Template Numerical Library: TNL::Matrices::Sandbox::SparseSandboxMatrix< Real, Device, Index, MatrixType, RealAllocator, IndexAllocator

Template of a sparse matrix that can be used for testing of new sparse-matrix formats. More...

#include <TNL/Matrices/Sandbox/SparseSandboxMatrix.h>

Inheritance diagram for TNL::Matrices::Sandbox::SparseSandboxMatrix< Real, Device, Index, MatrixType, RealAllocator, IndexAllocator >:

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Collaboration diagram for TNL::Matrices::Sandbox::SparseSandboxMatrix< Real, Device, Index, MatrixType, RealAllocator, IndexAllocator >:

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Public Types
using	ColumnsIndexesVectorType

using	ColumnsIndexesViewType = typename ColumnsIndexesVectorType::ViewType

using	ConstColumnsIndexesViewType = typename ColumnsIndexesViewType::ConstViewType

using	ConstRowCapacitiesView = typename RowCapacitiesView::ConstViewType

using	ConstRowView = SparseSandboxMatrixRowView< ConstValuesViewType, ConstColumnsIndexesViewType, isBinary() >
	Type for accessing constant matrix rows.

using	ConstValuesViewType = typename ValuesViewType::ConstViewType

using	ConstViewType = SparseSandboxMatrixView< std::add_const_t< Real >, Device, Index, MatrixType >
	Matrix view type for constant instances.

using	DeviceType = Device
	The device where the matrix is allocated.

using	IndexAllocatorType = IndexAllocator
	The allocator for matrix elements column indexes.

using	IndexType = Index
	The type used for matrix elements indexing.

using	RealAllocatorType = RealAllocator
	The allocator for matrix elements values.

using	RealType = std::remove_const_t< Real >
	The type of matrix elements.

using	RowCapacitiesType = Containers::Vector< std::remove_const_t< Index >, Device, Index, IndexAllocator >

using	RowCapacitiesView = Containers::VectorView< std::remove_const_t< Index >, Device, Index >

using	RowPointers = TNL::Containers::Vector< IndexType, DeviceType, IndexType >
	Type of container for CSR row pointers.

using	RowView = SparseSandboxMatrixRowView< ValuesViewType, ColumnsIndexesViewType, isBinary() >
	Type for accessing matrix rows.

template<typename _Real = Real, typename _Device = Device, typename _Index = Index, typename _MatrixType = MatrixType, typename _RealAllocator = typename Allocators::Default< _Device >::template Allocator< _Real >, typename _IndexAllocator = typename Allocators::Default< _Device >::template Allocator< _Index >>
using	Self = SparseSandboxMatrix< _Real, _Device, _Index, _MatrixType, _RealAllocator, _IndexAllocator >
	Helper type for getting self type or its modifications.

using	ValuesVectorType = Containers::Vector< Real, Device, Index, RealAllocator >

using	ValuesViewType = typename ValuesVectorType::ViewType

using	ViewType = SparseSandboxMatrixView< Real, Device, Index, MatrixType >
	Type of related matrix view.

Public Member Functions
	SparseSandboxMatrix (const RealAllocatorType &realAllocator=RealAllocatorType(), const IndexAllocatorType &indexAllocator=IndexAllocatorType())
	Constructor only with values and column indexes allocators.

template<typename RowCapacitiesVector , std::enable_if_t< TNL::IsArrayType< RowCapacitiesVector >::value, int > = 0>
	SparseSandboxMatrix (const RowCapacitiesVector &rowCapacities, IndexType columns, const RealAllocatorType &realAllocator=RealAllocatorType(), const IndexAllocatorType &indexAllocator=IndexAllocatorType())
	Constructor with matrix rows capacities given as a vector and number of columns.

	SparseSandboxMatrix (const SparseSandboxMatrix &matrix)=default
	Copy constructor.

template<typename ListIndex >
	SparseSandboxMatrix (const std::initializer_list< ListIndex > &rowCapacities, IndexType columns, const RealAllocatorType &realAllocator=RealAllocatorType(), const IndexAllocatorType &indexAllocator=IndexAllocatorType())
	Constructor with matrix rows capacities and number of columns.

template<typename Index_t , std::enable_if_t< std::is_integral< Index_t >::value, int > = 0>
	SparseSandboxMatrix (Index_t rows, Index_t columns, const RealAllocatorType &realAllocator=RealAllocatorType(), const IndexAllocatorType &indexAllocator=IndexAllocatorType())
	Constructor with matrix dimensions.

	SparseSandboxMatrix (IndexType rows, IndexType columns, const std::initializer_list< std::tuple< IndexType, IndexType, RealType > > &data, const RealAllocatorType &realAllocator=RealAllocatorType(), const IndexAllocatorType &indexAllocator=IndexAllocatorType())
	Constructor with matrix dimensions and data in initializer list.

template<typename MapIndex , typename MapValue >
	SparseSandboxMatrix (IndexType rows, IndexType columns, const std::map< std::pair< MapIndex, MapIndex >, MapValue > &map, const RealAllocatorType &realAllocator=RealAllocatorType(), const IndexAllocatorType &indexAllocator=IndexAllocatorType())
	Constructor with matrix dimensions and data in std::map.

	SparseSandboxMatrix (SparseSandboxMatrix &&matrix) noexcept=default
	Move constructor.

__cuda_callable__ void	addElement (IndexType row, IndexType column, const RealType &value, const RealType &thisElementMultiplicator)
	Add element at given row and column to given value.

template<typename Function >
void	forAllElements (Function &&function)
	Method for parallel iteration over all matrix elements for non-constant instances.

template<typename Function >
void	forAllElements (Function &&function) const
	Method for parallel iteration over all matrix elements for constant instances.

template<typename Function >
void	forAllRows (Function &&function)
	Method for parallel iteration over all matrix rows.

template<typename Function >
void	forAllRows (Function &&function) const
	Method for parallel iteration over all matrix rows for constant instances.

template<typename Function >
void	forElements (IndexType begin, IndexType end, Function &&function)
	Method for parallel iteration over all matrix elements of given rows for non-constant instances.

template<typename Function >
void	forElements (IndexType begin, IndexType end, Function &&function) const
	Method for parallel iteration over matrix elements of given rows for constant instances.

template<typename Function >
void	forRows (IndexType begin, IndexType end, Function &&function)
	Method for parallel iteration over matrix rows from interval `[begin, end)`.

template<typename Function >
void	forRows (IndexType begin, IndexType end, Function &&function) const
	Method for parallel iteration over matrix rows from interval `[begin, end)` for constant instances.

ColumnsIndexesVectorType &	getColumnIndexes ()
	Getter of column indexes for nonconstant instances.

const ColumnsIndexesVectorType &	getColumnIndexes () const
	Getter of segments for non-constant instances.

__cuda_callable__ IndexType	getColumns () const
	Returns number of matrix columns.

template<typename Vector >
void	getCompressedRowLengths (Vector &rowLengths) const
	Computes number of non-zeros in each row.

ConstViewType	getConstView () const
	Returns a non-modifiable view of the sparse matrix.

__cuda_callable__ RealType	getElement (IndexType row, IndexType column) const
	Returns value of matrix element at position given by its row and column index.

IndexType	getNonzeroElementsCount () const
	Returns number of non-zero matrix elements.

__cuda_callable__ RowView	getRow (const IndexType &rowIdx)
	Non-constant getter of simple structure for accessing given matrix row.

__cuda_callable__ ConstRowView	getRow (const IndexType &rowIdx) const
	Constant getter of simple structure for accessing given matrix row.

template<typename Vector >
void	getRowCapacities (Vector &rowCapacities) const
	Compute capacities of all rows.

__cuda_callable__ IndexType	getRowCapacity (IndexType row) const
	Returns capacity of given matrix row.

__cuda_callable__ IndexType	getRows () const
	Returns number of matrix rows.

std::string	getSerializationTypeVirtual () const override
	Returns string with serialization type.

template<typename Real2 , typename Index2 >
void	getTransposition (const SparseSandboxMatrix< Real2, Device, Index2, MatrixType > &matrix, const RealType &matrixMultiplicator=1.0)

ValuesVectorType &	getValues ()
	Returns a reference to a vector with the matrix elements values.

const ValuesVectorType &	getValues () const
	Returns a constant reference to a vector with the matrix elements values.

