TasGrid Namespace Reference

Encapsulates the Tasmanian Sparse Grid module. More...

Classes
class	TasmanianSparseGrid
	The master-class that represents an instance of a Tasmanian sparse grid. More...
class	Data2D
class	MultiIndexSet
class	StorageSet
struct	HandleDeleter
class	GpuVector
	Template class that wraps around a single GPU array, providing functionality that mimics std::vector.
struct	GpuEngine
	Wrapper class around calls GPU accelerated linear algebra libraries.
class	AccelerationDomainTransform
struct	AccelerationContext
struct	InternalSyclQueue
class	CacheLagrange
class	CacheLagrangeDerivative
class	CustomTabulated
	Class providing manipulation of custom tabulated rules, file I/O and structured access to the points, weights, and meta-data.
class	OneDimensionalWrapper
	A class to cache one dimensional rules, nodes, weight, meta-data, etc.
class	TableGaussPatterson
struct	NodeData
struct	TensorData
class	DynamicConstructorDataGlobal
struct	SimpleConstructData
class	CandidateManager
class	CompleteStorage
class	VectorToStreamBuffer

Typedefs
using	int_gpu_lapack = std::int64_t
using	ModelSignature = std::function< void(std::vector< double > const &x, std::vector< double > &y, size_t thread_id)>
	Signature of a model function to be used in the construction procedures. More...
using	ModelSignatureMPI = std::function< void(std::vector< double > const &x, std::vector< double > &y)>
	Signature of a model function to be used in the MPI construction procedures. More...

Enumerations
enum	TypeIndexRelation { type_abeforeb , type_bbeforea , type_asameb }
enum	TypeDepth {   type_none , type_level , type_curved , type_hyperbolic ,   type_iptotal , type_qptotal , type_ipcurved , type_qpcurved ,   type_iphyperbolic , type_qphyperbolic , type_tensor , type_iptensor ,   type_qptensor }
	Used by Global Sequence and Fourier grids, indicates the selection criteria. More...
enum	TypeOneDRule {   rule_none , rule_clenshawcurtis , rule_clenshawcurtis0 , rule_fejer2 ,   rule_chebyshev , rule_chebyshevodd , rule_leja , rule_lejaodd ,   rule_rleja , rule_rlejadouble2 , rule_rlejadouble4 , rule_rlejaodd ,   rule_rlejashifted , rule_rlejashiftedeven , rule_rlejashifteddouble , rule_maxlebesgue ,   rule_maxlebesgueodd , rule_minlebesgue , rule_minlebesgueodd , rule_mindelta ,   rule_mindeltaodd , rule_gausslegendre , rule_gausslegendreodd , rule_gausspatterson ,   rule_gausschebyshev1 , rule_gausschebyshev1odd , rule_gausschebyshev2 , rule_gausschebyshev2odd ,   rule_gaussgegenbauer , rule_gaussgegenbauerodd , rule_gaussjacobi , rule_gaussjacobiodd ,   rule_gausslaguerre , rule_gausslaguerreodd , rule_gausshermite , rule_gausshermiteodd ,   rule_customtabulated , rule_localp , rule_localp0 , rule_semilocalp ,   rule_localpb , rule_wavelet , rule_fourier }
	Used to specify the one dimensional family of rules that induces the sparse grid. More...
enum	TypeRefinement {   refine_classic , refine_parents_first , refine_direction_selective , refine_fds ,   refine_stable , refine_none }
	Refinement strategy for local polynomial and wavelet grids. More...
enum	TypeAcceleration {   accel_none , accel_cpu_blas , accel_gpu_default , accel_gpu_cublas ,   accel_gpu_cuda , accel_gpu_magma }
	Modes of acceleration. More...

