#include <SimpleStorageStrategy.hpp>

Inheritance diagram for lama::SimpleStorageStrategy< ValueType >:

Public Types
typedef std::string::const_iterator	StringIterator
typedef ValueType	ValueType
	the Type of elements of this.
enum	SyncKind { ASYNCHRONOUS, SYNCHRONOUS }
	SyncKind describes if the communication and computation should be done synchronously or asynchronously. More...
enum	MatrixKind { DENSE, SPARSE }
	MatrixKind describes if a matrix is dense or sparse. More...
typedef const Matrix &	ExpressionMemberType
	ExpressionMemberType is the type that is used the template Expression to store a Vector.
Public Member Functions
	SimpleStorageStrategy (Matrix &other)
	Construct a square matrix with the given size and the specified distribution.
virtual	~SimpleStorageStrategy ()
	virtual destructor of SimpleStorageStrategy
void	applyStrategy ()
virtual const char *	getTypeName () const
	Virtual method that delivers the class name to which a matrix belongs.
virtual void	clear ()
	Clear the full matrix, resets global and local sizes to 0.
virtual void	allocate (const IndexType numRows, const IndexType numColumns)
	Reallocate this matrix to a replicated zero-matrix of the given shape.
virtual void	allocate (DistributionPtr rowDistribution, DistributionPtr colDistribution)
	Reallocate this matrix to a distributed zero-matrix by the given distributions.
virtual void	setIdentity ()
	Operator that sets the matrix to the identity matrix.
virtual void	assign (const Matrix &other)
	This method assigns a matrix to this matrix with automatic conversion to the matrix type and the current row / column distribution of *this.
virtual void	assign (const _MatrixStorage &other)
	This method assigns replicated/global matrix storage data to this matrix.
virtual void	assign (const _MatrixStorage &storage, DistributionPtr rowDist, DistributionPtr colDist)
	Assign local storage for a distributed matrix.
virtual void	buildLocalStorage (_MatrixStorage &storage) const
	Get the local part (no splitted columns) of a matrix as if colDist is replicated.
virtual void	redistribute (DistributionPtr rowDistribution, DistributionPtr colDistribution)
	This method allows any arbitrary redistribution of the matrix.
virtual void	getRow (Vector &row, const IndexType globalRowIndex) const
	This method returns one row of the matrix.
virtual void	getDiagonal (Vector &diagonal) const
	This method returns the diagonal.
virtual void	setDiagonal (const Vector &diagonal)
	This method replaces the diagonal.
virtual void	setDiagonal (const Scalar scalar)
	This method replaces the diagonal by a diagonal value.
virtual void	scale (const Vector &scaling)
	This method scales all values with a vector.
virtual void	scale (const Scalar scaling)
	This method scales all matrix values with a scalar.
virtual Scalar	getValue (IndexType i, IndexType j) const
	returns a copy of the value at the passed global indexes.
virtual IndexType	getNumValues () const
	Get the total number of non-zero values in the matrix.
virtual void	matrixTimesVector (Vector &result, const Scalar alpha, const Vector &x, const Scalar beta, const Vector &y) const
	matrixTimesVector computes result = alpha * this * x + beta * y
virtual void	matrixTimesScalar (const Matrix &other, const Scalar alpha)
	matrixTimesScalar computes this = alpha * other
virtual void	matrixTimesMatrix (Matrix &result, const Scalar alpha, const Matrix &B, const Scalar beta, const Matrix &C) const
	matrixTimesMatrix computes result = alpha * this * B + beta * C
virtual IndexType	getLocalNumValues () const
	Getter routine for the local number of stored values.
virtual IndexType	getLocalNumRows () const
	Getter routine for the local number of rows.
virtual IndexType	getLocalNumColumns () const
	Getter routine for the local number of rows and columns.
virtual void	setContext (const ContextPtr context)
	specifies on which compute back end the matrix operations should take place.
virtual ContextPtr	getContextPtr () const
	Getter routine for the context.
virtual MatrixKind	getMatrixKind () const
	Each derived matrix must give info about its kind (DENSE or SPARSE).
virtual void	prefetch () const
	Prefetch matrix data to its 'preferred' context location.
virtual void	wait () const
	wait for a possibly running prefetch.
virtual void	invert (const Matrix &other)
	Compute the inverse of a matrix.
virtual Scalar	maxDiffNorm (const Matrix &other) const
	returns the max norm of ( this - other )
virtual std::auto_ptr< Matrix >	create () const
	Constructor function which creates a 'zero' matrix of same type as a given matrix.
virtual std::auto_ptr< Matrix >	copy () const
	Constructor function which creates a copy of this matrix.
virtual Scalar::ScalarType	getValueType () const
	Query the value type of the matrix elements, e.g.
virtual bool	hasDiagonalProperty () const
	hasDiagonalProperty returns the diagonalProperty of the local storage.
virtual void	resetDiagonalProperty ()
	resetDiagonalProperty rechecks the storages for their diagonal property
virtual size_t	getMemoryUsage () const
	getMemoryUsage returns the global memory that is allocated to hold this matrix.
const MatrixStorage< ValueType > &	getLocalStorage () const
	Getter routine for local part of the sparse matrix.
const MatrixStorage< ValueType > &	getHaloStorage () const
	Getter routine for halo part of the sparse matrix.
