< const ScaledPermutation< value_type, index_type > > column_permutation, bool invert=false) const
	Creates a scaled and permuted copy of this matrix.
std::unique_ptr< LinOp >	permute (const array< IndexType > *permutation_indices) const override
std::unique_ptr< LinOp >	inverse_permute (const array< IndexType > *inverse_permutation_indices) const override
std::unique_ptr< LinOp >	row_permute (const array< IndexType > *permutation_indices) const override
std::unique_ptr< LinOp >	column_permute (const array< IndexType > *permutation_indices) const override
std::unique_ptr< LinOp >	inverse_row_permute (const array< IndexType > *inverse_permutation_indices) const override
std::unique_ptr< LinOp >	inverse_column_permute (const array< IndexType > *inverse_permutation_indices) const override
std::unique_ptr< Diagonal< ValueType > >	extract_diagonal () const override
	Extracts the diagonal entries of the matrix into a vector.
std::unique_ptr< absolute_type >	compute_absolute () const override
	Gets the AbsoluteLinOp.
void	compute_absolute_inplace () override
	Compute absolute inplace on each element.
void	sort_by_column_index ()
	Sorts all (value, col_idx) pairs in each row by column index.
bool	is_sorted_by_column_index () const
value_type *	get_values () noexcept
	Returns the values of the matrix.
const value_type *	get_const_values () const noexcept
	Returns the values of the matrix.
std::unique_ptr< Dense< ValueType > >	create_value_view ()
	Creates a Dense view of the value array of this matrix as a column vector of dimensions nnz x 1.
std::unique_ptr< const Dense< ValueType > >	create_const_value_view () const
	Creates a const Dense view of the value array of this matrix as a column vector of dimensions nnz x 1.
index_type *	get_col_idxs () noexcept
	Returns the column indexes of the matrix.
const index_type *	get_const_col_idxs () const noexcept
	Returns the column indexes of the matrix.
index_type *	get_row_ptrs () noexcept
	Returns the row pointers of the matrix.
const index_type *	get_const_row_ptrs () const noexcept
	Returns the row pointers of the matrix.
index_type *	get_srow () noexcept
	Returns the starting rows.
const index_type *	get_const_srow () const noexcept
	Returns the starting rows.
size_type	get_num_srow_elements () const noexcept
	Returns the number of the srow stored elements (involved warps).
size_type	get_num_stored_elements () const noexcept
	Returns the number of elements explicitly stored in the matrix.
std::shared_ptr< strategy_type >	get_strategy () const noexcept
	Returns the strategy.
void	set_strategy (std::shared_ptr< strategy_type > strategy)
	Set the strategy.
void	scale (ptr_param< const LinOp > alpha)
	Scales the matrix with a scalar.
void	inv_scale (ptr_param< const LinOp > alpha)
	Scales the matrix with the inverse of a scalar.
std::unique_ptr< Csr< ValueType, IndexType > >	create_submatrix (const index_set< IndexType > &row_index_set, const index_set< IndexType > &column_index_set) const
	Creates a submatrix from this Csr matrix given row and column index_set objects.
std::unique_ptr< Csr< ValueType, IndexType > >	create_submatrix (const span &row_span, const span &column_span) const
	Creates a submatrix from this Csr matrix given row and column spans.
Csr &	operator= (const Csr &)
	Copy-assigns a Csr matrix.
Csr &	operator= (Csr &&)
	Move-assigns a Csr matrix.
	Csr (const Csr &)
	Copy-constructs a Csr matrix.
	Csr (Csr &&)
	Move-constructs a Csr matrix.
Public Member Functions inherited from gko::EnableLinOp< Csr< default_precision, int32 > >
const Csr< default_precision, int32 > *	apply (ptr_param< const LinOp > b, ptr_param< LinOp > x) const
Public Member Functions inherited from gko::EnablePolymorphicAssignment< Csr< default_precision, int32 > >
void	convert_to (result_type *result) const override
void	move_to (result_type *result) override
Public Member Functions inherited from gko::DiagonalExtractable< default_precision >
std::unique_ptr< LinOp >	extract_diagonal_linop () const override
	Extracts the diagonal entries of the matrix into a vector.
Public Member Functions inherited from gko::ReadableFromMatrixData< default_precision, int32 >
virtual void	read (const matrix_data< default_precision, int32 > &data)=0
	Reads a matrix from a matrix_data structure.
Public Member Functions inherited from gko::WritableToMatrixData< default_precision, int32 >
virtual void	write (matrix_data< default_precision, int32 > &data) const=0
	Writes a matrix to a matrix_data structure.
Public Member Functions inherited from gko::Permutable< int32 >
virtual std::unique_ptr< LinOp >	permute (const array< int32 > *permutation_indices) const
	Returns a LinOp representing the symmetric row and column permutation of the Permutable object.
virtual std::unique_ptr< LinOp >	inverse_permute (const array< int32 > *permutation_indices) const
	Returns a LinOp representing the symmetric inverse row and column permutation of the Permutable object.
virtual std::unique_ptr< LinOp >	row_permute (const array< int32 > *permutation_indices) const=0
	Returns a LinOp representing the row permutation of the Permutable object.
virtual std::unique_ptr< LinOp >	column_permute (const array< int32 > *permutation_indices) const=0
	Returns a LinOp representing the column permutation of the Permutable object.
virtual std::unique_ptr< LinOp >	inverse_row_permute (const array< int32 > *permutation_indices) const=0
	Returns a LinOp representing the row permutation of the inverse permuted object.
virtual std::unique_ptr< LinOp >	inverse_column_permute (const array< int32 > *permutation_indices) const=0
	Returns a LinOp representing the row permutation of the inverse permuted object.
Public Member Functions inherited from gko::EnableAbsoluteComputation< remove_complex< Csr< default_precision, int32 > > >
std::unique_ptr< LinOp >	compute_absolute_linop () const override
	Gets the absolute LinOp.
Public Member Functions inherited from gko::ScaledIdentityAddable
void	add_scaled_identity (ptr_param< const LinOp > const a, ptr_param< const LinOp > const b)
	Scales this and adds another scalar times the identity to it.

