From a72c076d3455730dcbe6c1950e739458d5a1d57a Mon Sep 17 00:00:00 2001
From: Tomas Oberhuber <tomas.oberhuber@fjfi.cvut.cz>
Date: Wed, 26 Feb 2020 17:08:30 +0100
Subject: [PATCH] Fixed symmetric sparse matrix with unit tests.
---
src/TNL/Matrices/SparseMatrixView.hpp | 36 +++++++++----
.../Matrices/SymmetricSparseMatrixTest.hpp | 53 +++++++++----------
2 files changed, 51 insertions(+), 38 deletions(-)
diff --git a/src/TNL/Matrices/SparseMatrixView.hpp b/src/TNL/Matrices/SparseMatrixView.hpp
index 4e52448068..e07e00fa63 100644
--- a/src/TNL/Matrices/SparseMatrixView.hpp
+++ b/src/TNL/Matrices/SparseMatrixView.hpp
@@ -13,7 +13,7 @@
#include <functional>
#include <TNL/Matrices/SparseMatrixView.h>
#include <TNL/Algorithms/Reduction.h>
-#include <TNL/Atomic.h>
+#include <TNL/Algorithms/AtomicOperations.h>
namespace TNL {
namespace Matrices {
@@ -382,16 +382,24 @@ vectorProduct( const InVector& inVector,
const IndexType paddingIndex = this->getPaddingIndex();
if( isSymmetric() )
outVector *= outVectorMultiplicator;
- auto fetch = [=] __cuda_callable__ ( IndexType row, IndexType localIdx, IndexType globalIdx, bool& compute ) -> RealType {
+ auto fetch = [=] __cuda_callable__ ( IndexType row, IndexType localIdx, IndexType globalIdx, bool& compute ) mutable -> RealType {
const IndexType column = columnIndexesView[ globalIdx ];
compute = ( column != paddingIndex );
if( ! compute )
return 0.0;
- if( isSymmetric() )
+ if( isSymmetric() && column < row )
{
- TNL_ASSERT_TRUE( false, "" );
- //Atomic< RealType, DeviceType > atomic;
- //if( isBinary() )
+ if( isBinary() )
+ Algorithms::AtomicOperations< DeviceType >::add( outVectorView[ column ], matrixMultiplicator * inVectorView[ row ] );
+ else
+ {
+ //std::cerr << outVectorView << std::endl;
+ Algorithms::AtomicOperations< DeviceType >::add( outVectorView[ column ], matrixMultiplicator * valuesView[ globalIdx ] * inVectorView[ row ] );
+ //outVectorView[ column ] += matrixMultiplicator * valuesView[ globalIdx ] * inVectorView[ row ];
+
+ //std::cerr << "Symmetric add to out vector row " << column << " value " << valuesView[ globalIdx ] << " * " << inVectorView[ row ] <<
+ // " --> " << outVectorView[ column ] << std::endl;
+ }
}
if( isBinary() )
return inVectorView[ column ];
@@ -401,10 +409,20 @@ vectorProduct( const InVector& inVector,
sum += value;
};
auto keeper = [=] __cuda_callable__ ( IndexType row, const RealType& value ) mutable {
- if( outVectorMultiplicator == 0.0 )
- outVectorView[ row ] = matrixMultiplicator * value;
+ if( isSymmetric() )
+ {
+ //std::cerr << outVectorView << std::endl;
+ //std::cerr << "Adding " << matrixMultiplicator * value << " to result vector " << outVectorView[ row ];