ViewType	getView ()
	Returns a modifiable view of the sparse matrix.

void	load (const String &fileName)
	Method for loading the matrix from the file with given filename.

void	load (File &file) override
	Method for loading the matrix from a file.

template<typename Matrix >
bool	operator!= (const Matrix &matrix) const
	Comparison operator with another arbitrary matrix type.

template<typename Real_ , typename Device_ , typename Index_ , ElementsOrganization Organization, typename RealAllocator_ >
SparseSandboxMatrix &	operator= (const DenseMatrix< Real_, Device_, Index_, Organization, RealAllocator_ > &matrix)
	Assignment of dense matrix.

template<typename Real_ , typename Device_ , typename Index_ , ElementsOrganization Organization, typename RealAllocator_ >
SparseSandboxMatrix< Real, Device, Index, MatrixType, RealAllocator, IndexAllocator > &	operator= (const DenseMatrix< Real_, Device_, Index_, Organization, RealAllocator_ > &matrix)

template<typename RHSMatrix >
SparseSandboxMatrix &	operator= (const RHSMatrix &matrix)
	Assignment of any matrix type other then this and dense.

template<typename RHSMatrix >
SparseSandboxMatrix< Real, Device, Index, MatrixType, RealAllocator, IndexAllocator > &	operator= (const RHSMatrix &matrix)

SparseSandboxMatrix &	operator= (const SparseSandboxMatrix &matrix)
	Assignment of exactly the same matrix type.

template<typename Device_ >
SparseSandboxMatrix &	operator= (const SparseSandboxMatrix< RealType, Device_, IndexType, MatrixType, RealAllocator, IndexAllocator > &matrix)
	Assignment of exactly the same matrix type but different device.

template<typename Device_ >
SparseSandboxMatrix< Real, Device, Index, MatrixType, RealAllocator, IndexAllocator > &	operator= (const SparseSandboxMatrix< RealType, Device_, IndexType, MatrixType, RealAllocator, IndexAllocator > &matrix)

template<typename Matrix >
bool	operator== (const Matrix &matrix) const
	Comparison operator with another arbitrary matrix type.

void	print (std::ostream &str) const
	Method for printing the matrix to output stream.

template<typename Fetch , typename Reduce , typename Keep , typename FetchReal >
void	reduceAllRows (Fetch &&fetch, const Reduce &reduce, Keep &&keep, const FetchReal &zero) const
	Method for performing general reduction on all matrix rows for constant instances.

template<typename Fetch , typename Reduce , typename Keep , typename FetchReal >
void	reduceRows (IndexType begin, IndexType end, Fetch &&fetch, const Reduce &reduce, Keep &&keep, const FetchReal &zero) const
	Method for performing general reduction on matrix rows for constant instances.

template<typename Fetch , typename Reduce , typename Keep , typename FetchValue >
void	reduceRows (IndexType begin, IndexType end, Fetch &&fetch, const Reduce &reduce, Keep &&keep, const FetchValue &zero) const

void	reset ()
	Resets the matrix to zero dimensions.

void	save (const String &fileName) const
	Method for saving the matrix to the file with given filename.

void	save (File &file) const override
	Method for saving the matrix to a file.

template<typename Function >
void	sequentialForAllRows (Function &function)
	This method calls sequentialForRows for all matrix rows.

template<typename Function >
void	sequentialForAllRows (Function &function) const
	This method calls sequentialForRows for all matrix rows (for constant instances).

template<typename Function >
void	sequentialForRows (IndexType begin, IndexType end, Function &function)
	Method for sequential iteration over all matrix rows for non-constant instances.

template<typename Function >
void	sequentialForRows (IndexType begin, IndexType end, Function &function) const
	Method for sequential iteration over all matrix rows for constant instances.

void	setDimensions (IndexType rows, IndexType columns)
	Set number of rows and columns of this matrix.

__cuda_callable__ void	setElement (IndexType row, IndexType column, const RealType &value)
	Sets element at given row and column to given value.

void	setElements (const std::initializer_list< std::tuple< IndexType, IndexType, RealType > > &data)
	This method sets the sparse matrix elements from initializer list.

template<typename MapIndex , typename MapValue >
void	setElements (const std::map< std::pair< MapIndex, MapIndex >, MapValue > &map)
	This method sets the sparse matrix elements from std::map.

template<typename Matrix >
void	setLike (const Matrix &matrix)
	Set the number of matrix rows and columns by the given matrix.

template<typename Matrix_ >
void	setLike (const Matrix_ &matrix)

template<typename RowCapacitiesVector >
void	setRowCapacities (const RowCapacitiesVector &rowCapacities)
	Allocates memory for non-zero matrix elements.

template<typename InVector , typename OutVector >
void	vectorProduct (const InVector &inVector, OutVector &outVector, RealType matrixMultiplicator=1.0, RealType outVectorMultiplicator=0.0, IndexType begin=0, IndexType end=0) const
	Computes product of matrix and vector.

Public Member Functions inherited from TNL::Object
virtual	~Object ()=default
	Destructor.

void	load (const String &fileName)
	Method for restoring the object from a file.

void	save (const String &fileName) const
	Method for saving the object to a file as a binary data.

Static Public Member Functions
static std::string	getSerializationType ()
	Returns string with serialization type.

static constexpr bool	isBinary ()
	Test of binary matrix type.

static constexpr bool	isSymmetric ()
	Test of symmetric matrix type.

Static Public Member Functions inherited from TNL::Object
static std::string	getSerializationType ()
	Static serialization type getter.

Protected Attributes
ColumnsIndexesVectorType	columnIndexes
	Vector containing the column indices of non-zero matrix elements.

IndexType	columns

IndexAllocator	indexAllocator

RowPointers	rowPointers
	Container for CSR row pointers.

IndexType	rows

ValuesVectorType	values
	Array containing the allocated matrix elements.

ViewType	view
	Instance of the sparse matrix view.

Detailed Description

template<typename Real = double, typename Device = Devices::Host, typename Index = int, typename MatrixType = GeneralMatrix, typename RealAllocator = typename Allocators::Default< Device >::template Allocator< Real >, typename IndexAllocator = typename Allocators::Default< Device >::template Allocator< Index >>
class TNL::Matrices::Sandbox::SparseSandboxMatrix< Real, Device, Index, MatrixType, RealAllocator, IndexAllocator >

Template of a sparse matrix that can be used for testing of new sparse-matrix formats.

Template Parameters

Real	is a type of matrix elements. If Real equals bool the matrix is treated as binary and so the matrix elements values are not stored in the memory since we need to remember only coordinates of non-zero elements( which equal one).
Device	is a device where the matrix is allocated.
Index	is a type for indexing of the matrix elements.
MatrixType	specifies a symmetry of matrix. See MatrixType. Symmetric matrices store only lower part of the matrix and its diagonal. The upper part is reconstructed on the fly. GeneralMatrix with no symmetry is used by default.
RealAllocator	is allocator for the matrix elements values.
IndexAllocator	is allocator for the matrix elements column indexes.

This class can be used for rapid testing and development of new formats for sparse matrices. One may profit from several TNL tools compatible with interface of this templated class like:

Large set of existing unit tests.
Matrix reading from MTX files - to use TNL::Matrices::MatrixReader, the following methods must be functional a. SparseSandboxMatrix::setRowCapacities b. SparseSandboxMatrix::setElement c. SparseSandboxMatrix::operator= between different devices
Matrix benchmarks - the following methods must be functional a. SparseSandboxMatrix::vectorProduct - for SpMV benchmark
Linear solvers
Simple comparison of performance with other matrix formats

In the core of this class there is:

Vector 'values(\ref SparseSandboxMatrix::values) which is used for storing of matrix elements values.
VectorcolumnIndexes` (SparseSandboxMatrix::columnIndexes). This vector is used for storing of matrix elements column indexes.