Static Public Member Functions
static std::unique_ptr< Csr >	create (std::shared_ptr< const Executor > exec, std::shared_ptr< strategy_type > strategy)
	Creates an uninitialized CSR matrix of the specified size.
static std::unique_ptr< Csr >	create (std::shared_ptr< const Executor > exec, const dim< 2 > &size={}, size_type num_nonzeros={}, std::shared_ptr< strategy_type > strategy=nullptr)
	Creates an uninitialized CSR matrix of the specified size.
static std::unique_ptr< Csr >	create (std::shared_ptr< const Executor > exec, const dim< 2 > &size, array< value_type > values, array< index_type > col_idxs, array< index_type > row_ptrs, std::shared_ptr< strategy_type > strategy=nullptr)
	Creates a CSR matrix from already allocated (and initialized) row pointer, column index and value arrays.
template<typename InputValueType, typename InputColumnIndexType, typename InputRowPtrType>
static std::unique_ptr< Csr >	create (std::shared_ptr< const Executor > exec, const dim< 2 > &size, std::initializer_list< InputValueType > values, std::initializer_list< InputColumnIndexType > col_idxs, std::initializer_list< InputRowPtrType > row_ptrs)
	create(std::shared_ptr<const Executor>,const dim<2>&, array<value_type>, array<index_type>, array<index_type>)
static std::unique_ptr< const Csr >	create_const (std::shared_ptr< const Executor > exec, const dim< 2 > &size, gko::detail::const_array_view< ValueType > &&values, gko::detail::const_array_view< IndexType > &&col_idxs, gko::detail::const_array_view< IndexType > &&row_ptrs, std::shared_ptr< strategy_type > strategy=nullptr)
	Creates a constant (immutable) Csr matrix from a set of constant arrays.

Friends
class	EnablePolymorphicObject< Csr, LinOp >
class	Coo< ValueType, IndexType >
class	Dense< ValueType >
class	Diagonal< ValueType >
class	Ell< ValueType, IndexType >
class	Hybrid< ValueType, IndexType >
class	Sellp< ValueType, IndexType >
class	SparsityCsr< ValueType, IndexType >
class	Fbcsr< ValueType, IndexType >
class	CsrBuilder< ValueType, IndexType >
class	Csr< to_complex< ValueType >, IndexType >
class	Csr< previous_precision< ValueType >, IndexType >

Detailed Description

template<typename ValueType = default_precision, typename IndexType = int32>
class gko::matrix::Csr< ValueType, IndexType >

CSR is a matrix format which stores only the nonzero coefficients by compressing each row of the matrix (compressed sparse row format).

The nonzero elements are stored in a 1D array row-wise, and accompanied with a row pointer array which stores the starting index of each row. An additional column index array is used to identify the column of each nonzero element.

