Serialize pointer-to-pointer-... array into one 1D array, keeping track of the offsets in few additional 1D arrays. More...

#include <ndflattener.hpp>

Public Types
using	Data_t = Data_t_

using	Index_t = Index_t_

using	Base = NdFlattener< K - 1, Data_t, Index_t >

using	CurrentLayer = NdFlattener< K, Data_t, Index_t >

using	LowestLayer = typename Base::LowestLayer

Public Member Functions
Index_t *	GetIndices ()

const Index_t *	GetIndices () const

	NdFlattener ()
	Basic constructor. Afterwards, initialization can be done with initialize_or_refresh.

template<class... ARGS>
	NdFlattener (ARGS const &... args)
	Constructor which calls initialize_or_refresh.

template<class... ARGS>
void	initialize_or_refresh (ARGS const &... args)
	Initialize or refresh the NdFlattener.

bool	initialized ()
	Initialization status of the NdFlattener.

template<class f_type >
void	initialize (f_type f)
	Initialize from a 'recursive function'.

template<class f_type >
void	refresh (f_type f)
	Refresh the data according to the 'recursive function'.

template<typename MPI_Environment_type >
void	initialize (MPI_Environment_type const &mpi_env, Base const &local_version)
	Initialize a flattener with K indices by combining distributed flatteners with (K-1) indices each.

template<typename MPI_Environment_type >
void	refresh (MPI_Environment_type const &mpi_env, Base const &local_version)
	Refresh the data by MPI collective communication.

Index_t	size () const

helpers::IndexAccumulator< K - 1, NdFlattener< K - 1, Data_t, Index_t > >	operator[] (Index_t i0)
	Look-up with IndexAccumulator, non-const version.

helpers::IndexAccumulator< K - 1, const NdFlattener< K - 1, Data_t, Index_t > >	operator[] (Index_t i0) const
	Look-up with IndexAccumulator, const version.

Static Public Attributes
static constexpr size_t	K = K_

Protected Member Functions
void	toPythonString_fromto (std::ostream &output, Index_t from, Index_t to) const
	Write to stream in Python-list style, using the data of the indices array between 'from' (inclusive) and 'to' (exclusive).

void	allocate ()
	Allocate the indices array after nNodes has been determined.

void	reset_iNode ()
	Set iNode to 0 in all layers.

template<class f_type >
void	count_f (f_type f)
	Determine the space required for reading the 'recursive function'.

template<class f_type >
void	set_f (f_type f, bool refresh)
	Read the 'recursive function' into the allocated arrays.

void	count_g (Nd_MPI_Environment const &mpi_env, su2matrix< Index_t > &Nodes_all, CurrentLayer const &local_version)
	Count the distributed flatteners' numbers of nodes, and set nNodes.

void	set_g (Nd_MPI_Environment const &mpi_env, su2matrix< Index_t > const &Nodes_all, CurrentLayer const &local_version)
	Gather the distributed flatteners' data and index arrays into the allocated arrays.

Friends
std::ostream &	operator<< (std::ostream &output, NdFlattener const &nd)
	Write in Python-list style.

Detailed Description

template<size_t K_, typename Data_t_, typename Index_t_>
class NdFlattener< K_, Data_t_, Index_t_ >

Serialize pointer-to-pointer-... array into one 1D array, keeping track of the offsets in few additional 1D arrays.

The pointer-to-pointer-... array can be provided by a nested lambda function ('recursive function') or by gathering such arrays from MPI processes ('collective communication'). After initializing an NdFlattener with either of these data, it can be refreshed in the same way after the the pointer-to-pointer-... array's values (but not its structure) have changed.

Template Parameters

K	- number of indices
Data_t	- Type of stored array data
Index	- Type of index

Member Typedef Documentation

◆ Base

template<size_t K_, typename Data_t_ , typename Index_t_ >

using NdFlattener< K_, Data_t_, Index_t_ >::Base = NdFlattener<K - 1, Data_t, Index_t>

◆ CurrentLayer

template<size_t K_, typename Data_t_ , typename Index_t_ >

using NdFlattener< K_, Data_t_, Index_t_ >::CurrentLayer = NdFlattener<K, Data_t, Index_t>

◆ Data_t

template<size_t K_, typename Data_t_ , typename Index_t_ >

using NdFlattener< K_, Data_t_, Index_t_ >::Data_t = Data_t_

◆ Index_t

template<size_t K_, typename Data_t_ , typename Index_t_ >

using NdFlattener< K_, Data_t_, Index_t_ >::Index_t = Index_t_

◆ LowestLayer

template<size_t K_, typename Data_t_ , typename Index_t_ >

using NdFlattener< K_, Data_t_, Index_t_ >::LowestLayer = typename Base::LowestLayer

Constructor & Destructor Documentation

◆ NdFlattener() [1/2]

template<size_t K_, typename Data_t_ , typename Index_t_ >

NdFlattener< K_, Data_t_, Index_t_ >::NdFlattener ( )

inline

Basic constructor. Afterwards, initialization can be done with initialize_or_refresh.