Functions
TasmanianSparseGrid	makeEmpty ()
	Returns an empty sparse grid. More...
TasmanianSparseGrid	makeGlobalGrid (int dimensions, int outputs, int depth, TypeDepth type, TypeOneDRule rule, std::vector< int > const &anisotropic_weights=std::vector< int >(), double alpha=0.0, double beta=0.0, const char *custom_filename=nullptr, std::vector< int > const &level_limits=std::vector< int >())
	Factory method, creates a new grid and calls TasmanianSparseGrid::makeGlobalGrid(). More...
TasmanianSparseGrid	makeSequenceGrid (int dimensions, int outputs, int depth, TypeDepth type, TypeOneDRule rule, std::vector< int > const &anisotropic_weights=std::vector< int >(), std::vector< int > const &level_limits=std::vector< int >())
	Factory method, creates a new grid and calls TasmanianSparseGrid::makeSequenceGrid(). More...
TasmanianSparseGrid	makeLocalPolynomialGrid (int dimensions, int outputs, int depth, int order=1, TypeOneDRule rule=rule_localp, std::vector< int > const &level_limits=std::vector< int >())
	Factory method, creates a new grid and calls TasmanianSparseGrid::makeLocalPolynomialGrid(). More...
TasmanianSparseGrid	makeWaveletGrid (int dimensions, int outputs, int depth, int order=1, std::vector< int > const &level_limits=std::vector< int >())
	Factory method, creates a new grid and calls TasmanianSparseGrid::makeWaveletGrid(). More...
TasmanianSparseGrid	makeFourierGrid (int dimensions, int outputs, int depth, TypeDepth type, std::vector< int > const &anisotropic_weights=std::vector< int >(), std::vector< int > const &level_limits=std::vector< int >())
	Factory method, creates a new grid and calls TasmanianSparseGrid::makeFourierGrid(). More...
TasmanianSparseGrid	readGrid (const char *filename)
	Factory method, creates a new grid and calls TasmanianSparseGrid::read(). More...
TasmanianSparseGrid	readGrid (std::string const &filename)
	Overload using std::string. More...
TasmanianSparseGrid	copyGrid (TasmanianSparseGrid const &source, int outputs_begin=0, int outputs_end=-1)
	Returns a grid that is a copy of the source. More...
template<typename T >
std::vector< T >	spltVector2D (std::vector< T > const &x, size_t stride, int ibegin, int iend)
template<typename >
void	deleteHandle (int *)
CustomTabulated	getSubrules (CustomTabulated &ct, int start_index, int stride, std::string description)
template<class T >
std::vector< const T * >	makeReverseReferenceVector (const std::forward_list< T > &list)
template<bool use_ascii>
void	writeNodeDataList (const std::forward_list< NodeData > &data, std::ostream &os)
template<typename iomode >
std::forward_list< NodeData >	readNodeDataList (std::istream &is, size_t num_dimensions, size_t num_outputs)
template<typename iomode >
std::forward_list< TensorData >	readTensorDataList (std::istream &is, size_t num_dimensions)
template<class RuleLike >
std::vector< double >	listToNodes (std::forward_list< NodeData > const &node_list, size_t num_dimensions, RuleLike const &rule)
template<typename callable_method >
std::vector< double >	listToLocalNodes (std::forward_list< NodeData > const &node_list, size_t num_dimensions, callable_method rule)
double	poly_eval (const std::vector< double > &roots, double x)
double	lagrange_eval (size_t idx, const std::vector< double > &roots, double x)
template<bool is_symmetric>
void	getGaussNodesAndWeights (const int n, const std::vector< double > &ref_points, const std::vector< double > &ref_weights, std::vector< std::vector< double >> &points_cache, std::vector< std::vector< double >> &weights_cache)
TasGrid::CustomTabulated	getShiftedExoticQuadrature (const int n, const double shift, const std::vector< double > &shifted_weights, const std::vector< double > &ref_points, const char *description, const bool is_symmetric=false)
void	shiftReferenceWeights (std::vector< double > const &vals, double shift, std::vector< double > &ref_weights)
TasGrid::CustomTabulated	getExoticQuadrature (const int num_levels, const double shift, const TasGrid::TasmanianSparseGrid &grid, const char *description, const bool is_symmetric=false)
	Constructs an exotic quadrature from a sparse grid object. More...
TasGrid::CustomTabulated	getExoticQuadrature (const int num_levels, const double shift, std::function< double(double)> weight_fn, const int num_ref_points, const char *description, const bool is_symmetric=false)
	Similar to getExoticQuadrature() but the weight function is defined by a lambda expression. More...
template<bool parallel_construction, bool use_initial_guess>
void	constructCommon (ModelSignature model, size_t max_num_points, size_t num_parallel_jobs, size_t max_samples_per_job, TasmanianSparseGrid &grid, std::function< std::vector< double >(TasmanianSparseGrid &)> candidates, std::string const &checkpoint_filename)
template<bool parallel_construction = TasGrid::mode_parallel, bool initial_guess = no_initial_guess>
void	constructSurrogate (ModelSignature model, size_t max_num_points, size_t num_parallel_jobs, size_t max_samples_per_job, TasmanianSparseGrid &grid, double tolerance, TypeRefinement criteria, int output=-1, std::vector< int > const &level_limits=std::vector< int >(), std::string const &checkpoint_filename=std::string())
	Construct a sparse grid surrogate to the model defined by the lambda. More...
template<bool parallel_construction = TasGrid::mode_parallel, bool initial_guess = no_initial_guess>
void	constructSurrogate (ModelSignature model, size_t max_num_points, size_t num_parallel_jobs, size_t max_samples_per_job, TasmanianSparseGrid &grid, TypeDepth type, std::vector< int > const &anisotropic_weights=std::vector< int >(), std::vector< int > const &level_limits=std::vector< int >(), std::string const &checkpoint_filename=std::string())
	Construct a sparse grid surrogate to the model defined by the lambda. More...
template<bool parallel_construction = TasGrid::mode_parallel, bool initial_guess = no_initial_guess>
