From 53c48f25e7391132e07bb5570468da5b044e4b72 Mon Sep 17 00:00:00 2001
From: =?UTF-8?q?Tom=C3=A1=C5=A1=20Oberhuber?= <oberhuber.tomas@gmail.com>
Date: Sun, 14 Mar 2021 14:42:19 +0100
Subject: [PATCH] Changing order of parameters of
Algorithms::Reduction::reduce(withArgument) from (reduce,fetch) to
(fetch,reduce).
---
.../DenseMatrixExample_getConstRow.cpp | 2 +-
.../DenseMatrixViewExample_getConstRow.cpp | 2 +-
...MultidiagonalMatrixExample_getConstRow.cpp | 2 +-
...idiagonalMatrixViewExample_getConstRow.cpp | 4 +-
.../SparseMatrixExample_getConstRow.cpp | 2 +-
.../SparseMatrixViewExample_getConstRow.cpp | 2 +-
.../TridiagonalMatrixExample_getConstRow.cpp | 2 +-
...idiagonalMatrixViewExample_getConstRow.cpp | 2 +-
...orithms_and_lambda_functions_reduction.cpp | 2 +-
...ithms_and_lambda_functions_reduction_2.cpp | 2 +-
.../ReductionAndScan/ComparisonExample.cpp | 2 +-
.../ReductionAndScan/MapReduceExample-1.cpp | 2 +-
.../ReductionAndScan/MapReduceExample-2.cpp | 2 +-
.../ReductionAndScan/MapReduceExample-3.cpp | 2 +-
.../ReductionAndScan/MaximumNormExample.cpp | 2 +-
.../ReductionAndScan/ProductExample.cpp | 2 +-
.../ReductionWithArgument.cpp | 2 +-
.../ReductionAndScan/ScalarProductExample.cpp | 2 +-
.../Tutorials/ReductionAndScan/SumExample.cpp | 2 +-
.../UpdateAndResidueExample.cpp | 2 +-
.../BLAS/CommonVectorOperations.hpp | 34 +--
src/TNL/Algorithms/MemoryOperationsCuda.hpp | 6 +-
src/TNL/Algorithms/MemoryOperationsHost.hpp | 6 +-
src/TNL/Algorithms/Reduction.h | 166 +++++++--------
src/TNL/Algorithms/Reduction.hpp | 196 +++++++++---------
src/TNL/Containers/ArrayView.hpp | 4 +-
src/TNL/Containers/Expressions/Comparison.h | 30 +--
.../DistributedExpressionTemplates.h | 12 +-
.../Expressions/ExpressionTemplates.h | 12 +-
.../Expressions/VerticalOperations.h | 20 +-
src/TNL/Matrices/DenseMatrixView.hpp | 2 +-
src/TNL/Matrices/Matrix.hpp | 2 +-
src/TNL/Matrices/MatrixView.hpp | 2 +-
src/TNL/Matrices/MultidiagonalMatrixView.hpp | 2 +-
src/TNL/Matrices/SparseMatrixView.hpp | 4 +-
src/TNL/Matrices/TridiagonalMatrixView.hpp | 2 +-
.../MeshDetails/layers/EntityTags/Layer.h | 4 +-
src/UnitTests/Matrices/DenseMatrixTest.h | 2 +-
38 files changed, 274 insertions(+), 274 deletions(-)
diff --git a/Documentation/Examples/Matrices/DenseMatrix/DenseMatrixExample_getConstRow.cpp b/Documentation/Examples/Matrices/DenseMatrix/DenseMatrixExample_getConstRow.cpp
index 445ba2d518..c61a1c8221 100644
--- a/Documentation/Examples/Matrices/DenseMatrix/DenseMatrixExample_getConstRow.cpp
+++ b/Documentation/Examples/Matrices/DenseMatrix/DenseMatrixExample_getConstRow.cpp
@@ -36,7 +36,7 @@ void getRowExample()
/***
* Compute the matrix trace.
*/
- int trace = TNL::Algorithms::Reduction< Device >::reduce( 0, matrix->getRows(), std::plus<>{}, fetch, 0 );
+ int trace = TNL::Algorithms::Reduction< Device >::reduce( 0, matrix->getRows(), fetch, std::plus<>{}, 0 );
std::cout << "Matrix trace is " << trace << "." << std::endl;
}
diff --git a/Documentation/Examples/Matrices/DenseMatrix/DenseMatrixViewExample_getConstRow.cpp b/Documentation/Examples/Matrices/DenseMatrix/DenseMatrixViewExample_getConstRow.cpp
index 1e139fa4b2..a0b9980242 100644
--- a/Documentation/Examples/Matrices/DenseMatrix/DenseMatrixViewExample_getConstRow.cpp
+++ b/Documentation/Examples/Matrices/DenseMatrix/DenseMatrixViewExample_getConstRow.cpp
@@ -29,7 +29,7 @@ void getRowExample()
return row.getElement( rowIdx );
};
- int trace = TNL::Algorithms::Reduction< Device >::reduce( 0, matrix.getRows(), std::plus<>{}, fetch, 0 );
+ int trace = TNL::Algorithms::Reduction< Device >::reduce( 0, matrix.getRows(), fetch, std::plus<>{}, 0 );
std::cout << "Matrix trace is " << trace << "." << std::endl;
}
diff --git a/Documentation/Examples/Matrices/MultidiagonalMatrix/MultidiagonalMatrixExample_getConstRow.cpp b/Documentation/Examples/Matrices/MultidiagonalMatrix/MultidiagonalMatrixExample_getConstRow.cpp
index 6d0f7aeb35..b8ebf91817 100644
--- a/Documentation/Examples/Matrices/MultidiagonalMatrix/MultidiagonalMatrixExample_getConstRow.cpp
+++ b/Documentation/Examples/Matrices/MultidiagonalMatrix/MultidiagonalMatrixExample_getConstRow.cpp
@@ -41,7 +41,7 @@ void getRowExample()
/***
* Compute the matrix trace.
*/
- int trace = TNL::Algorithms::Reduction< Device >::reduce( 0, matrix->getRows(), std::plus<>{}, fetch, 0 );
+ int trace = TNL::Algorithms::Reduction< Device >::reduce( 0, matrix->getRows(), fetch, std::plus<>{}, 0 );
std::cout << "Matrix reads as: " << std::endl << *matrix << std::endl;
std::cout << "Matrix trace is: " << trace << "." << std::endl;
}
diff --git a/Documentation/Examples/Matrices/MultidiagonalMatrix/MultidiagonalMatrixViewExample_getConstRow.cpp b/Documentation/Examples/Matrices/MultidiagonalMatrix/MultidiagonalMatrixViewExample_getConstRow.cpp
index 65fa867f15..346e331dba 100644
--- a/Documentation/Examples/Matrices/MultidiagonalMatrix/MultidiagonalMatrixViewExample_getConstRow.cpp
+++ b/Documentation/Examples/Matrices/MultidiagonalMatrix/MultidiagonalMatrixViewExample_getConstRow.cpp
@@ -13,7 +13,7 @@ void getRowExample()
using MatrixType = TNL::Matrices::MultidiagonalMatrix< double, Device >;
MatrixType matrix (
matrixSize, // number of matrix columns
- diagonalsOffsets,
+ diagonalsOffsets,
{ { 0.0, 0.0, 1.0 }, // matrix elements
{ 0.0, 2.0, 1.0 },
{ 3.0, 2.0, 1.0 },
@@ -32,7 +32,7 @@ void getRowExample()
/***
* Compute the matrix trace.
*/
- int trace = TNL::Algorithms::Reduction< Device >::reduce( 0, matrix.getRows(), std::plus<>{}, fetch, 0 );
+ int trace = TNL::Algorithms::Reduction< Device >::reduce( 0, matrix.getRows(), fetch, std::plus<>{}, 0 );
std::cout << "Matrix reads as: " << std::endl << matrix << std::endl;
std::cout << "Matrix trace is: " << trace << "." << std::endl;
}
diff --git a/Documentation/Examples/Matrices/SparseMatrix/SparseMatrixExample_getConstRow.cpp b/Documentation/Examples/Matrices/SparseMatrix/SparseMatrixExample_getConstRow.cpp
index 01689a6621..4d3ae4ff51 100644
--- a/Documentation/Examples/Matrices/SparseMatrix/SparseMatrixExample_getConstRow.cpp
+++ b/Documentation/Examples/Matrices/SparseMatrix/SparseMatrixExample_getConstRow.cpp
@@ -36,7 +36,7 @@ void getRowExample()
/***
* Compute the matrix trace.
*/
- int trace = TNL::Algorithms::Reduction< Device >::reduce( 0, matrix->getRows(), std::plus<>{}, fetch, 0 );
+ int trace = TNL::Algorithms::Reduction< Device >::reduce( 0, matrix->getRows(), fetch, std::plus<>{}, 0 );
std::cout << "Matrix trace is " << trace << "." << std::endl;
}
diff --git a/Documentation/Examples/Matrices/SparseMatrix/SparseMatrixViewExample_getConstRow.cpp b/Documentation/Examples/Matrices/SparseMatrix/SparseMatrixViewExample_getConstRow.cpp
index d2e4d971ce..2b5f0faed2 100644
--- a/Documentation/Examples/Matrices/SparseMatrix/SparseMatrixViewExample_getConstRow.cpp
+++ b/Documentation/Examples/Matrices/SparseMatrix/SparseMatrixViewExample_getConstRow.cpp
@@ -28,7 +28,7 @@ void getRowExample()
/***
* Compute the matrix trace.
*/
- int trace = TNL::Algorithms::Reduction< Device >::reduce( 0, matrix.getRows(), std::plus<>{}, fetch, 0 );
+ int trace = TNL::Algorithms::Reduction< Device >::reduce( 0, matrix.getRows(), fetch, std::plus<>{}, 0 );
std::cout << "Matrix trace is " << trace << "." << std::endl;
}
diff --git a/Documentation/Examples/Matrices/TridiagonalMatrix/TridiagonalMatrixExample_getConstRow.cpp b/Documentation/Examples/Matrices/TridiagonalMatrix/TridiagonalMatrixExample_getConstRow.cpp
index e008c03a02..30bf9249ec 100644
--- a/Documentation/Examples/Matrices/TridiagonalMatrix/TridiagonalMatrixExample_getConstRow.cpp
+++ b/Documentation/Examples/Matrices/TridiagonalMatrix/TridiagonalMatrixExample_getConstRow.cpp
@@ -40,7 +40,7 @@ void getRowExample()
/***
* Compute the matrix trace.