This class contains fully functional implementation of CSR format and so the user have to replace just what he needs to. Once you have successfully implemented the sparse matrix format in this form, you may consider to extract it into a form of segments to make it accessible even for other algorithms then SpMV.

Parts of the code, that need to be modified are marked by SANDBOX_TODO tag. The whole implementation consits of the following classes:

SparseSandboxMatrix - this class, it serves for matrix setup and performing of the main operations.
SparseSandboxMatrixView - view class which is necessary mainly for passing the matrix to GPU kernels. Most methods of SparseSandboxMatrix are common with SparseSandboxMatrixView and in this case they are implemented in the view class (and there is just redirection from this class). For this reason, SparseSandboxMatrix contains instance of the view class (SparseSandboxMatrix::view) which needs to be regularly updated each time when metadata are changed. This is usually done by the means of method SparseSandboxMatrix::getView.
SparseSandboxMatrixRowView - is a class for accessing particular matrix rows. It will, likely, require some changes as well.

We suggest the following way of implementation of the new sparse matrix format:

Add metadata required by your format next to SparseSandboxMatrix::rowPointers but do not replace the row pointers. It will allow you to implement your new format next to the original CSR and to check/compare with the valid CSR implementation any time you get into troubles. The adventage is that all unit tests are working properly and you may just focus on modifying one method after another. The unit tests are called from src/UnitTests/Matrices/SparseMatrixTests_SandboxMatrix.h and src/UnitTests/Matrices/SparseMatrixVectorProductTests_SandboxMatrix.h
Modify first the method SparseSandboxMatrix::setRowCapacities which is responsible for the setup of the format metadata.
Continue with modification of constructors, view class, SparseSandboxMatrix::getView and SparseSandboxMatrix::getConstView.
Next you need to modify SparseSandboxMatrix::setElement and SparseSandboxMatrix::getElement methods and assignment operator at least for copying the matrix across different devices (i.e. from CPU to GPU). It will allow you to use TNL::Matrices::MatrixReader. We recommend to have the same data layout on both CPU and GPU so that the transfer of the matrix from CPU to GPU is trivial.
Finally proceed to SparseSandboxMatrix::vectorProduct to implement SpMV operation. We recommend to implement first the CPU version which is easier to debug. Next proceed to GPU version.
When SpMV works it is time to delete the original CSR implementation, i.e. everything around rowPointers.
Optimize your implementation to the best performance and test with tnl-benchmark-spmv - you need to include your new matrix to src/Benchmarks/SpMV/spmv.h and modify this file accordingly.
If you want, you may now generalize SpMV to SparseSandboxMatrix::reduceRows method.
If you have reduceRows implemented, you may use the original implementation of SpMV based just on the reduceRows method.
You may implement SparseSandboxMatrix::forRows and SparseSandboxMatrix::forElements.
Now you have complete implementation of new sparse matrix format. You may turn it into new type of segments (TNL::Algorithms::Segments).

During the implementation some unit tests may crash. If you do not need them at the moment, you may comment them in files src/UnitTests/Matrices/SparseMatrixTests.h and src/UnitTests/Matrices/SparseMatrixVectorProductTests.h

Member Typedef Documentation

◆ ColumnsIndexesVectorType

using TNL::Matrices::Sandbox::SparseSandboxMatrix< Real, Device, Index, MatrixType, RealAllocator, IndexAllocator >::ColumnsIndexesVectorType

Initial value:

Containers::Vector< typename TNL::copy_const< Index >::template from< Real >::type, Device, Index, IndexAllocator >

◆ ConstViewType

using TNL::Matrices::Sandbox::SparseSandboxMatrix< Real, Device, Index, MatrixType, RealAllocator, IndexAllocator >::ConstViewType = SparseSandboxMatrixView< std::add_const_t< Real >, Device, Index, MatrixType >

Matrix view type for constant instances.

See SparseSandboxMatrixView.

◆ RowPointers

using TNL::Matrices::Sandbox::SparseSandboxMatrix< Real, Device, Index, MatrixType, RealAllocator, IndexAllocator >::RowPointers = TNL::Containers::Vector< IndexType, DeviceType, IndexType >

Type of container for CSR row pointers.

SANDBOX_TODO: You may replace it with containers for metadata of your format.

◆ ViewType

using TNL::Matrices::Sandbox::SparseSandboxMatrix< Real, Device, Index, MatrixType, RealAllocator, IndexAllocator >::ViewType = SparseSandboxMatrixView< Real, Device, Index, MatrixType >

Type of related matrix view.

See SparseSandboxMatrixView.

Constructor & Destructor Documentation

◆ SparseSandboxMatrix() [1/8]

template<typename Real , typename Device , typename Index , typename MatrixType , typename RealAllocator , typename IndexAllocator >

TNL::Matrices::Sandbox::SparseSandboxMatrix< Real, Device, Index, MatrixType, RealAllocator, IndexAllocator >::SparseSandboxMatrix	(	const RealAllocatorType &	realAllocator = RealAllocatorType(),
		const IndexAllocatorType &	indexAllocator = IndexAllocatorType() )

Constructor only with values and column indexes allocators.

Parameters

realAllocator	is used for allocation of matrix elements values.
indexAllocator	is used for allocation of matrix elements column indexes.

◆ SparseSandboxMatrix() [2/8]

TNL::Matrices::Sandbox::SparseSandboxMatrix< Real, Device, Index, MatrixType, RealAllocator, IndexAllocator >::SparseSandboxMatrix ( const SparseSandboxMatrix< Real, Device, Index, MatrixType, RealAllocator, IndexAllocator > & matrix )

default

Copy constructor.

Parameters

matrix is the source matrix

◆ SparseSandboxMatrix() [3/8]

TNL::Matrices::Sandbox::SparseSandboxMatrix< Real, Device, Index, MatrixType, RealAllocator, IndexAllocator >::SparseSandboxMatrix ( SparseSandboxMatrix< Real, Device, Index, MatrixType, RealAllocator, IndexAllocator > && matrix )

defaultnoexcept

Move constructor.

Parameters

matrix is the source matrix

◆ SparseSandboxMatrix() [4/8]

template<typename Real , typename Device , typename Index , typename MatrixType , typename RealAllocator , typename IndexAllocator >

template<typename Index_t , std::enable_if_t< std::is_integral< Index_t >::value, int > >

TNL::Matrices::Sandbox::SparseSandboxMatrix< Real, Device, Index, MatrixType, RealAllocator, IndexAllocator >::SparseSandboxMatrix	(	Index_t	rows,
		Index_t	columns,
		const RealAllocatorType &	realAllocator = RealAllocatorType(),
		const IndexAllocatorType &	indexAllocator = IndexAllocatorType() )

Constructor with matrix dimensions.

Parameters

rows	is number of matrix rows.
columns	is number of matrix columns.
realAllocator	is used for allocation of matrix elements values.
indexAllocator	is used for allocation of matrix elements column indexes.

◆ SparseSandboxMatrix() [5/8]

template<typename Real , typename Device , typename Index , typename MatrixType , typename RealAllocator , typename IndexAllocator >

template<typename ListIndex >

TNL::Matrices::Sandbox::SparseSandboxMatrix< Real, Device, Index, MatrixType, RealAllocator, IndexAllocator >::SparseSandboxMatrix	(	const std::initializer_list< ListIndex > &	rowCapacities,
		IndexType	columns,
		const RealAllocatorType &	realAllocator = RealAllocatorType(),
		const IndexAllocatorType &	indexAllocator = IndexAllocatorType() )

explicit

Constructor with matrix rows capacities and number of columns.

The number of matrix rows is given by the size of rowCapacities list.

Template Parameters

ListIndex is the initializer list values type.

Parameters

rowCapacities	is a list telling how many matrix elements must be allocated in each row.
columns	is the number of matrix columns.
realAllocator	is used for allocation of matrix elements values.
indexAllocator	is used for allocation of matrix elements column indexes.

