`gko::matrix::Csr`#

Compressed sparse row format. Stores each row’s nonzeros as a contiguous slice of value and column-index arrays, with a row-pointer array locating the start of each row. The general-purpose default for sparse matrices in Ginkgo. The SpMV kernel is selected by gko::matrix::csr::spmv_strategy (automatic, load_balance, merge_path, classical, sparselib), passed to Csr::create and returned by get_strategy().

Attention

The strategy_type classes (Csr::classical, Csr::merge_path, and so on) are deprecated in favour of the spmv_strategy enum.

enum class gko::matrix::csr::spmv_strategy#

Type describes the Csr SpMV strategy.

Values:

enumerator automatic#: automatic is the strategy choosing between load_balance and classical based on the maximum number of entries per row and the number of entries of the matrix.

enumerator load_balance#: load_balance is the strategy trying to distribute the work equally in terms of the number of matrix entries. More detail can be checked in Goran and Enrique: Balanced CSR sparse matrix-vector product on graphics processors (https://doi.org/10.1007/978-3-319-64203-1_50).

enumerator merge_path#: merge_path is the strategy trying to distribute the work equally in terms of the number of matrix entries and row pointers. More detail can be checked in Merrill and Garland: Merge-Based Parallel Sparse Matrix-Vector Multiplication (https://doi.org/10.1109/SC.2016.57).

enumerator classical#: classical is the strategy assigning the same amount of the working resource to each row.

enumerator sparselib#

sparselib is the strategy calling the backend sparse library implementation when it is supported.

reference/omp: ginkgo’s classical spmv
cuda: cuSPARSE
hip: hipSPARSE
dpcpp: oneMKL

template<typename ValueType = default_precision, typename IndexType = int32> class Csr #

Inherits from

public gko::LinOp
public gko::EnableCloneable<Csr<default_precision, int32>>
public gko::ConvertibleTo<Csr<next_precision<default_precision>, int32>>
public gko::ConvertibleTo<Csr<next_precision<default_precision, 2>, int32>>
public gko::ConvertibleTo<Csr<next_precision<default_precision, 3>, int32>>
public gko::ConvertibleTo<Dense<default_precision>>
public gko::ConvertibleTo<Coo<default_precision, int32>>
public gko::ConvertibleTo<Ell<default_precision, int32>>
public gko::ConvertibleTo<Fbcsr<default_precision, int32>>
public gko::ConvertibleTo<Hybrid<default_precision, int32>>
public gko::ConvertibleTo<Sellp<default_precision, int32>>
public gko::ConvertibleTo<SparsityCsr<default_precision, int32>>
public gko::DiagonalExtractable<default_precision>
public gko::ReadableFromMatrixData<default_precision, int32>
public gko::WritableToMatrixData<default_precision, int32>
public gko::Transposable
public gko::Permutable<int32>
public gko::EnableAbsoluteComputation<remove_complex<Csr<default_precision, int32>>>
public gko::ScaledIdentityAddable

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

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

The Csr LinOp supports three families of apply operations, dispatched on the type of the right operand:

Against a Dense operand b, apply computes a sparse matrix-vector (or matrix-multivector) product:
\[ x = A b, \qquad x = \alpha A b + \beta x. \]
Against another Csr operand B, apply computes a sparse-sparse matrix product (SpGEMM):
\[ C = A B, \qquad C = \alpha A B + \beta C. \]
Against an Identity operand, apply reduces to a sparse-sparse matrix addition (SpGEAM):
\[ B = \alpha A + \beta B. \]

In code:

matrix::Csr *A, *B, *C;      // matrices
matrix::Dense *b, *x;        // vectors tall-and-skinny matrices
matrix::Dense *alpha, *beta; // scalars of dimension 1x1
matrix::Identity *I;         // identity matrix

// Applying to Dense matrices computes an SpMV/SpMM product
A->apply(b, x)              // x = A*b
A->apply(alpha, b, beta, x) // x = alpha*A*b + beta*x

// Applying to Csr matrices computes a SpGEMM product of two sparse matrices
A->apply(B, C)              // C = A*B
A->apply(alpha, B, beta, C) // C = alpha*A*B + beta*C

// Applying to an Identity matrix computes a SpGEAM sparse matrix addition
A->apply(alpha, I, beta, B) // B = alpha*A + beta*B

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

Template Parameters:

ValueType – precision of matrix elements
IndexType – precision of matrix indexes

Public Functions

virtual std::unique_ptr<LinOp> transpose() const override#

Returns a LinOp representing the transpose of the Transposable object.

Returns:: a pointer to the new transposed object

virtual std::unique_ptr<LinOp> conj_transpose() const override#

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

Returns:: a pointer to the new conjugate transposed object

device_view get_device_view()#

Returns a non-owning device view of this matrix.