The Csr LinOp supports different operations:

matrix::Csr *A, *B, *C;      // matrices
matrix::Dense *b, *x;        // vectors tall-and-skinny matrices
matrix::Dense *alpha, *beta; // scalars of dimension 1x1
matrix::Identity *I;         // identity matrix
 
// Applying to Dense matrices computes an SpMV/SpMM product
A->apply(b, x)              // x = A*b
A->apply(alpha, b, beta, x) // x = alpha*A*b + beta*x
 
// Applying to Csr matrices computes a SpGEMM product of two sparse matrices
A->apply(B, C)              // C = A*B
A->apply(alpha, B, beta, C) // C = alpha*A*B + beta*C
 
// Applying to an Identity matrix computes a SpGEAM sparse matrix addition
A->apply(alpha, I, beta, B) // B = alpha*A + beta*B

Both the SpGEMM and SpGEAM operation require the input matrices to be sorted by column index, otherwise the algorithms will produce incorrect results.

Template Parameters

ValueType	precision of matrix elements
IndexType	precision of matrix indexes

Constructor & Destructor Documentation

◆ Csr() [1/2]

template<typename ValueType = default_precision, typename IndexType = int32>

gko::matrix::Csr< ValueType, IndexType >::Csr ( const Csr< ValueType, IndexType > & )

Copy-constructs a Csr matrix.

Inherits executor, strategy and data.

◆ Csr() [2/2]

template<typename ValueType = default_precision, typename IndexType = int32>

gko::matrix::Csr< ValueType, IndexType >::Csr ( Csr< ValueType, IndexType > && )

Move-constructs a Csr matrix.

Inherits executor and strategy, moves the data and leaves the moved-from object in an empty state (0x0 LinOp with unchanged executor and strategy, no nonzeros and valid row pointers).

Member Function Documentation

◆ add_scale_reuse()

template<typename ValueType = default_precision, typename IndexType = int32>

std::pair< std::unique_ptr< Csr >, scale_add_reuse_info > gko::matrix::Csr< ValueType, IndexType >::add_scale_reuse	(	ptr_param< const Dense< value_type > >	scale_this,
		ptr_param< const Dense< value_type > >	scale_other,
		ptr_param< const Csr< ValueType, IndexType > >	mtx_other ) const

Computes the sparse matrix sum scale_this * this + scale_other * mtx_add on the executor of this matrix, and necessary data for value updates:

auto [result, reuse] = mtx->add_scale_reuse(alpha, beta, mtx2);
change_values(alpha, mtx, beta, mtx2);
reuse->update_values(alpha, mtx, beta, mtx2, result);

This matrix needs to be sorted by column index, otherwise the result will be incorrect.

Parameters

scale_this	the scalar by which this matrix will be scaled.
scale_other	the scalar by which this matrix will be scaled.
mtx_other	the matrix which will be added to this, scaled by scale_other. It needs to be sorted by column index, otherwise the result will be incorrect.

Returns: std::pair containing the result of the computation, stored on the same executor as this matrix, and a scale_add_reuse_info object allowing value updates to the output matrix.

◆ compute_absolute()

template<typename ValueType = default_precision, typename IndexType = int32>

std::unique_ptr< absolute_type > gko::matrix::Csr< ValueType, IndexType >::compute_absolute ( ) const

overridevirtual

Gets the AbsoluteLinOp.

Returns: a pointer to the new absolute object

Implements gko::EnableAbsoluteComputation< remove_complex< Csr< default_precision, int32 > > >.

◆ compute_absolute_inplace()

template<typename ValueType = default_precision, typename IndexType = int32>

void gko::matrix::Csr< ValueType, IndexType >::compute_absolute_inplace ( )

overridevirtual

Compute absolute inplace on each element.

Implements gko::AbsoluteComputable.

◆ conj_transpose()

template<typename ValueType = default_precision, typename IndexType = int32>

std::unique_ptr< LinOp > gko::matrix::Csr< ValueType, IndexType >::conj_transpose ( ) const

overridevirtual

Returns a LinOp representing the conjugate transpose of the Transposable object.

Returns: a pointer to the new conjugate transposed object

Implements gko::Transposable.

◆ create() [1/4]

template<typename ValueType = default_precision, typename IndexType = int32>

std::unique_ptr< Csr > gko::matrix::Csr< ValueType, IndexType >::create	(	std::shared_ptr< const Executor >	exec,
		const dim< 2 > &	size,
		array< value_type >	values,
		array< index_type >	col_idxs,
		array< index_type >	row_ptrs,
		std::shared_ptr< strategy_type >	strategy = nullptr )

static

Creates a CSR matrix from already allocated (and initialized) row pointer, column index and value arrays.