◆ SparseSandboxMatrix() [6/8]

template<typename Real , typename Device , typename Index , typename MatrixType , typename RealAllocator , typename IndexAllocator >

template<typename RowCapacitiesVector , std::enable_if_t< TNL::IsArrayType< RowCapacitiesVector >::value, int > >

TNL::Matrices::Sandbox::SparseSandboxMatrix< Real, Device, Index, MatrixType, RealAllocator, IndexAllocator >::SparseSandboxMatrix	(	const RowCapacitiesVector &	rowCapacities,
		IndexType	columns,
		const RealAllocatorType &	realAllocator = RealAllocatorType(),
		const IndexAllocatorType &	indexAllocator = IndexAllocatorType() )

explicit

Constructor with matrix rows capacities given as a vector and number of columns.

The number of matrix rows is given by the size of rowCapacities vector.

Template Parameters

RowCapacitiesVector is the row capacities vector type. Usually it is some of TNL::Containers::Array, TNL::Containers::ArrayView, TNL::Containers::Vector or TNL::Containers::VectorView.

Parameters

rowCapacities	is a vector telling how many matrix elements must be allocated in each row.
columns	is the number of matrix columns.
realAllocator	is used for allocation of matrix elements values.
indexAllocator	is used for allocation of matrix elements column indexes.

◆ SparseSandboxMatrix() [7/8]

template<typename Real , typename Device , typename Index , typename MatrixType , typename RealAllocator , typename IndexAllocator >

TNL::Matrices::Sandbox::SparseSandboxMatrix< Real, Device, Index, MatrixType, RealAllocator, IndexAllocator >::SparseSandboxMatrix	(	IndexType	rows,
		IndexType	columns,
		const std::initializer_list< std::tuple< IndexType, IndexType, RealType > > &	data,
		const RealAllocatorType &	realAllocator = RealAllocatorType(),
		const IndexAllocatorType &	indexAllocator = IndexAllocatorType() )

explicit

Constructor with matrix dimensions and data in initializer list.

The matrix elements values are given as a list data of triples: { { row1, column1, value1 }, { row2, column2, value2 }, ... }.

Parameters

rows	is number of matrix rows.
columns	is number of matrix columns.
data	is a list of matrix elements values.
realAllocator	is used for allocation of matrix elements values.
indexAllocator	is used for allocation of matrix elements column indexes.

◆ SparseSandboxMatrix() [8/8]

template<typename Real , typename Device , typename Index , typename MatrixType , typename RealAllocator , typename IndexAllocator >

template<typename MapIndex , typename MapValue >

TNL::Matrices::Sandbox::SparseSandboxMatrix< Real, Device, Index, MatrixType, RealAllocator, IndexAllocator >::SparseSandboxMatrix	(	IndexType	rows,
		IndexType	columns,
		const std::map< std::pair< MapIndex, MapIndex >, MapValue > &	map,
		const RealAllocatorType &	realAllocator = RealAllocatorType(),
		const IndexAllocatorType &	indexAllocator = IndexAllocatorType() )

explicit

Constructor with matrix dimensions and data in std::map.

The matrix elements values are given as a map data where keys are std::pair of matrix coordinates ( {row, column} ) and value is the matrix element value.

Template Parameters

MapIndex	is a type for indexing rows and columns.
MapValue	is a type for matrix elements values in the map.

Parameters

rows	is number of matrix rows.
columns	is number of matrix columns.
map	is std::map containing matrix elements.
realAllocator	is used for allocation of matrix elements values.
indexAllocator	is used for allocation of matrix elements column indexes.

Member Function Documentation

◆ addElement()

template<typename Real , typename Device , typename Index , typename MatrixType , typename RealAllocator , typename IndexAllocator >

__cuda_callable__ void TNL::Matrices::Sandbox::SparseSandboxMatrix< Real, Device, Index, MatrixType, RealAllocator, IndexAllocator >::addElement	(	IndexType	row,
		IndexType	column,
		const RealType &	value,
		const RealType &	thisElementMultiplicator )

Add element at given row and column to given value.

This method can be called from the host system (CPU) no matter where the matrix is allocated. If the matrix is allocated on GPU this method can be called even from device kernels. If the matrix is allocated in GPU device this method is called from CPU, it transfers values of each matrix element separately and so the performance is very low. For higher performance see. SparseMatrix::getRow or SparseMatrix::forElements and SparseMatrix::forAllElements. The call may fail if the matrix row capacity is exhausted.

Parameters

row	is row index of the element.
column	is columns index of the element.
value	is the value the element will be set to.
thisElementMultiplicator	is multiplicator the original matrix element value is multiplied by before addition of given value.

◆ forAllElements() [1/2]

template<typename Real , typename Device , typename Index , typename MatrixType , typename RealAllocator , typename IndexAllocator >

template<typename Function >

void TNL::Matrices::Sandbox::SparseSandboxMatrix< Real, Device, Index, MatrixType, RealAllocator, IndexAllocator >::forAllElements ( Function && function )

Method for parallel iteration over all matrix elements for non-constant instances.

See SparseMatrix::forElements.

Template Parameters

Function is a type of lambda function that will operate on matrix elements.

Parameters

function is an instance of the lambda function to be called for each matrix element.

◆ forAllElements() [2/2]

template<typename Real , typename Device , typename Index , typename MatrixType , typename RealAllocator , typename IndexAllocator >

template<typename Function >

void TNL::Matrices::Sandbox::SparseSandboxMatrix< Real, Device, Index, MatrixType, RealAllocator, IndexAllocator >::forAllElements ( Function && function ) const

Method for parallel iteration over all matrix elements for constant instances.

See SparseMatrix::forElements.

Template Parameters

Function is a type of lambda function that will operate on matrix elements.

Parameters

function is an instance of the lambda function to be called for each matrix element.

◆ forAllRows() [1/2]

template<typename Real , typename Device , typename Index , typename MatrixType , typename RealAllocator , typename IndexAllocator >

template<typename Function >

void TNL::Matrices::Sandbox::SparseSandboxMatrix< Real, Device, Index, MatrixType, RealAllocator, IndexAllocator >::forAllRows ( Function && function )

Method for parallel iteration over all matrix rows.

In each row, given lambda function is performed. Each row is processed by at most one thread unlike the method SparseMatrix::forAllElements where more than one thread can be mapped to each row.

Template Parameters

Function is type of the lambda function.

Parameters

function is an instance of the lambda function to be called for each row.

auto function = [] __cuda_callable__ ( RowView& row ) { ... };

__cuda_callable__

#define __cuda_callable__

Definition Macros.h:49

TNL::Matrices::Sandbox::SparseSandboxMatrixRowView

RowView is a simple structure for accessing rows of sparse matrix.

Definition SparseSandboxMatrixRowView.h:24

RowView represents matrix row - see TNL::Matrices::SparseMatrix::RowView.

◆ forAllRows() [2/2]

template<typename Real , typename Device , typename Index , typename MatrixType , typename RealAllocator , typename IndexAllocator >

template<typename Function >

void TNL::Matrices::Sandbox::SparseSandboxMatrix< Real, Device, Index, MatrixType, RealAllocator, IndexAllocator >::forAllRows ( Function && function ) const

Method for parallel iteration over all matrix rows for constant instances.

In each row, given lambda function is performed. Each row is processed by at most one thread unlike the method SparseMatrix::forAllElements where more than one thread can be mapped to each row.

Template Parameters

Function is type of the lambda function.

Parameters

function is an instance of the lambda function to be called for each row.

auto function = [] __cuda_callable__ ( const ConstRowView& row ) { ... };

ConstRowView represents matrix row - see TNL::Matrices::SparseMatrix::ConstRowView.

◆ forElements() [1/2]

template<typename Real , typename Device , typename Index , typename MatrixType , typename RealAllocator , typename IndexAllocator >

template<typename Function >

void TNL::Matrices::Sandbox::SparseSandboxMatrix< Real, Device, Index, MatrixType, RealAllocator, IndexAllocator >::forElements	(	IndexType	begin,
		IndexType	end,
		Function &&	function )

Method for parallel iteration over all matrix elements of given rows for non-constant instances.