Returns:: a device view of this matrix.

const_device_view get_const_device_view() const#

Returns a non-owning const device view of this matrix.

Returns:: a const device view of this matrix.

std::unique_ptr<Csr> multiply(ptr_param<const Csr> other) const#

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

Parameters:: other – the matrix with which the product will be computed. It needs to be sorted by column indices when using OmpExecutor or DpcppExecutor for this.
Returns:: the product of the two matrices, stored on the same executor as this matrix.

std::pair<std::unique_ptr<Csr>, multiply_reuse_info> multiply_reuse( ptr_param<const Csr> other, ) const#

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

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

Parameters:: other – the matrix with which the product will be computed. It needs to be sorted by column indices when using OmpExecutor or DpcppExecutor for this.
Returns:: std::pair containing the product of the two matrices, stored on the same executor as this matrix, and a multiply_reuse_info object allowing value updates to the output matrix.

std::unique_ptr<Csr> multiply_add( ptr_param<const Dense<value_type>> scale_mult, ptr_param<const Csr> mtx_mult, ptr_param<const Dense<value_type>> scale_add, ptr_param<const Csr> mtx_add, ) const#

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

Parameters:

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

Returns:

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

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

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

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

Parameters:

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

Returns:

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

std::unique_ptr<Csr> scale_add( ptr_param<const Dense<value_type>> scale_this, ptr_param<const Dense<value_type>> scale_other, ptr_param<const Csr> mtx_other, ) const#

Computes the sparse matrix sum scale_this * this + scale_other * mtx_add on the executor of this matrix. This matrix needs to be sorted by column index, otherwise the result will be incorrect.

Parameters:

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

Returns:

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

std::pair<std::unique_ptr<Csr>, scale_add_reuse_info> add_scale_reuse( ptr_param<const Dense<value_type>> scale_this, ptr_param<const Dense<value_type>> scale_other, ptr_param<const Csr> mtx_other, ) const#

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

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

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

Parameters:

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

Returns:

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

std::pair<std::unique_ptr<Csr>, permuting_reuse_info> transpose_reuse( ) const#

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

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

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

std::unique_ptr<Csr> permute( ptr_param<const Permutation<index_type>> permutation, permute_mode mode = permute_mode::symmetric, ) const#

Creates a permuted copy \(A'\) of this matrix \(A\) with the given permutation \(P\). By default, this computes a symmetric permutation (permute_mode::symmetric). For the effect of the different permutation modes, see permute_mode.

Parameters:

permutation – The input permutation.
mode – The permutation mode, see permute_mode.

Returns:

The permuted matrix.

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

Creates a non-symmetrically permuted copy \(A'\) of this matrix \(A\) with the given row and column permutations \(P\) and \(Q\). The operation will compute \(A'(i, j) = A(p[i], q[j])\), or \(A' = P A Q^T\) if invert is false, and \(A'(p[i], q[j]) = A(i,j)\), or \(A' = P^{-1} A Q^{-T}\) if invert is true.

Parameters:

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

Returns:

The permuted matrix.

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

Computes the operations necessary to propagate changed values from a matrix A to a permuted matrix. The semantics of this function match those of permute(ptr_param<const Permutation<index_type>>, permute_mode). Updating values works as follows:

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

Parameters:

permutation – The input permutation.
mode – The permutation mode, see permute_mode.

Returns:

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

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

Permutation<index_type>>, bool). Updating values works as follows:

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

Parameters:

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

Returns:

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

std::unique_ptr<Csr> scale_permute( ptr_param<const ScaledPermutation<value_type, index_type>> permutation, permute_mode = permute_mode::symmetric, ) const#

Creates a scaled and permuted copy of this matrix. For an explanation of the permutation modes, see permute(ptr_param<const Permutation<index_type>>, permute_mode)

Parameters:

permutation – The scaled permutation.
mode – The permutation mode.

Returns:

The permuted matrix.

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

Creates a scaled and permuted copy of this matrix. For an explanation of the parameters, see permute(ptr_param<const Permutation<index_type>>, ptr_param<const

Permutation<index_type>>, permute_mode)

Parameters:

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

Returns:

The permuted matrix.

virtual std::unique_ptr<Diagonal<ValueType>> extract_diagonal( ) const override#

Extracts the diagonal entries of the matrix into a vector.

Parameters:: diag – the vector into which the diagonal will be written

virtual std::unique_ptr<absolute_type> compute_absolute( ) const override#

Gets the AbsoluteLinOp

Returns:: a pointer to the new absolute object

virtual void compute_absolute_inplace() override#: Compute absolute inplace on each element.

void sort_by_column_index()#: Sorts all (value, col_idx) pairs in each row by column index

inline value_type *get_values() noexcept#

Returns the values of the matrix.