void	constructSurrogate (ModelSignature model, size_t max_num_points, size_t num_parallel_jobs, size_t max_samples_per_job, TasmanianSparseGrid &grid, TypeDepth type, int output, std::vector< int > const &level_limits=std::vector< int >(), std::string const &checkpoint_filename=std::string())
	Construct a sparse grid surrogate to the model defined by the lambda. More...
bool	hasGPUBasis (TasmanianSparseGrid const &grid)
template<typename scalar_type >
void	generateCoefficientsGPU (double const data_points[], int num_data, scalar_type model_values[], double tolerance, TasmanianSparseGrid &grid)
template<typename scalar_type >
Data2D< scalar_type >	generateCoefficients (double const data_points[], int num_data, double const model_values[], double tolerance, TasmanianSparseGrid &grid)
template<typename scalar_type >
void	loadUnstructuredDataL2tmpl (double const data_points[], int num_data, double const model_values[], double tolerance, TasmanianSparseGrid &grid)
void	loadUnstructuredDataL2 (double const data_points[], int num_data, double const model_values[], double tolerance, TasmanianSparseGrid &grid)
	Construct a sparse grid surrogate using a least-squares fit. More...
void	loadUnstructuredDataL2 (std::vector< double > const &data_points, std::vector< double > const &model_values, double tolerance, TasmanianSparseGrid &grid)
	Overload that used vectors and infers the number of data points from the size of the vectors. More...
template<bool use_initial_guess>
void	mpiConstructCommon (ModelSignatureMPI model, int num_dimensions, int num_outputs, size_t max_num_points, size_t max_samples_per_job, size_t max_num_ranks, int tagx, int tagy, int root, MPI_Comm comm, TasmanianSparseGrid &grid, std::function< std::vector< double >(TasmanianSparseGrid &)> candidates, std::string const &checkpoint_filename)
template<bool use_initial_guess = no_initial_guess>
void	mpiConstructSurrogate (ModelSignatureMPI model, int num_dimensions, int num_outputs, size_t max_num_points, size_t max_samples_per_job, size_t max_num_ranks, int tagx, int tagy, int root, MPI_Comm comm, TasmanianSparseGrid &grid, double tolerance, TypeRefinement criteria, int output=-1, std::vector< int > const &level_limits=std::vector< int >(), std::vector< double > const &scale_correction=std::vector< double >(), std::string const &checkpoint_filename=std::string())
	Construct a sparse grid surrogate to the model defined by the lambda, MPI version. More...
template<bool use_initial_guess = no_initial_guess>
void	mpiConstructSurrogate (ModelSignatureMPI model, int num_dimensions, int num_outputs, size_t max_num_points, size_t max_samples_per_job, size_t max_num_ranks, int tagx, int tagy, int root, MPI_Comm comm, TasmanianSparseGrid &grid, TypeDepth type, std::vector< int > const &anisotropic_weights=std::vector< int >(), std::vector< int > const &level_limits=std::vector< int >(), std::string const &checkpoint_filename=std::string())
	Construct a sparse grid surrogate to the model defined by the lambda, MPI version. More...
template<bool use_initial_guess = no_initial_guess>
void	mpiConstructSurrogate (ModelSignatureMPI model, int num_dimensions, int num_outputs, size_t max_num_points, size_t max_samples_per_job, size_t max_num_ranks, int tagx, int tagy, int root, MPI_Comm comm, TasmanianSparseGrid &grid, TypeDepth type, int output, std::vector< int > const &level_limits=std::vector< int >(), std::string const &checkpoint_filename=std::string())
	Construct a sparse grid surrogate to the model defined by the lambda, MPI version. More...
template<bool use_initial_guess = no_initial_guess>
void	mpiConstructWorker (ModelSignatureMPI model, int num_dimensions, int num_outputs, size_t max_samples_per_job, size_t max_num_ranks, int tagx, int tagy, int root, MPI_Comm comm)
	Executes the worker (i.e., non-root) portion of the MPI sampling. More...
int	getMPIRank (MPI_Comm comm)
template<bool binary = TasGrid::mode_binary>
int	MPIGridSend (TasmanianSparseGrid const &grid, int destination, int tag, MPI_Comm comm)
	Send a grid to another process in the MPI comm. More...
template<bool binary = TasGrid::mode_binary>
int	MPIGridRecv (TasmanianSparseGrid &grid, int source, int tag, MPI_Comm comm, MPI_Status *status=MPI_STATUS_IGNORE)
	Receive a grid from another process in the MPI comm. More...
template<bool binary = TasGrid::mode_binary>
int	MPIGridBcast (TasmanianSparseGrid &grid, int root, MPI_Comm comm)
	Broadcast a grid to all processes in an MPI comm. More...
template<bool binary = TasGrid::mode_binary>
int	MPIGridScatterOutputs (TasmanianSparseGrid const &source, TasmanianSparseGrid &destination, int root, int tag, MPI_Comm comm)
	Split the grid across the comm where each rank receives an equal portion of the total outputs. More...
template<bool parallel_construction = true, bool overwrite_loaded = false>
void	loadNeededValues (std::function< void(double const x[], double y[], size_t thread_id)> model, TasmanianSparseGrid &grid, size_t num_threads)
	Loads the current grid with model values, does not perform any refinement. More...
template<bool parallel_construction = true, bool overwrite_loaded = false>
void	loadNeededValues (std::function< void(std::vector< double > const &x, std::vector< double > &y, size_t thread_id)> model, TasmanianSparseGrid &grid, size_t num_threads)
	Overload that uses vectors for the model inputs and outputs. More...
template<bool parallel_construction = true, bool overwrite_loaded = false>
void	loadNeededPoints (std::function< void(double const x[], double y[], size_t thread_id)> model, TasmanianSparseGrid &grid, size_t num_threads)
	Alias to loadNeededValues(), array variant.
template<bool parallel_construction = true, bool overwrite_loaded = false>
void	loadNeededPoints (std::function< void(std::vector< double > const &x, std::vector< double > &y, size_t thread_id)> model, TasmanianSparseGrid &grid, size_t num_threads)
	Alias to loadNeededValues(), vector variant.