virtual void	setContext (const ContextPtr localContext, const ContextPtr haloContext)
	Only sparse matrices will override this method, others will ignore second argument.
virtual const Context &	getContext () const
void	setRawDenseData (const IndexType numRows, const IndexType numColumns, const OtherValueType values[], const OtherValueType eps=0.0)
	Set sparse matrix with global dense data.
void	assign (const _SparseMatrix &matrix)
	Method that assigns a sparse matrix, specialization of assign( const Matrix& )
void	swap (SparseMatrix< ValueType > &other)
	Swap will swap all member variables of the two sparse matrices.
void	matrixTimesVectorImpl (DenseVector< ValueType > &result, const ValueType alpha, const DenseVector< ValueType > &x, const ValueType beta, const DenseVector< ValueType > &y) const
virtual void	matrixTimesVectorNImpl (DenseMatrix< ValueType > &result, const ValueType alpha, const DenseMatrix< ValueType > &x, const ValueType beta, const DenseMatrix< ValueType > &y) const
	matrix times matrix for multiple dense vectors.
ValueType	maxDiffNormImpl (const SparseMatrix< ValueType > &other) const
	Get the maximal difference between two elements for sparse matrices of same type.
IndexType	getPartitialNumValues () const
const Halo &	getHalo () const
	Read access to the halo of the distributed matrix.
virtual void	writeAt (std::ostream &stream) const
	Write some information about this to the passed stream.
virtual void	setComputeKind (SyncKind computeKind)
	set the compute kind.
std::auto_ptr< Matrix >	create (const IndexType numRows, const IndexType numColumns) const
	Constructor creates a replicated matrix of same type as a given matrix.
std::auto_ptr< Matrix >	create (const IndexType size) const
	Constructor creates a distributed zero matrix of same type as a given matrix.
std::auto_ptr< Matrix >	create (DistributionPtr rowDistribution, DistributionPtr colDistribution) const
	Constructor creates a distributed zero matrix of same type as a given matrix.
std::auto_ptr< Matrix >	create (DistributionPtr distribution) const
	Constructor creates a distributed zero matrix of same type as a given matrix.
void	assignTranspose (const Matrix &matrix)
	Assign another matrix transposed to this matrix.
void	writeToFile (const std::string &fileName, const File::FileType fileType=File::BINARY, const File::DataType dataType=File::INTERNAL, const File::IndexDataType indexDataTypeIA=File::INT, const File::IndexDataType indexDataTypeJA=File::INT) const
	Writes this sparse matrix to a file in CSR format.
void	readFromFile (const std::string &filename)
	Assigns this matrix with a replicated sparse matrix read from file.
std::auto_ptr< _LAMAArray >	createArray () const
	This routine creates a LAMA array with the same value type as the matrix.
Scalar	operator() (IndexType i, IndexType j) const
	returns a copy of the value at the passed global indexes.
IndexType	getNumRows () const
	Returns the number of global rows.
IndexType	getNumColumns () const
	Returns the number of columns.
double	getSparsityRate () const
virtual void	matrix2CSRGraph (IndexType xadj, IndexType adjncy, IndexType vwgt, CommunicatorPtr comm, const IndexType globalRowIndices=NULL, IndexType *vtxdist=NULL) const
	transformation from matrix type to a csr graph
const Distribution &	getColDistribution () const
	gets a constant reference to the column distribution.
DistributionPtr	getColDistributionPtr () const
	gets a pointer to the column distribution.
SyncKind	getCommunicationKind () const
	get the communication kind.
SyncKind	getComputeKind () const
	get the compute kind.
void	setCommunicationKind (SyncKind communicationKind)
	set the communication kind.
void	inheritAttributes (const Matrix &other)
	Inherit context and kind arguments from another matrix.
VectorPtr	createDenseVector (DistributionPtr distribution, const Scalar value) const
	Constructor creates a distributed dense vector of same type as a given matrix.
const Distribution &	getDistribution () const
DistributionPtr	getDistributionPtr () const
Static Public Member Functions
static const char *	typeName ()
	Getter for the type name of the class.
Protected Member Functions
void	swap (Distributed &other)
void	checkSettings ()
	Test consistency of sparse matrix data, only used if ASSERT_DEBUG is enabled.
void	matrixTimesMatrixImpl (const ValueType alpha, const SparseMatrix< ValueType > &A, const SparseMatrix< ValueType > &B, const ValueType beta, const SparseMatrix< ValueType > &C)
	Set this matrix = alpha * A * B + beta * C.
void	setReplicatedMatrix (const IndexType numRows, const IndexType numColumns)
	Set the global/local size of replicated matrix.
void	setDistributedMatrix (DistributionPtr distribution, DistributionPtr colDistribution)
	Set the global and local size of distributed matrix.
void	swapMatrix (Matrix &other)
void	setDistributionPtr (DistributionPtr distributionPtr)
Protected Attributes
boost::shared_ptr < MatrixStorage< ValueType > >	mLocalData
	local columns of sparse matrix
boost::shared_ptr < MatrixStorage< ValueType > >	mHaloData
	local columns of sparse matrix
Halo	mHalo
	Exchange plans for halo part due to column distribution.
DistributionPtr	mColDistribution
IndexType	mNumRows
IndexType	mNumColumns
Private Member Functions
	LAMA_LOG_DECL_STATIC_LOGGER (logger)
Private Attributes
MatrixPtr	mInnerMatrix

