TNL::Containers::ArrayView< Value, Device, Index > Class Template Reference

ArrayView is a simple data structure which provides a non-owning encapsulation of array data. That is, ArrayView is like Array without memory management. More...

#include <TNL/Containers/ArrayView.h>

Public Types
using	ConstViewType = ArrayView< std::add_const_t< Value >, Device, Index >
	Compatible constant ArrayView type.
using	DeviceType = Device
	Device used to run operations on the array.
using	IndexType = Index
	Type being used for the array elements indexing.
template<typename _Value , typename _Device = Device, typename _Index = Index>
using	Self = ArrayView< _Value, _Device, _Index >
	A template which allows to quickly obtain an ArrayView type with changed template parameters.
using	ValueType = Value
	Type of elements stored in this array.
using	ViewType = ArrayView< Value, Device, Index >
	Compatible ArrayView type.

Public Member Functions
__cuda_callable__	ArrayView ()=default
	Constructs an empty array view.
__cuda_callable__	ArrayView (ArrayView &&view) noexcept=default
	Move constructor for initialization from rvalues.
__cuda_callable__	ArrayView (const ArrayView &view)=default
	Shallow copy constructor.
template<typename Value_ >
__cuda_callable__	ArrayView (const ArrayView< Value_, Device, Index > &view)
	"Templated" shallow copy constructor.
__cuda_callable__	ArrayView (ValueType *data, IndexType size)
	Constructs an array view by binding to the given data pointer and size.
__cuda_callable__ void	bind (ArrayView view)
	Method for rebinding (reinitialization) using another array view.
__cuda_callable__ void	bind (ValueType *data, IndexType size)
	Method for rebinding (reinitialization) using a raw pointer and size.
__cuda_callable__ bool	empty () const
	Returns true if the current array view size is zero.
template<typename Function >
void	forAllElements (Function &&f)
	Process the lambda function f for each array element.
template<typename Function >
void	forAllElements (Function &&f) const
	Process the lambda function f for each array element for constant instances.
template<typename Function >
void	forElements (IndexType begin, IndexType end, Function &&f)
	Process the lambda function f for each array element in interval [ begin, end).
template<typename Function >
void	forElements (IndexType begin, IndexType end, Function &&f) const
	Process the lambda function f for each array element in interval [ begin, end) for constant instances of the array.
__cuda_callable__ ValueType *	getArrayData ()
	Returns a raw pointer to the data.
__cuda_callable__ const ValueType *	getArrayData () const
	Returns a const-qualified raw pointer to the data.
__cuda_callable__ ConstViewType	getConstView (IndexType begin=0, IndexType end=0) const
	Returns a non-modifiable view of the array view.
__cuda_callable__ ValueType *	getData ()
	Returns a raw pointer to the data.
__cuda_callable__ const ValueType *	getData () const
	Returns a const-qualified raw pointer to the data.
__cuda_callable__ ValueType	getElement (IndexType i) const
	Returns the value of the i-th element.
__cuda_callable__ IndexType	getSize () const
	Returns the current size of the array view.
__cuda_callable__ ViewType	getView (IndexType begin=0, IndexType end=0)
	Returns a modifiable view of the array view.
void	load (const std::string &fileName)
	Method for loading the data from a binary file fileName.
template<typename ArrayT >
bool	operator!= (const ArrayT &array) const
	Compares the array view with another array-like container.
__cuda_callable__ Value &	operator() (IndexType i)
	Accesses the i-th element of the array.
__cuda_callable__ const Value &	operator() (IndexType i) const
	Accesses the i-th element of the array.
ArrayView &	operator= (const ArrayView &view)
	Deep copy assignment operator for copying data from another array view.
template<typename T , typename... , typename = std::enable_if_t< std::is_convertible_v< T, ValueType > || IsArrayType< T >::value >>
ArrayView &	operator= (const T &data)
	Assigns either array-like container or a single value.
template<typename T , typename... , typename >
ArrayView< Value, Device, Index > &	operator= (const T &data)
template<typename ArrayT >
bool	operator== (const ArrayT &array) const
	Compares the array view with another array-like container.
__cuda_callable__ Value &	operator[] (IndexType i)
	Accesses the i-th element of the array view.
__cuda_callable__ const Value &	operator[] (IndexType i) const
	Accesses the i-th element of the array view.
__cuda_callable__ void	reset ()
void	save (const std::string &fileName) const
	Method for saving the data to a binary file fileName.
__cuda_callable__ void	setElement (IndexType i, ValueType value)
	Sets the value of the i-th element to v.
void	setValue (ValueType value, IndexType begin=0, IndexType end=0)
	Sets elements of the array view to given value.
__cuda_callable__ void	swap (ArrayView &view)
	Swaps this array view with another.