Template Parameters

Function is type of lambda function that will operate on matrix elements.

Parameters

begin	defines beginning of the range `[begin, end)` of rows to be processed.
end	defines ending of the range `[begin, end)` of rows to be processed.
function	is an instance of the lambda function to be called for each element of given rows.

The lambda function function should be declared like follows:

auto function = [] __cuda_callable__ ( IndexType rowIdx, IndexType localIdx, IndexType columnIdx, RealType& value )

{ ... }

TNL::Matrices::Sandbox::SparseSandboxMatrix::RealType

std::remove_const_t< Real > RealType

The type of matrix elements.

Definition SparseSandboxMatrix.h:177

TNL::Matrices::Sandbox::SparseSandboxMatrix::IndexType

Index IndexType

The type used for matrix elements indexing.

Definition SparseSandboxMatrix.h:187

The localIdx parameter is a rank of the non-zero element in given row.

◆ forElements() [2/2]

template<typename Real , typename Device , typename Index , typename MatrixType , typename RealAllocator , typename IndexAllocator >

template<typename Function >

void TNL::Matrices::Sandbox::SparseSandboxMatrix< Real, Device, Index, MatrixType, RealAllocator, IndexAllocator >::forElements	(	IndexType	begin,
		IndexType	end,
		Function &&	function ) const

Method for parallel iteration over matrix elements of given rows for constant instances.

Template Parameters

Function is type of lambda function that will operate on matrix elements.

Parameters

begin	defines beginning of the range `[begin, end)` of rows to be processed.
end	defines ending of the range `[begin, end)` of rows to be processed.
function	is an instance of the lambda function to be called for element of given rows.

The lambda function function should be declared like follows:

auto function = [] __cuda_callable__ ( IndexType rowIdx, IndexType localIdx, IndexType columnIdx, const RealType& value )

{ ... };

The localIdx parameter is a rank of the non-zero element in given row.

◆ forRows() [1/2]

template<typename Real , typename Device , typename Index , typename MatrixType , typename RealAllocator , typename IndexAllocator >

template<typename Function >

void TNL::Matrices::Sandbox::SparseSandboxMatrix< Real, Device, Index, MatrixType, RealAllocator, IndexAllocator >::forRows	(	IndexType	begin,
		IndexType	end,
		Function &&	function )

Method for parallel iteration over matrix rows from interval [begin, end).

In each row, given lambda function is performed. Each row is processed by at most one thread unlike the method SparseMatrix::forElements where more than one thread can be mapped to each row.

Template Parameters

Function is type of the lambda function.

Parameters

begin	defines beginning of the range `[begin, end)` of rows to be processed.
end	defines ending of the range `[begin, end)` of rows to be processed.
function	is an instance of the lambda function to be called for each row.

auto function = [] __cuda_callable__ ( RowView& row ) { ... };

RowView represents matrix row - see TNL::Matrices::SparseMatrix::RowView.

◆ forRows() [2/2]

template<typename Real , typename Device , typename Index , typename MatrixType , typename RealAllocator , typename IndexAllocator >

template<typename Function >

void TNL::Matrices::Sandbox::SparseSandboxMatrix< Real, Device, Index, MatrixType, RealAllocator, IndexAllocator >::forRows	(	IndexType	begin,
		IndexType	end,
		Function &&	function ) const

Method for parallel iteration over matrix rows from interval [begin, end) for constant instances.

In each row, given lambda function is performed. Each row is processed by at most one thread unlike the method SparseMatrix::forElements where more than one thread can be mapped to each row.

Template Parameters

Function is type of the lambda function.

Parameters

begin	defines beginning of the range `[begin, end)` of rows to be processed.
end	defines ending of the range `[begin, end)` of rows to be processed.
function	is an instance of the lambda function to be called for each row.

auto function = [] __cuda_callable__ ( const ConstRowView& row ) { ... };

ConstRowView represents matrix row - see TNL::Matrices::SparseMatrix::ConstRowView.

◆ getColumnIndexes() [1/2]

template<typename Real , typename Device , typename Index , typename MatrixType , typename RealAllocator , typename IndexAllocator >

auto TNL::Matrices::Sandbox::SparseSandboxMatrix< Real, Device, Index, MatrixType, RealAllocator, IndexAllocator >::getColumnIndexes ( )

nodiscard

Getter of column indexes for nonconstant instances.

Returns: Reference to a vector with matrix elements column indexes.

◆ getColumnIndexes() [2/2]

template<typename Real , typename Device , typename Index , typename MatrixType , typename RealAllocator , typename IndexAllocator >

auto TNL::Matrices::Sandbox::SparseSandboxMatrix< Real, Device, Index, MatrixType, RealAllocator, IndexAllocator >::getColumnIndexes ( ) const

nodiscard

Getter of segments for non-constant instances.

Segments are a structure for addressing the matrix elements columns and values. In fact, Segments represent the sparse matrix format.

Returns: Non-constant reference to segments.

Getter of segments for constant instances.

Segments are a structure for addressing the matrix elements columns and values. In fact, Segments represent the sparse matrix format.

Returns: Constant reference to segments.

Getter of column indexes for constant instances.

Returns: Constant reference to a vector with matrix elements column indexes.

◆ getColumns()

template<typename Real , typename Device , typename Index , typename MatrixType , typename RealAllocator , typename IndexAllocator >

__cuda_callable__ Index TNL::Matrices::Sandbox::SparseSandboxMatrix< Real, Device, Index, MatrixType, RealAllocator, IndexAllocator >::getColumns ( ) const

nodiscard

Returns number of matrix columns.

Returns: number of matrix columns.

◆ getCompressedRowLengths()

template<typename Real , typename Device , typename Index , typename MatrixType , typename RealAllocator , typename IndexAllocator >

template<typename Vector >

void TNL::Matrices::Sandbox::SparseSandboxMatrix< Real, Device, Index, MatrixType, RealAllocator, IndexAllocator >::getCompressedRowLengths ( Vector & rowLengths ) const

Computes number of non-zeros in each row.

Parameters

rowLengths is a vector into which the number of non-zeros in each row will be stored.

◆ getConstView()

template<typename Real , typename Device , typename Index , typename MatrixType , typename RealAllocator , typename IndexAllocator >

auto TNL::Matrices::Sandbox::SparseSandboxMatrix< Real, Device, Index, MatrixType, RealAllocator, IndexAllocator >::getConstView ( ) const

nodiscard

Returns a non-modifiable view of the sparse matrix.

See SparseSandboxMatrixView.

Returns: sparse matrix view.

◆ getElement()

template<typename Real , typename Device , typename Index , typename MatrixType , typename RealAllocator , typename IndexAllocator >

__cuda_callable__ auto TNL::Matrices::Sandbox::SparseSandboxMatrix< Real, Device, Index, MatrixType, RealAllocator, IndexAllocator >::getElement	(	IndexType	row,
		IndexType	column ) const

nodiscard

Returns value of matrix element at position given by its row and column index.

Parameters

row	is a row index of the matrix element.
column	i a column index of the matrix element.

Returns: value of given matrix element.

◆ getNonzeroElementsCount()

template<typename Real , typename Device , typename Index , typename MatrixType , typename RealAllocator , typename IndexAllocator >

Index TNL::Matrices::Sandbox::SparseSandboxMatrix< Real, Device, Index, MatrixType, RealAllocator, IndexAllocator >::getNonzeroElementsCount ( ) const

nodiscard

Returns number of non-zero matrix elements.

This method really counts the non-zero matrix elements and so it returns zero for matrix having all allocated elements set to zero.

Returns: number of non-zero matrix elements.

◆ getRow() [1/2]

template<typename Real , typename Device , typename Index , typename MatrixType , typename RealAllocator , typename IndexAllocator >

__cuda_callable__ auto TNL::Matrices::Sandbox::SparseSandboxMatrix< Real, Device, Index, MatrixType, RealAllocator, IndexAllocator >::getRow ( const IndexType & rowIdx )

nodiscard

Non-constant getter of simple structure for accessing given matrix row.