template<typename ValueType>
class lama::SimpleStorageStrategy< ValueType >

Member Typedef Documentation

typedef const Matrix& lama::Matrix::ExpressionMemberType [inherited]

ExpressionMemberType is the type that is used the template Expression to store a Vector.

template<typename ValueType >

typedef std::string::const_iterator lama::SimpleStorageStrategy< ValueType >::StringIterator

typedef ValueType lama::SparseMatrix< ValueType >::ValueType [inherited]

the Type of elements of this.

Member Enumeration Documentation

enum lama::Matrix::MatrixKind [inherited]

MatrixKind describes if a matrix is dense or sparse.

Enumerator:

DENSE	matrix kind for a dense matrix
SPARSE	matrix kind for a sparse matrix

enum lama::Matrix::SyncKind [inherited]

SyncKind describes if the communication and computation should be done synchronously or asynchronously.

Enumerator:

ASYNCHRONOUS
SYNCHRONOUS

Constructor & Destructor Documentation

template<typename ValueType >

lama::SimpleStorageStrategy< ValueType >::SimpleStorageStrategy ( Matrix & other )

Construct a square matrix with the given size and the specified distribution.

Parameters:

[in]	matrixInput	Specifies the file name of the Matrix which used to create the generated matrix.
[in]	config	Configuration which will be parsed to generate the defined matrix

template<typename ValueType >

lama::SimpleStorageStrategy< ValueType >::~SimpleStorageStrategy ( ) [virtual]

virtual destructor of SimpleStorageStrategy

Member Function Documentation

template<typename ValueType >

void lama::SimpleStorageStrategy< ValueType >::allocate	(	const IndexType	numRows,
		const IndexType	numColumns
	)		`[virtual]`

Reallocate this matrix to a replicated zero-matrix of the given shape.

Parameters:

[in]	numRows	number of rows
[in]	numColumns	number of columns

Remarks:: The call allocate( 0, 0 ) implies a clear on all arrays used internally for the presentation of the matrix data.

Reimplemented from lama::SparseMatrix< ValueType >.

References LAMA_ASSERT.

template<typename ValueType >

void lama::SimpleStorageStrategy< ValueType >::allocate	(	DistributionPtr	rowDistribution,
		DistributionPtr	colDistribution
	)		`[virtual]`

Reallocate this matrix to a distributed zero-matrix by the given distributions.

Parameters:

[in]	distribution	is row distribution, number of rows given by getGlobalSize()
[in]	colDistribution	is col distribution, number of columns given by getGlobalSize()

The distributions specify the new global and the new local sizes for a distributed matrix.

Reimplemented from lama::SparseMatrix< ValueType >.

References LAMA_ASSERT.

template<typename ValueType >

void lama::SimpleStorageStrategy< ValueType >::applyStrategy ( )

References lama::Context::CUDA, and lama::Matrix::setDistributedMatrix().

template<typename ValueType >

void lama::SimpleStorageStrategy< ValueType >::assign ( const Matrix & other ) [virtual]

This method assigns a matrix to this matrix with automatic conversion to the matrix type and the current row / column distribution of *this.

Note: other.getNumRows() == getNumRows(), other.getNumColumns() == getNumColumns() is mandatory

Reimplemented from lama::SparseMatrix< ValueType >.

References LAMA_ASSERT.

template<typename ValueType >

void lama::SimpleStorageStrategy< ValueType >::assign ( const _MatrixStorage & other ) [virtual]

This method assigns replicated/global matrix storage data to this matrix.

Note: the current matrix data will be overridden.

Reimplemented from lama::SparseMatrix< ValueType >.

References LAMA_ASSERT.

template<typename ValueType >

void lama::SimpleStorageStrategy< ValueType >::assign	(	const _MatrixStorage &	storage,
		DistributionPtr	rowDist,
		DistributionPtr	colDist
	)		`[virtual]`

Assign local storage for a distributed matrix.

Reimplemented from lama::SparseMatrix< ValueType >.

References LAMA_ASSERT.

void lama::SparseMatrix< ValueType >::assign ( const _SparseMatrix< ValueType > & matrix ) [inherited]

Method that assigns a sparse matrix, specialization of assign( const Matrix& )

void lama::SparseMatrix< ValueType >::assignTranspose ( const Matrix & matrix ) [inherited]

Assign another matrix transposed to this matrix.

template<typename ValueType >

void lama::SimpleStorageStrategy< ValueType >::buildLocalStorage ( _MatrixStorage & storage ) const [virtual]

Get the local part (no splitted columns) of a matrix as if colDist is replicated.

Parameters:

[out] storage will contain the local part of the matrix with all columns.

As splitting of columns is differently handled for sparse and dense matrices, this method is useful for conversion between them. An alternative solution of copying the whole matrix and replication of columns might be too expensive.

Reimplemented from lama::SparseMatrix< ValueType >.

References LAMA_ASSERT.

void lama::SparseMatrix< ValueType >::checkSettings ( ) [protected, inherited]

Test consistency of sparse matrix data, only used if ASSERT_DEBUG is enabled.

Reimplemented from lama::Matrix.

template<typename ValueType >

void lama::SimpleStorageStrategy< ValueType >::clear ( ) [virtual]

Clear the full matrix, resets global and local sizes to 0.

     CSRSparseMatrix<double> a ( ... );
     a = CSRSparseMatrix<double> ();     \\ will free all arrays
     a.clear();                          \\ same functionality, clears involved arrays

Reimplemented from lama::SparseMatrix< ValueType >.

References LAMA_ASSERT.

template<typename ValueType >

std::auto_ptr< Matrix > lama::SimpleStorageStrategy< ValueType >::copy ( ) const [virtual]

Constructor function which creates a copy of this matrix.

 std::auto_ptr<Matrix> newMatrix = matrix.copy();
 // More convenient to use, but exactly same as follows:
 std::auto_ptr<Matrix> newMatrix = matrix.create(); *newMatrix = matrix;

This method is a workaround to call the copy constructor of a derived matrix class where the derived class is not known at compile time.