*/
- int trace = TNL::Algorithms::Reduction< Device >::reduce( 0, matrix->getRows(), std::plus<>{}, fetch, 0 );
+ int trace = TNL::Algorithms::Reduction< Device >::reduce( 0, matrix->getRows(), fetch, std::plus<>{}, 0 );
std::cout << "Matrix reads as: " << std::endl << *matrix << std::endl;
std::cout << "Matrix trace is: " << trace << "." << std::endl;
}
diff --git a/Documentation/Examples/Matrices/TridiagonalMatrix/TridiagonalMatrixViewExample_getConstRow.cpp b/Documentation/Examples/Matrices/TridiagonalMatrix/TridiagonalMatrixViewExample_getConstRow.cpp
index 83463d8686..20d55ff121 100644
--- a/Documentation/Examples/Matrices/TridiagonalMatrix/TridiagonalMatrixViewExample_getConstRow.cpp
+++ b/Documentation/Examples/Matrices/TridiagonalMatrix/TridiagonalMatrixViewExample_getConstRow.cpp
@@ -30,7 +30,7 @@ void getRowExample()
/***
* Compute the matrix trace.
*/
- int trace = TNL::Algorithms::Reduction< Device >::reduce( 0, view.getRows(), std::plus<>{}, fetch, 0 );
+ int trace = TNL::Algorithms::Reduction< Device >::reduce( 0, view.getRows(), fetch, std::plus<>{}, 0 );
std::cout << "Matrix reads as: " << std::endl << matrix << std::endl;
std::cout << "Matrix trace is: " << trace << "." << std::endl;
}
diff --git a/Documentation/Tutorials/GeneralConcepts/snippet_algorithms_and_lambda_functions_reduction.cpp b/Documentation/Tutorials/GeneralConcepts/snippet_algorithms_and_lambda_functions_reduction.cpp
index 85ba934080..fda9a41b99 100644
--- a/Documentation/Tutorials/GeneralConcepts/snippet_algorithms_and_lambda_functions_reduction.cpp
+++ b/Documentation/Tutorials/GeneralConcepts/snippet_algorithms_and_lambda_functions_reduction.cpp
@@ -6,5 +6,5 @@ void scalarProduct( double* v1, double* v2, double* product, const int size )
}
auto reduce = [] __cuda_callable__ ( const double& a, const double& b ) {
return a + b; };
- TNL::Algorithms::Reduction< Device >::reduce( 0, size, reduce, fetch, 0.0 );
+ TNL::Algorithms::Reduction< Device >::reduce( 0, size, fetch, reduce, 0.0 );
}
\ No newline at end of file
diff --git a/Documentation/Tutorials/GeneralConcepts/snippet_algorithms_and_lambda_functions_reduction_2.cpp b/Documentation/Tutorials/GeneralConcepts/snippet_algorithms_and_lambda_functions_reduction_2.cpp
index deeb49dd51..ef17140ce0 100644
--- a/Documentation/Tutorials/GeneralConcepts/snippet_algorithms_and_lambda_functions_reduction_2.cpp
+++ b/Documentation/Tutorials/GeneralConcepts/snippet_algorithms_and_lambda_functions_reduction_2.cpp
@@ -8,5 +8,5 @@ void scalarProduct( double* u1, double* u2,
}
auto reduce = [] __cuda_callable__ ( const double& a, const double& b ) {
return a + b; };
- TNL::Algorithms::Reduction< Device >::reduce( 0, size, reduce, fetch, 0.0 );
+ TNL::Algorithms::Reduction< Device >::reduce( 0, size, fetch, reduce, 0.0 );
}
\ No newline at end of file
diff --git a/Documentation/Tutorials/ReductionAndScan/ComparisonExample.cpp b/Documentation/Tutorials/ReductionAndScan/ComparisonExample.cpp
index b8c73530ce..3ef168a251 100644
--- a/Documentation/Tutorials/ReductionAndScan/ComparisonExample.cpp
+++ b/Documentation/Tutorials/ReductionAndScan/ComparisonExample.cpp
@@ -22,7 +22,7 @@ bool comparison( const Vector< double, Device >& u, const Vector< double, Device
* Reduce performs logical AND on intermediate results obtained by fetch.
*/
auto reduce = [] __cuda_callable__ ( const bool& a, const bool& b ) { return a && b; };
- return Reduction< Device >::reduce( 0, v_view.getSize(), reduce, fetch, true );
+ return Reduction< Device >::reduce( 0, v_view.getSize(), fetch, reduce, true );
}
int main( int argc, char* argv[] )
diff --git a/Documentation/Tutorials/ReductionAndScan/MapReduceExample-1.cpp b/Documentation/Tutorials/ReductionAndScan/MapReduceExample-1.cpp
index ddcb5e2f97..eeccc728fb 100644
--- a/Documentation/Tutorials/ReductionAndScan/MapReduceExample-1.cpp
+++ b/Documentation/Tutorials/ReductionAndScan/MapReduceExample-1.cpp
@@ -14,7 +14,7 @@ double mapReduce( Vector< double, Device >& u )
auto fetch = [=] __cuda_callable__ ( int i )->double {
return u_view[ i ] > 0 ? u_view[ i ] : 0.0; };
auto reduce = [] __cuda_callable__ ( const double& a, const double& b ) { return a + b; };
- return Reduction< Device >::reduce( 0, u_view.getSize(), reduce, fetch, 0.0 );
+ return Reduction< Device >::reduce( 0, u_view.getSize(), fetch, reduce, 0.0 );
}
int main( int argc, char* argv[] )
diff --git a/Documentation/Tutorials/ReductionAndScan/MapReduceExample-2.cpp b/Documentation/Tutorials/ReductionAndScan/MapReduceExample-2.cpp
index 64f7be8cae..da7c1c9c6c 100644
--- a/Documentation/Tutorials/ReductionAndScan/MapReduceExample-2.cpp
+++ b/Documentation/Tutorials/ReductionAndScan/MapReduceExample-2.cpp
@@ -16,7 +16,7 @@ double mapReduce( Vector< double, Device >& u )
if( i % 2 == 0 ) return u_view[ i ];
return 0.0; };
auto reduce = [] __cuda_callable__ ( const double& a, const double& b ) { return a + b; };
- return Reduction< Device >::reduce( 0, u_view.getSize(), reduce, fetch, 0.0 );
+ return Reduction< Device >::reduce( 0, u_view.getSize(), fetch, reduce, 0.0 );
}
int main( int argc, char* argv[] )
diff --git a/Documentation/Tutorials/ReductionAndScan/MapReduceExample-3.cpp b/Documentation/Tutorials/ReductionAndScan/MapReduceExample-3.cpp
index bfbf63f3b8..5b5f31131c 100644
--- a/Documentation/Tutorials/ReductionAndScan/MapReduceExample-3.cpp
+++ b/Documentation/Tutorials/ReductionAndScan/MapReduceExample-3.cpp
@@ -15,7 +15,7 @@ double mapReduce( Vector< double, Device >& u )
auto fetch = [=] __cuda_callable__ ( int i )->double {
return u_view[ 2 * i ]; };
auto reduce = [] __cuda_callable__ ( const double& a, const double& b ) { return a + b; };
- return Reduction< Device >::reduce( 0, u_view.getSize() / 2, reduce, fetch, 0.0 );
+ return Reduction< Device >::reduce( 0, u_view.getSize() / 2, fetch, reduce, 0.0 );
}
int main( int argc, char* argv[] )
diff --git a/Documentation/Tutorials/ReductionAndScan/MaximumNormExample.cpp b/Documentation/Tutorials/ReductionAndScan/MaximumNormExample.cpp
index 7dcd9a92b4..1b31eb5e53 100644
--- a/Documentation/Tutorials/ReductionAndScan/MaximumNormExample.cpp
+++ b/Documentation/Tutorials/ReductionAndScan/MaximumNormExample.cpp
@@ -13,7 +13,7 @@ double maximumNorm( const Vector< double, Device >& v )
auto view = v.getConstView();
auto fetch = [=] __cuda_callable__ ( int i ) { return abs( view[ i ] ); };
auto reduce = [] __cuda_callable__ ( const double& a, const double& b ) { return max( a, b ); };
- return Reduction< Device >::reduce( 0, view.getSize(), reduce, fetch, 0.0 );
+ return Reduction< Device >::reduce( 0, view.getSize(), fetch, reduce, 0.0 );
}
int main( int argc, char* argv[] )
diff --git a/Documentation/Tutorials/ReductionAndScan/ProductExample.cpp b/Documentation/Tutorials/ReductionAndScan/ProductExample.cpp
index 6f37861dc6..9df9a6e4b5 100644
--- a/Documentation/Tutorials/ReductionAndScan/ProductExample.cpp
+++ b/Documentation/Tutorials/ReductionAndScan/ProductExample.cpp
@@ -17,7 +17,7 @@ double product( const Vector< double, Device >& v )
/***
* Since we compute the product of all elements, the reduction must be initialized by 1.0 not by 0.0.
*/
- return Reduction< Device >::reduce( 0, view.getSize(), reduce, fetch, 1.0 );
+ return Reduction< Device >::reduce( 0, view.getSize(), fetch, reduce, 1.0 );
}
int main( int argc, char* argv[] )
diff --git a/Documentation/Tutorials/ReductionAndScan/ReductionWithArgument.cpp b/Documentation/Tutorials/ReductionAndScan/ReductionWithArgument.cpp
index 0d9c160208..689d8b599c 100644
--- a/Documentation/Tutorials/ReductionAndScan/ReductionWithArgument.cpp
+++ b/Documentation/Tutorials/ReductionAndScan/ReductionWithArgument.cpp
@@ -22,7 +22,7 @@ maximumNorm( const Vector< double, Device >& v )
else if( a == b && bIdx < aIdx )
aIdx = bIdx;
};
- return Reduction< Device >::reduceWithArgument( 0, view.getSize(), reduction, fetch, std::numeric_limits< double >::max() );
+ return Reduction< Device >::reduceWithArgument( 0, view.getSize(), fetch, reduction, std::numeric_limits< double >::max() );
}
int main( int argc, char* argv[] )
diff --git a/Documentation/Tutorials/ReductionAndScan/ScalarProductExample.cpp b/Documentation/Tutorials/ReductionAndScan/ScalarProductExample.cpp
index e830f7884e..5a63b460b8 100644
--- a/Documentation/Tutorials/ReductionAndScan/ScalarProductExample.cpp
+++ b/Documentation/Tutorials/ReductionAndScan/ScalarProductExample.cpp
@@ -18,7 +18,7 @@ double scalarProduct( const Vector< double, Device >& u, const Vector< double, D
*/
auto fetch = [=] __cuda_callable__ ( int i ) { return u_view[ i ] * v_view[ i ]; };
auto reduce = [] __cuda_callable__ ( const double& a, const double& b ) { return a + b; };
- return Reduction< Device >::reduce( 0, v_view.getSize(), reduce, fetch, 0.0 );
+ return Reduction< Device >::reduce( 0, v_view.getSize(), fetch, reduce, 0.0 );
}
int main( int argc, char* argv[] )
diff --git a/Documentation/Tutorials/ReductionAndScan/SumExample.cpp b/Documentation/Tutorials/ReductionAndScan/SumExample.cpp
index 5db872f5e0..90c6f724a7 100644
--- a/Documentation/Tutorials/ReductionAndScan/SumExample.cpp
+++ b/Documentation/Tutorials/ReductionAndScan/SumExample.cpp
@@ -30,7 +30,7 @@ double sum( const Vector< double, Device >& v )
* lambdas defined above and finally value of idempotent element, zero in this case, which serve for the
* reduction initiation.