Returns:: the values of the matrix.

inline const value_type *get_const_values() const noexcept#

Returns the values of the matrix.

Note

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

Returns:: the values of the matrix.

std::unique_ptr<Dense<ValueType>> create_value_view()#: Creates a Dense view of the value array of this matrix as a column vector of dimensions nnz x 1.

std::unique_ptr<const Dense<ValueType>> create_const_value_view( ) const#: Creates a const Dense view of the value array of this matrix as a column vector of dimensions nnz x 1.

inline index_type *get_col_idxs() noexcept#

Returns the column indexes of the matrix.

Returns:: the column indexes of the matrix.

inline const index_type *get_const_col_idxs() const noexcept#

Returns the column indexes of the matrix.

Note

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

Returns:: the column indexes of the matrix.

inline index_type *get_row_ptrs() noexcept#

Returns the row pointers of the matrix.

Returns:: the row pointers of the matrix.

inline const index_type *get_const_row_ptrs() const noexcept#

Returns the row pointers of the matrix.

Note

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

Returns:: the row pointers of the matrix.

inline index_type *get_srow() noexcept#

Returns the starting rows.

Returns:: the starting rows.

inline const index_type *get_const_srow() const noexcept#

Returns the starting rows.

Note

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

Returns:: the starting rows.

inline size_type get_num_srow_elements() const noexcept#

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

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

inline size_type get_num_stored_elements() const noexcept#

Returns the number of elements explicitly stored in the matrix.

Returns:: the number of elements explicitly stored in the matrix

csr::spmv_strategy get_strategy() const noexcept#

Returns the strategy

Returns:: the strategy

inline void set_strategy(csr::spmv_strategy strategy)#

Set the strategy

Parameters:: strategy – the csr strategy

inline void scale(ptr_param<const LinOp> alpha)#

Scales the matrix with a scalar.

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

inline void inv_scale(ptr_param<const LinOp> alpha)#

Scales the matrix with the inverse of a scalar.

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

virtual void validate_data() const override#: Throws gko::InvalidData exception if we found the data inside the object does not fulfill certain property up to our knowledge.

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

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

Note

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

Parameters:

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

Returns:

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

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

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

Note

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

Parameters:

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

Returns:

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

Csr &operator=(const Csr&)#: Copy-assigns a Csr matrix. Preserves executor, copies everything else.

Csr &operator=(Csr&&)#: Move-assigns a Csr matrix. Preserves executor, moves the data and leaves the moved-from object in an empty state (0x0 LinOp with unchanged executor and strategy, no nonzeros and valid row pointers).

Csr(const Csr&)#: Copy-constructs a Csr matrix. Inherits executor, strategy and data.

Csr(Csr&&)#: Move-constructs a Csr matrix. Inherits executor and strategy, moves the data and leaves the moved-from object in an empty state (0x0 LinOp with unchanged executor and strategy, no nonzeros and valid row pointers).

Public Static Functions

static std::unique_ptr<Csr> create( std::shared_ptr<const Executor> exec, csr::spmv_strategy strategy, )#

Creates an uninitialized CSR matrix of the specified size.

Parameters:

exec – Executor associated to the matrix
strategy – the strategy of CSR

Returns:

A smart pointer to the newly created matrix.

static std::unique_ptr<Csr> create( std::shared_ptr<const Executor> exec, const dim<2> &size = {}, size_type num_nonzeros = {}, csr::spmv_strategy strategy = csr::spmv_strategy::automatic, )#

Creates an uninitialized CSR matrix of the specified size.

Parameters:

exec – Executor associated to the matrix
size – size of the matrix
num_nonzeros – number of nonzeros
strategy – the strategy the matrix uses for SpMV operations, default is automatic.

Returns:

A smart pointer to the newly created matrix.

static std::unique_ptr<Csr> create( std::shared_ptr<const Executor> exec, const dim<2> &size, array<value_type> values, array<index_type> col_idxs, array<index_type> row_ptrs, csr::spmv_strategy strategy = csr::spmv_strategy::automatic, )#

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

Note

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

Parameters:

exec – Executor associated to the matrix
size – size of the matrix
values – array of matrix values
col_idxs – array of column indexes
row_ptrs – array of row pointers
strategy – the strategy the matrix uses for SpMV operations, default is automatic.