Reimplemented from lama::SparseMatrix< ValueType >.

References LAMA_ASSERT.

template<typename ValueType >

std::auto_ptr< Matrix > lama::SimpleStorageStrategy< ValueType >::create ( ) const [virtual]

Constructor function which creates a 'zero' matrix of same type as a given matrix.

 void sub( ..., const Matrix& matrix, ...)
 {
     ...
     // Create a copy of the input matrix

     std::auto_ptr<Matrix> newMatrix = matrix.create();
     *newMatrix = matrix;

     // Create a unity matrix of same type and same row distribution as matrix

     std::auto_ptr<Matrix> newMatrix = matrix.create();
     newMatrix->allocate( matrix.getRowDistributionPtr(), matrix.getRowDistributionPtr() );
     newMatrix->setIdentity();
     ...
 }

This method is a workaround to call the constructor of a derived matrix class where the derived class is not known at compile time.

Reimplemented from lama::SparseMatrix< ValueType >.

References LAMA_ASSERT.

std::auto_ptr< Matrix > lama::Matrix::create	(	const IndexType	numRows,
		const IndexType	numColumns
	)		const `[inherited]`

Constructor creates a replicated matrix of same type as a given matrix.

References lama::Matrix::create().

std::auto_ptr< Matrix > lama::Matrix::create ( const IndexType size ) const [inherited]

Constructor creates a distributed zero matrix of same type as a given matrix.

References lama::Matrix::create().

std::auto_ptr< Matrix > lama::Matrix::create	(	DistributionPtr	rowDistribution,
		DistributionPtr	colDistribution
	)		const `[inherited]`

Constructor creates a distributed zero matrix of same type as a given matrix.

References lama::Matrix::create().

std::auto_ptr< Matrix > lama::Matrix::create ( DistributionPtr distribution ) const [inherited]

Constructor creates a distributed zero matrix of same type as a given matrix.

References lama::Matrix::create().

std::auto_ptr< _LAMAArray > lama::Matrix::createArray ( ) const [inherited]

This routine creates a LAMA array with the same value type as the matrix.

: returns an auto pointer to the LAMA array.

Same as _LAMAArray::create( this.getValueType() )

Value type is known only at runtime, so pointer to the base class is returned. Auto pointer indicates that calling routine takes ownership of the allocated array.

References lama::Matrix::create(), and lama::Matrix::getValueType().

VectorPtr lama::Matrix::createDenseVector	(	DistributionPtr	distribution,
		const Scalar	value
	)		const `[inherited]`

Constructor creates a distributed dense vector of same type as a given matrix.

References lama::Scalar::DOUBLE, lama::Scalar::FLOAT, lama::Scalar::getValue(), lama::Matrix::getValueType(), and LAMA_THROWEXCEPTION.

const Distribution & lama::Matrix::getColDistribution ( ) const [inline, inherited]

gets a constant reference to the column distribution.

Returns:: a constant reference to the column distribution.

References LAMA_ASSERT_ERROR, and lama::Matrix::mColDistribution.

Referenced by lama::DenseMatrix< T >::assignSparse(), lama::Matrix::checkSettings(), lama::LUSolver::computeLUFactorization(), lama::InverseSolver::decompose(), lama::DenseMatrix< T >::DenseMatrix(), lama::DenseMatrixOps::invert(), lama::InverseSolver::invert(), lama::DenseMatrixOps::invertReplicated(), lama::SpecializedJacobi::iterateTyped(), lama::Matrix::Matrix(), lama::DenseMatrix< T >::matrixTimesMatrix(), lama::SparseMatrix< T >::matrixTimesMatrixImpl(), lama::CRTPMatrix< DenseMatrix< T >, T >::matrixTimesVector(), lama::SparseMatrix< T >::matrixTimesVectorNImpl(), lama::DenseMatrix< T >::maxDiffNorm(), lama::SparseMatrix< T >::maxDiffNorm(), lama::DenseMatrix< T >::maxDiffNormImpl(), lama::SparseMatrix< T >::maxDiffNormImpl(), lama::Matrix::operator=(), lama::LUSolver::solve(), and lama::Solver::solveInit().

DistributionPtr lama::Matrix::getColDistributionPtr ( ) const [inline, inherited]

gets a pointer to the column distribution.

Returns:: a pointer to the column distribution.

References LAMA_ASSERT_ERROR, and lama::Matrix::mColDistribution.

Referenced by lama::SparseMatrix< T >::assign(), lama::DenseMatrix< T >::assign(), lama::DenseMatrix< T >::assignSparse(), lama::SparseMatrix< T >::assignTransposeImpl(), lama::DenseMatrix< T >::copyDenseMatrix(), lama::MatrixCreator< T >::fillRandom(), lama::DenseMatrixOps::invert(), lama::DenseMatrix< T >::invert(), lama::DenseMatrix< T >::matrixTimesMatrix(), lama::SparseMatrix< T >::matrixTimesMatrixImpl(), and lama::SparseMatrix< T >::matrixTimesVectorNImpl().

Matrix::SyncKind lama::Matrix::getCommunicationKind ( ) const [inline, inherited]

get the communication kind.

Returns:: the communication kind.

References lama::Matrix::mCommunicationKind.

Referenced by lama::Matrix::inheritAttributes(), lama::SpecializedJacobi::iterateTyped(), and lama::DenseMatrix< T >::matrixTimesVectorImpl().

Matrix::SyncKind lama::Matrix::getComputeKind ( ) const [inline, inherited]

get the compute kind.