Parameters

exec	Executor associated to the matrix
size	size of the matrix
values	array of matrix values
col_idxs	array of column indexes
row_ptrs	array of row pointers
strategy	the strategy the matrix uses for SpMV operations

Note: If one of row_ptrs, col_idxs or values is not an rvalue, not an array of IndexType, IndexType and ValueType, respectively, or is on the wrong executor, an internal copy of that array will be created, and the original array data will not be used in the matrix.

Returns: A smart pointer to the newly created matrix.

◆ create() [2/4]

template<typename ValueType = default_precision, typename IndexType = int32>

template<typename InputValueType, typename InputColumnIndexType, typename InputRowPtrType>

std::unique_ptr< Csr > gko::matrix::Csr< ValueType, IndexType >::create	(	std::shared_ptr< const Executor >	exec,
		const dim< 2 > &	size,
		std::initializer_list< InputValueType >	values,
		std::initializer_list< InputColumnIndexType >	col_idxs,
		std::initializer_list< InputRowPtrType >	row_ptrs )

inlinestatic

create(std::shared_ptr<const Executor>,const dim<2>&, array<value_type>, array<index_type>, array<index_type>)

◆ create() [3/4]

template<typename ValueType = default_precision, typename IndexType = int32>

std::unique_ptr< Csr > gko::matrix::Csr< ValueType, IndexType >::create	(	std::shared_ptr< const Executor >	exec,
		const dim< 2 > &	size = {},
		size_type	num_nonzeros = {},
		std::shared_ptr< strategy_type >	strategy = nullptr )

static

Creates an uninitialized CSR matrix of the specified size.

Parameters

exec	Executor associated to the matrix
size	size of the matrix
num_nonzeros	number of nonzeros
strategy	the strategy of CSR, or the default strategy if set to nullptr

Returns: A smart pointer to the newly created matrix.

◆ create() [4/4]

template<typename ValueType = default_precision, typename IndexType = int32>

std::unique_ptr< Csr > gko::matrix::Csr< ValueType, IndexType >::create	(	std::shared_ptr< const Executor >	exec,
		std::shared_ptr< strategy_type >	strategy )

static

Creates an uninitialized CSR matrix of the specified size.

Parameters

exec	Executor associated to the matrix
strategy	the strategy of CSR

Returns: A smart pointer to the newly created matrix.

Referenced by gko::matrix::Csr< ValueType, IndexType >::automatical::automatical(), gko::matrix::Csr< value_type, index_type >::create(), and gko::matrix::Csr< ValueType, IndexType >::load_balance::load_balance().

◆ create_const()

template<typename ValueType = default_precision, typename IndexType = int32>

std::unique_ptr< const Csr > gko::matrix::Csr< ValueType, IndexType >::create_const	(	std::shared_ptr< const Executor >	exec,
		const dim< 2 > &	size,
		gko::detail::const_array_view< ValueType > &&	values,
		gko::detail::const_array_view< IndexType > &&	col_idxs,
		gko::detail::const_array_view< IndexType > &&	row_ptrs,
		std::shared_ptr< strategy_type >	strategy = nullptr )

static

Creates a constant (immutable) Csr matrix from a set of constant arrays.

Parameters

exec	the executor to create the matrix on
size	the dimensions of the matrix
values	the value array of the matrix
col_idxs	the column index array of the matrix
row_ptrs	the row pointer array of the matrix
strategy	the strategy the matrix uses for SpMV operations

Returns: A smart pointer to the constant matrix wrapping the input arrays (if they reside on the same executor as the matrix) or a copy of these arrays on the correct executor.; A smart pointer to the newly created matrix.

◆ create_submatrix() [1/2]

template<typename ValueType = default_precision, typename IndexType = int32>

std::unique_ptr< Csr< ValueType, IndexType > > gko::matrix::Csr< ValueType, IndexType >::create_submatrix	(	const index_set< IndexType > &	row_index_set,
		const index_set< IndexType > &	column_index_set ) const

Creates a submatrix from this Csr matrix given row and column index_set objects.

Parameters

row_index_set	the row index set containing the set of rows to be in the submatrix.
column_index_set	the col index set containing the set of columns to be in the submatrix.

Returns: A new CSR matrix with the elements that belong to the row and columns of this matrix as specified by the index sets.

Note: This is not a view but creates a new, separate CSR matrix.

◆ create_submatrix() [2/2]

template<typename ValueType = default_precision, typename IndexType = int32>

std::unique_ptr< Csr< ValueType, IndexType > > gko::matrix::Csr< ValueType, IndexType >::create_submatrix	(	const span &	row_span,
		const span &	column_span ) const

Creates a submatrix from this Csr matrix given row and column spans.

