gko::ext::cuda::solver::Cudss< ValueType, IndexType > Class Template Reference

A direct solver using NVIDIA's cuDSS library. More...

#include <ginkgo/extensions/cuda/solver/cudss.hpp>

Inheritance diagram for gko::ext::cuda::solver::Cudss< ValueType, IndexType >:

Collaboration diagram for gko::ext::cuda::solver::Cudss< ValueType, IndexType >:

Classes
class	Factory
struct	parameters_type

Public Types
using	value_type = ValueType
using	index_type = IndexType

Public Member Functions
const parameters_type &	get_parameters () const
	Cudss (const Cudss &)
	Creates a copy of the solver (shares factorization state).
	Cudss (Cudss &&) noexcept
	Moves from the given solver, leaving it empty.
Cudss &	operator= (const Cudss &)
Cudss &	operator= (Cudss &&) noexcept
void	refactorize (std::shared_ptr< const LinOp > new_matrix)
	Re-run the numeric factorization with updated matrix values. More...
Public Member Functions inherited from gko::LinOp
void	apply (ptr_param< const LinOp > b, ptr_param< LinOp > x) const
	Applies a linear operator to a vector (or a sequence of vectors). More...
void	apply (ptr_param< const LinOp > alpha, ptr_param< const LinOp > b, ptr_param< const LinOp > beta, ptr_param< LinOp > x) const
	Performs the operation x = alpha * op(b) + beta * x. More...
const dim< 2 > &	get_size () const noexcept
	Returns the size of the operator. More...
virtual bool	apply_uses_initial_guess () const
	Returns true if the linear operator uses the data given in x as an initial guess. More...
LinOp &	operator= (const LinOp &)=default
	Copy-assigns a LinOp. More...
LinOp &	operator= (LinOp &&other)
	Move-assigns a LinOp. More...
	LinOp (const LinOp &)=default
	Copy-constructs a LinOp. More...
	LinOp (LinOp &&other)
	Move-constructs a LinOp. More...
Public Member Functions inherited from gko::PolymorphicObject
PolymorphicObject &	operator= (const PolymorphicObject &)
std::shared_ptr< const Executor >	get_executor () const noexcept
	Returns the Executor of the object. More...
Public Member Functions inherited from gko::log::EnableLogging< PolymorphicObject >
void	add_logger (std::shared_ptr< const Logger > logger) override
void	remove_logger (const Logger *logger) override
void	remove_logger (ptr_param< const Logger > logger)
const std::vector< std::shared_ptr< const Logger > > &	get_loggers () const override
void	clear_loggers () override
Public Member Functions inherited from gko::log::Loggable
void	remove_logger (ptr_param< const Logger > logger)

Static Public Member Functions
static auto	build () -> decltype(Factory ::create())
static parameters_type	parse (const config::pnode &config, const config::registry &context, const config::type_descriptor &td_for_child=config::make_type_descriptor< ValueType, IndexType >())
	Parse parameters from a configuration property tree.
static config::configuration_map	get_config_map ()
	Returns a configuration_map for registering this type with a config::registry. More...

Detailed Description

template<typename ValueType, typename IndexType = int32>
class gko::ext::cuda::solver::Cudss< ValueType, IndexType >

A direct solver using NVIDIA's cuDSS library.

This solver is only supported on the CudaExecutor. It wraps the cuDSS sparse direct solver, performing analysis, factorization, and solve phases. The factorization is computed during construction (generate) and reused across apply calls.

The solver is opaque — factorization data is stored internally in cuDSS-native format and cannot be extracted.

Template Parameters

ValueType	the value type of the system matrix and vectors
IndexType	the index type of the system matrix

Member Function Documentation

get_config_map()

template<typename ValueType , typename IndexType = int32>

static config::configuration_map gko::ext::cuda::solver::Cudss< ValueType, IndexType >::get_config_map ( )

static

Returns a configuration_map for registering this type with a config::registry.

Users can pass this to the registry constructor to enable JSON/YAML configuration of Cudss.

refactorize()

template<typename ValueType , typename IndexType = int32>

void gko::ext::cuda::solver::Cudss< ValueType, IndexType >::refactorize

(

std::shared_ptr< const LinOp >

new_matrix

)

Re-run the numeric factorization with updated matrix values.

The new matrix must have the same sparsity pattern (dimensions and number of non-zeros) as the matrix used in generate(). Only the numeric factorization phase is re-executed; the symbolic analysis from the initial generate() is reused.

Parameters

new_matrix

the updated system matrix (same sparsity pattern)

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

• ginkgo/extensions/cuda/solver/cudss.hpp