Called recursively when a derived class (higher K) is constructed.

◆ NdFlattener() [2/2]

template<size_t K_, typename Data_t_ , typename Index_t_ >

template<class... ARGS>

NdFlattener< K_, Data_t_, Index_t_ >::NdFlattener ( ARGS const &... args )

inline

Constructor which calls initialize_or_refresh.

Member Function Documentation

◆ allocate()

template<size_t K_, typename Data_t_ , typename Index_t_ >

void NdFlattener< K_, Data_t_, Index_t_ >::allocate ( )

inlineprotected

Allocate the indices array after nNodes has been determined.

◆ count_f()

template<size_t K_, typename Data_t_ , typename Index_t_ >

template<class f_type >

void NdFlattener< K_, Data_t_, Index_t_ >::count_f ( f_type f )

inlineprotected

Determine the space required for reading the 'recursive function'.

Parameters

f	- the 'recursive function'

◆ count_g()

template<size_t K_, typename Data_t_ , typename Index_t_ >

void NdFlattener< K_, Data_t_, Index_t_ >::count_g	(	Nd_MPI_Environment const &	mpi_env,
		su2matrix< Index_t > &	Nodes_all,
		CurrentLayer const &	local_version
	)

inlineprotected

Count the distributed flatteners' numbers of nodes, and set nNodes.

Parameters

[in]	mpi_env	- MPI environment for communication
[out]	Nodes_all	- [k][r] is set to number of nodes in layer (k+1), rank r.
[in]	local_version	- local instance to be send to the other processes

◆ GetIndices() [1/2]

template<size_t K_, typename Data_t_ , typename Index_t_ >

Index_t * NdFlattener< K_, Data_t_, Index_t_ >::GetIndices ( )

inline

◆ GetIndices() [2/2]

template<size_t K_, typename Data_t_ , typename Index_t_ >

const Index_t * NdFlattener< K_, Data_t_, Index_t_ >::GetIndices ( ) const

inline

◆ initialize() [1/2]

template<size_t K_, typename Data_t_ , typename Index_t_ >

template<class f_type >

void NdFlattener< K_, Data_t_, Index_t_ >::initialize ( f_type f )

inline

Initialize from a 'recursive function'.

The function should return a pair. Its first entry is the number of children. Its second entry is a function with the same meaning, recursively one layer down.

Parameters

f	- the 'recursive function'

◆ initialize() [2/2]

template<size_t K_, typename Data_t_ , typename Index_t_ >

template<typename MPI_Environment_type >

void NdFlattener< K_, Data_t_, Index_t_ >::initialize	(	MPI_Environment_type const &	mpi_env,
		Base const &	local_version
	)

inline

Initialize a flattener with K indices by combining distributed flatteners with (K-1) indices each.

The new first index will encode the rank of the process. Data is exchanged in MPI::Allgatherv-style collective communication.

Parameters

[in]	mpi_env	- The MPI environment used for communication.
[in]	local_version	- The local NdFlattener structure with (K-1) indices.

◆ initialize_or_refresh()

template<size_t K_, typename Data_t_ , typename Index_t_ >

template<class... ARGS>

void NdFlattener< K_, Data_t_, Index_t_ >::initialize_or_refresh ( ARGS const &... args )

inline

Initialize or refresh the NdFlattener.

Either a 'recursive function' or 'collective communication' may be used. When the NdFlattener does not hold data yet, it is initialized, meaning that the data are collected and the indices arrays are allocated and filled. Otherwise it is refreshed, meaning that the data are recollected under the assumption that the indices arrays did not change.

◆ initialized()

template<size_t K_, typename Data_t_ , typename Index_t_ >

bool NdFlattener< K_, Data_t_, Index_t_ >::initialized ( )

inline

Initialization status of the NdFlattener.

Returns: true if the NdFlattener has been initialized

◆ operator[]() [1/2]

template<size_t K_, typename Data_t_ , typename Index_t_ >

helpers::IndexAccumulator< K - 1, NdFlattener< K - 1, Data_t, Index_t > > NdFlattener< K_, Data_t_, Index_t_ >::operator[] ( Index_t i0 )

inline

Look-up with IndexAccumulator, non-const version.

◆ operator[]() [2/2]

template<size_t K_, typename Data_t_ , typename Index_t_ >

helpers::IndexAccumulator< K - 1, const NdFlattener< K - 1, Data_t, Index_t > > NdFlattener< K_, Data_t_, Index_t_ >::operator[] ( Index_t i0 ) const

inline

Look-up with IndexAccumulator, const version.