Returns:: the compute kind.

References lama::Matrix::mComputeKind.

Referenced by lama::Matrix::inheritAttributes().

virtual const Context& lama::SparseMatrix< ValueType >::getContext ( ) const [inline, virtual, inherited]

Reimplemented from lama::Matrix.

template<typename ValueType >

ContextPtr lama::SimpleStorageStrategy< ValueType >::getContextPtr ( ) const [virtual]

Getter routine for the context.

Note: Only for SparseMatrix the context of the halo can be queried.

Reimplemented from lama::SparseMatrix< ValueType >.

References LAMA_ASSERT.

template<typename ValueType >

void lama::SimpleStorageStrategy< ValueType >::getDiagonal ( Vector & diagonal ) const [virtual]

This method returns the diagonal.

Parameters:

[out] diagonal is the destination array

Calculations are dependent to the diagonal property.

Reimplemented from lama::SparseMatrix< ValueType >.

References LAMA_ASSERT.

const Distribution & lama::Distributed::getDistribution ( ) const [inline, inherited]

References lama::Distributed::mDistribution.

DistributionPtr lama::Distributed::getDistributionPtr ( ) const [inline, inherited]

const Halo& lama::SparseMatrix< ValueType >::getHalo ( ) const [virtual, inherited]

Read access to the halo of the distributed matrix.

Implements lama::_SparseMatrix.

const MatrixStorage<ValueType>& lama::SparseMatrix< ValueType >::getHaloStorage ( ) const [inline, virtual, inherited]

Getter routine for halo part of the sparse matrix.

Implements lama::_SparseMatrix.

template<typename ValueType >

IndexType lama::SimpleStorageStrategy< ValueType >::getLocalNumColumns ( ) const [virtual]

Getter routine for the local number of rows and columns.

Reimplemented from lama::SparseMatrix< ValueType >.

References LAMA_ASSERT.

template<typename ValueType >

IndexType lama::SimpleStorageStrategy< ValueType >::getLocalNumRows ( ) const [virtual]

Getter routine for the local number of rows.

Reimplemented from lama::SparseMatrix< ValueType >.

References LAMA_ASSERT.

template<typename ValueType >

IndexType lama::SimpleStorageStrategy< ValueType >::getLocalNumValues ( ) const [virtual]

Getter routine for the local number of stored values.

Reimplemented from lama::SparseMatrix< ValueType >.

References LAMA_ASSERT.

const MatrixStorage<ValueType>& lama::SparseMatrix< ValueType >::getLocalStorage ( ) const [inline, virtual, inherited]

Getter routine for local part of the sparse matrix.

Implements lama::_SparseMatrix.

template<typename ValueType >

Matrix::MatrixKind lama::SimpleStorageStrategy< ValueType >::getMatrixKind ( ) const [virtual]

Each derived matrix must give info about its kind (DENSE or SPARSE).

Reimplemented from lama::_SparseMatrix.

References LAMA_ASSERT.

template<typename ValueType >

size_t lama::SimpleStorageStrategy< ValueType >::getMemoryUsage ( ) const [virtual]

getMemoryUsage returns the global memory that is allocated to hold this matrix.

getMemoryUsage returns the global memory that is allocated to hold this matrix. For a distributed matrix all partitions are summed together.

Returns:: the memory consumption of this matrix.

Reimplemented from lama::SparseMatrix< ValueType >.

References LAMA_ASSERT.

IndexType lama::Matrix::getNumColumns ( ) const [inline, inherited]

Returns the number of columns.

Returns:: the number of columns.

References lama::Matrix::mNumColumns.

Referenced by lama::DenseMatrix< T >::assignSparse(), lama::LUSolver::computeLUFactorization(), lama::InverseSolver::decompose(), lama::DenseMatrix< T >::DenseMatrix(), lama::MatrixCreator< T >::fillRandom(), lama::SparseMatrix< T >::getLocalNumColumns(), lama::InverseSolver::invert(), lama::DenseMatrix< T >::invert(), lama::DenseMatrixOps::invertCyclic(), lama::MatrixConfigGrammar::MatrixConfigGrammar(), lama::Matrix::operator=(), lama::LUSolver::solve(), and lama::Solver::solveInit().

IndexType lama::Matrix::getNumRows ( ) const [inline, inherited]

Returns the number of global rows.

Returns:: the number of rows.

References lama::Matrix::mNumRows.

Referenced by lama::InverseSolver::computeInverse(), lama::LUSolver::computeLUFactorization(), lama::InverseSolver::decompose(), lama::SparseMatrix< T >::getLocalNumRows(), lama::LUSolver::initialize(), lama::DenseMatrixOps::invert(), lama::InverseSolver::invert(), lama::DenseMatrix< T >::invert(), lama::DenseMatrixOps::invertCyclic(), lama::SpecializedJacobi::iterateTyped(), lama::MatrixConfigGrammar::MatrixConfigGrammar(), lama::Matrix::operator=(), lama::LUSolver::solve(), and lama::Solver::solveInit().

template<typename ValueType >

IndexType lama::SimpleStorageStrategy< ValueType >::getNumValues ( ) const [virtual]

Get the total number of non-zero values in the matrix.

An element is considered to be non-zero if its absolute value is greater equal than mEpsilon. Zero diagonal elements are also counted if this->hasDiagonalProperty() is given.

This routine does not count zero elements even if they are stored (e.g. for dense or dia storage data).

Returns:: the number of non-zero values in this matrix.

Reimplemented from lama::SparseMatrix< ValueType >.