Protected Attributes
ValueType *	data = nullptr
	Pointer to the data.
IndexType	size = 0
	Array view size.

Detailed Description

template<typename Value, typename Device = Devices::Host, typename Index = int>
class TNL::Containers::ArrayView< Value, Device, Index >

ArrayView is a simple data structure which provides a non-owning encapsulation of array data. That is, ArrayView is like Array without memory management.

The meaning of the template parameters is the same as in Array.

Template Parameters

Value	The type of array elements.
Device	The device where the array is allocated. This ensures the compile-time checks of correct pointers manipulation. It can be any class defined in the TNL::Devices namespace.
Index	The indexing type.

ArrayView provides access to array elements and general array operations same as Array, but it does not manage memory. The construction of an ArrayView does not make a memory allocation, but it can be bound to an existing portion of memory specified by a raw pointer, another ArrayView, or an Array. Similarly, ArrayView is not resizable, so there is no setSize method. Note that ArrayView does not own its data, so it does not deallocate it in the destructor.

Another important difference between ArrayView and Array is in the copy semantics. While the copy-constructor of Array makes a deep copy of the data, ArrayView makes only a shallow copy in the copy-constructor, i.e. it changes only its pointer and size. As a result, array views can be efficiently passed by value (even to device kernels) or captured by value in lambda functions (even in device lambda functions). Note that operator= in both ArrayView and Array still makes a deep copy of the data.

See also Array, Vector, VectorView.

Example

#include <iostream>
#include <TNL/Containers/Array.h>
#include <TNL/Containers/ArrayView.h>
 
using namespace TNL;
 
/***
 * The following works for any device (CPU, GPU ...).
 */
template< typename Device >
void
arrayViewExample()
{
   const int size = 10;
   using ArrayType = Containers::Array< int, Device >;
   using IndexType = typename ArrayType::IndexType;
   using ViewType = Containers::ArrayView< int, Device >;
   ArrayType a1( size ), a2( size );
   ViewType a1_view = a1.getView();
   ViewType a2_view = a2.getView();
 
   /***
    * You may initiate the array view using setElement
    */
   for( int i = 0; i < size; i++ )
      a1_view.setElement( i, i );
 
   /***
    * You may also assign value to all array view elements ...
    */
   a2_view = 0;
 
   /***
    * More efficient way of array view elements manipulation is with the lambda functions
    */
   ArrayType a3( size );
   ViewType a3_view = a3.getView();
   auto f1 = [] __cuda_callable__( IndexType i, int& value )
   {
      value = 2 * i;
   };
   a3_view.forAllElements( f1 );
 
   for( int i = 0; i < size; i++ )
      if( a3_view.getElement( i ) != 2 * i )
         std::cerr << "Something is wrong!!!" << std::endl;
 
   /***
    * You may swap array view data with the swap method.
    */
   a1_view.swap( a3_view );
 
   /***
    * You may save it to file and load again
    */
   File( "a1_view.tnl", std::ios_base::out ) << a1_view;
   File( "a1_view.tnl", std::ios_base::in ) >> a2_view;
 
   std::remove( "a1_view.tnl" );
 
   if( a2_view != a1_view )
      std::cerr << "Something is wrong!!!" << std::endl;
 
   std::cout << "a2_view = " << a2_view << std::endl;
}
 
int
main()
{
   std::cout << "The first test runs on CPU ..." << std::endl;
   arrayViewExample< Devices::Host >();
#ifdef __CUDACC__
   std::cout << "The second test runs on GPU ..." << std::endl;
   arrayViewExample< Devices::Cuda >();
#endif
}

Output

The first test runs on CPU ...
a2_view = [ 0, 2, 4, 6, 8, 10, 12, 14, 16, 18 ]
The second test runs on GPU ...
a2_view = [ 0, 2, 4, 6, 8, 10, 12, 14, 16, 18 ]

Member Typedef Documentation

DeviceType

template<typename Value , typename Device = Devices::Host, typename Index = int>

using TNL::Containers::ArrayView< Value, Device, Index >::DeviceType = Device

Device used to run operations on the array.