Parameters

row_span	the row span containing the contiguous set of rows to be in the submatrix.
column_span	the column span containing the contiguous set of columns to be in the submatrix.

Returns: A new CSR matrix with the elements that belong to the row and columns of this matrix as specified by the index sets.

Note: This is not a view but creates a new, separate CSR matrix.

◆ extract_diagonal()

template<typename ValueType = default_precision, typename IndexType = int32>

std::unique_ptr< Diagonal< ValueType > > gko::matrix::Csr< ValueType, IndexType >::extract_diagonal ( ) const

overridevirtual

Extracts the diagonal entries of the matrix into a vector.

Parameters

diag	the vector into which the diagonal will be written

Implements gko::DiagonalExtractable< default_precision >.

◆ get_col_idxs()

template<typename ValueType = default_precision, typename IndexType = int32>

index_type * gko::matrix::Csr< ValueType, IndexType >::get_col_idxs ( )

inlinenoexcept

Returns the column indexes of the matrix.

Returns: the column indexes of the matrix.

◆ get_const_col_idxs()

template<typename ValueType = default_precision, typename IndexType = int32>

const index_type * gko::matrix::Csr< ValueType, IndexType >::get_const_col_idxs ( ) const

inlinenoexcept

Returns the column indexes of the matrix.

Returns: the column indexes of the matrix.

Note: This is the constant version of the function, which can be significantly more memory efficient than the non-constant version, so always prefer this version.

◆ get_const_row_ptrs()

template<typename ValueType = default_precision, typename IndexType = int32>

const index_type * gko::matrix::Csr< ValueType, IndexType >::get_const_row_ptrs ( ) const

inlinenoexcept

Returns the row pointers of the matrix.

Returns: the row pointers of the matrix.

Note: This is the constant version of the function, which can be significantly more memory efficient than the non-constant version, so always prefer this version.

◆ get_const_srow()

template<typename ValueType = default_precision, typename IndexType = int32>

const index_type * gko::matrix::Csr< ValueType, IndexType >::get_const_srow ( ) const

inlinenoexcept

Returns the starting rows.

Returns: the starting rows.

Note: This is the constant version of the function, which can be significantly more memory efficient than the non-constant version, so always prefer this version.

◆ get_const_values()

template<typename ValueType = default_precision, typename IndexType = int32>

const value_type * gko::matrix::Csr< ValueType, IndexType >::get_const_values ( ) const

inlinenoexcept

Returns the values of the matrix.

Returns: the values of the matrix.

Note: This is the constant version of the function, which can be significantly more memory efficient than the non-constant version, so always prefer this version.

◆ get_num_srow_elements()

template<typename ValueType = default_precision, typename IndexType = int32>

size_type gko::matrix::Csr< ValueType, IndexType >::get_num_srow_elements ( ) const

inlinenoexcept

Returns the number of the srow stored elements (involved warps).

Returns: the number of the srow stored elements (involved warps)

◆ get_num_stored_elements()

template<typename ValueType = default_precision, typename IndexType = int32>

size_type gko::matrix::Csr< ValueType, IndexType >::get_num_stored_elements ( ) const

inlinenoexcept

Returns the number of elements explicitly stored in the matrix.

Returns: the number of elements explicitly stored in the matrix

◆ get_row_ptrs()

template<typename ValueType = default_precision, typename IndexType = int32>

index_type * gko::matrix::Csr< ValueType, IndexType >::get_row_ptrs ( )

inlinenoexcept

Returns the row pointers of the matrix.

Returns: the row pointers of the matrix.

◆ get_srow()

template<typename ValueType = default_precision, typename IndexType = int32>

index_type * gko::matrix::Csr< ValueType, IndexType >::get_srow ( )

inlinenoexcept

Returns the starting rows.

Returns: the starting rows.

◆ get_strategy()

template<typename ValueType = default_precision, typename IndexType = int32>

std::shared_ptr< strategy_type > gko::matrix::Csr< ValueType, IndexType >::get_strategy ( ) const

inlinenoexcept

Returns the strategy.

Returns: the strategy

◆ get_values()

template<typename ValueType = default_precision, typename IndexType = int32>

value_type * gko::matrix::Csr< ValueType, IndexType >::get_values ( )

inlinenoexcept

Returns the values of the matrix.

Returns: the values of the matrix.

◆ inv_scale()

template<typename ValueType = default_precision, typename IndexType = int32>

void gko::matrix::Csr< ValueType, IndexType >::inv_scale ( ptr_param< const LinOp > alpha )

inline

Scales the matrix with the inverse of a scalar.