References LAMA_ASSERT.

IndexType lama::SparseMatrix< ValueType >::getPartitialNumValues ( ) const [inherited]

template<typename ValueType >

void lama::SimpleStorageStrategy< ValueType >::getRow	(	Vector &	row,
		const IndexType	globalRowIndex
	)		const `[virtual]`

This method returns one row of the matrix.

Parameters:

[out] row is a replicated vector with all values of the row

Note: the value type of the vector should be the same type as the matrix (otherwise conversion) and it should be a replicated vector (otherwise reallocation)

Unclear: should the distribution always be unchanged ?

Reimplemented from lama::SparseMatrix< ValueType >.

References LAMA_ASSERT.

double lama::Matrix::getSparsityRate ( ) const [inherited]

References lama::Matrix::getNumValues(), lama::Matrix::mNumColumns, and lama::Matrix::mNumRows.

template<typename ValueType >

const char * lama::SimpleStorageStrategy< ValueType >::getTypeName ( ) const [virtual]

Virtual method that delivers the class name to which a matrix belongs.

Reimplemented from lama::SparseMatrix< ValueType >.

References LAMA_ASSERT.

template<typename ValueType >

Scalar lama::SimpleStorageStrategy< ValueType >::getValue	(	IndexType	i,
		IndexType	j
	)		const `[virtual]`

returns a copy of the value at the passed global indexes.

Parameters:

[in]	i	the global row index
[in]	j	the global column index

Returns:: a copy of the value at the passed global position.

As this operation requires communication ins SPMD mode it can be very inefficient in some situations.

Reimplemented from lama::SparseMatrix< ValueType >.

References LAMA_ASSERT.

template<typename ValueType >

Scalar::ScalarType lama::SimpleStorageStrategy< ValueType >::getValueType ( ) const [virtual]

Query the value type of the matrix elements, e.g.

DOUBLE or FLOAT.

Reimplemented from lama::SparseMatrix< ValueType >.

References LAMA_ASSERT.

template<typename ValueType >

bool lama::SimpleStorageStrategy< ValueType >::hasDiagonalProperty ( ) const [virtual]

hasDiagonalProperty returns the diagonalProperty of the local storage.

Returns:: if the diagonal property is full filled.

Reimplemented from lama::SparseMatrix< ValueType >.

References LAMA_ASSERT.

void lama::Matrix::inheritAttributes ( const Matrix & other ) [inherited]

Inherit context and kind arguments from another matrix.

This routine will also be used by copy constructors in base classes.

References lama::Matrix::getCommunicationKind(), lama::Matrix::getComputeKind(), lama::Matrix::getContextPtr(), lama::Matrix::setCommunicationKind(), lama::Matrix::setComputeKind(), and lama::Matrix::setContext().

template<typename ValueType >

void lama::SimpleStorageStrategy< ValueType >::invert ( const Matrix & other ) [virtual]

Compute the inverse of a matrix.

Reimplemented from lama::SparseMatrix< ValueType >.

References LAMA_ASSERT.

template<typename ValueType >

lama::SimpleStorageStrategy< ValueType >::LAMA_LOG_DECL_STATIC_LOGGER ( logger ) [private]

Reimplemented from lama::SparseMatrix< ValueType >.

void lama::Matrix::matrix2CSRGraph	(	IndexType *	xadj,
		IndexType *	adjncy,
		IndexType *	vwgt,
		CommunicatorPtr	comm,
		const IndexType *	globalRowIndices = `NULL`,
		IndexType *	vtxdist = `NULL`
	)		const `[virtual, inherited]`

transformation from matrix type to a csr graph

transformation from matrix type to a csr graph, so that it (Par)Metis can work with it.

Parameters:

[out]	xadj	the ia array of the csr graph
[out]	adjncy	the ja array of the csr graph

References LAMA_THROWEXCEPTION.

template<typename ValueType >

void lama::SimpleStorageStrategy< ValueType >::matrixTimesMatrix	(	Matrix &	result,
		const Scalar	alpha,
		const Matrix &	B,
		const Scalar	beta,
		const Matrix &	C
	)		const `[virtual]`

matrixTimesMatrix computes result = alpha * this * B + beta * C

Parameters:

[out]	result	the Matrix to store the result to
[in]	alpha	the Scalar alpha of the expression
[in]	B	the Matrix B of the expression
[in]	beta	the Scalar beta of the expression
[in]	C	the Matrix C of the expression

matrixTimesMatrix computes result = alpha * this * x + beta * y.

Reimplemented from lama::SparseMatrix< ValueType >.

References LAMA_ASSERT.

void lama::SparseMatrix< ValueType >::matrixTimesMatrixImpl	(	const ValueType	alpha,
		const SparseMatrix< ValueType > &	A,
		const SparseMatrix< ValueType > &	B,
		const ValueType	beta,
		const SparseMatrix< ValueType > &	C
	)		`[protected, inherited]`

Set this matrix = alpha * A * B + beta * C.

template<typename ValueType >

void lama::SimpleStorageStrategy< ValueType >::matrixTimesScalar	(	const Matrix &	other,
		const Scalar	alpha
	)		`[virtual]`

matrixTimesScalar computes this = alpha * other

Parameters:

[out]	other	the Matrix to mutliply
[in]	alpha	the Scalar of the expression

matrixTimesScalar computes this = alpha * other.

Reimplemented from lama::SparseMatrix< ValueType >.