*/
- return Reduction< Device >::reduce( 0, view.getSize(), reduce, fetch, 0.0 );
+ return Reduction< Device >::reduce( 0, view.getSize(), fetch, reduce, 0.0 );
}
int main( int argc, char* argv[] )
diff --git a/Documentation/Tutorials/ReductionAndScan/UpdateAndResidueExample.cpp b/Documentation/Tutorials/ReductionAndScan/UpdateAndResidueExample.cpp
index fa2717ac32..8bd08e900d 100644
--- a/Documentation/Tutorials/ReductionAndScan/UpdateAndResidueExample.cpp
+++ b/Documentation/Tutorials/ReductionAndScan/UpdateAndResidueExample.cpp
@@ -17,7 +17,7 @@ double updateAndResidue( Vector< double, Device >& u, const Vector< double, Devi
u_view[ i ] += tau * add;
return add * add; };
auto reduce = [] __cuda_callable__ ( const double& a, const double& b ) { return a + b; };
- return sqrt( Reduction< Device >::reduce( 0, u_view.getSize(), reduce, fetch, 0.0 ) );
+ return sqrt( Reduction< Device >::reduce( 0, u_view.getSize(), fetch, reduce, 0.0 ) );
}
int main( int argc, char* argv[] )
diff --git a/src/Benchmarks/BLAS/CommonVectorOperations.hpp b/src/Benchmarks/BLAS/CommonVectorOperations.hpp
index acb96fabbe..d6a459677d 100644
--- a/src/Benchmarks/BLAS/CommonVectorOperations.hpp
+++ b/src/Benchmarks/BLAS/CommonVectorOperations.hpp
@@ -30,7 +30,7 @@ getVectorMax( const Vector& v )
const auto* data = v.getData();
auto fetch = [=] __cuda_callable__ ( IndexType i ) -> ResultType { return data[ i ]; };
auto reduction = [] __cuda_callable__ ( const ResultType& a, const ResultType& b ) { return TNL::max( a, b ); };
- return Algorithms::Reduction< DeviceType >::reduce( ( IndexType ) 0, v.getSize(), reduction, fetch, std::numeric_limits< ResultType >::lowest() );
+ return Algorithms::Reduction< DeviceType >::reduce( ( IndexType ) 0, v.getSize(), fetch, reduction, std::numeric_limits< ResultType >::lowest() );
}
template< typename Device >
@@ -47,7 +47,7 @@ getVectorMin( const Vector& v )
const auto* data = v.getData();
auto fetch = [=] __cuda_callable__ ( IndexType i ) -> RealType { return data[ i ]; };
auto reduction = [] __cuda_callable__ ( const ResultType& a, const ResultType& b ) { return TNL::min( a, b ); };
- return Algorithms::Reduction< DeviceType >::reduce( ( IndexType ) 0, v.getSize(), reduction, fetch, std::numeric_limits< ResultType >::max() );
+ return Algorithms::Reduction< DeviceType >::reduce( ( IndexType ) 0, v.getSize(), fetch, reduction, std::numeric_limits< ResultType >::max() );
}
template< typename Device >
@@ -64,7 +64,7 @@ getVectorAbsMax( const Vector& v )
const auto* data = v.getData();
auto fetch = [=] __cuda_callable__ ( IndexType i ) { return TNL::abs( data[ i ] ); };
auto reduction = [] __cuda_callable__ ( const ResultType& a, const ResultType& b ) { return TNL::max( a, b ); };
- return Algorithms::Reduction< DeviceType >::reduce( ( IndexType ) 0, v.getSize(), reduction, fetch, std::numeric_limits< ResultType >::lowest() );
+ return Algorithms::Reduction< DeviceType >::reduce( ( IndexType ) 0, v.getSize(), fetch, reduction, std::numeric_limits< ResultType >::lowest() );
}
template< typename Device >
@@ -81,7 +81,7 @@ getVectorAbsMin( const Vector& v )
const auto* data = v.getData();
auto fetch = [=] __cuda_callable__ ( IndexType i ) { return TNL::abs( data[ i ] ); };
auto reduction = [] __cuda_callable__ ( const ResultType& a, const ResultType& b ) { return TNL::min( a, b ); };
- return Algorithms::Reduction< DeviceType >::reduce( ( IndexType ) 0, v.getSize(), reduction, fetch, std::numeric_limits< ResultType >::max() );
+ return Algorithms::Reduction< DeviceType >::reduce( ( IndexType ) 0, v.getSize(), fetch, reduction, std::numeric_limits< ResultType >::max() );
}
template< typename Device >
@@ -97,7 +97,7 @@ getVectorL1Norm( const Vector& v )
const auto* data = v.getData();
auto fetch = [=] __cuda_callable__ ( IndexType i ) { return TNL::abs( data[ i ] ); };
- return Algorithms::Reduction< DeviceType >::reduce( ( IndexType ) 0, v.getSize(), std::plus<>{}, fetch, ( ResultType ) 0 );
+ return Algorithms::Reduction< DeviceType >::reduce( ( IndexType ) 0, v.getSize(), fetch, std::plus<>{}, ( ResultType ) 0 );
}
template< typename Device >
@@ -113,7 +113,7 @@ getVectorL2Norm( const Vector& v )
const auto* data = v.getData();
auto fetch = [=] __cuda_callable__ ( IndexType i ) { return data[ i ] * data[ i ]; };
- return std::sqrt( Algorithms::Reduction< DeviceType >::reduce( ( IndexType ) 0, v.getSize(), std::plus<>{}, fetch, ( ResultType ) 0 ) );
+ return std::sqrt( Algorithms::Reduction< DeviceType >::reduce( ( IndexType ) 0, v.getSize(), fetch, std::plus<>{}, ( ResultType ) 0 ) );
}
template< typename Device >
@@ -136,7 +136,7 @@ getVectorLpNorm( const Vector& v,
const auto* data = v.getData();
auto fetch = [=] __cuda_callable__ ( IndexType i ) { return TNL::pow( TNL::abs( data[ i ] ), p ); };
- return std::pow( Algorithms::Reduction< DeviceType >::reduce( ( IndexType ) 0, v.getSize(), std::plus<>{}, fetch, ( ResultType ) 0 ), 1.0 / p );
+ return std::pow( Algorithms::Reduction< DeviceType >::reduce( ( IndexType ) 0, v.getSize(), fetch, std::plus<>{}, ( ResultType ) 0 ), 1.0 / p );
}
template< typename Device >
@@ -155,7 +155,7 @@ getVectorSum( const Vector& v )
const auto* data = v.getData();
auto fetch = [=] __cuda_callable__ ( IndexType i ) -> ResultType { return data[ i ]; };
- return Algorithms::Reduction< DeviceType >::reduce( ( IndexType ) 0, v.getSize(), std::plus<>{}, fetch, ( ResultType ) 0 );
+ return Algorithms::Reduction< DeviceType >::reduce( ( IndexType ) 0, v.getSize(), fetch, std::plus<>{}, ( ResultType ) 0 );
}
template< typename Device >
@@ -175,7 +175,7 @@ getVectorDifferenceMax( const Vector1& v1,
const auto* data2 = v2.getData();
auto fetch = [=] __cuda_callable__ ( IndexType i ) { return data1[ i ] - data2[ i ]; };
auto reduction = [] __cuda_callable__ ( const ResultType& a, const ResultType& b ) { return TNL::max( a, b ); };
- return Algorithms::Reduction< DeviceType >::reduce( ( IndexType ) 0, v1.getSize(), reduction, fetch, std::numeric_limits< ResultType >::lowest() );
+ return Algorithms::Reduction< DeviceType >::reduce( ( IndexType ) 0, v1.getSize(), fetch, reduction, std::numeric_limits< ResultType >::lowest() );
}
template< typename Device >
@@ -195,7 +195,7 @@ getVectorDifferenceMin( const Vector1& v1,
const auto* data2 = v2.getData();
auto fetch = [=] __cuda_callable__ ( IndexType i ) { return data1[ i ] - data2[ i ]; };
auto reduction = [] __cuda_callable__ ( const ResultType& a, const ResultType& b ) { return TNL::min( a, b ); };
- return Algorithms::Reduction< DeviceType >::reduce( ( IndexType ) 0, v1.getSize(), reduction, fetch, std::numeric_limits< ResultType >::max() );
+ return Algorithms::Reduction< DeviceType >::reduce( ( IndexType ) 0, v1.getSize(), fetch, reduction, std::numeric_limits< ResultType >::max() );
}
template< typename Device >
@@ -215,7 +215,7 @@ getVectorDifferenceAbsMax( const Vector1& v1,
const auto* data2 = v2.getData();
auto fetch = [=] __cuda_callable__ ( IndexType i ) { return TNL::abs( data1[ i ] - data2[ i ] ); };
auto reduction = [] __cuda_callable__ ( const ResultType& a, const ResultType& b ) { return TNL::max( a, b ); };
- return Algorithms::Reduction< DeviceType >::reduce( ( IndexType ) 0, v1.getSize(), reduction, fetch, std::numeric_limits< ResultType >::lowest() );
+ return Algorithms::Reduction< DeviceType >::reduce( ( IndexType ) 0, v1.getSize(), fetch, reduction, std::numeric_limits< ResultType >::lowest() );
}
template< typename Device >
@@ -235,7 +235,7 @@ getVectorDifferenceAbsMin( const Vector1& v1,
const auto* data2 = v2.getData();
auto fetch = [=] __cuda_callable__ ( IndexType i ) { return TNL::abs( data1[ i ] - data2[ i ] ); };
auto reduction = [] __cuda_callable__ ( const ResultType& a, const ResultType& b ) { return TNL::min( a, b ); };
- return Algorithms::Reduction< DeviceType >::reduce( ( IndexType ) 0, v1.getSize(), reduction, fetch, std::numeric_limits< ResultType >::max() );
+ return Algorithms::Reduction< DeviceType >::reduce( ( IndexType ) 0, v1.getSize(), fetch, reduction, std::numeric_limits< ResultType >::max() );
}
template< typename Device >
@@ -254,7 +254,7 @@ getVectorDifferenceL1Norm( const Vector1& v1,
const auto* data1 = v1.getData();
const auto* data2 = v2.getData();
auto fetch = [=] __cuda_callable__ ( IndexType i ) { return TNL::abs( data1[ i ] - data2[ i ] ); };
- return Algorithms::Reduction< DeviceType >::reduce( ( IndexType ) 0, v1.getSize(), std::plus<>{}, fetch, ( ResultType ) 0 );