Parameters

alpha The entire matrix is scaled by 1 / alpha. alpha has to be a 1x1 Dense matrix.

◆ multiply()

template<typename ValueType = default_precision, typename IndexType = int32>

std::unique_ptr< Csr > gko::matrix::Csr< ValueType, IndexType >::multiply ( ptr_param< const Csr< ValueType, IndexType > > other ) const

Computes the sparse matrix product this * other on the executor of this matrix.

Parameters

other the matrix with which the product will be computed. It needs to be sorted by column indices when using OmpExecutor or DpcppExecutor for this.

Returns: the product of the two matrices, stored on the same executor as this matrix.

◆ multiply_add()

template<typename ValueType = default_precision, typename IndexType = int32>

std::unique_ptr< Csr > gko::matrix::Csr< ValueType, IndexType >::multiply_add	(	ptr_param< const Dense< value_type > >	scale_mult,
		ptr_param< const Csr< ValueType, IndexType > >	mtx_mult,
		ptr_param< const Dense< value_type > >	scale_add,
		ptr_param< const Csr< ValueType, IndexType > >	mtx_add ) const

Computes the sparse matrix product scale_mult * this * mtx_mult + scale_add * mtx_add on the executor of this matrix.

Parameters

scale_mult	the scalar by which the matrix product will be scaled.
mtx_mult	the matrix with which the product will be computed. It needs to be sorted by column indices when using OmpExecutor or DpcppExecutor for this.
scale_add	the scalar by which the matrix mtx_add will be scaled.
mtx_add	the matrix which will be added to the product, scaled by scale_add.

Returns: the result of the computation, stored on the same executor as this matrix.

◆ multiply_add_reuse()

template<typename ValueType = default_precision, typename IndexType = int32>

std::pair< std::unique_ptr< Csr >, multiply_add_reuse_info > gko::matrix::Csr< ValueType, IndexType >::multiply_add_reuse	(	ptr_param< const Dense< value_type > >	scale_mult,
		ptr_param< const Csr< ValueType, IndexType > >	mtx_mult,
		ptr_param< const Dense< value_type > >	scale_add,
		ptr_param< const Csr< ValueType, IndexType > >	mtx_add ) const

Computes the sparse matrix product scale_mult * this * mtx_mult + scale_add * mtx_add on the executor of this matrix, and necessary data for value updates:

auto [result, reuse] = mtx->multiply_add_reuse(sm, mm, sa, ma);
change_values(mtx, sm, mm, sa, ma);
reuse->update_values(mtx, sm, mm, sa, ma, result);

Parameters

scale_mult	the scalar by which the matrix product will be scaled.
mtx_mult	the matrix with which the product will be computed. It needs to be sorted by column indices when using OmpExecutor or DpcppExecutor for this.
scale_add	the scalar by which the matrix mtx_add will be scaled.
mtx_add	the matrix which will be added to the product, scaled by scale_add.

Returns: std::pair containing the result of the computation, stored on the same executor as this matrix, and a multiply_add_reuse_info object allowing value updates to the output matrix.

◆ multiply_reuse()

template<typename ValueType = default_precision, typename IndexType = int32>

std::pair< std::unique_ptr< Csr >, multiply_reuse_info > gko::matrix::Csr< ValueType, IndexType >::multiply_reuse ( ptr_param< const Csr< ValueType, IndexType > > other ) const

Computes the sparse matrix product this * other on the executor of this matrix, and necessary data for value updates:

auto [C, reuse] = A->multiply_reuse(B);
change_values(A, B);
reuse->update_values(A, B, C);

Parameters

other the matrix with which the product will be computed. It needs to be sorted by column indices when using OmpExecutor or DpcppExecutor for this.

Returns: std::pair containing the product of the two matrices, stored on the same executor as this matrix, and a multiply_reuse_info object allowing value updates to the output matrix.

◆ operator=() [1/2]

template<typename ValueType = default_precision, typename IndexType = int32>

Csr & gko::matrix::Csr< ValueType, IndexType >::operator= ( const Csr< ValueType, IndexType > & )

Copy-assigns a Csr matrix.

Preserves executor, copies everything else.

◆ operator=() [2/2]

template<typename ValueType = default_precision, typename IndexType = int32>

Csr & gko::matrix::Csr< ValueType, IndexType >::operator= ( Csr< ValueType, IndexType > && )

Move-assigns a Csr matrix.

Preserves executor, moves the data and leaves the moved-from object in an empty state (0x0 LinOp with unchanged executor and strategy, no nonzeros and valid row pointers).