References LAMA_ASSERT.

template<typename ValueType >

void lama::SimpleStorageStrategy< ValueType >::matrixTimesVector	(	Vector &	result,
		const Scalar	alpha,
		const Vector &	x,
		const Scalar	beta,
		const Vector &	y
	)		const `[virtual]`

matrixTimesVector computes result = alpha * this * x + beta * y

Parameters:

[out]	result	the Vector to store the result to
[in]	alpha	the Scalar alpha of the expression
[in]	x	the Vector x of the expression
[in]	beta	the Scalar beta of the expression
[in]	y	the Vector y of the expression

matrixTimesVector computes result = alpha * this * x + beta * y. If result == x or result == y new storage is allocated to store the result.

Reimplemented from lama::SparseMatrix< ValueType >.

References LAMA_ASSERT.

void lama::SparseMatrix< ValueType >::matrixTimesVectorImpl	(	DenseVector< ValueType > &	result,
		const ValueType	alpha,
		const DenseVector< ValueType > &	x,
		const ValueType	beta,
		const DenseVector< ValueType > &	y
	)		const `[inherited]`

virtual void lama::SparseMatrix< ValueType >::matrixTimesVectorNImpl	(	DenseMatrix< ValueType > &	result,
		const ValueType	alpha,
		const DenseMatrix< ValueType > &	x,
		const ValueType	beta,
		const DenseMatrix< ValueType > &	y
	)		const `[virtual, inherited]`

matrix times matrix for multiple dense vectors.

template<typename ValueType >

Scalar lama::SimpleStorageStrategy< ValueType >::maxDiffNorm ( const Matrix & other ) const [virtual]

returns the max norm of ( this - other )

Parameters:

[in] other another matrix with the same shape as this matrix

Returns:: the max norm of ( this - other )

The maximal value is given by the largest difference between two elements at the same position of the matrices.

Reimplemented from lama::SparseMatrix< ValueType >.

References LAMA_ASSERT.

ValueType lama::SparseMatrix< ValueType >::maxDiffNormImpl ( const SparseMatrix< ValueType > & other ) const [inherited]

Get the maximal difference between two elements for sparse matrices of same type.

Scalar lama::Matrix::operator()	(	IndexType	i,
		IndexType	j
	)		const `[inherited]`

returns a copy of the value at the passed global indexes.

Parameters:

[in]	i	the global row index
[in]	j	the global column index

Returns:: a copy of the value at the passed global position.

As this operator requires communication ins SPMD mode it can be very inefficient in some situations.

References lama::Matrix::getValue().

template<typename ValueType >

void lama::SimpleStorageStrategy< ValueType >::prefetch ( ) const [virtual]

Prefetch matrix data to its 'preferred' context location.

Reimplemented from lama::SparseMatrix< ValueType >.

References LAMA_ASSERT.

void lama::SparseMatrix< ValueType >::readFromFile ( const std::string & filename ) [inherited]

Assigns this matrix with a replicated sparse matrix read from file.

Creates a replicated sparse matrix read from file. Currently supported is the matrix market format, XDR, formatted, unformatted, binary.

ToDo: set reference to description in StorageIO.

Parameters:

[in] filename the filename to read from

Note: Derived classes might use this routine within a constructor for convenience. This class does not support such a constructor as no file format is known.

template<typename ValueType >

void lama::SimpleStorageStrategy< ValueType >::redistribute	(	DistributionPtr	rowDistribution,
		DistributionPtr	colDistribution
	)		`[virtual]`

This method allows any arbitrary redistribution of the matrix.

Parameters:

[in]	rowDistribution	is new distribution of rows, global size must be mNumRows
[in]	colDistribution	is new distribution of columns, global size must be mNumColumns

Reimplemented from lama::SparseMatrix< ValueType >.

template<typename ValueType >

void lama::SimpleStorageStrategy< ValueType >::resetDiagonalProperty ( ) [virtual]

resetDiagonalProperty rechecks the storages for their diagonal property

Returns:: if the diagonal property is full filled.

Reimplemented from lama::SparseMatrix< ValueType >.

References LAMA_ASSERT.

template<typename ValueType >

void lama::SimpleStorageStrategy< ValueType >::scale ( const Vector & scaling ) [virtual]

This method scales all values with a vector.

Parameters:

[in] values is the source array

row wise calculations

Reimplemented from lama::SparseMatrix< ValueType >.

References LAMA_ASSERT.

template<typename ValueType >

void lama::SimpleStorageStrategy< ValueType >::scale ( const Scalar scaling ) [virtual]

This method scales all matrix values with a scalar.

Parameters:

[in] value is the source value

Reimplemented from lama::SparseMatrix< ValueType >.

References LAMA_ASSERT.

void lama::Matrix::setCommunicationKind ( SyncKind communicationKind ) [inherited]

set the communication kind.

Parameters:

[in] communicationKind the communication kind.

References lama::Matrix::mCommunicationKind.

Referenced by lama::Matrix::inheritAttributes().

virtual void lama::SparseMatrix< ValueType >::setComputeKind ( SyncKind computeKind ) [virtual, inherited]

set the compute kind.

Parameters:

[in] computeKind the compute kind.

Reimplemented from lama::Matrix.

virtual void lama::SparseMatrix< ValueType >::setContext	(	const ContextPtr	localContext,
		const ContextPtr	haloContext
	)		`[inline, virtual, inherited]`

Only sparse matrices will override this method, others will ignore second argument.

Reimplemented from lama::Matrix.

template<typename ValueType >

void lama::SimpleStorageStrategy< ValueType >::setContext ( const ContextPtr context ) [virtual]

specifies on which compute back end the matrix operations should take place.