Parameters

rowIdx is matrix row index.

Returns: RowView for accessing given matrix row.

See SparseMatrixRowView.

◆ getRow() [2/2]

template<typename Real , typename Device , typename Index , typename MatrixType , typename RealAllocator , typename IndexAllocator >

__cuda_callable__ auto TNL::Matrices::Sandbox::SparseSandboxMatrix< Real, Device, Index, MatrixType, RealAllocator, IndexAllocator >::getRow ( const IndexType & rowIdx ) const

nodiscard

Constant getter of simple structure for accessing given matrix row.

Parameters

rowIdx is matrix row index.

Returns: RowView for accessing given matrix row.

See SparseMatrixRowView.

◆ getRowCapacities()

template<typename Real , typename Device , typename Index , typename MatrixType , typename RealAllocator , typename IndexAllocator >

template<typename Vector >

void TNL::Matrices::Sandbox::SparseSandboxMatrix< Real, Device, Index, MatrixType, RealAllocator, IndexAllocator >::getRowCapacities ( Vector & rowCapacities ) const

Compute capacities of all rows.

The row capacities are not stored explicitly and must be computed.

Parameters

rowCapacities is a vector where the row capacities will be stored.

◆ getRowCapacity()

template<typename Real , typename Device , typename Index , typename MatrixType , typename RealAllocator , typename IndexAllocator >

__cuda_callable__ Index TNL::Matrices::Sandbox::SparseSandboxMatrix< Real, Device, Index, MatrixType, RealAllocator, IndexAllocator >::getRowCapacity ( IndexType row ) const

nodiscard

Returns capacity of given matrix row.

Parameters

row	index of matrix row.

Returns: number of matrix elements allocated for the row.

◆ getRows()

template<typename Real , typename Device , typename Index , typename MatrixType , typename RealAllocator , typename IndexAllocator >

__cuda_callable__ Index TNL::Matrices::Sandbox::SparseSandboxMatrix< Real, Device, Index, MatrixType, RealAllocator, IndexAllocator >::getRows ( ) const

nodiscard

Returns number of matrix rows.

Returns: number of matrix row.

◆ getSerializationType()

template<typename Real , typename Device , typename Index , typename MatrixType , typename RealAllocator , typename IndexAllocator >

std::string TNL::Matrices::Sandbox::SparseSandboxMatrix< Real, Device, Index, MatrixType, RealAllocator, IndexAllocator >::getSerializationType ( )

staticnodiscard

Returns string with serialization type.

The string has a form Matrices::SparseSandboxMatrix< RealType, [any_device], IndexType, General/Symmetric, Format, [any_allocator] >.

Returns: String with the serialization type.

◆ getSerializationTypeVirtual()

template<typename Real , typename Device , typename Index , typename MatrixType , typename RealAllocator , typename IndexAllocator >

std::string TNL::Matrices::Sandbox::SparseSandboxMatrix< Real, Device, Index, MatrixType, RealAllocator, IndexAllocator >::getSerializationTypeVirtual ( ) const

nodiscardoverridevirtual

Returns string with serialization type.

See SparseSandboxMatrix::getSerializationType.

Returns: String with the serialization type.

Reimplemented from TNL::Object.

◆ getValues() [1/2]

template<typename Real , typename Device , typename Index , typename MatrixType , typename RealAllocator , typename IndexAllocator >

auto TNL::Matrices::Sandbox::SparseSandboxMatrix< Real, Device, Index, MatrixType, RealAllocator, IndexAllocator >::getValues ( )

nodiscard

Returns a reference to a vector with the matrix elements values.

Returns: constant reference to a vector with the matrix elements values.

◆ getValues() [2/2]

template<typename Real , typename Device , typename Index , typename MatrixType , typename RealAllocator , typename IndexAllocator >

auto TNL::Matrices::Sandbox::SparseSandboxMatrix< Real, Device, Index, MatrixType, RealAllocator, IndexAllocator >::getValues ( ) const

nodiscard

Returns a constant reference to a vector with the matrix elements values.

Returns: constant reference to a vector with the matrix elements values.

◆ getView()

template<typename Real , typename Device , typename Index , typename MatrixType , typename RealAllocator , typename IndexAllocator >

auto TNL::Matrices::Sandbox::SparseSandboxMatrix< Real, Device, Index, MatrixType, RealAllocator, IndexAllocator >::getView ( )

nodiscard

Returns a modifiable view of the sparse matrix.

See SparseSandboxMatrixView.

Returns: sparse matrix view.

◆ isBinary()

static constexpr bool TNL::Matrices::Sandbox::SparseSandboxMatrix< Real, Device, Index, MatrixType, RealAllocator, IndexAllocator >::isBinary ( )

inlinestaticconstexpr

Test of binary matrix type.

Returns: true if the matrix is stored as binary and false otherwise.

◆ isSymmetric()

static constexpr bool TNL::Matrices::Sandbox::SparseSandboxMatrix< Real, Device, Index, MatrixType, RealAllocator, IndexAllocator >::isSymmetric ( )

inlinestaticconstexpr

Test of symmetric matrix type.

Returns: true if the matrix is stored as symmetric and false otherwise.

◆ load() [1/2]

template<typename Real , typename Device , typename Index , typename MatrixType , typename RealAllocator , typename IndexAllocator >

void TNL::Matrices::Sandbox::SparseSandboxMatrix< Real, Device, Index, MatrixType, RealAllocator, IndexAllocator >::load ( const String & fileName )

Method for loading the matrix from the file with given filename.

Parameters

fileName is name of the file.

◆ load() [2/2]

template<typename Real , typename Device , typename Index , typename MatrixType , typename RealAllocator , typename IndexAllocator >

void TNL::Matrices::Sandbox::SparseSandboxMatrix< Real, Device, Index, MatrixType, RealAllocator, IndexAllocator >::load ( File & file )

overridevirtual

Method for loading the matrix from a file.

Parameters

file	is the input file.

Reimplemented from TNL::Object.

◆ operator!=()

template<typename Real , typename Device , typename Index , typename MatrixType , typename RealAllocator , typename IndexAllocator >

template<typename Matrix >

bool TNL::Matrices::Sandbox::SparseSandboxMatrix< Real, Device, Index, MatrixType, RealAllocator, IndexAllocator >::operator!= ( const Matrix & matrix ) const

nodiscard

Comparison operator with another arbitrary matrix type.

Parameters

matrix is the right-hand side matrix.

Returns: true if the RHS matrix is equal, false otherwise.

◆ operator=() [1/4]

template<typename Real_ , typename Device_ , typename Index_ , ElementsOrganization Organization, typename RealAllocator_ >

SparseSandboxMatrix & TNL::Matrices::Sandbox::SparseSandboxMatrix< Real, Device, Index, MatrixType, RealAllocator, IndexAllocator >::operator= ( const DenseMatrix< Real_, Device_, Index_, Organization, RealAllocator_ > & matrix )

Assignment of dense matrix.

Parameters

matrix is input matrix for the assignment.

Returns: reference to this matrix.

◆ operator=() [2/4]

template<typename RHSMatrix >

SparseSandboxMatrix & TNL::Matrices::Sandbox::SparseSandboxMatrix< Real, Device, Index, MatrixType, RealAllocator, IndexAllocator >::operator= ( const RHSMatrix & matrix )

Assignment of any matrix type other then this and dense.

Warning: Assignment of symmetric sparse matrix to general sparse matrix does not give correct result, currently. Only the diagonal and the lower part of the matrix is assigned.

Parameters

matrix is input matrix for the assignment.

Returns: reference to this matrix.

◆ operator=() [3/4]

template<typename Real , typename Device , typename Index , typename MatrixType , typename RealAllocator , typename IndexAllocator >

SparseSandboxMatrix< Real, Device, Index, MatrixType, RealAllocator, IndexAllocator > & TNL::Matrices::Sandbox::SparseSandboxMatrix< Real, Device, Index, MatrixType, RealAllocator, IndexAllocator >::operator= ( const SparseSandboxMatrix< Real, Device, Index, MatrixType, RealAllocator, IndexAllocator > & matrix )

Assignment of exactly the same matrix type.

