/***************************************************************************
                          Diagonal_impl.h  -  description
                             -------------------
    begin                : Dec 17, 2015
    copyright            : (C) 2015 by Tomas Oberhuber et al.
    email                : tomas.oberhuber@fjfi.cvut.cz
 ***************************************************************************/

/* See Copyright Notice in tnl/Copyright */

// Implemented by: Jakub Klinkovsky

#pragma once

#include "Diagonal.h"

#include <TNL/Algorithms/ParallelFor.h>

namespace TNL {
namespace Solvers {
namespace Linear {
namespace Preconditioners {

template< typename Matrix >
void
Diagonal< Matrix >::
update( const MatrixPointer& matrixPointer )
{
   TNL_ASSERT_GT( matrixPointer->getRows(), 0, "empty matrix" );
   TNL_ASSERT_EQ( matrixPointer->getRows(), matrixPointer->getColumns(), "matrix must be square" );

   diagonal.setSize( matrixPointer->getRows() );

   VectorViewType diag_view( diagonal );

   const auto kernel_matrix = matrixPointer->getView();

   // TODO: Rewrite this with SparseMatrix::forAllRows
   auto kernel = [=] __cuda_callable__ ( IndexType i ) mutable
   {
      diag_view[ i ] = kernel_matrix.getElement( i, i );
   };

   Algorithms::ParallelFor< DeviceType >::exec( (IndexType) 0, diagonal.getSize(), kernel );
}

template< typename Matrix >
void
Diagonal< Matrix >::
solve( ConstVectorViewType b, VectorViewType x ) const
{
   x = b / diagonal;
}


template< typename Matrix >
void
Diagonal< Matrices::DistributedMatrix< Matrix > >::
update( const MatrixPointer& matrixPointer )
{
   TNL_ASSERT_GT( matrixPointer->getRows(), 0, "empty matrix" );
   diagonal.setSize( matrixPointer->getLocalMatrix().getRows() );

   LocalViewType diag_view( diagonal );
   // FIXME: SparseMatrix::getConstView is broken
//   const auto matrix_view = matrixPointer->getLocalMatrix().getConstView();
   const auto matrix_view = matrixPointer->getLocalMatrix().getView();

   if( matrixPointer->getRows() == matrixPointer->getColumns() ) {
      // square matrix, assume global column indices
      const auto row_range = matrixPointer->getLocalRowRange();
      auto kernel = [=] __cuda_callable__ ( IndexType i ) mutable
      {
         const IndexType gi = row_range.getGlobalIndex( i );
         diag_view[ i ] = matrix_view.getElement( i, gi );
      };
      Algorithms::ParallelFor< DeviceType >::exec( (IndexType) 0, diagonal.getSize(), kernel );
   }
   else {
      // non-square matrix, assume ghost indexing
      TNL_ASSERT_LT( matrixPointer->getLocalMatrix().getRows(), matrixPointer->getLocalMatrix().getColumns(), "the local matrix should have more columns than rows" );
      auto kernel = [=] __cuda_callable__ ( IndexType i ) mutable
      {
         diag_view[ i ] = matrix_view.getElement( i, i );
      };
      Algorithms::ParallelFor< DeviceType >::exec( (IndexType) 0, diagonal.getSize(), kernel );
   }
}

template< typename Matrix >
void
Diagonal< Matrices::DistributedMatrix< Matrix > >::
solve( ConstVectorViewType b, VectorViewType x ) const
{
   ConstLocalViewType diag_view( diagonal );
   const auto b_view = b.getConstLocalView();
   auto x_view = x.getLocalView();

   // compute without ghosts (diagonal includes only local rows)
   x_view = b_view / diag_view;

   // synchronize ghosts
   x.startSynchronization();
}

} // namespace Preconditioners
} // namespace Linear
} // namespace Solvers
} // namespace TNL