Parameters:

[in] context the compute back to use for calculations with matrix

Note: Only for sparse matrices it is possible to specify separate locations for local and halo computations.

Reimplemented from lama::SparseMatrix< ValueType >.

template<typename ValueType >

void lama::SimpleStorageStrategy< ValueType >::setDiagonal ( const Vector & diagonal ) [virtual]

This method replaces the diagonal.

Parameters:

[in] diagonal is the source array

Calculations are dependent to the diagonal property

Reimplemented from lama::SparseMatrix< ValueType >.

References LAMA_ASSERT.

template<typename ValueType >

void lama::SimpleStorageStrategy< ValueType >::setDiagonal ( const Scalar scalar ) [virtual]

This method replaces the diagonal by a diagonal value.

Parameters:

[in] scalar is the source value

Calculations are dependent to the diagonal property

Reimplemented from lama::SparseMatrix< ValueType >.

References LAMA_ASSERT.

void lama::Matrix::setDistributedMatrix	(	DistributionPtr	distribution,
		DistributionPtr	colDistribution
	)		`[protected, inherited]`

Set the global and local size of distributed matrix.

Parameters:

[in]	distribution	is distribution for rows
[in]	colDistribution	is distribution for columns

Global and local size is given implicitly by the distributions itself.

References LAMA_ASSERT_ERROR, lama::Matrix::mColDistribution, lama::Matrix::mNumColumns, lama::Matrix::mNumRows, and lama::Distributed::setDistributionPtr().

Referenced by lama::DenseMatrix< T >::allocate(), lama::SimpleStorageStrategy< ValueType >::applyStrategy(), lama::DenseMatrix< T >::assign(), lama::DenseMatrix< T >::assignSparse(), lama::COOSparseMatrix< T >::COOSparseMatrix(), lama::DenseMatrix< T >::copyDenseMatrix(), lama::CSRSparseMatrix< T >::CSRSparseMatrix(), lama::DIASparseMatrix< T >::DIASparseMatrix(), lama::ELLSparseMatrix< T >::ELLSparseMatrix(), lama::JDSSparseMatrix< T >::JDSSparseMatrix(), lama::Matrix::Matrix(), lama::Matrix::setReplicatedMatrix(), lama::DenseMatrix< T >::splitColumns(), and lama::XXXSparseMatrix< T >::XXXSparseMatrix().

void lama::Distributed::setDistributionPtr ( DistributionPtr distributionPtr ) [protected, inherited]

References lama::Distributed::mDistribution.

Referenced by lama::Vector::resize(), and lama::Matrix::setDistributedMatrix().

template<typename ValueType >

void lama::SimpleStorageStrategy< ValueType >::setIdentity ( ) [virtual]

Operator that sets the matrix to the identity matrix.

 void sub( ..., Matrix& a, ... )
 ...
 LAMA_ASSERT_EQUAL_DEBUG( a.getNumRows(), a.getNumColumns() );
 A.setIdentity();

Reimplemented from lama::SparseMatrix< ValueType >.

References LAMA_ASSERT.

void lama::SparseMatrix< ValueType >::setRawDenseData	(	const IndexType	numRows,
		const IndexType	numColumns,
		const OtherValueType	values[],
		const OtherValueType	eps = `0.0`
	)		`[inherited]`

Set sparse matrix with global dense data.

void lama::Matrix::setReplicatedMatrix	(	const IndexType	numRows,
		const IndexType	numColumns
	)		`[protected, inherited]`

Set the global/local size of replicated matrix.

Parameters:

[in]	numRows	number of rows, must be non-negative.
[in]	numColumns	number of columns, must be non-negative.

References lama::Matrix::setDistributedMatrix().

Referenced by lama::DenseMatrix< T >::allocate(), lama::DenseMatrix< T >::assign(), lama::DenseMatrix< T >::clear(), and lama::DenseMatrix< T >::DenseMatrix().

void lama::Distributed::swap ( Distributed & other ) [protected, inherited]

References lama::Distributed::mDistribution.

Referenced by lama::Matrix::swapMatrix().

void lama::SparseMatrix< ValueType >::swap ( SparseMatrix< ValueType > & other ) [inherited]

Swap will swap all member variables of the two sparse matrices.

This operation might be useful in iteration loops where a sparse matrix is updated each iteration. It is more convenient than a solution that is based on using pointers in the application.

void lama::Matrix::swapMatrix ( Matrix & other ) [protected, inherited]

References lama::Matrix::mColDistribution, lama::Matrix::mNumColumns, lama::Matrix::mNumRows, and lama::Distributed::swap().

Referenced by lama::DenseMatrix< T >::swap().

static const char* lama::SparseMatrix< ValueType >::typeName ( ) [static, inherited]

Getter for the type name of the class.

template<typename ValueType >

void lama::SimpleStorageStrategy< ValueType >::wait ( ) const [virtual]

wait for a possibly running prefetch.

Reimplemented from lama::SparseMatrix< ValueType >.

References LAMA_ASSERT.

virtual void lama::SparseMatrix< ValueType >::writeAt ( std::ostream & stream ) const [virtual, inherited]

Write some information about this to the passed stream.

Parameters:

[out] stream the stream to write to.

Reimplemented from lama::Matrix.

void lama::SparseMatrix< ValueType >::writeToFile	(	const std::string &	fileName,
		const File::FileType	fileType = `File::BINARY`,
		const File::DataType	dataType = `File::INTERNAL`,
		const File::IndexDataType	indexDataTypeIA = `File::INT`,
		const File::IndexDataType	indexDataTypeJA = `File::INT`
	)		const `[inherited]`