See TNL::Devices for the available options.

Constructor & Destructor Documentation

ArrayView() [1/5]

template<typename Value , typename Device = Devices::Host, typename Index = int>

__cuda_callable__ TNL::Containers::ArrayView< Value, Device, Index >::ArrayView ( )

default

Constructs an empty array view.

This method can be called from device kernels.

ArrayView() [2/5]

template<typename Value , typename Device , typename Index >

__cuda_callable__ TNL::Containers::ArrayView< Value, Device, Index >::ArrayView	(	ValueType *	data,
		IndexType	size )

Constructs an array view by binding to the given data pointer and size.

This method can be called from device kernels.

Parameters

data	The data pointer to be bound.
size	The number of elements in the array view.

ArrayView() [3/5]

template<typename Value , typename Device = Devices::Host, typename Index = int>

__cuda_callable__ TNL::Containers::ArrayView< Value, Device, Index >::ArrayView ( const ArrayView< Value, Device, Index > & view )

default

Shallow copy constructor.

This method can be called from device kernels.

Parameters

view	The array view to be copied.

ArrayView() [4/5]

template<typename Value , typename Device = Devices::Host, typename Index = int>

template<typename Value_ >

__cuda_callable__ TNL::Containers::ArrayView< Value, Device, Index >::ArrayView ( const ArrayView< Value_, Device, Index > & view )

inline

"Templated" shallow copy constructor.

This method can be called from device kernels.

Template Parameters

Value_

The template parameter can be any cv-qualified variant of ValueType.

Parameters

view	The array view to be copied.

ArrayView() [5/5]

template<typename Value , typename Device = Devices::Host, typename Index = int>

__cuda_callable__ TNL::Containers::ArrayView< Value, Device, Index >::ArrayView ( ArrayView< Value, Device, Index > && view )

defaultnoexcept

Move constructor for initialization from rvalues.

This method can be called from device kernels.

Parameters

view	The array view to be moved.

Member Function Documentation

bind() [1/2]

template<typename Value , typename Device , typename Index >

__cuda_callable__ void TNL::Containers::ArrayView< Value, Device, Index >::bind

(

ArrayView< Value, Device, Index >

view

)

Method for rebinding (reinitialization) using another array view.

Note that you can also bind directly to an Array instance and other objects whose type is implicitly convertible to ArrayView.

This method can be called from device kernels.

Parameters

view	The array view to be bound.

bind() [2/2]

template<typename Value , typename Device , typename Index >

__cuda_callable__ void TNL::Containers::ArrayView< Value, Device, Index >::bind	(	ValueType *	data,
		IndexType	size )

Method for rebinding (reinitialization) using a raw pointer and size.

This method can be called from device kernels.

Parameters

data	The data pointer to be bound to the array view.
size	The number of elements in the array view.

empty()

template<typename Value , typename Device , typename Index >

__cuda_callable__ bool TNL::Containers::ArrayView< Value, Device, Index >::empty ( ) const

nodiscard

Returns true if the current array view size is zero.

This method can be called from device kernels.

forAllElements() [1/2]

template<typename Value , typename Device , typename Index >

template<typename Function >

void TNL::Containers::ArrayView< Value, Device, Index >::forAllElements

(

Function &&

f

)

Process the lambda function f for each array element.

The lambda function is supposed to be declared as

f( IndexType elementIdx, ValueType& elementValue )

where

• elementIdx is an index of the array element being currently processed
• elementValue is a value of the array element being currently processed

This is performed at the same place where the array is allocated, i.e. it is efficient even on GPU.

Parameters

f	The lambda function to be processed.