◆ refresh() [1/2]

template<size_t K_, typename Data_t_ , typename Index_t_ >

template<class f_type >

void NdFlattener< K_, Data_t_, Index_t_ >::refresh ( f_type f )

inline

Refresh the data according to the 'recursive function'.

The NdFlattener must have been constructed with a 'recursive function'. Now refresh the values with another 'recursive function'. The subarray lengths resulting from both 'recursive functions' must coincide, as the indices arrays are not changed.

Parameters

f	- the 'recursive function'

Template Parameters

f_type - to allow for any type of the 'recursive function'

◆ refresh() [2/2]

template<size_t K_, typename Data_t_ , typename Index_t_ >

template<typename MPI_Environment_type >

void NdFlattener< K_, Data_t_, Index_t_ >::refresh	(	MPI_Environment_type const &	mpi_env,
		Base const &	local_version
	)

inline

Refresh the data by MPI collective communication.

The NdFlattener must have been constructed by MPI collective communication. Now refresh the values with another collective communication. The subarray lengths resulting from both collective communications must coincide, as the indices arrays are not changed.

Parameters

[in]	mpi_env	- The MPI environment used for communication.
[in]	local_version	- The local NdFlattener structure.

◆ reset_iNode()

template<size_t K_, typename Data_t_ , typename Index_t_ >

void NdFlattener< K_, Data_t_, Index_t_ >::reset_iNode ( )

inlineprotected

Set iNode to 0 in all layers.

◆ set_f()

template<size_t K_, typename Data_t_ , typename Index_t_ >

template<class f_type >

void NdFlattener< K_, Data_t_, Index_t_ >::set_f	(	f_type	f,
		bool	refresh
	)

inlineprotected

Read the 'recursive function' into the allocated arrays.

Parameters

f	- the 'recursive function'
refresh	- if true, the object is already initialized and only the data in layer 1 have to be overwritten

Template Parameters

f_type - to allow for any type of the 'recursive function'

◆ set_g()

template<size_t K_, typename Data_t_ , typename Index_t_ >

void NdFlattener< K_, Data_t_, Index_t_ >::set_g	(	Nd_MPI_Environment const &	mpi_env,
		su2matrix< Index_t > const &	Nodes_all,
		CurrentLayer const &	local_version
	)

inlineprotected

Gather the distributed flatteners' data and index arrays into the allocated arrays.

Parameters

[in]	mpi_env	- MPI environment for communication
[in]	Nodes_all	- [k][r] is the number of nodes in layer (k+1), rank r.
[in]	local_version	- local instance to be sent to the other processes

◆ size()

template<size_t K_, typename Data_t_ , typename Index_t_ >

Index_t NdFlattener< K_, Data_t_, Index_t_ >::size ( ) const

inline

◆ toPythonString_fromto()

template<size_t K_, typename Data_t_ , typename Index_t_ >

void NdFlattener< K_, Data_t_, Index_t_ >::toPythonString_fromto	(	std::ostream &	output,
		Index_t	from,
		Index_t	to
	)		const

inlineprotected

Write to stream in Python-list style, using the data of the indices array between 'from' (inclusive) and 'to' (exclusive).

Like this: [[1, 2], [10, 20, 30]]

Parameters

[in]	output	- Stream
[in]	from	- Beginning of the representation in the indices array.
[in]	to	- Ending of the representation in the indices array.

Friends And Related Symbol Documentation

◆ operator<<

template<size_t K_, typename Data_t_ , typename Index_t_ >

std::ostream & operator<<	(	std::ostream &	output,
		NdFlattener< K_, Data_t_, Index_t_ > const &	nd
	)

friend

Write in Python-list style.

Like this: [[1, 2], [10, 20, 30]]

Member Data Documentation

◆ K

template<size_t K_, typename Data_t_ , typename Index_t_ >

constexpr size_t NdFlattener< K_, Data_t_, Index_t_ >::K = K_

staticconstexpr

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

Common/include/toolboxes/ndflattener.hpp

Public Types

Public Member Functions

Static Public Attributes

Protected Member Functions

Friends

Detailed Description

Member Typedef Documentation

◆ Base

◆ CurrentLayer

◆ Data_t

◆ Index_t

◆ LowestLayer

Constructor & Destructor Documentation

◆ NdFlattener() [1/2]

◆ NdFlattener() [2/2]

Member Function Documentation

◆ allocate()

◆ count_f()

◆ count_g()

◆ GetIndices() [1/2]

◆ GetIndices() [2/2]

◆ initialize() [1/2]

◆ initialize() [2/2]

◆ initialize_or_refresh()

◆ initialized()

◆ operator[]() [1/2]

◆ operator[]() [2/2]

◆ refresh() [1/2]

◆ refresh() [2/2]

◆ reset_iNode()

◆ set_f()

◆ set_g()

◆ size()

◆ toPythonString_fromto()

Friends And Related Symbol Documentation

◆ operator<<

Member Data Documentation

◆ K