Parameters

matrix is input matrix for the assignment.

Returns: reference to this matrix.

◆ operator=() [4/4]

template<typename Device_ >

SparseSandboxMatrix & TNL::Matrices::Sandbox::SparseSandboxMatrix< Real, Device, Index, MatrixType, RealAllocator, IndexAllocator >::operator= ( const SparseSandboxMatrix< RealType, Device_, IndexType, MatrixType, RealAllocator, IndexAllocator > & matrix )

Assignment of exactly the same matrix type but different device.

Parameters

matrix is input matrix for the assignment.

Returns: reference to this matrix.

◆ operator==()

template<typename Real , typename Device , typename Index , typename MatrixType , typename RealAllocator , typename IndexAllocator >

template<typename Matrix >

bool TNL::Matrices::Sandbox::SparseSandboxMatrix< Real, Device, Index, MatrixType, RealAllocator, IndexAllocator >::operator== ( const Matrix & matrix ) const

nodiscard

Comparison operator with another arbitrary matrix type.

Parameters

matrix is the right-hand side matrix.

Returns: true if the RHS matrix is equal, false otherwise.

◆ print()

template<typename Real , typename Device , typename Index , typename MatrixType , typename RealAllocator , typename IndexAllocator >

void TNL::Matrices::Sandbox::SparseSandboxMatrix< Real, Device, Index, MatrixType, RealAllocator, IndexAllocator >::print ( std::ostream & str ) const

Method for printing the matrix to output stream.

Parameters

str	is the output stream.

◆ reduceAllRows()

template<typename Real , typename Device , typename Index , typename MatrixType , typename RealAllocator , typename IndexAllocator >

template<typename Fetch , typename Reduce , typename Keep , typename FetchReal >

void TNL::Matrices::Sandbox::SparseSandboxMatrix< Real, Device, Index, MatrixType, RealAllocator, IndexAllocator >::reduceAllRows	(	Fetch &&	fetch,
		const Reduce &	reduce,
		Keep &&	keep,
		const FetchReal &	zero ) const

Method for performing general reduction on all matrix rows for constant instances.

Template Parameters

Fetch	is a type of lambda function for data fetch declared as `fetch( IndexType rowIdx, IndexType& columnIdx, RealType& elementValue ) -> FetchValue`. The return type of this lambda can be any non void.
Reduce	is a type of lambda function for reduction declared as `reduce( const FetchValue& v1, const FetchValue& v2 ) -> FetchValue`.
Keep	is a type of lambda function for storing results of reduction in each row. It is declared as `keep( const IndexType rowIdx, const double& value )`.
FetchValue	is type returned by the Fetch lambda function.

Parameters

fetch	is an instance of lambda function for data fetch.
reduce	is an instance of lambda function for reduction.
keep	in an instance of lambda function for storing results.
zero	is the identity element of given reduction operation.

◆ reduceRows()

template<typename Fetch , typename Reduce , typename Keep , typename FetchReal >

void TNL::Matrices::Sandbox::SparseSandboxMatrix< Real, Device, Index, MatrixType, RealAllocator, IndexAllocator >::reduceRows	(	IndexType	begin,
		IndexType	end,
		Fetch &&	fetch,
		const Reduce &	reduce,
		Keep &&	keep,
		const FetchReal &	zero ) const

Method for performing general reduction on matrix rows for constant instances.

Template Parameters

Fetch	is a type of lambda function for data fetch declared as `fetch( IndexType rowIdx, IndexType& columnIdx, RealType& elementValue ) -> FetchValue`. The return type of this lambda can be any non void.
Reduce	is a type of lambda function for reduction declared as `reduce( const FetchValue& v1, const FetchValue& v2 ) -> FetchValue`.
Keep	is a type of lambda function for storing results of reduction in each row. It is declared as `keep( const IndexType rowIdx, const double& value )`.
FetchValue	is type returned by the Fetch lambda function.

Parameters

begin	defines beginning of the range `[begin, end)` of rows to be processed.
end	defines ending of the range `[begin, end)` of rows to be processed.
fetch	is an instance of lambda function for data fetch.
reduce	is an instance of lambda function for reduction.
keep	in an instance of lambda function for storing results.
zero	is the identity element of given reduction operation.

◆ save() [1/2]

template<typename Real , typename Device , typename Index , typename MatrixType , typename RealAllocator , typename IndexAllocator >

void TNL::Matrices::Sandbox::SparseSandboxMatrix< Real, Device, Index, MatrixType, RealAllocator, IndexAllocator >::save ( const String & fileName ) const

Method for saving the matrix to the file with given filename.

Parameters

fileName is name of the file.

◆ save() [2/2]

template<typename Real , typename Device , typename Index , typename MatrixType , typename RealAllocator , typename IndexAllocator >

void TNL::Matrices::Sandbox::SparseSandboxMatrix< Real, Device, Index, MatrixType, RealAllocator, IndexAllocator >::save ( File & file ) const

overridevirtual

Method for saving the matrix to a file.

Parameters

file	is the output file.

Reimplemented from TNL::Object.

◆ sequentialForAllRows() [1/2]

template<typename Real , typename Device , typename Index , typename MatrixType , typename RealAllocator , typename IndexAllocator >

template<typename Function >

void TNL::Matrices::Sandbox::SparseSandboxMatrix< Real, Device, Index, MatrixType, RealAllocator, IndexAllocator >::sequentialForAllRows ( Function & function )

This method calls sequentialForRows for all matrix rows.

See SparseMatrix::sequentialForAllRows.

Template Parameters

Function is a type of lambda function that will operate on matrix elements.

Parameters

function is an instance of the lambda function to be called in each row.

◆ sequentialForAllRows() [2/2]

template<typename Real , typename Device , typename Index , typename MatrixType , typename RealAllocator , typename IndexAllocator >

template<typename Function >

void TNL::Matrices::Sandbox::SparseSandboxMatrix< Real, Device, Index, MatrixType, RealAllocator, IndexAllocator >::sequentialForAllRows ( Function & function ) const

This method calls sequentialForRows for all matrix rows (for constant instances).

See SparseMatrix::sequentialForRows.

Template Parameters

Function is a type of lambda function that will operate on matrix elements.

Parameters

function is an instance of the lambda function to be called in each row.

◆ sequentialForRows() [1/2]

template<typename Real , typename Device , typename Index , typename MatrixType , typename RealAllocator , typename IndexAllocator >

template<typename Function >

void TNL::Matrices::Sandbox::SparseSandboxMatrix< Real, Device, Index, MatrixType, RealAllocator, IndexAllocator >::sequentialForRows	(	IndexType	begin,
		IndexType	end,
		Function &	function )

Method for sequential iteration over all matrix rows for non-constant instances.

Template Parameters

Function is type of lambda function that will operate on matrix elements. It should have form like function( IndexType rowIdx, IndexType columnIdx, IndexType columnIdx_, RealType& value ). The column index repeats twice only for compatibility with sparse matrices.

Parameters

begin	defines beginning of the range [begin,end) of rows to be processed.
end	defines ending of the range [begin,end) of rows to be processed.
function	is an instance of the lambda function to be called in each row.

◆ sequentialForRows() [2/2]

template<typename Real , typename Device , typename Index , typename MatrixType , typename RealAllocator , typename IndexAllocator >

template<typename Function >

void TNL::Matrices::Sandbox::SparseSandboxMatrix< Real, Device, Index, MatrixType, RealAllocator, IndexAllocator >::sequentialForRows	(	IndexType	begin,
		IndexType	end,
		Function &	function ) const

Method for sequential iteration over all matrix rows for constant instances.

Template Parameters

Function is type of lambda function that will operate on matrix elements. It should have form like function( IndexType rowIdx, IndexType columnIdx, IndexType columnIdx_, const RealType& value ). The column index repeats twice only for compatibility with sparse matrices.

Parameters

begin	defines beginning of the range [begin,end) of rows to be processed.
end	defines ending of the range [begin,end) of rows to be processed.
function	is an instance of the lambda function to be called in each row.