Example

#include <iostream>
#include <TNL/Containers/Array.h>
#include <TNL/Containers/ArrayView.h>
 
using namespace TNL;
 
template< typename Device >
void
forElementsExample()
{
   /****
    * Create new arrays
    */
   const int size = 10;
   Containers::Array< float, Device > a( size ), b( size );
   b = 0;
 
   /****
    * Create an ArrayView and use it for initiation of elements of array `a`
    */
   auto a_view = a.getView();
   a_view.forAllElements(
      [] __cuda_callable__( int i, float& value )
      {
         value = i;
      } );
 
   /****
    * Initiate elements of array `b` with indexes 0-4 using `a_view`
    */
   b.getView().forElements( 0,
                            5,
                            [ = ] __cuda_callable__( int i, float& value )
                            {
                               value = a_view[ i ] + 4.0;
                            } );
 
   /****
    * Print the results
    */
   std::cout << " a = " << a << std::endl;
   std::cout << " b = " << b << std::endl;
}
 
int
main( int argc, char* argv[] )
{
   std::cout << "Running example on the host system: " << std::endl;
   forElementsExample< Devices::Host >();
 
#ifdef __CUDACC__
   std::cout << "Running example on the CUDA device: " << std::endl;
   forElementsExample< Devices::Cuda >();
#endif
}

Output

Running example on the host system: 
 a = [ 0, 1, 2, 3, 4, 5, 6, 7, 8, 9 ]
 b = [ 4, 5, 6, 7, 8, 0, 0, 0, 0, 0 ]
Running example on the CUDA device: 
 a = [ 0, 1, 2, 3, 4, 5, 6, 7, 8, 9 ]
 b = [ 4, 5, 6, 7, 8, 0, 0, 0, 0, 0 ]

forAllElements() [2/2]

template<typename Value , typename Device , typename Index >

template<typename Function >

void TNL::Containers::ArrayView< Value, Device, Index >::forAllElements

(

Function &&

f

)

const

Process the lambda function f for each array element for constant instances.

The lambda function is supposed to be declared as

f( IndexType elementIdx, const ValueType& elementValue )

where

• elementIdx is an index of the array element being currently processed
• elementValue is a value of the array element being currently processed

This is performed at the same place where the array is allocated, i.e. it is efficient even on GPU.

Parameters

f	The lambda function to be processed.

Example

#include <iostream>
#include <TNL/Containers/Array.h>
#include <TNL/Containers/ArrayView.h>
 
using namespace TNL;
 
template< typename Device >
void
forElementsExample()
{
   /****
    * Create new arrays
    */
   const int size = 10;
   Containers::Array< float, Device > a( size ), b( size );
   b = 0;
 
   /****
    * Create an ArrayView and use it for initiation of elements of array `a`
    */
   auto a_view = a.getView();
   a_view.forAllElements(
      [] __cuda_callable__( int i, float& value )
      {
         value = i;
      } );
 
   /****
    * Initiate elements of array `b` with indexes 0-4 using `a_view`
    */
   b.getView().forElements( 0,
                            5,
                            [ = ] __cuda_callable__( int i, float& value )
                            {
                               value = a_view[ i ] + 4.0;
                            } );
 
   /****
    * Print the results
    */
   std::cout << " a = " << a << std::endl;
   std::cout << " b = " << b << std::endl;
}
 
int
main( int argc, char* argv[] )
{
   std::cout << "Running example on the host system: " << std::endl;
   forElementsExample< Devices::Host >();
 
#ifdef __CUDACC__
   std::cout << "Running example on the CUDA device: " << std::endl;
   forElementsExample< Devices::Cuda >();
#endif
}

Output

Running example on the host system: 
 a = [ 0, 1, 2, 3, 4, 5, 6, 7, 8, 9 ]
 b = [ 4, 5, 6, 7, 8, 0, 0, 0, 0, 0 ]
Running example on the CUDA device: 
 a = [ 0, 1, 2, 3, 4, 5, 6, 7, 8, 9 ]
 b = [ 4, 5, 6, 7, 8, 0, 0, 0, 0, 0 ]

forElements() [1/2]

template<typename Value , typename Device , typename Index >

template<typename Function >

void TNL::Containers::ArrayView< Value, Device, Index >::forElements	(	IndexType	begin,
		IndexType	end,
		Function &&	f )

Process the lambda function f for each array element in interval [ begin, end).

The lambda function is supposed to be declared as

f( IndexType elementIdx, ValueType& elementValue )

where

• elementIdx is an index of the array element being currently processed
• elementValue is a value of the array element being currently processed

This is performed at the same place where the array is allocated, i.e. it is efficient even on GPU.

Parameters

begin	The beginning of the array elements interval.
end	The end of the array elements interval.
f	The lambda function to be processed.