+ return Algorithms::Reduction< DeviceType >::reduce( ( IndexType ) 0, v1.getSize(), fetch, std::plus<>{}, ( ResultType ) 0 );
}
template< typename Device >
@@ -276,7 +276,7 @@ getVectorDifferenceL2Norm( const Vector1& v1,
auto diff = data1[ i ] - data2[ i ];
return diff * diff;
};
- return std::sqrt( Algorithms::Reduction< DeviceType >::reduce( ( IndexType ) 0, v1.getSize(), std::plus<>{}, fetch, ( ResultType ) 0 ) );
+ return std::sqrt( Algorithms::Reduction< DeviceType >::reduce( ( IndexType ) 0, v1.getSize(), fetch, std::plus<>{}, ( ResultType ) 0 ) );
}
template< typename Device >
@@ -302,7 +302,7 @@ getVectorDifferenceLpNorm( const Vector1& v1,
const auto* data1 = v1.getData();
const auto* data2 = v2.getData();
auto fetch = [=] __cuda_callable__ ( IndexType i ) { return TNL::pow( TNL::abs( data1[ i ] - data2[ i ] ), p ); };
- return std::pow( Algorithms::Reduction< DeviceType >::reduce( ( IndexType ) 0, v1.getSize(), std::plus<>{}, fetch, ( ResultType ) 0 ), 1.0 / p );
+ return std::pow( Algorithms::Reduction< DeviceType >::reduce( ( IndexType ) 0, v1.getSize(), fetch, std::plus<>{}, ( ResultType ) 0 ), 1.0 / p );
}
template< typename Device >
@@ -321,7 +321,7 @@ getVectorDifferenceSum( const Vector1& v1,
const auto* data1 = v1.getData();
const auto* data2 = v2.getData();
auto fetch = [=] __cuda_callable__ ( IndexType i ) { return data1[ i ] - data2[ i ]; };
- return Algorithms::Reduction< DeviceType >::reduce( ( IndexType ) 0, v1.getSize(), std::plus<>{}, fetch, ( ResultType ) 0 );
+ return Algorithms::Reduction< DeviceType >::reduce( ( IndexType ) 0, v1.getSize(), fetch, std::plus<>{}, ( ResultType ) 0 );
}
template< typename Device >
@@ -340,7 +340,7 @@ getScalarProduct( const Vector1& v1,
const auto* data1 = v1.getData();
const auto* data2 = v2.getData();
auto fetch = [=] __cuda_callable__ ( IndexType i ) { return data1[ i ] * data2[ i ]; };
- return Algorithms::Reduction< DeviceType >::reduce( ( IndexType ) 0, v1.getSize(), std::plus<>{}, fetch, ( ResultType ) 0 );
+ return Algorithms::Reduction< DeviceType >::reduce( ( IndexType ) 0, v1.getSize(), fetch, std::plus<>{}, ( ResultType ) 0 );
}
} // namespace Benchmarks
diff --git a/src/TNL/Algorithms/MemoryOperationsCuda.hpp b/src/TNL/Algorithms/MemoryOperationsCuda.hpp
index 53b60bb392..5351b69625 100644
--- a/src/TNL/Algorithms/MemoryOperationsCuda.hpp
+++ b/src/TNL/Algorithms/MemoryOperationsCuda.hpp
@@ -148,7 +148,7 @@ compare( const Element1* destination,
TNL_ASSERT_TRUE( source, "Attempted to compare data through a nullptr." );
auto fetch = [=] __cuda_callable__ ( Index i ) -> bool { return destination[ i ] == source[ i ]; };
- return Reduction< Devices::Cuda >::reduce( ( Index ) 0, size, std::logical_and<>{}, fetch, true );
+ return Reduction< Devices::Cuda >::reduce( ( Index ) 0, size, fetch, std::logical_and<>{}, true );
}
template< typename Element,
@@ -164,7 +164,7 @@ containsValue( const Element* data,
TNL_ASSERT_GE( size, (Index) 0, "" );
auto fetch = [=] __cuda_callable__ ( Index i ) -> bool { return data[ i ] == value; };
- return Reduction< Devices::Cuda >::reduce( ( Index ) 0, size, std::logical_or<>{}, fetch, false );
+ return Reduction< Devices::Cuda >::reduce( ( Index ) 0, size, fetch, std::logical_or<>{}, false );
}
template< typename Element,
@@ -180,7 +180,7 @@ containsOnlyValue( const Element* data,
TNL_ASSERT_GE( size, 0, "" );
auto fetch = [=] __cuda_callable__ ( Index i ) -> bool { return data[ i ] == value; };
- return Reduction< Devices::Cuda >::reduce( ( Index ) 0, size, std::logical_and<>{}, fetch, true );
+ return Reduction< Devices::Cuda >::reduce( ( Index ) 0, size, fetch, std::logical_and<>{}, true );
}
} // namespace Algorithms
diff --git a/src/TNL/Algorithms/MemoryOperationsHost.hpp b/src/TNL/Algorithms/MemoryOperationsHost.hpp
index 090d0bb9ed..92b44f8cf5 100644
--- a/src/TNL/Algorithms/MemoryOperationsHost.hpp
+++ b/src/TNL/Algorithms/MemoryOperationsHost.hpp
@@ -113,7 +113,7 @@ compare( const DestinationElement* destination,
if( Devices::Host::isOMPEnabled() && Devices::Host::getMaxThreadsCount() > 1 ) {
auto fetch = [destination, source] ( Index i ) -> bool { return destination[ i ] == source[ i ]; };
- return Reduction< Devices::Host >::reduce( ( Index ) 0, size, std::logical_and<>{}, fetch, true );
+ return Reduction< Devices::Host >::reduce( ( Index ) 0, size, fetch, std::logical_and<>{}, true );
}
else {
// sequential algorithm can return as soon as it finds a mismatch
@@ -135,7 +135,7 @@ containsValue( const Element* data,
if( Devices::Host::isOMPEnabled() && Devices::Host::getMaxThreadsCount() > 1 ) {
auto fetch = [=] ( Index i ) -> bool { return data[ i ] == value; };
- return Reduction< Devices::Host >::reduce( ( Index ) 0, size, std::logical_or<>{}, fetch, false );
+ return Reduction< Devices::Host >::reduce( ( Index ) 0, size, fetch, std::logical_or<>{}, false );
}
else {
// sequential algorithm can return as soon as it finds a match
@@ -157,7 +157,7 @@ containsOnlyValue( const Element* data,
if( Devices::Host::isOMPEnabled() && Devices::Host::getMaxThreadsCount() > 1 ) {
auto fetch = [data, value] ( Index i ) -> bool { return data[ i ] == value; };
- return Reduction< Devices::Host >::reduce( ( Index ) 0, size, std::logical_and<>{}, fetch, true );
+ return Reduction< Devices::Host >::reduce( ( Index ) 0, size, fetch, std::logical_and<>{}, true );
}
else {
// sequential algorithm can return as soon as it finds a mismatch
diff --git a/src/TNL/Algorithms/Reduction.h b/src/TNL/Algorithms/Reduction.h
index e36a706c13..d928ec6875 100644
--- a/src/TNL/Algorithms/Reduction.h
+++ b/src/TNL/Algorithms/Reduction.h
@@ -45,27 +45,27 @@ struct Reduction< Devices::Sequential >
*
* \tparam Index is a type for indexing.
* \tparam Result is a type of the reduction result.
- * \tparam ReductionOperation is a lambda function performing the reduction.
- * \tparam DataFetcher is a lambda function for fetching the input data.
+ * \tparam Fetch is a lambda function for fetching the input data.
+ * \tparam Reduce is a lambda function performing the reduction.
*
* \param begin defines range [begin, end) of indexes which will be used for the reduction.
* \param end defines range [begin, end) of indexes which will be used for the reduction.
- * \param reduction is a lambda function defining the reduction operation.
- * \param dataFetcher is a lambda function fetching the input data.
+ * \param fetch is a lambda function fetching the input data.
+ * \param reduce is a lambda function defining the reduction operation.
* \param zero is the idempotent element for the reduction operation, i.e. element which
* does not change the result of the reduction.
* \return result of the reduction
*
- * The dataFetcher lambda function takes one argument which is index of the element to be fetched:
+ * The `fetch` lambda function takes one argument which is index of the element to be fetched:
*
* ```
- * auto dataFetcher1 = [=] __cuda_callable__ ( Index i ) { return ... };
+ * auto fetch = [=] __cuda_callable__ ( Index i ) { return ... };
* ```
*
- * The reduction lambda function takes two variables which are supposed to be reduced:
+ * The `reduce` lambda function takes two variables which are supposed to be reduced:
*
* ```
- * auto reduction = [] __cuda_callable__ ( const Result& a, const Result& b ) { return ... };
+ * auto reduce = [] __cuda_callable__ ( const Result& a, const Result& b ) { return ... };
* ```
*
* \par Example
@@ -78,13 +78,13 @@ struct Reduction< Devices::Sequential >
*/
template< typename Index,
typename Result,
- typename ReductionOperation,
- typename DataFetcher >
+ typename Fetch,
+ typename Reduce >
static constexpr Result
reduce( const Index begin,
const Index end,
- const ReductionOperation& reduction,
- DataFetcher& dataFetcher,
+ Fetch&& fetch,
+ Reduce&& reduce,
const Result& zero );
/**
@@ -96,28 +96,28 @@ struct Reduction< Devices::Sequential >
*
* \tparam Index is a type for indexing.
* \tparam Result is a type of the reduction result.
- * \tparam ReductionOperation is a lambda function performing the reduction.
- * \tparam DataFetcher is a lambda function for fetching the input data.
+ * \tparam Fetch is a lambda function for fetching the input data.
+ * \tparam Reduce is a lambda function performing the reduction.
*
* \param begin defines range [begin, end) of indexes which will be used for the reduction.