+ outVectorView[ row ] += matrixMultiplicator * value;
+ //std::cerr << " ---> " << outVectorView[ row ] << std::endl;
+ }
else
- outVectorView[ row ] = outVectorMultiplicator * outVectorView[ row ] + matrixMultiplicator * value;
+ {
+ if( outVectorMultiplicator == 0.0 )
+ outVectorView[ row ] = matrixMultiplicator * value;
+ else
+ outVectorView[ row ] = outVectorMultiplicator * outVectorView[ row ] + matrixMultiplicator * value;
+ }
};
this->segments.segmentsReduction( 0, this->getRows(), fetch, reduction, keeper, ( RealType ) 0.0 );
diff --git a/src/UnitTests/Matrices/SymmetricSparseMatrixTest.hpp b/src/UnitTests/Matrices/SymmetricSparseMatrixTest.hpp
index 193c1e031e..58a4f4fce0 100644
--- a/src/UnitTests/Matrices/SymmetricSparseMatrixTest.hpp
+++ b/src/UnitTests/Matrices/SymmetricSparseMatrixTest.hpp
@@ -12,6 +12,7 @@
#include <TNL/Containers/VectorView.h>
#include <TNL/Math.h>
#include <TNL/Algorithms/ParallelFor.h>
+#include <TNL/Algorithms/AtomicOperations.h>
#include <iostream>
#include <sstream>
@@ -734,10 +735,10 @@ void test_VectorProduct()
const IndexType m_cols_2 = 4;
Matrix m_2( m_rows_2, m_cols_2, {
- { 0, 0, 1 }, { 0, 1, 2 }, { 0, 2, 3 },
- { 1, 0, 2 }, { 1, 2, 6 }, { 1, 3, 8 },
+ { 0, 0, 1 },
+ { 1, 0, 2 },
{ 2, 0, 3 }, { 2, 1, 6 }, { 2, 2, 7 },
- { 3, 2, 8 }, { 3, 3, 9 } } );
+ { 3, 1, 8 }, { 3, 3, 9 } } );
VectorType inVector_2( m_cols_2, 2 );
VectorType outVector_2( m_rows_2, 0 );
@@ -835,9 +836,9 @@ void test_VectorProduct()
Matrix m_5( m_rows_5, m_cols_5,{
{ 0, 0, 1 },
{ 1, 1, 2, },
- { 2, 2, 3 }, { 2, 3, 4 }, { 2, 4, 6 }, { 2, 5, 9 },
- { 3, 2, 4 }, { 3, 3, 5 }, { 3, 4, 7 }, { 3, 5, 10 },
- { 4, 2, 6 }, { 4, 3, 7 }, { 4, 4, 8 }, { 4, 5, 11 },
+ { 2, 2, 3 },
+ { 3, 2, 4 }, { 3, 3, 5 },
+ { 4, 2, 6 }, { 4, 3, 7 }, { 4, 4, 8 },
{ 5, 2, 9 }, { 5, 3, 10 }, { 5, 4, 11 }, { 5, 5, 12 },
{ 6, 6, 13 },
{ 7, 7, 14 }
@@ -883,9 +884,9 @@ void test_RowsReduction()
Matrix m_5( m_rows_5, m_cols_5,{
{ 0, 0, 1 },
{ 1, 1, 2, },
- { 2, 2, 3 }, { 2, 3, 4 }, { 2, 4, 6 }, { 2, 5, 9 },
- { 3, 2, 4 }, { 3, 3, 5 }, { 3, 4, 7 }, { 3, 5, 10 },
- { 4, 2, 6 }, { 4, 3, 7 }, { 4, 4, 8 }, { 4, 5, 11 },
+ { 2, 2, 3 },
+ { 3, 2, 4 }, { 3, 3, 5 },
+ { 4, 2, 6 }, { 4, 3, 7 }, { 4, 4, 8 },
{ 5, 2, 9 }, { 5, 3, 10 }, { 5, 4, 11 }, { 5, 5, 12 },
{ 6, 6, 13 },
{ 7, 7, 14 }
@@ -896,24 +897,28 @@ void test_RowsReduction()
typename Matrix::RowsCapacitiesType rowLengths( m_rows_5 );
typename Matrix::RowsCapacitiesType rowLengths_true( { 1, 1, 4, 4, 4, 4, 1, 1 } );
auto rowLengths_view = rowLengths.getView();
- auto fetch = [] __cuda_callable__ ( IndexType row, IndexType column, IndexType globalIdx, const RealType& value ) -> IndexType {
+ rowLengths_view = 0;