Example

#include <iostream>
#include <TNL/Containers/Array.h>
#include <TNL/Containers/ArrayView.h>
 
using namespace TNL;
 
template< typename Device >
void
forElementsExample()
{
   /****
    * Create new arrays
    */
   const int size = 10;
   Containers::Array< float, Device > a( size ), b( size );
   b = 0;
 
   /****
    * Create an ArrayView and use it for initiation of elements of array `a`
    */
   auto a_view = a.getView();
   a_view.forAllElements(
      [] __cuda_callable__( int i, float& value )
      {
         value = i;
      } );
 
   /****
    * Initiate elements of array `b` with indexes 0-4 using `a_view`
    */
   b.getView().forElements( 0,
                            5,
                            [ = ] __cuda_callable__( int i, float& value )
                            {
                               value = a_view[ i ] + 4.0;
                            } );
 
   /****
    * Print the results
    */
   std::cout << " a = " << a << std::endl;
   std::cout << " b = " << b << std::endl;
}
 
int
main( int argc, char* argv[] )
{
   std::cout << "Running example on the host system: " << std::endl;
   forElementsExample< Devices::Host >();
 
#ifdef __CUDACC__
   std::cout << "Running example on the CUDA device: " << std::endl;
   forElementsExample< Devices::Cuda >();
#endif
}

Output

Running example on the host system: 
 a = [ 0, 1, 2, 3, 4, 5, 6, 7, 8, 9 ]
 b = [ 4, 5, 6, 7, 8, 0, 0, 0, 0, 0 ]
Running example on the CUDA device: 
 a = [ 0, 1, 2, 3, 4, 5, 6, 7, 8, 9 ]
 b = [ 4, 5, 6, 7, 8, 0, 0, 0, 0, 0 ]

forElements() [2/2]

template<typename Value , typename Device , typename Index >

template<typename Function >

void TNL::Containers::ArrayView< Value, Device, Index >::forElements	(	IndexType	begin,
		IndexType	end,
		Function &&	f ) const

Process the lambda function f for each array element in interval [ begin, end) for constant instances of the array.

The lambda function is supposed to be declared as

f( IndexType elementIdx, const ValueType& elementValue )

where

• elementIdx is an index of the array element being currently processed
• elementValue is a value of the array element being currently processed

This is performed at the same place where the array is allocated, i.e. it is efficient even on GPU.

Parameters

begin	The beginning of the array elements interval.
end	The end of the array elements interval.
f	The lambda function to be processed.

Example

#include <iostream>
#include <TNL/Containers/Array.h>
#include <TNL/Containers/ArrayView.h>
 
using namespace TNL;
 
template< typename Device >
void
forElementsExample()
{
   /****
    * Create new arrays
    */
   const int size = 10;
   Containers::Array< float, Device > a( size ), b( size );
   b = 0;
 
   /****
    * Create an ArrayView and use it for initiation of elements of array `a`
    */
   auto a_view = a.getView();
   a_view.forAllElements(
      [] __cuda_callable__( int i, float& value )
      {
         value = i;
      } );
 
   /****
    * Initiate elements of array `b` with indexes 0-4 using `a_view`
    */
   b.getView().forElements( 0,
                            5,
                            [ = ] __cuda_callable__( int i, float& value )
                            {
                               value = a_view[ i ] + 4.0;
                            } );
 
   /****
    * Print the results
    */
   std::cout << " a = " << a << std::endl;
   std::cout << " b = " << b << std::endl;
}
 
int
main( int argc, char* argv[] )
{
   std::cout << "Running example on the host system: " << std::endl;
   forElementsExample< Devices::Host >();
 
#ifdef __CUDACC__
   std::cout << "Running example on the CUDA device: " << std::endl;
   forElementsExample< Devices::Cuda >();
#endif
}

Output

Running example on the host system: 
 a = [ 0, 1, 2, 3, 4, 5, 6, 7, 8, 9 ]
 b = [ 4, 5, 6, 7, 8, 0, 0, 0, 0, 0 ]
Running example on the CUDA device: 
 a = [ 0, 1, 2, 3, 4, 5, 6, 7, 8, 9 ]
 b = [ 4, 5, 6, 7, 8, 0, 0, 0, 0, 0 ]

getArrayData() [1/2]

template<typename Value , typename Device , typename Index >

__cuda_callable__ Value * TNL::Containers::ArrayView< Value, Device, Index >::getArrayData ( )

nodiscard

Returns a raw pointer to the data.

Use this method in algorithms where you want to emphasize that C-style array pointer is required.