* \param end defines range [begin, end) of indexes which will be used for the reduction.
- * \param reduction is a lambda function defining the reduction operation and managing the elements positions.
- * \param dataFetcher is a lambda function fetching the input data.
+ * \param fetch is a lambda function fetching the input data.
+ * \param reduce is a lambda function defining the reduction operation and managing the elements positions.
* \param zero is the idempotent element for the reduction operation, i.e. element which
* does not change the result of the reduction.
* \return result of the reduction in a form of std::pair< Index, Result> structure. `pair.first'
* is the element position and `pair.second` is the reduction result.
*
- * The dataFetcher lambda function takes one argument which is index of the element to be fetched:
+ * The `fetch` lambda function takes one argument which is index of the element to be fetched:
*
* ```
- * auto dataFetcher1 = [=] __cuda_callable__ ( Index i ) { return ... };
+ * auto fetch = [=] __cuda_callable__ ( Index i ) { return ... };
* ```
*
- * The reduction lambda function takes two variables which are supposed to be reduced:
+ * The `reduce` lambda function takes two variables which are supposed to be reduced:
*
* ```
- * auto reduction = [] __cuda_callable__ ( const Result& a, const Result& b, Index& aIdx, const Index& bIdx ) { return ... };
+ * auto reduce = [] __cuda_callable__ ( const Result& a, const Result& b, Index& aIdx, const Index& bIdx ) { return ... };
* ```
*
* \par Example
@@ -130,13 +130,13 @@ struct Reduction< Devices::Sequential >
*/
template< typename Index,
typename Result,
- typename ReductionOperation,
- typename DataFetcher >
+ typename Fetch,
+ typename Reduce >
static constexpr std::pair< Result, Index >
reduceWithArgument( const Index begin,
const Index end,
- const ReductionOperation& reduction,
- DataFetcher& dataFetcher,
+ Fetch&& fetch,
+ Reduce&& reduce,
const Result& zero );
};
@@ -148,27 +148,27 @@ struct Reduction< Devices::Host >
*
* \tparam Index is a type for indexing.
* \tparam Result is a type of the reduction result.
- * \tparam ReductionOperation is a lambda function performing the reduction.
- * \tparam DataFetcher is a lambda function for fetching the input data.
+ * \tparam Fetch is a lambda function for fetching the input data.
+ * \tparam Reduce is a lambda function performing the reduction.
*
* \param begin defines range [begin, end) of indexes which will be used for the reduction.
* \param end defines range [begin, end) of indexes which will be used for the reduction.
- * \param reduction is a lambda function defining the reduction operation.
- * \param dataFetcher is a lambda function fetching the input data.
+ * \param fetch is a lambda function fetching the input data.
+ * \param reduce is a lambda function defining the reduction operation.
* \param zero is the idempotent element for the reduction operation, i.e. element which
* does not change the result of the reduction.
* \return result of the reduction
*
- * The dataFetcher lambda function takes one argument which is index of the element to be fetched:
+ * The `fetch` lambda function takes one argument which is index of the element to be fetched:
*
* ```
- * auto dataFetcher1 = [=] __cuda_callable__ ( Index i ) { return ... };
+ * auto fetch = [=] __cuda_callable__ ( Index i ) { return ... };
* ```
*
- * The reduction lambda function takes two variables which are supposed to be reduced:
+ * The `reduce` lambda function takes two variables which are supposed to be reduced:
*
* ```
- * auto reduction = [] __cuda_callable__ ( const Result& a, const Result& b ) { return ... };
+ * auto reduce = [] __cuda_callable__ ( const Result& a, const Result& b ) { return ... };
* ```
*
* \par Example
@@ -181,65 +181,65 @@ struct Reduction< Devices::Host >
*/
template< typename Index,
typename Result,
- typename ReductionOperation,
- typename DataFetcher >
+ typename Fetch,
+ typename Reduce >
static Result
reduce( const Index begin,
const Index end,
- const ReductionOperation& reduction,
- DataFetcher& dataFetcher,
+ Fetch&& fetch,
+ Reduce&& reduce,
const Result& zero );
/**
* \brief Computes reduction on CPU and returns position of an element of interest.
- *
- * For example in case of computing minimal or maximal element in array/vector,
+ *
+ * For example in case of computing minimal or maximal element in array/vector,
* the position of the element having given value can be obtained. The use of this method
* is, however, more flexible.
- *
+ *
* \tparam Index is a type for indexing.
* \tparam Result is a type of the reduction result.
* \tparam ReductionOperation is a lambda function performing the reduction.
* \tparam DataFetcher is a lambda function for fetching the input data.
- *
+ *
* \param begin defines range [begin, end) of indexes which will be used for the reduction.
* \param end defines range [begin, end) of indexes which will be used for the reduction.
- * \param reduction is a lambda function defining the reduction operation and managing the elements positions.
- * \param dataFetcher is a lambda function fetching the input data.
+ * \param fetch is a lambda function fetching the input data.
+ * \param reduce is a lambda function defining the reduction operation and managing the elements positions.
* \param zero is the idempotent element for the reduction operation, i.e. element which
* does not change the result of the reduction.
* \return result of the reduction in a form of std::pair< Index, Result> structure. `pair.first'
* is the element position and `pair.second` is the reduction result.
- *
- * The dataFetcher lambda function takes one argument which is index of the element to be fetched:
- *
+ *
+ * The `fetch` lambda function takes one argument which is index of the element to be fetched:
+ *
* ```
- * auto dataFetcher1 = [=] __cuda_callable__ ( Index i ) { return ... };
+ * auto fetch = [=] __cuda_callable__ ( Index i ) { return ... };
* ```
- *
- * The reduction lambda function takes two variables which are supposed to be reduced:
- *
+ *
+ * The `reduce` lambda function takes two variables which are supposed to be reduced:
+ *
* ```
- * auto reduction = [] __cuda_callable__ ( const Result& a, const Result& b, Index& aIdx, const Index& bIdx ) { return ... };
+ * auto reduce = [] __cuda_callable__ ( const Result& a, const Result& b, Index& aIdx, const Index& bIdx ) { return ... };
* ```
- *
+ *
* \par Example
- *
+ *
* \include ReductionAndScan/ReductionWithArgument.cpp
- *
+ *
* \par Output
- *
+ *
* \include ReductionWithArgument.out
*/
template< typename Index,
typename Result,
- typename ReductionOperation,
- typename DataFetcher >
+ typename Fetch,
+ typename Reduce >
static std::pair< Result, Index >
reduceWithArgument( const Index begin,
const Index end,
- const ReductionOperation& reduction,
- DataFetcher& dataFetcher,
+ Fetch&& fetch,
+ Reduce&& reduce,
const Result& zero );
};
@@ -251,27 +251,27 @@ struct Reduction< Devices::Cuda >
*
* \tparam Index is a type for indexing.
* \tparam Result is a type of the reduction result.
- * \tparam ReductionOperation is a lambda function performing the reduction.
- * \tparam DataFetcher is a lambda function for fetching the input data.
+ * \tparam Fetch is a lambda function for fetching the input data.
+ * \tparam Reduce is a lambda function performing the reduction.
*
* \param begin defines range [begin, end) of indexes which will be used for the reduction.
* \param end defines range [begin, end) of indexes which will be used for the reduction.
- * \param reduction is a lambda function defining the reduction operation.
- * \param dataFetcher is a lambda function fetching the input data.
+ * \param fetch is a lambda function fetching the input data.
+ * \param reduce is a lambda function defining the reduction operation.
* \param zero is the idempotent element for the reduction operation, i.e. element which
* does not change the result of the reduction.
* \return result of the reduction
*
- * The dataFetcher lambda function takes one argument which is index of the element to be fetched:
+ * The `fetch` lambda function takes one argument which is index of the element to be fetched:
*
* ```
- * auto dataFetcher1 = [=] __cuda_callable__ ( Index i ) { return ... };
+ * auto fetch = [=] __cuda_callable__ ( Index i ) { return ... };
* ```
*
- * The reduction lambda function takes two variables which are supposed to be reduced:
+ * The `reduce` lambda function takes two variables which are supposed to be reduced:
*
* ```
- * auto reduction = [] __cuda_callable__ ( const Result& a, const Result& b ) { return ... };
+ * auto reduce = [] __cuda_callable__ ( const Result& a, const Result& b ) { return ... };
* ```
*
* \par Example
@@ -284,46 +284,46 @@ struct Reduction< Devices::Cuda >
*/
template< typename Index,
typename Result,
- typename ReductionOperation,
- typename DataFetcher >
+ typename Fetch,
+ typename Reduce >
static Result
reduce( const Index begin,
const Index end,
- const ReductionOperation& reduction,
- DataFetcher& dataFetcher,
+ Fetch&& fetch,
+ Reduce&& reduce,
const Result& zero );
/**
* \brief Computes reduction on GPU and returns position of an element of interest.
*
- * For example in case of computing minimal or maximal element in array/vector,
+ * For example in case of computing minimal or maximal element in array/vector,
* the position of the element having given value can be obtained. The use of this method
* is, however, more flexible.
*
* \tparam Index is a type for indexing.
* \tparam Result is a type of the reduction result.
- * \tparam ReductionOperation is a lambda function performing the reduction.
- * \tparam DataFetcher is a lambda function for fetching the input data.
+ * \tparam Fetch is a lambda function for fetching the input data.
+ * \tparam Reduce is a lambda function performing the reduction.
*
* \param begin defines range [begin, end) of indexes which will be used for the reduction.
* \param end defines range [begin, end) of indexes which will be used for the reduction.
- * \param reduction is a lambda function defining the reduction operation and managing the elements positions.
- * \param dataFetcher is a lambda function fetching the input data.
+ * \param fetch is a lambda function fetching the input data.
+ * \param reduce is a lambda function defining the reduction operation and managing the elements positions.
* \param zero is the idempotent element for the reduction operation, i.e. element which
* does not change the result of the reduction.
* \return result of the reduction in a form of std::pair< Index, Result> structure. `pair.first'
* is the element position and `pair.second` is the reduction result.