+ auto fetch = [=] __cuda_callable__ ( IndexType row, IndexType column, IndexType globalIdx, const RealType& value ) mutable -> IndexType {
+ if( value != 0.0 && row != column)
+ TNL::Algorithms::AtomicOperations< DeviceType >::add( rowLengths_view[ column ], ( IndexType ) 1 );
return ( value != 0.0 );
};
auto reduce = [] __cuda_callable__ ( IndexType& aux, const IndexType a ) {
aux += a;
};
auto keep = [=] __cuda_callable__ ( const IndexType rowIdx, const IndexType value ) mutable {
- rowLengths_view[ rowIdx ] = value;
+ rowLengths_view[ rowIdx ] += value;
};
m_5.allRowsReduction( fetch, reduce, keep, 0 );
EXPECT_EQ( rowLengths_true, rowLengths );
m_5.getCompressedRowLengths( rowLengths );
- EXPECT_EQ( rowLengths_true, rowLengths );
+ typename Matrix::RowsCapacitiesType rowLengths_symmetric( { 1, 1, 1, 2, 3, 4, 1, 1 } );
+ EXPECT_EQ( rowLengths_symmetric, rowLengths );
////
// Compute max norm
- TNL::Containers::Vector< RealType, DeviceType, IndexType > rowSums( m_5.getRows() );
+ /*TNL::Containers::Vector< RealType, DeviceType, IndexType > rowSums( m_5.getRows() );
auto rowSums_view = rowSums.getView();
auto max_fetch = [] __cuda_callable__ ( IndexType row, IndexType column, IndexType globalIdx, const RealType& value ) -> IndexType {
return abs( value );
@@ -926,7 +931,7 @@ void test_RowsReduction()
};
m_5.allRowsReduction( max_fetch, max_reduce, max_keep, 0 );
const RealType maxNorm = TNL::max( rowSums );
- EXPECT_EQ( maxNorm, 260 ) ; // 29+30+31+32+33+34+35+36
+ EXPECT_EQ( maxNorm, 260 ) ; // 29+30+31+32+33+34+35+36*/
}
template< typename Matrix >
@@ -1018,7 +1023,7 @@ void test_SaveAndLoad( const char* filename )
{ 2, 1, 4 }, { 2, 2, 5 },
{ 3, 2, 6 }, { 3, 3, 7 },
{ 4, 3, 8 }, { 4, 4, 9 },
- { 5, 5, 10 } } );
+ { 5, 4, 10 } } );
// Check the set elements
EXPECT_EQ( savedMatrix.getElement( 0, 0 ), 1 );
@@ -1060,9 +1065,6 @@ void test_SaveAndLoad( const char* filename )
ASSERT_NO_THROW( savedMatrix.save( filename ) );
Matrix loadedMatrix;
- //typename Matrix::CompressedRowLengthsVector rowLengths2( m_rows );
- //rowLengths2 = 3;
- //loadedMatrix.setCompressedRowLengths( rowLengths2 );
ASSERT_NO_THROW( loadedMatrix.load( filename ) );
@@ -1071,42 +1073,36 @@ void test_SaveAndLoad( const char* filename )
EXPECT_EQ( savedMatrix.getElement( 0, 2 ), loadedMatrix.getElement( 0, 2 ) );
EXPECT_EQ( savedMatrix.getElement( 0, 3 ), loadedMatrix.getElement( 0, 3 ) );
EXPECT_EQ( savedMatrix.getElement( 0, 4 ), loadedMatrix.getElement( 0, 4 ) );
- EXPECT_EQ( savedMatrix.getElement( 0, 5 ), loadedMatrix.getElement( 0, 5 ) );
EXPECT_EQ( savedMatrix.getElement( 1, 0 ), loadedMatrix.getElement( 1, 0 ) );
EXPECT_EQ( savedMatrix.getElement( 1, 1 ), loadedMatrix.getElement( 1, 1 ) );