Returns:

A smart pointer to the newly created matrix.

template<typename InputValueType, typename InputColumnIndexType, typename InputRowPtrType> static inline std::unique_ptr<Csr> create( std::shared_ptr<const Executor> exec, const dim<2> &size, std::initializer_list<InputValueType> values, std::initializer_list<InputColumnIndexType> col_idxs, std::initializer_list<InputRowPtrType> row_ptrs, )#

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

static std::unique_ptr<const Csr> create_const( std::shared_ptr<const Executor> exec, const dim<2> &size, gko::detail::const_array_view<ValueType> &&values, gko::detail::const_array_view<IndexType> &&col_idxs, gko::detail::const_array_view<IndexType> &&row_ptrs, csr::spmv_strategy strategy = csr::spmv_strategy::automatic, )#

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

Parameters:

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

Returns:

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

Returns:

A smart pointer to the newly created matrix.

static std::unique_ptr<Csr> create( std::shared_ptr<const Executor> exec, std::shared_ptr<strategy_type> strategy, )#

create(std::shared_ptr<const Executor>,csr::spmv_strategy)

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

Csr> create_const(std::shared_ptr<constExecutor>, const dim<2>&, gko::detail::const_array_view<ValueType>&&,gko::detail::const_array_view<IndexType>&&,gko::detail::const_array_view<IndexType>&&, csr::spmv_strategy)

class strategy_type#

Subclassed by

gko::matrix::Csr< ValueType, IndexType >::automatical
gko::matrix::Csr< ValueType, IndexType >::classical
gko::matrix::Csr< ValueType, IndexType >::cusparse
gko::matrix::Csr< ValueType, IndexType >::load_balance
gko::matrix::Csr< ValueType, IndexType >::merge_path
gko::matrix::Csr< ValueType, IndexType >::sparselib

class classical #

Inherits from

public gko::matrix::Csr<ValueType, IndexType>::strategy_type

class merge_path #

Inherits from

public gko::matrix::Csr<ValueType, IndexType>::strategy_type

class cusparse #

Inherits from

public gko::matrix::Csr<ValueType, IndexType>::strategy_type

class sparselib #

Inherits from

public gko::matrix::Csr<ValueType, IndexType>::strategy_type

class load_balance #

Inherits from

public gko::matrix::Csr<ValueType, IndexType>::strategy_type

class automatical #

Inherits from

public gko::matrix::Csr<ValueType, IndexType>::strategy_type

class multiply_reuse_info#

Class describing the internal lookup structures created by multiply_reuse(const Csr*) to recompute a sparse matrix-matrix product with updated values.

Public Functions

void update_values( ptr_param<const Csr> mtx1, ptr_param<const Csr> mtx2, ptr_param<Csr> out, ) const#: Recomputes the sparse matrix-matrix product out = mtx1 * mtx2 when only the values of mtx1 and mtx2 changed, but the sparsity patterns of mtx1, mtx2 and out are unchanged.

class multiply_add_reuse_info#

Class describing the internal lookup structures created by multiply_add_reuse to recompute a sparse matrix-matrix product with updated values.

Public Functions

void update_values( ptr_param<const Csr> mtx, ptr_param<const Dense<value_type>> scale_mult, ptr_param<const Csr> mtx_mult, ptr_param<const Dense<value_type>> scale_add, ptr_param<const Csr> mtx_add, ptr_param<Csr> out, ) const#: Recomputes the sparse matrix-matrix product out = scale_mult * mtx * mtx_mult + scale_add * mtx_add when only the values of mtx, scale_mult, mtx_mult, scale_add, mtx_add changed, but the sparsity patterns of mtx, mtx_mult, mtx_add and out are unchanged.

class scale_add_reuse_info#

Class describing the internal lookup structures created by scale_add_reuse to recompute a sparse matrix-matrix sum with updated values.

Public Functions

void update_values( ptr_param<const Dense<value_type>> scale1, ptr_param<const Csr> mtx1, ptr_param<const Dense<value_type>> scale2, ptr_param<const Csr> mtx2, ptr_param<Csr> out, ) const#: Recomputes the sparse matrix-matrix sum out = scale1 * mtx1 + scale2 * mtx2 when only the values of mtx1, scale1, mtx2, scale2 changed, but the sparsity patterns of mtx1, mtx2 and out are unchanged.

struct permuting_reuse_info#

A struct describing a transformation of the matrix that reorders the values of the matrix into the transformed matrix.

Public Functions

explicit permuting_reuse_info()#: Creates an empty reuse info.

explicit permuting_reuse_info( std::unique_ptr<Permutation<index_type>> value_permutation, )#: Creates a reuse info structure from its value permutation.

void update_values( ptr_param<const Csr> input, ptr_param<Csr> output, ) const#: Propagates the values from an input matrix to the transformed matrix. The output matrix needs to have been computed using the transformation that was also used to generate this reuse data. Internally, this permutes the input value vector into the output value vector.

gko::matrix::Csr#

`gko::matrix::Csr`#