*
- * The dataFetcher lambda function takes one argument which is index of the element to be fetched:
+ * The `fetch` lambda function takes one argument which is index of the element to be fetched:
*
* ```
- * auto dataFetcher1 = [=] __cuda_callable__ ( Index i ) { return ... };
+ * auto fetch = [=] __cuda_callable__ ( Index i ) { return ... };
* ```
*
- * The reduction lambda function takes two variables which are supposed to be reduced:
+ * The `reduce` lambda function takes two variables which are supposed to be reduced:
*
* ```
- * auto reduction = [] __cuda_callable__ ( const Result& a, const Result& b, Index& aIdx, const Index& bIdx ) { return ... };
+ * auto reduce = [] __cuda_callable__ ( const Result& a, const Result& b, Index& aIdx, const Index& bIdx ) { return ... };
* ```
*
* \par Example
@@ -336,13 +336,13 @@ struct Reduction< Devices::Cuda >
*/
template< typename Index,
typename Result,
- typename ReductionOperation,
- typename DataFetcher >
+ typename Fetch,
+ typename Reduce >
static std::pair< Result, Index >
reduceWithArgument( const Index begin,
const Index end,
- const ReductionOperation& reduction,
- DataFetcher& dataFetcher,
+ Fetch&& fetch,
+ Reduce&& reduce,
const Result& zero );
};
diff --git a/src/TNL/Algorithms/Reduction.hpp b/src/TNL/Algorithms/Reduction.hpp
index 70e725af6b..7873f9c3c4 100644
--- a/src/TNL/Algorithms/Reduction.hpp
+++ b/src/TNL/Algorithms/Reduction.hpp
@@ -37,14 +37,14 @@ static constexpr int Reduction_minGpuDataSize = 256;//65536; //16384;//1024;//25
template< typename Index,
typename Result,
- typename ReductionOperation,
- typename DataFetcher >
+ typename Fetch,
+ typename Reduce >
constexpr Result
Reduction< Devices::Sequential >::
reduce( const Index begin,
const Index end,
- const ReductionOperation& reduction,
- DataFetcher& dataFetcher,
+ Fetch&& fetch,
+ Reduce&& reduce,
const Result& zero )
{
constexpr int block_size = 128;
@@ -55,45 +55,45 @@ reduce( const Index begin,
// initialize array for unrolled results
Result r[ 4 ] = { zero, zero, zero, zero };
- // main reduction (explicitly unrolled loop)
+ // main reduce (explicitly unrolled loop)
for( Index b = 0; b < blocks; b++ ) {
const Index offset = begin + b * block_size;
for( int i = 0; i < block_size; i += 4 ) {
- r[ 0 ] = reduction( r[ 0 ], dataFetcher( offset + i ) );
- r[ 1 ] = reduction( r[ 1 ], dataFetcher( offset + i + 1 ) );
- r[ 2 ] = reduction( r[ 2 ], dataFetcher( offset + i + 2 ) );
- r[ 3 ] = reduction( r[ 3 ], dataFetcher( offset + i + 3 ) );
+ r[ 0 ] = reduce( r[ 0 ], fetch( offset + i ) );
+ r[ 1 ] = reduce( r[ 1 ], fetch( offset + i + 1 ) );
+ r[ 2 ] = reduce( r[ 2 ], fetch( offset + i + 2 ) );
+ r[ 3 ] = reduce( r[ 3 ], fetch( offset + i + 3 ) );
}
}
- // reduction of the last, incomplete block (not unrolled)
+ // reduce of the last, incomplete block (not unrolled)
for( Index i = begin + blocks * block_size; i < end; i++ )
- r[ 0 ] = reduction( r[ 0 ], dataFetcher( i ) );
+ r[ 0 ] = reduce( r[ 0 ], fetch( i ) );
- // reduction of unrolled results
- r[ 0 ] = reduction( r[ 0 ], r[ 2 ] );
- r[ 1 ] = reduction( r[ 1 ], r[ 3 ] );
- r[ 0 ] = reduction( r[ 0 ], r[ 1 ] );
+ // reduce of unrolled results
+ r[ 0 ] = reduce( r[ 0 ], r[ 2 ] );
+ r[ 1 ] = reduce( r[ 1 ], r[ 3 ] );
+ r[ 0 ] = reduce( r[ 0 ], r[ 1 ] );
return r[ 0 ];
}
else {
Result result = zero;
for( Index i = begin; i < end; i++ )
- result = reduction( result, dataFetcher( i ) );
+ result = reduce( result, fetch( i ) );
return result;
}
}
template< typename Index,
typename Result,
- typename ReductionOperation,
- typename DataFetcher >
+ typename Fetch,
+ typename Reduce >
constexpr std::pair< Result, Index >
Reduction< Devices::Sequential >::
reduceWithArgument( const Index begin,
const Index end,
- const ReductionOperation& reduction,
- DataFetcher& dataFetcher,
+ Fetch&& fetch,
+ Reduce&& reduce,
const Result& zero )
{
constexpr int block_size = 128;
@@ -106,7 +106,7 @@ reduceWithArgument( const Index begin,
Result r[ 4 ] = { zero, zero, zero, zero };
bool initialized( false );
- // main reduction (explicitly unrolled loop)
+ // main reduce (explicitly unrolled loop)
for( Index b = 0; b < blocks; b++ ) {
const Index offset = begin + b * block_size;
for( int i = 0; i < block_size; i += 4 ) {
@@ -116,48 +116,48 @@ reduceWithArgument( const Index begin,
arg[ 1 ] = offset + i + 1;
arg[ 2 ] = offset + i + 2;
arg[ 3 ] = offset + i + 3;
- r[ 0 ] = dataFetcher( offset + i );
- r[ 1 ] = dataFetcher( offset + i + 1 );
- r[ 2 ] = dataFetcher( offset + i + 2 );
- r[ 3 ] = dataFetcher( offset + i + 3 );
+ r[ 0 ] = fetch( offset + i );
+ r[ 1 ] = fetch( offset + i + 1 );
+ r[ 2 ] = fetch( offset + i + 2 );
+ r[ 3 ] = fetch( offset + i + 3 );
initialized = true;
continue;
}
- reduction( r[ 0 ], dataFetcher( offset + i ), arg[ 0 ], offset + i );
- reduction( r[ 1 ], dataFetcher( offset + i + 1 ), arg[ 1 ], offset + i + 1 );
- reduction( r[ 2 ], dataFetcher( offset + i + 2 ), arg[ 2 ], offset + i + 2 );
- reduction( r[ 3 ], dataFetcher( offset + i + 3 ), arg[ 3 ], offset + i + 3 );
+ reduce( r[ 0 ], fetch( offset + i ), arg[ 0 ], offset + i );
+ reduce( r[ 1 ], fetch( offset + i + 1 ), arg[ 1 ], offset + i + 1 );
+ reduce( r[ 2 ], fetch( offset + i + 2 ), arg[ 2 ], offset + i + 2 );
+ reduce( r[ 3 ], fetch( offset + i + 3 ), arg[ 3 ], offset + i + 3 );
}
}
- // reduction of the last, incomplete block (not unrolled)
+ // reduce of the last, incomplete block (not unrolled)
for( Index i = begin + blocks * block_size; i < size; i++ )
- reduction( r[ 0 ], dataFetcher( i ), arg[ 0 ], i );
+ reduce( r[ 0 ], fetch( i ), arg[ 0 ], i );
- // reduction of unrolled results
- reduction( r[ 0 ], r[ 2 ], arg[ 0 ], arg[ 2 ] );
- reduction( r[ 1 ], r[ 3 ], arg[ 1 ], arg[ 3 ] );
- reduction( r[ 0 ], r[ 1 ], arg[ 0 ], arg[ 1 ] );
+ // reduce of unrolled results
+ reduce( r[ 0 ], r[ 2 ], arg[ 0 ], arg[ 2 ] );
+ reduce( r[ 1 ], r[ 3 ], arg[ 1 ], arg[ 3 ] );
+ reduce( r[ 0 ], r[ 1 ], arg[ 0 ], arg[ 1 ] );
return std::make_pair( r[ 0 ], arg[ 0 ] );
}
else {
- std::pair< Result, Index > result( dataFetcher( begin ), begin );
+ std::pair< Result, Index > result( fetch( begin ), begin );
for( Index i = begin + 1; i < end; i++ )
- reduction( result.first, dataFetcher( i ), result.second, i );
+ reduce( result.first, fetch( i ), result.second, i );
return result;
}
}
template< typename Index,
typename Result,
- typename ReductionOperation,
- typename DataFetcher >
+ typename Fetch,
+ typename Reduce >
Result
Reduction< Devices::Host >::
reduce( const Index begin,
const Index end,
- const ReductionOperation& reduction,
- DataFetcher& dataFetcher,
+ Fetch&& fetch,
+ Reduce&& reduce,
const Result& zero )
{
#ifdef HAVE_OPENMP
@@ -178,10 +178,10 @@ reduce( const Index begin,
for( Index b = 0; b < blocks; b++ ) {
const Index offset = begin + b * block_size;
for( int i = 0; i < block_size; i += 4 ) {
- r[ 0 ] = reduction( r[ 0 ], dataFetcher( offset + i ) );
- r[ 1 ] = reduction( r[ 1 ], dataFetcher( offset + i + 1 ) );
- r[ 2 ] = reduction( r[ 2 ], dataFetcher( offset + i + 2 ) );
- r[ 3 ] = reduction( r[ 3 ], dataFetcher( offset + i + 3 ) );
+ r[ 0 ] = reduce( r[ 0 ], fetch( offset + i ) );
+ r[ 1 ] = reduce( r[ 1 ], fetch( offset + i + 1 ) );
+ r[ 2 ] = reduce( r[ 2 ], fetch( offset + i + 2 ) );
+ r[ 3 ] = reduce( r[ 3 ], fetch( offset + i + 3 ) );
}
}
@@ -189,37 +189,37 @@ reduce( const Index begin,
#pragma omp single nowait
{
for( Index i = begin + blocks * block_size; i < end; i++ )
- r[ 0 ] = reduction( r[ 0 ], dataFetcher( i ) );
+ r[ 0 ] = reduce( r[ 0 ], fetch( i ) );
}
- // local reduction of unrolled results
- r[ 0 ] = reduction( r[ 0 ], r[ 2 ] );
- r[ 1 ] = reduction( r[ 1 ], r[ 3 ] );
- r[ 0 ] = reduction( r[ 0 ], r[ 1 ] );
+ // local reduce of unrolled results
+ r[ 0 ] = reduce( r[ 0 ], r[ 2 ] );
+ r[ 1 ] = reduce( r[ 1 ], r[ 3 ] );
+ r[ 0 ] = reduce( r[ 0 ], r[ 1 ] );
- // inter-thread reduction of local results
+ // inter-thread reduce of local results
#pragma omp critical
{
- result = reduction( result, r[ 0 ] );
+ result = reduce( result, r[ 0 ] );
}
}
return result;
}
else
#endif
- return Reduction< Devices::Sequential >::reduce( begin, end, reduction, dataFetcher, zero );
+ return Reduction< Devices::Sequential >::reduce( begin, end, fetch, reduce, zero );
}
template< typename Index,
typename Result,
- typename ReductionOperation,
- typename DataFetcher >
+ typename Fetch,
+ typename Reduce >
std::pair< Result, Index >
Reduction< Devices::Host >::
reduceWithArgument( const Index begin,