◆ setDimensions()

template<typename Real , typename Device , typename Index , typename MatrixType , typename RealAllocator , typename IndexAllocator >

void TNL::Matrices::Sandbox::SparseSandboxMatrix< Real, Device, Index, MatrixType, RealAllocator, IndexAllocator >::setDimensions	(	IndexType	rows,
		IndexType	columns )

Set number of rows and columns of this matrix.

Parameters

rows	is the number of matrix rows.
columns	is the number of matrix columns.

◆ setElement()

template<typename Real , typename Device , typename Index , typename MatrixType , typename RealAllocator , typename IndexAllocator >

__cuda_callable__ void TNL::Matrices::Sandbox::SparseSandboxMatrix< Real, Device, Index, MatrixType, RealAllocator, IndexAllocator >::setElement	(	IndexType	row,
		IndexType	column,
		const RealType &	value )

Sets element at given row and column to given value.

Parameters

row	is row index of the element.
column	is columns index of the element.
value	is the value the element will be set to.

◆ setElements() [1/2]

template<typename Real , typename Device , typename Index , typename MatrixType , typename RealAllocator , typename IndexAllocator >

void TNL::Matrices::Sandbox::SparseSandboxMatrix< Real, Device, Index, MatrixType, RealAllocator, IndexAllocator >::setElements ( const std::initializer_list< std::tuple< IndexType, IndexType, RealType > > & data )

This method sets the sparse matrix elements from initializer list.

The number of matrix rows and columns must be set already. The matrix elements values are given as a list data of triples: { { row1, column1, value1 }, { row2, column2, value2 }, ... }.

Parameters

data	is a initializer list of initializer lists representing list of matrix rows.

◆ setElements() [2/2]

template<typename Real , typename Device , typename Index , typename MatrixType , typename RealAllocator , typename IndexAllocator >

template<typename MapIndex , typename MapValue >

void TNL::Matrices::Sandbox::SparseSandboxMatrix< Real, Device, Index, MatrixType, RealAllocator, IndexAllocator >::setElements ( const std::map< std::pair< MapIndex, MapIndex >, MapValue > & map )

This method sets the sparse matrix elements from std::map.

The matrix elements values are given as a map data where keys are std::pair of matrix coordinates ( {row, column} ) and value is the matrix element value.

Template Parameters

MapIndex	is a type for indexing rows and columns.
MapValue	is a type for matrix elements values in the map.

Parameters

map	is std::map containing matrix elements.

◆ setLike()

template<typename Matrix >

void TNL::Matrices::Sandbox::SparseSandboxMatrix< Real, Device, Index, MatrixType, RealAllocator, IndexAllocator >::setLike ( const Matrix & matrix )

Set the number of matrix rows and columns by the given matrix.

Template Parameters

Matrix is matrix type. This can be any matrix having methods getRows and getColumns.

Parameters

matrix in the input matrix dimensions of which are to be adopted.

◆ setRowCapacities()

template<typename Real , typename Device , typename Index , typename MatrixType , typename RealAllocator , typename IndexAllocator >

template<typename RowCapacitiesVector >

void TNL::Matrices::Sandbox::SparseSandboxMatrix< Real, Device, Index, MatrixType, RealAllocator, IndexAllocator >::setRowCapacities ( const RowCapacitiesVector & rowCapacities )

Allocates memory for non-zero matrix elements.

The size of the input vector must be equal to the number of matrix rows. The number of allocated matrix elements for each matrix row depends on the sparse matrix format. Some formats may allocate more elements than required.

Template Parameters

RowCapacitiesVector is a type of vector/array used for row capacities setting.

Parameters

rowCapacities is a vector telling the number of required non-zero matrix elements in each row.

◆ vectorProduct()

template<typename Real , typename Device , typename Index , typename MatrixType , typename RealAllocator , typename IndexAllocator >

template<typename InVector , typename OutVector >

void TNL::Matrices::Sandbox::SparseSandboxMatrix< Real, Device, Index, MatrixType, RealAllocator, IndexAllocator >::vectorProduct	(	const InVector &	inVector,
		OutVector &	outVector,
		RealType	matrixMultiplicator = 1.0,
		RealType	outVectorMultiplicator = 0.0,
		IndexType	begin = 0,
		IndexType	end = 0 ) const

Computes product of matrix and vector.

More precisely, it computes:

outVector = matrixMultiplicator * ( * this ) * inVector + outVectorMultiplicator * outVector

Template Parameters

InVector	is type of input vector. It can be TNL::Containers::Vector, TNL::Containers::VectorView, TNL::Containers::Array, TNL::Containers::ArrayView, or similar container.
OutVector	is type of output vector. It can be TNL::Containers::Vector, TNL::Containers::VectorView, TNL::Containers::Array, TNL::Containers::ArrayView, or similar container.

Parameters

inVector	is input vector.
outVector	is output vector.
matrixMultiplicator	is a factor by which the matrix is multiplied. It is one by default.
outVectorMultiplicator	is a factor by which the outVector is multiplied before added to the result of matrix-vector product. It is zero by default.
begin	is the beginning of the rows range for which the vector product is computed. It is zero by default.
end	is the end of the rows range for which the vector product is computed. It is number if the matrix rows by default.

Member Data Documentation

◆ rowPointers

RowPointers TNL::Matrices::Sandbox::SparseSandboxMatrix< Real, Device, Index, MatrixType, RealAllocator, IndexAllocator >::rowPointers

protected

Container for CSR row pointers.

SANDBOX_TODO: You may replace it with containers and metadata required by you format.

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

src/TNL/Matrices/Sandbox/SparseSandboxMatrix.h
src/TNL/Matrices/Sandbox/SparseSandboxMatrix.hpp

Public Types

Public Member Functions

Static Public Member Functions

Protected Attributes

Detailed Description

Member Typedef Documentation

◆ ColumnsIndexesVectorType

◆ ConstViewType

◆ RowPointers

◆ ViewType

Constructor & Destructor Documentation

◆ SparseSandboxMatrix() [1/8]

◆ SparseSandboxMatrix() [2/8]

◆ SparseSandboxMatrix() [3/8]

◆ SparseSandboxMatrix() [4/8]

◆ SparseSandboxMatrix() [5/8]

◆ SparseSandboxMatrix() [6/8]

◆ SparseSandboxMatrix() [7/8]

◆ SparseSandboxMatrix() [8/8]

Member Function Documentation

◆ addElement()

◆ forAllElements() [1/2]

◆ forAllElements() [2/2]

◆ forAllRows() [1/2]

◆ forAllRows() [2/2]

◆ forElements() [1/2]

◆ forElements() [2/2]

◆ forRows() [1/2]

◆ forRows() [2/2]

◆ getColumnIndexes() [1/2]

◆ getColumnIndexes() [2/2]

◆ getColumns()

◆ getCompressedRowLengths()

◆ getConstView()

◆ getElement()

◆ getNonzeroElementsCount()

◆ getRow() [1/2]

◆ getRow() [2/2]

◆ getRowCapacities()

◆ getRowCapacity()

◆ getRows()

◆ getSerializationType()

◆ getSerializationTypeVirtual()

◆ getValues() [1/2]

◆ getValues() [2/2]

◆ getView()

◆ isBinary()

◆ isSymmetric()

◆ load() [1/2]

◆ load() [2/2]

◆ operator!=()

◆ operator=() [1/4]

◆ operator=() [2/4]

◆ operator=() [3/4]

◆ operator=() [4/4]

◆ operator==()

◆ print()

◆ reduceAllRows()

◆ reduceRows()

◆ save() [1/2]

◆ save() [2/2]

◆ sequentialForAllRows() [1/2]

◆ sequentialForAllRows() [2/2]

◆ sequentialForRows() [1/2]

◆ sequentialForRows() [2/2]

◆ setDimensions()

◆ setElement()

◆ setElements() [1/2]

◆ setElements() [2/2]

◆ setLike()

◆ setRowCapacities()

◆ vectorProduct()

Member Data Documentation

◆ rowPointers