◆ permute() [1/2]

template<typename ValueType = default_precision, typename IndexType = int32>

std::unique_ptr< Csr > gko::matrix::Csr< ValueType, IndexType >::permute	(	ptr_param< const Permutation< index_type > >	permutation,
		permute_mode	mode = permute_mode::symmetric ) const

Creates a permuted copy of this matrix with the given permutation .

By default, this computes a symmetric permutation (permute_mode::symmetric). For the effect of the different permutation modes, see permute_mode

Parameters

permutation	The input permutation.
mode	The permutation mode. If permute_mode::inverse is set, we use the inverse permutation $P^{-1}$ instead of . If permute_mode::rows is set, the rows will be permuted. If permute_mode::columns is set, the columns will be permuted.

Returns: The permuted matrix.

◆ permute() [2/2]

template<typename ValueType = default_precision, typename IndexType = int32>

std::unique_ptr< Csr > gko::matrix::Csr< ValueType, IndexType >::permute	(	ptr_param< const Permutation< index_type > >	row_permutation,
		ptr_param< const Permutation< index_type > >	column_permutation,
		bool	invert = false ) const

Creates a non-symmetrically permuted copy of this matrix with the given row and column permutations and .

The operation will compute , or if invert is false, and , or $A' = P^{-1} A Q^{-T}$ if invert is true.

Parameters

row_permutation	The permutation to apply to the rows
column_permutation	The permutation to apply to the columns
invert	If set to false, uses the input permutations, otherwise uses their inverses $P^{-1}, Q^{-1}$

Returns: The permuted matrix.

◆ permute_reuse() [1/2]

template<typename ValueType = default_precision, typename IndexType = int32>

std::pair< std::unique_ptr< Csr >, permuting_reuse_info > gko::matrix::Csr< ValueType, IndexType >::permute_reuse	(	ptr_param< const Permutation< index_type > >	permutation,
		permute_mode	mode = permute_mode::symmetric ) const

Computes the operations necessary to propagate changed values from a matrix A to a permuted matrix.

The semantics of this function match those of permute(ptr_param<const Permutation<index_type>>, permute_mode). Updating values works as follows:

auto [permuted, reuse] = matrix->permute_reuse(permutation, mode);
change_values(matrix);
reuse->update_values(matrix, permuted);

Parameters

permutation	The input permutation.
mode	The permutation mode. If permute_mode::inverse is set, we use the inverse permutation $P^{-1}$ instead of . If permute_mode::rows is set, the rows will be permuted. If permute_mode::columns is set, the columns will be permuted.

Returns: an std::pair consisting of the permuted matrix and the reuse info that can be used to update values in the permuted matrix.

◆ permute_reuse() [2/2]

template<typename ValueType = default_precision, typename IndexType = int32>

std::pair< std::unique_ptr< Csr >, permuting_reuse_info > gko::matrix::Csr< ValueType, IndexType >::permute_reuse	(	ptr_param< const Permutation< index_type > >	row_permutation,
		ptr_param< const Permutation< index_type > >	column_permutation,
		bool	invert = false ) const

Computes the operations necessary to propagate changed values from a matrix A to a permuted matrix.

The semantics of this function match those of permute(ptr_param<const Permutation<index_type>>, ptr_param<const Permutation<index_type>>, bool). Updating values works as follows:

auto [permuted, reuse] = matrix->permute_reuse(row_perm, col_perm, inv);
change_values(matrix);
reuse->update_values(matrix, permuted);

Parameters

row_permutation	The permutation to apply to the rows
column_permutation	The permutation to apply to the columns
invert	If set to false, uses the input permutations, otherwise uses their inverses $P^{-1}, Q^{-1}$

Returns: an std::pair consisting of the permuted matrix and the reuse info that can be used to update values in the permuted matrix.

◆ scale()

template<typename ValueType = default_precision, typename IndexType = int32>

void gko::matrix::Csr< ValueType, IndexType >::scale ( ptr_param< const LinOp > alpha )

inline

Scales the matrix with a scalar.

Parameters

alpha The entire matrix is scaled by alpha. alpha has to be a 1x1 Dense matrix.

◆ scale_add()

template<typename ValueType = default_precision, typename IndexType = int32>

std::unique_ptr< Csr > gko::matrix::Csr< ValueType, IndexType >::scale_add	(	ptr_param< const Dense< value_type > >	scale_this,
		ptr_param< const Dense< value_type > >	scale_other,
		ptr_param< const Csr< ValueType, IndexType > >	mtx_other ) const

Computes the sparse matrix sum scale_this * this + scale_other * mtx_add on the executor of this matrix.

This matrix needs to be sorted by column index, otherwise the result will be incorrect.