const Index end,
- const ReductionOperation& reduction,
- DataFetcher& dataFetcher,
+ Fetch&& fetch,
+ Reduce&& reduce,
const Result& zero )
{
#ifdef HAVE_OPENMP
@@ -247,17 +247,17 @@ reduceWithArgument( const Index begin,
arg[ 1 ] = offset + i + 1;
arg[ 2 ] = offset + i + 2;
arg[ 3 ] = offset + i + 3;
- r[ 0 ] = dataFetcher( offset + i );
- r[ 1 ] = dataFetcher( offset + i + 1 );
- r[ 2 ] = dataFetcher( offset + i + 2 );
- r[ 3 ] = dataFetcher( offset + i + 3 );
+ r[ 0 ] = fetch( offset + i );
+ r[ 1 ] = fetch( offset + i + 1 );
+ r[ 2 ] = fetch( offset + i + 2 );
+ r[ 3 ] = fetch( offset + i + 3 );
initialized = true;
continue;
}
- reduction( r[ 0 ], dataFetcher( offset + i ), arg[ 0 ], offset + i );
- reduction( r[ 1 ], dataFetcher( offset + i + 1 ), arg[ 1 ], offset + i + 1 );
- reduction( r[ 2 ], dataFetcher( offset + i + 2 ), arg[ 2 ], offset + i + 2 );
- reduction( r[ 3 ], dataFetcher( offset + i + 3 ), arg[ 3 ], offset + i + 3 );
+ reduce( r[ 0 ], fetch( offset + i ), arg[ 0 ], offset + i );
+ reduce( r[ 1 ], fetch( offset + i + 1 ), arg[ 1 ], offset + i + 1 );
+ reduce( r[ 2 ], fetch( offset + i + 2 ), arg[ 2 ], offset + i + 2 );
+ reduce( r[ 3 ], fetch( offset + i + 3 ), arg[ 3 ], offset + i + 3 );
}
}
@@ -265,44 +265,44 @@ reduceWithArgument( const Index begin,
#pragma omp single nowait
{
for( Index i = begin + blocks * block_size; i < end; i++ )
- reduction( r[ 0 ], dataFetcher( i ), arg[ 0 ], i );
+ reduce( r[ 0 ], fetch( i ), arg[ 0 ], i );
}
- // local reduction of unrolled results
- reduction( r[ 0 ], r[ 2 ], arg[ 0 ], arg[ 2 ] );
- reduction( r[ 1 ], r[ 3 ], arg[ 1 ], arg[ 3 ] );
- reduction( r[ 0 ], r[ 1 ], arg[ 0 ], arg[ 1 ] );
+ // local reduce of unrolled results
+ reduce( r[ 0 ], r[ 2 ], arg[ 0 ], arg[ 2 ] );
+ reduce( r[ 1 ], r[ 3 ], arg[ 1 ], arg[ 3 ] );
+ reduce( r[ 0 ], r[ 1 ], arg[ 0 ], arg[ 1 ] );
- // inter-thread reduction of local results
+ // inter-thread reduce of local results
#pragma omp critical
{
if( result.second == -1 )
result.second = arg[ 0 ];
- reduction( result.first, r[ 0 ], result.second, arg[ 0 ] );
+ reduce( result.first, r[ 0 ], result.second, arg[ 0 ] );
}
}
return result;
}
else
#endif
- return Reduction< Devices::Sequential >::reduceWithArgument( begin, end, reduction, dataFetcher, zero );
+ return Reduction< Devices::Sequential >::reduceWithArgument( begin, end, fetch, reduce, zero );
}
template< typename Index,
typename Result,
- typename ReductionOperation,
- typename DataFetcher >
+ typename Fetch,
+ typename Reduce >
Result
Reduction< Devices::Cuda >::
reduce( const Index begin,
const Index end,
- const ReductionOperation& reduction,
- DataFetcher& dataFetcher,
+ Fetch&& fetch,
+ Reduce&& reduce,
const Result& zero )
{
// Only fundamental and pointer types can be safely reduced on host. Complex
// objects stored on the device might contain pointers into the device memory,
- // in which case reduction on host might fail.
+ // in which case reduce on host might fail.
constexpr bool can_reduce_later_on_host = std::is_fundamental< Result >::value || std::is_pointer< Result >::value;
#ifdef CUDA_REDUCTION_PROFILING
@@ -313,11 +313,11 @@ reduce( const Index begin,
CudaReductionKernelLauncher< Index, Result > reductionLauncher( begin, end );
- // start the reduction on the GPU
+ // start the reduce on the GPU
Result* deviceAux1( 0 );
const int reducedSize = reductionLauncher.start(
- reduction,
- dataFetcher,
+ reduce,
+ fetch,
zero,
deviceAux1 );
@@ -353,9 +353,9 @@ reduce( const Index begin,
timer.start();
#endif
- // finish the reduction on the host
+ // finish the reduce on the host
auto fetch = [&] ( Index i ) { return resultArray[ i ]; };
- const Result result = Reduction< Devices::Sequential >::reduce( 0, reducedSize, reduction, fetch, zero );
+ const Result result = Reduction< Devices::Sequential >::reduce( 0, reducedSize, fetch, reduce, zero );
#ifdef CUDA_REDUCTION_PROFILING
timer.stop();
@@ -364,8 +364,8 @@ reduce( const Index begin,
return result;
}
else {
- // data can't be safely reduced on host, so continue with the reduction on the GPU
- auto result = reductionLauncher.finish( reduction, zero );
+ // data can't be safely reduced on host, so continue with the reduce on the GPU
+ auto result = reductionLauncher.finish( reduce, zero );
#ifdef CUDA_REDUCTION_PROFILING
timer.stop();
@@ -380,19 +380,19 @@ reduce( const Index begin,
template< typename Index,
typename Result,
- typename ReductionOperation,
- typename DataFetcher >
+ typename Fetch,
+ typename Reduce >
std::pair< Result, Index >
Reduction< Devices::Cuda >::
reduceWithArgument( const Index begin,
const Index end,
- const ReductionOperation& reduction,
- DataFetcher& dataFetcher,
+ Fetch&& fetch,
+ Reduce&& reduce,
const Result& zero )
{
// Only fundamental and pointer types can be safely reduced on host. Complex
// objects stored on the device might contain pointers into the device memory,
- // in which case reduction on host might fail.
+ // in which case reduce on host might fail.
constexpr bool can_reduce_later_on_host = std::is_fundamental< Result >::value || std::is_pointer< Result >::value;
#ifdef CUDA_REDUCTION_PROFILING
@@ -403,12 +403,12 @@ reduceWithArgument( const Index begin,
CudaReductionKernelLauncher< Index, Result > reductionLauncher( begin, end );
- // start the reduction on the GPU
+ // start the reduce on the GPU
Result* deviceAux1( nullptr );
Index* deviceIndexes( nullptr );
const int reducedSize = reductionLauncher.startWithArgument(
- reduction,
- dataFetcher,
+ reduce,
+ fetch,
zero,
deviceAux1,
deviceIndexes );
@@ -460,11 +460,11 @@ reduceWithArgument( const Index begin,
timer.start();
#endif
- // finish the reduction on the host
+ // finish the reduce on the host
// auto fetch = [&] ( Index i ) { return resultArray[ i ]; };
-// const Result result = Reduction< Devices::Sequential >::reduceWithArgument( reducedSize, argument, reduction, fetch, zero );
+// const Result result = Reduction< Devices::Sequential >::reduceWithArgument( reducedSize, argument, reduce, fetch, zero );
for( Index i = 1; i < reducedSize; i++ )
- reduction( resultArray[ 0 ], resultArray[ i ], indexArray[ 0 ], indexArray[ i ] );
+ reduce( resultArray[ 0 ], resultArray[ i ], indexArray[ 0 ], indexArray[ i ] );
#ifdef CUDA_REDUCTION_PROFILING
timer.stop();
@@ -473,8 +473,8 @@ reduceWithArgument( const Index begin,
return std::make_pair( resultArray[ 0 ], indexArray[ 0 ] );
}
else {
- // data can't be safely reduced on host, so continue with the reduction on the GPU
- auto result = reductionLauncher.finishWithArgument( reduction, zero );
+ // data can't be safely reduced on host, so continue with the reduce on the GPU
+ auto result = reductionLauncher.finishWithArgument( reduce, zero );
#ifdef CUDA_REDUCTION_PROFILING
timer.stop();
diff --git a/src/TNL/Containers/ArrayView.hpp b/src/TNL/Containers/ArrayView.hpp
index 2c0d3d631f..9143dea1ac 100644
--- a/src/TNL/Containers/ArrayView.hpp
+++ b/src/TNL/Containers/ArrayView.hpp
@@ -380,7 +380,7 @@ reduceElements( Index begin, Index end, Fetch&& fetch, Reduce&& reduce, const Re
ValueType* d = this->getData();
auto main_fetch = [=] __cuda_callable__ ( IndexType i ) mutable -> Result { return fetch( i, d[ i ] ); };
- return Algorithms::Reduction< DeviceType >::reduce( begin, end, reduce, main_fetch, zero );
+ return Algorithms::Reduction< DeviceType >::reduce( begin, end, main_fetch, reduce, zero );
}
template< typename Value,
@@ -397,7 +397,7 @@ reduceElements( Index begin, Index end, Fetch&& fetch, Reduce&& reduce, const Re
const ValueType* d = this->getData();
auto main_fetch = [=] __cuda_callable__ ( IndexType i ) mutable -> Result { return fetch( i, d[ i ] ); };
- return Algorithms::Reduction< DeviceType >::reduce( begin, end, reduce, main_fetch, zero );
+ return Algorithms::Reduction< DeviceType >::reduce( begin, end, main_fetch, reduce, zero );
}
template< typename Value,
diff --git a/src/TNL/Containers/Expressions/Comparison.h b/src/TNL/Containers/Expressions/Comparison.h
index 33986e1edd..738409cc40 100644
--- a/src/TNL/Containers/Expressions/Comparison.h
+++ b/src/TNL/Containers/Expressions/Comparison.h
@@ -68,7 +68,7 @@ struct VectorComparison< T1, T2, false >
const auto view_a = a.getConstView();
const auto view_b = b.getConstView();
auto fetch = [=] __cuda_callable__ ( IndexType i ) -> bool { return view_a[ i ] == view_b[ i ]; };
- return Algorithms::Reduction< DeviceType >::reduce( ( IndexType ) 0, a.getSize(), std::logical_and<>{}, fetch, true );