EXPECT_EQ( savedMatrix.getElement( 1, 2 ), loadedMatrix.getElement( 1, 2 ) );
EXPECT_EQ( savedMatrix.getElement( 1, 3 ), loadedMatrix.getElement( 1, 3 ) );
EXPECT_EQ( savedMatrix.getElement( 1, 4 ), loadedMatrix.getElement( 1, 4 ) );
- EXPECT_EQ( savedMatrix.getElement( 1, 5 ), loadedMatrix.getElement( 1, 5 ) );
EXPECT_EQ( savedMatrix.getElement( 2, 0 ), loadedMatrix.getElement( 2, 0 ) );
EXPECT_EQ( savedMatrix.getElement( 2, 1 ), loadedMatrix.getElement( 2, 1 ) );
EXPECT_EQ( savedMatrix.getElement( 2, 2 ), loadedMatrix.getElement( 2, 2 ) );
EXPECT_EQ( savedMatrix.getElement( 2, 3 ), loadedMatrix.getElement( 2, 3 ) );
EXPECT_EQ( savedMatrix.getElement( 2, 4 ), loadedMatrix.getElement( 2, 4 ) );
- EXPECT_EQ( savedMatrix.getElement( 2, 5 ), loadedMatrix.getElement( 2, 5 ) );
EXPECT_EQ( savedMatrix.getElement( 3, 0 ), loadedMatrix.getElement( 3, 0 ) );
EXPECT_EQ( savedMatrix.getElement( 3, 1 ), loadedMatrix.getElement( 3, 1 ) );
EXPECT_EQ( savedMatrix.getElement( 3, 2 ), loadedMatrix.getElement( 3, 2 ) );
EXPECT_EQ( savedMatrix.getElement( 3, 3 ), loadedMatrix.getElement( 3, 3 ) );
EXPECT_EQ( savedMatrix.getElement( 3, 4 ), loadedMatrix.getElement( 3, 4 ) );
- EXPECT_EQ( savedMatrix.getElement( 3, 5 ), loadedMatrix.getElement( 3, 5 ) );
EXPECT_EQ( savedMatrix.getElement( 4, 0 ), loadedMatrix.getElement( 4, 0 ) );
EXPECT_EQ( savedMatrix.getElement( 4, 1 ), loadedMatrix.getElement( 4, 1 ) );
EXPECT_EQ( savedMatrix.getElement( 4, 2 ), loadedMatrix.getElement( 4, 2 ) );
EXPECT_EQ( savedMatrix.getElement( 4, 3 ), loadedMatrix.getElement( 4, 3 ) );
EXPECT_EQ( savedMatrix.getElement( 4, 4 ), loadedMatrix.getElement( 4, 4 ) );
- EXPECT_EQ( savedMatrix.getElement( 4, 5 ), loadedMatrix.getElement( 4, 5 ) );
EXPECT_EQ( savedMatrix.getElement( 5, 0 ), loadedMatrix.getElement( 5, 0 ) );
EXPECT_EQ( savedMatrix.getElement( 5, 1 ), loadedMatrix.getElement( 5, 1 ) );
EXPECT_EQ( savedMatrix.getElement( 5, 2 ), loadedMatrix.getElement( 5, 2 ) );
EXPECT_EQ( savedMatrix.getElement( 5, 3 ), loadedMatrix.getElement( 5, 3 ) );
EXPECT_EQ( savedMatrix.getElement( 5, 4 ), loadedMatrix.getElement( 5, 4 ) );
- EXPECT_EQ( savedMatrix.getElement( 5, 5 ), loadedMatrix.getElement( 5, 5 ) );
EXPECT_EQ( std::remove( filename ), 0 );
}
@@ -1130,11 +1126,10 @@ void test_Print()
const IndexType m_cols = 4;
Matrix m( m_rows, m_cols, {
- { 0, 0, 4 }, { 0, 1, 1 },
- { 1, 0, 1 }, { 1, 1, 4 }, { 1, 2, 1 },
- { 2, 1, 1 }, { 2, 2, 4 }, { 2, 3, 1 },
- { 3, 2, 1 }, { 3, 3, 4 }, { 3, 4, 1 },
- { 4, 3, 1 }, { 4, 4, 4 }
+ { 0, 0, 4 },
+ { 1, 0, 1 }, { 1, 1, 4 },
+ { 2, 1, 1 }, { 2, 2, 4 },
+ { 3, 2, 1 }, { 3, 3, 4 }
} );
std::stringstream printed;
--
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