This method can be called from device kernels.

getArrayData() [2/2]

template<typename Value , typename Device , typename Index >

__cuda_callable__ const Value * TNL::Containers::ArrayView< Value, Device, Index >::getArrayData ( ) const

nodiscard

Returns a const-qualified raw pointer to the data.

Use this method in algorithms where you want to emphasize that C-style array pointer is required.

This method can be called from device kernels.

getConstView()

template<typename Value , typename Device , typename Index >

__cuda_callable__ ArrayView< Value, Device, Index >::ConstViewType TNL::Containers::ArrayView< Value, Device, Index >::getConstView ( IndexType begin = 0, IndexType end = 0 ) const

nodiscard

Returns a non-modifiable view of the array view.

By default, a view for the whole array is returned. If begin or end is set to a non-zero value, a view only for the sub-interval [begin, end) is returned.

Parameters

begin	The beginning of the array view sub-interval. It is 0 by default.
end	The end of the array view sub-interval. The default value is 0 which is, however, replaced with the array size.

getData() [1/2]

template<typename Value , typename Device , typename Index >

__cuda_callable__ Value * TNL::Containers::ArrayView< Value, Device, Index >::getData ( )

nodiscard

Returns a raw pointer to the data.

This method can be called from device kernels.

getData() [2/2]

template<typename Value , typename Device , typename Index >

__cuda_callable__ const Value * TNL::Containers::ArrayView< Value, Device, Index >::getData ( ) const

nodiscard

Returns a const-qualified raw pointer to the data.

This method can be called from device kernels.

getElement()

template<typename Value , typename Device , typename Index >

__cuda_callable__ Value TNL::Containers::ArrayView< Value, Device, Index >::getElement ( IndexType i ) const

nodiscard

Returns the value of the i-th element.

This method can be called from both the host system and the device where the array is allocated.

Parameters

i	The index of the element to be returned.

getSize()

template<typename Value , typename Device , typename Index >

__cuda_callable__ Index TNL::Containers::ArrayView< Value, Device, Index >::getSize ( ) const

nodiscard

Returns the current size of the array view.

This method can be called from device kernels.

getView()

template<typename Value , typename Device , typename Index >

__cuda_callable__ ArrayView< Value, Device, Index >::ViewType TNL::Containers::ArrayView< Value, Device, Index >::getView ( IndexType begin = 0, IndexType end = 0 )

nodiscard

Returns a modifiable view of the array view.

By default, a view for the whole array is returned. If begin or end is set to a non-zero value, a view only for the sub-interval [begin, end) is returned.

Parameters

begin	The beginning of the array view sub-interval. It is 0 by default.
end	The end of the array view sub-interval. The default value is 0 which is, however, replaced with the array size.

load()

template<typename Value , typename Device , typename Index >

void TNL::Containers::ArrayView< Value, Device, Index >::load

(

const std::string &

fileName

)

Method for loading the data from a binary file fileName.

Parameters

fileName

The input file name.

operator!=()

template<typename Value , typename Device , typename Index >

template<typename ArrayT >

bool TNL::Containers::ArrayView< Value, Device, Index >::operator!= ( const ArrayT & array ) const

nodiscard

Compares the array view with another array-like container.

Template Parameters

ArrayT

The type of the parameter can be any array-like container, e.g. Array, ArrayView, Vector, VectorView, etc.

Parameters

array

Reference to the array-like container.

Returns

The negated result of operator==.

operator()() [1/2]

template<typename Value , typename Device , typename Index >

__cuda_callable__ Value & TNL::Containers::ArrayView< Value, Device, Index >::operator() ( IndexType i )

nodiscard

Accesses the i-th element of the array.

Equivalent to operator[], with the same notes and caveats.

operator()() [2/2]

template<typename Value , typename Device , typename Index >

__cuda_callable__ const Value & TNL::Containers::ArrayView< Value, Device, Index >::operator() ( IndexType i ) const

nodiscard

Accesses the i-th element of the array.

Equivalent to operator[], with the same notes and caveats.

operator=() [1/2]

template<typename Value , typename Device , typename Index >

ArrayView< Value, Device, Index > & TNL::Containers::ArrayView< Value, Device, Index >::operator=

(

const ArrayView< Value, Device, Index > &

view

)

Deep copy assignment operator for copying data from another array view.

Parameters

view	Reference to the source array view.

