Commit 0a4527d0 authored by Tomáš Oberhuber's avatar Tomáš Oberhuber
Browse files

Fixing Array - constructors and method empty.

parent 17bea751

src/Examples/Containers/ArrayExample.cpp

+6 −2
Original line number Diff line number Diff line
@@ -22,19 +22,23 @@ void arrayExample() 
    */

   for( int i = 0; i< size; i++ )

      a1.setElement( i, i );

   std::cout << "a1 = " << a1 << std::endl;



   /***

    * You may also assign value to all array elements ...

    */

   a2 = 0.0;

   std::cout << "a2 = " << a2 << std::endl;



   /***

    * ... or assign STL list and vector.

    */

   std::list< float > l = { 1.0, 2.0, 3.0 };

   std::vector< float > v = { 5.0, 6.0, 7.0 };

   a1 = v;

   a1 = l;

   std::cout << "a1 = " << a1 << std::endl;

   a1 = v;

   std::cout << "a1 = " << a1 << std::endl;



   /***

    * Simple array values checks can be done as follows ...

src/TNL/Containers/Algorithms/ArrayAssignment.h

+2 −2
Original line number Diff line number Diff line
@@ -74,10 +74,10 @@ struct ArrayAssignment< Array, T, false > 
{

   static void resize( Array& a, const T& t )

   {

      TNL_ASSERT_TRUE( !a.empty(), "Cannot assign value to empty array." );

   };

   static void assign( Array& a, const T& t )

   {

      TNL_ASSERT_FALSE( a.empty(), "Cannot assign value to empty array." );

      ArrayOperations< typename Array::DeviceType >::template

         setMemory< typename Array::ValueType, typename Array::IndexType >

         ( a.getArrayData(), ( typename Array::ValueType ) t, a.getSize() );

src/TNL/Containers/Array.hpp

+5 −4
Original line number Diff line number Diff line
@@ -142,7 +142,7 @@ Array( const std::initializer_list< InValue >& list ) 
   // Here we assume that the underlying array for initializer_list is const T[N]

   // as noted here:

   // https://en.cppreference.com/w/cpp/utility/initializer_list

   Algorithms::ArrayOperations< Device >::copyMemory( this->getData(), &( *list.begin() ), list.size() );

   Algorithms::ArrayOperations< Device, Devices::Host >::copyMemory( this->getData(), &( *list.begin() ), list.size() );

}



template< typename Value,
@@ -172,7 +172,7 @@ Array( const std::vector< InValue >& vector ) 
  referenceCounter( 0 )

{

   this->setSize( vector.size() );

   Algorithms::ArrayOperations< Device >::copyMemory( this->getData(), vector.data(), vector.size() );

   Algorithms::ArrayOperations< Device, Devices::Host >::copyMemory( this->getData(), vector.data(), vector.size() );

}



template< typename Value,
@@ -622,10 +622,11 @@ template< typename Value, 
          typename Device,

          typename Index >

bool

__cuda_callable__

Array< Value, Device, Index >::

empty() const

{

   return data;

   return ( data == nullptr );

}



template< typename Value,

src/TNL/Containers/ArrayView.h

+12 −12
Original line number Diff line number Diff line
@@ -26,7 +26,7 @@ template< int Size, typename Value > 
class StaticArray;



/**

 * \brief ArrayView serves for accessing array of data allocated by TNL::Array or

 * \brief ArrayView serves for managing array of data allocated by TNL::Array or

 * another way. It makes no data deallocation at the end of its life cycle. Compared

 * to TNL Array, it is lighter data structure and therefore it is more efficient

 * especially when it is being passed on GPU. The ArrayView can also be created
@@ -39,17 +39,17 @@ class StaticArray; 
 * In the \e Device type, the Array remembers where the memory is allocated.

 * This ensures the compile-time checks of correct pointers manipulation.

 * Methods defined as \ref __cuda_callable__ can be called even from kernels

 * running on device. Array elements can be changed either using the \ref operator[]

 * which is more efficient but it can be called from CPU only for arrays

 * allocated on host (CPU). If the array is allocated on GPU, the operator[]

 * can be called only from kernels running on the device (GPU). On the other

 * hand, methods \ref setElement and \ref getElement, can be called only from the

 * host (CPU) does not matter if the array resides on the host or the device.

 * In the latter case, explicit data transfer between host and device (via PCI

 * express or NVlink in more lucky systems) is invoked and so it can be very

 * slow. In not time critical parts of code, this is not an issue, however.

 * Another way to change data being accessed by the ArrayView is \ref evaluate which evaluates

 * given lambda function. This is performed at the same place where the array is

 * running on device. Array elements can be changed either using the \ref operator[].

 * This can be called from CPU only for arrays allocated on host (CPU). If the

 * array is allocated on GPU, the operator[] can be called only from kernels

 * running on the device (GPU). On the other hand, methods \ref setElement and

 * \ref getElement, can be called only from the host (CPU) does not matter if

 * the array resides on the host or the device. In the latter case, explicit data

 * transfer between host and device (via PCI express or NVlink in more lucky

 * systems) is invoked and so it can be very slow. In not time critical parts

 * of code, this is not an issue, however. Another way to change data being

 * accessed by the ArrayView is \ref evaluate which evaluates given lambda

 * function. This is performed at the same place where the array is

 * allocated i.e. it is efficient even on GPU. For simple checking of the array

 * contents, one may use methods \ref containValue and \ref containsValue and

 * \ref containsOnlyValue.

src/TNL/Containers/ArrayView.hpp

+1 −1
Original line number Diff line number Diff line
@@ -364,7 +364,7 @@ bool 
ArrayView< Value, Device, Index >::

empty() const

{

   return data;

   return ( data == nullptr );

}



template< typename Value,