Parameters

scale_this	the scalar by which this matrix will be scaled.
scale_other	the scalar by which this matrix will be scaled.
mtx_other	the matrix which will be added to this, scaled by scale_other. It needs to be sorted by column index, otherwise the result will be incorrect.

Returns: the result of the computation, stored on the same executor as this matrix.

Referenced by gko::matrix::Csr< ValueType, IndexType >::multiply_add_reuse_info::update_values().

◆ scale_permute() [1/2]

template<typename ValueType = default_precision, typename IndexType = int32>

std::unique_ptr< Csr > gko::matrix::Csr< ValueType, IndexType >::scale_permute	(	ptr_param< const ScaledPermutation< value_type, index_type > >	permutation,
		permute_mode	= permute_mode::symmetric ) const

Creates a scaled and permuted copy of this matrix.

For an explanation of the permutation modes, see permute(ptr_param<const Permutation<index_type>>, permute_mode)

Parameters

permutation	The scaled permutation.
mode	The permutation mode.

Returns: The permuted matrix.

◆ scale_permute() [2/2]

template<typename ValueType = default_precision, typename IndexType = int32>

std::unique_ptr< Csr > gko::matrix::Csr< ValueType, IndexType >::scale_permute	(	ptr_param< const ScaledPermutation< value_type, index_type > >	row_permutation,
		ptr_param< const ScaledPermutation< value_type, index_type > >	column_permutation,
		bool	invert = false ) const

Creates a scaled and permuted copy of this matrix.

For an explanation of the parameters, see permute(ptr_param<const Permutation<index_type>>, ptr_param<const Permutation<index_type>>, permute_mode)

Parameters

row_permutation	The scaled row permutation.
column_permutation	The scaled column permutation.
invert	If set to false, uses the input permutations, otherwise uses their inverses $P^{-1}, Q^{-1}$

Returns: The permuted matrix.

◆ set_strategy()

template<typename ValueType = default_precision, typename IndexType = int32>

void gko::matrix::Csr< ValueType, IndexType >::set_strategy ( std::shared_ptr< strategy_type > strategy )

inline

Set the strategy.

Parameters

strategy the csr strategy

◆ transpose()

template<typename ValueType = default_precision, typename IndexType = int32>

std::unique_ptr< LinOp > gko::matrix::Csr< ValueType, IndexType >::transpose ( ) const

overridevirtual

Returns a LinOp representing the transpose of the Transposable object.

Returns: a pointer to the new transposed object

Implements gko::Transposable.

◆ transpose_reuse()

template<typename ValueType = default_precision, typename IndexType = int32>

std::pair< std::unique_ptr< Csr >, permuting_reuse_info > gko::matrix::Csr< ValueType, IndexType >::transpose_reuse ( ) const

Computes the necessary data to update a transposed matrix from its original matrix.

auto [transposed, reuse] = matrix->transpose_reuse();
change_values(matrix);
reuse->update_values(matrix, transposed);

Returns: an std::pair consisting of the transposed matrix and a reuse info struct that can be used to update values in the transposed matrix.

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

ginkgo/core/distributed/matrix.hpp
ginkgo/core/matrix/csr.hpp

Classes

Public Types

Public Member Functions

Static Public Member Functions

Friends

Detailed Description

Constructor & Destructor Documentation

◆ Csr() [1/2]

◆ Csr() [2/2]

Member Function Documentation

◆ add_scale_reuse()

◆ compute_absolute()

◆ compute_absolute_inplace()

◆ conj_transpose()

◆ create() [1/4]

◆ create() [2/4]

◆ create() [3/4]

◆ create() [4/4]

◆ create_const()

◆ create_submatrix() [1/2]

◆ create_submatrix() [2/2]

◆ extract_diagonal()

◆ get_col_idxs()

◆ get_const_col_idxs()

◆ get_const_row_ptrs()

◆ get_const_srow()

◆ get_const_values()

◆ get_num_srow_elements()

◆ get_num_stored_elements()

◆ get_row_ptrs()

◆ get_srow()

◆ get_strategy()

◆ get_values()

◆ inv_scale()

◆ multiply()

◆ multiply_add()

◆ multiply_add_reuse()

◆ multiply_reuse()

◆ operator=() [1/2]

◆ operator=() [2/2]

◆ permute() [1/2]

◆ permute() [2/2]

◆ permute_reuse() [1/2]

◆ permute_reuse() [2/2]

◆ scale()

◆ scale_add()

◆ scale_permute() [1/2]

◆ scale_permute() [2/2]

◆ set_strategy()

◆ transpose()

◆ transpose_reuse()