Returns

Reference to this array view.

operator=() [2/2]

template<typename Value , typename Device = Devices::Host, typename Index = int>

template<typename T , typename... , typename = std::enable_if_t< std::is_convertible_v< T, ValueType > || IsArrayType< T >::value >>

ArrayView & TNL::Containers::ArrayView< Value, Device, Index >::operator=

(

const T &

data

)

Assigns either array-like container or a single value.

If T is an array type, e.g. Array, ArrayView, StaticArray, Vector, VectorView, or StaticVector, the elements from data are copied into this array view. Otherwise, if it is a type convertible to ValueType, all array elements are set to the value data.

Template Parameters

T	The type of the source array or value.

Parameters

data	Reference to the source array or value.

Returns

Reference to this array view.

operator==()

template<typename Value , typename Device , typename Index >

template<typename ArrayT >

bool TNL::Containers::ArrayView< Value, Device, Index >::operator== ( const ArrayT & array ) const

nodiscard

Compares the array view with another array-like container.

Template Parameters

ArrayT

The type of the parameter can be any array-like container, e.g. Array, ArrayView, Vector, VectorView, etc.

Parameters

array

Reference to the array-like container.

Returns

true if the array view is element-wise equal to the array-like container and false otherwise.

operator[]() [1/2]

template<typename Value , typename Device , typename Index >

__cuda_callable__ Value & TNL::Containers::ArrayView< Value, Device, Index >::operator[] ( IndexType i )

nodiscard

Accesses the i-th element of the array view.

This method can be called only from the device which has direct access to the memory space where the data was allocated. For example, if the data was allocated in the host memory, it can be called only from host, and if the data was allocated in the device memory, it can be called only from device kernels. If NDEBUG is not defined, assertions inside this methods performs runtime checks for cross-device memory accesses which lead to segmentation fault. If you need to do just a pointer arithmetics use getData instead.

Parameters

i	The index of the element to be accessed.

Returns

Reference to the i-th element.

operator[]() [2/2]

template<typename Value , typename Device , typename Index >

__cuda_callable__ const Value & TNL::Containers::ArrayView< Value, Device, Index >::operator[] ( IndexType i ) const

nodiscard

Accesses the i-th element of the array view.

This method can be called only from the device which has direct access to the memory space where the data was allocated. For example, if the data was allocated in the host memory, it can be called only from host, and if the data was allocated in the device memory, it can be called only from device kernels. If NDEBUG is not defined, assertions inside this methods performs runtime checks for cross-device memory accesses which lead to segmentation fault. If you need to do just a pointer arithmetics use getData instead.

Parameters

i	The index of the element to be accessed.

Returns

Constant reference to the i-th element.

save()

template<typename Value , typename Device , typename Index >

void TNL::Containers::ArrayView< Value, Device, Index >::save

(

const std::string &

fileName

)

const

Method for saving the data to a binary file fileName.

Parameters

fileName

The output file name.

setElement()

template<typename Value , typename Device , typename Index >

__cuda_callable__ void TNL::Containers::ArrayView< Value, Device, Index >::setElement	(	IndexType	i,
		ValueType	value )

Sets the value of the i-th element to v.

This method can be called from both the host system and the device where the array is allocated.

Parameters

i	The index of the element to be set.
value	The new value of the element.

setValue()

template<typename Value , typename Device , typename Index >

void TNL::Containers::ArrayView< Value, Device, Index >::setValue	(	ValueType	value,
		IndexType	begin = 0,
		IndexType	end = 0 )

Sets elements of the array view to given value.

By default, all array view elements are set to the given value. If begin or end is set to a non-zero value, only elements in the sub-interval [begin, end) are set.

Parameters

value	The new value for the array view elements.
begin	The beginning of the array view sub-interval. It is 0 by default.
end	The end of the array view sub-interval. The default value is 0 which is, however, replaced with the array view size.

swap()

template<typename Value , typename Device , typename Index >

__cuda_callable__ void TNL::Containers::ArrayView< Value, Device, Index >::swap

(

ArrayView< Value, Device, Index > &

view

)

Swaps this array view with another.

Swapping is done in a shallow way, i.e. only pointers and sizes are swapped.

Parameters

view	The array view to be swapped with this array view.

---

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

• src/TNL/Containers/ArrayView.h
• src/TNL/Containers/ArrayView.hpp