Variables
constexpr TypeAcceleration	accel_gpu_hip = accel_gpu_cuda
	At the front API, the HIP and CUDA options are equivalent, see TasGrid::TypeAcceleration.
constexpr TypeAcceleration	accel_gpu_rocblas = accel_gpu_cublas
	At the front API, the HIP and CUDA options are equivalent, see TasGrid::TypeAcceleration.
constexpr bool	mode_ascii = false
	Constant allowing for more expressive selection of ascii and binary mode in IO methods.
constexpr bool	mode_binary = true
	Constant allowing for more expressive selection of ascii and binary mode in IO methods.
InternalSyclQueue	test_queue
constexpr bool	mode_parallel = true
	Allows for expressive calls to TasGrid::constructSurrogate().
constexpr bool	mode_sequential = false
	Allows for expressive calls to TasGrid::constructSurrogate().
constexpr bool	with_initial_guess = true
	Allows for expressive calls to TasGrid::constructSurrogate().
constexpr bool	no_initial_guess = false
	Allows for expressive calls to TasGrid::constructSurrogate().

Detailed Description

Encapsulates the Tasmanian Sparse Grid module.

Enumeration Type Documentation

TypeIndexRelation

enum TasGrid::TypeIndexRelation

The standard C++11 algorithms std::merge and std::binary_search use bools as return types when comparing entries, thus in the case of many repeated entries, two comparisons have to be performed. Repeated entries are encountered in many sparse grids algorithms, especially when dealing with nested one dimensional rules. Thus, for performance reasons, Tasmanian implements merging and searching methods that compare multi-indexes and produce three outcomes in a single comparison.

Specifically, see SetManipulations::push_merge_map() and MultiIndexSet::getSlot() and MultiIndexSet::addSortedInsexes()

Enumerator
type_abeforeb	Indicates that the first multi-index in the call to compare comes before the second.
type_bbeforea	Indicates that the second multi-index in the call to compare after the second.
type_asameb	Indicates that the two multi-indexes are the same.