+ return Algorithms::Reduction< DeviceType >::reduce( ( IndexType ) 0, a.getSize(), fetch, std::logical_and<>{}, true );
}
};
@@ -100,7 +100,7 @@ struct Comparison< T1, T2, VectorExpressionVariable, VectorExpressionVariable >
const auto view_a = a.getConstView();
const auto view_b = b.getConstView();
auto fetch = [=] __cuda_callable__ ( IndexType i ) -> bool { return view_a[ i ] > view_b[ i ]; };
- return Algorithms::Reduction< DeviceType >::reduce( ( IndexType ) 0, a.getSize(), std::logical_and<>{}, fetch, true );
+ return Algorithms::Reduction< DeviceType >::reduce( ( IndexType ) 0, a.getSize(), fetch, std::logical_and<>{}, true );
}
static bool GE( const T1& a, const T2& b )
@@ -115,7 +115,7 @@ struct Comparison< T1, T2, VectorExpressionVariable, VectorExpressionVariable >
const auto view_a = a.getConstView();
const auto view_b = b.getConstView();
auto fetch = [=] __cuda_callable__ ( IndexType i ) -> bool { return view_a[ i ] >= view_b[ i ]; };
- return Algorithms::Reduction< DeviceType >::reduce( ( IndexType ) 0, a.getSize(), std::logical_and<>{}, fetch, true );
+ return Algorithms::Reduction< DeviceType >::reduce( ( IndexType ) 0, a.getSize(), fetch, std::logical_and<>{}, true );
}
static bool LT( const T1& a, const T2& b )
@@ -130,7 +130,7 @@ struct Comparison< T1, T2, VectorExpressionVariable, VectorExpressionVariable >
const auto view_a = a.getConstView();
const auto view_b = b.getConstView();
auto fetch = [=] __cuda_callable__ ( IndexType i ) -> bool { return view_a[ i ] < view_b[ i ]; };
- return Algorithms::Reduction< DeviceType >::reduce( ( IndexType ) 0, a.getSize(), std::logical_and<>{}, fetch, true );
+ return Algorithms::Reduction< DeviceType >::reduce( ( IndexType ) 0, a.getSize(), fetch, std::logical_and<>{}, true );
}
static bool LE( const T1& a, const T2& b )
@@ -145,7 +145,7 @@ struct Comparison< T1, T2, VectorExpressionVariable, VectorExpressionVariable >
const auto view_a = a.getConstView();
const auto view_b = b.getConstView();
auto fetch = [=] __cuda_callable__ ( IndexType i ) -> bool { return view_a[ i ] <= view_b[ i ]; };
- return Algorithms::Reduction< DeviceType >::reduce( ( IndexType ) 0, a.getSize(), std::logical_and<>{}, fetch, true );
+ return Algorithms::Reduction< DeviceType >::reduce( ( IndexType ) 0, a.getSize(), fetch, std::logical_and<>{}, true );
}
};
@@ -162,7 +162,7 @@ struct Comparison< T1, T2, ArithmeticVariable, VectorExpressionVariable >
const auto view_b = b.getConstView();
auto fetch = [=] __cuda_callable__ ( IndexType i ) -> bool { return a == view_b[ i ]; };
- return Algorithms::Reduction< DeviceType >::reduce( ( IndexType ) 0, b.getSize(), std::logical_and<>{}, fetch, true );
+ return Algorithms::Reduction< DeviceType >::reduce( ( IndexType ) 0, b.getSize(), fetch, std::logical_and<>{}, true );
}
static bool NE( const T1& a, const T2& b )
@@ -177,7 +177,7 @@ struct Comparison< T1, T2, ArithmeticVariable, VectorExpressionVariable >
const auto view_b = b.getConstView();
auto fetch = [=] __cuda_callable__ ( IndexType i ) -> bool { return a > view_b[ i ]; };
- return Algorithms::Reduction< DeviceType >::reduce( ( IndexType ) 0, b.getSize(), std::logical_and<>{}, fetch, true );
+ return Algorithms::Reduction< DeviceType >::reduce( ( IndexType ) 0, b.getSize(), fetch, std::logical_and<>{}, true );
}
static bool GE( const T1& a, const T2& b )
@@ -187,7 +187,7 @@ struct Comparison< T1, T2, ArithmeticVariable, VectorExpressionVariable >
const auto view_b = b.getConstView();
auto fetch = [=] __cuda_callable__ ( IndexType i ) -> bool { return a >= view_b[ i ]; };
- return Algorithms::Reduction< DeviceType >::reduce( ( IndexType ) 0, b.getSize(), std::logical_and<>{}, fetch, true );
+ return Algorithms::Reduction< DeviceType >::reduce( ( IndexType ) 0, b.getSize(), fetch, std::logical_and<>{}, true );
}
static bool LT( const T1& a, const T2& b )
@@ -197,7 +197,7 @@ struct Comparison< T1, T2, ArithmeticVariable, VectorExpressionVariable >
const auto view_b = b.getConstView();
auto fetch = [=] __cuda_callable__ ( IndexType i ) -> bool { return a < view_b[ i ]; };
- return Algorithms::Reduction< DeviceType >::reduce( ( IndexType ) 0, b.getSize(), std::logical_and<>{}, fetch, true );
+ return Algorithms::Reduction< DeviceType >::reduce( ( IndexType ) 0, b.getSize(), fetch, std::logical_and<>{}, true );
}
static bool LE( const T1& a, const T2& b )
@@ -207,7 +207,7 @@ struct Comparison< T1, T2, ArithmeticVariable, VectorExpressionVariable >
const auto view_b = b.getConstView();
auto fetch = [=] __cuda_callable__ ( IndexType i ) -> bool { return a <= view_b[ i ]; };
- return Algorithms::Reduction< DeviceType >::reduce( ( IndexType ) 0, b.getSize(), std::logical_and<>{}, fetch, true );
+ return Algorithms::Reduction< DeviceType >::reduce( ( IndexType ) 0, b.getSize(), fetch, std::logical_and<>{}, true );
}
};
@@ -224,7 +224,7 @@ struct Comparison< T1, T2, VectorExpressionVariable, ArithmeticVariable >
const auto view_a = a.getConstView();
auto fetch = [=] __cuda_callable__ ( IndexType i ) -> bool { return view_a[ i ] == b; };
- return Algorithms::Reduction< DeviceType >::reduce( ( IndexType ) 0, a.getSize(), std::logical_and<>{}, fetch, true );
+ return Algorithms::Reduction< DeviceType >::reduce( ( IndexType ) 0, a.getSize(), fetch, std::logical_and<>{}, true );
}
static bool NE( const T1& a, const T2& b )
@@ -239,7 +239,7 @@ struct Comparison< T1, T2, VectorExpressionVariable, ArithmeticVariable >
const auto view_a = a.getConstView();
auto fetch = [=] __cuda_callable__ ( IndexType i ) -> bool { return view_a[ i ] > b; };
- return Algorithms::Reduction< DeviceType >::reduce( ( IndexType ) 0, a.getSize(), std::logical_and<>{}, fetch, true );
+ return Algorithms::Reduction< DeviceType >::reduce( ( IndexType ) 0, a.getSize(), fetch, std::logical_and<>{}, true );
}
static bool GE( const T1& a, const T2& b )
@@ -249,7 +249,7 @@ struct Comparison< T1, T2, VectorExpressionVariable, ArithmeticVariable >
const auto view_a = a.getConstView();
auto fetch = [=] __cuda_callable__ ( IndexType i ) -> bool { return view_a[ i ] >= b; };
- return Algorithms::Reduction< DeviceType >::reduce( ( IndexType ) 0, a.getSize(), std::logical_and<>{}, fetch, true );
+ return Algorithms::Reduction< DeviceType >::reduce( ( IndexType ) 0, a.getSize(), fetch, std::logical_and<>{}, true );
}
static bool LT( const T1& a, const T2& b )
@@ -259,7 +259,7 @@ struct Comparison< T1, T2, VectorExpressionVariable, ArithmeticVariable >
const auto view_a = a.getConstView();
auto fetch = [=] __cuda_callable__ ( IndexType i ) -> bool { return view_a[ i ] < b; };
- return Algorithms::Reduction< DeviceType >::reduce( ( IndexType ) 0, a.getSize(), std::logical_and<>{}, fetch, true );
+ return Algorithms::Reduction< DeviceType >::reduce( ( IndexType ) 0, a.getSize(), fetch, std::logical_and<>{}, true );
}
static bool LE( const T1& a, const T2& b )
@@ -269,7 +269,7 @@ struct Comparison< T1, T2, VectorExpressionVariable, ArithmeticVariable >
const auto view_a = a.getConstView();
auto fetch = [=] __cuda_callable__ ( IndexType i ) -> bool { return view_a[ i ] <= b; };
- return Algorithms::Reduction< DeviceType >::reduce( ( IndexType ) 0, a.getSize(), std::logical_and<>{}, fetch, true );
+ return Algorithms::Reduction< DeviceType >::reduce( ( IndexType ) 0, a.getSize(), fetch, std::logical_and<>{}, true );
}
};
diff --git a/src/TNL/Containers/Expressions/DistributedExpressionTemplates.h b/src/TNL/Containers/Expressions/DistributedExpressionTemplates.h
index 5f67084fd8..6959a95fed 100644
--- a/src/TNL/Containers/Expressions/DistributedExpressionTemplates.h
+++ b/src/TNL/Containers/Expressions/DistributedExpressionTemplates.h
@@ -1073,7 +1073,7 @@ Result evaluateAndReduce( Vector& lhs,
RealType* lhs_data = lhs.getData();
auto fetch = [=] __cuda_callable__ ( IndexType i ) -> RealType { return ( lhs_data[ i ] = expression[ i ] ); };
- return Algorithms::Reduction< DeviceType >::reduce( lhs.getSize(), reduction, fetch, zero );
+ return Algorithms::Reduction< DeviceType >::reduce( lhs.getSize(), fetch, reduction, zero );
}
template< typename Vector,
@@ -1092,7 +1092,7 @@ Result evaluateAndReduce( Vector& lhs,
RealType* lhs_data = lhs.getData();
auto fetch = [=] __cuda_callable__ ( IndexType i ) -> RealType { return ( lhs_data[ i ] = expression[ i ] ); };
- return Algorithms::Reduction< DeviceType >::reduce( lhs.getSize(), reduction, fetch, zero );
+ return Algorithms::Reduction< DeviceType >::reduce( lhs.getSize(), fetch, reduction, zero );
}
////
@@ -1118,7 +1118,7 @@ Result addAndReduce( Vector& lhs,
lhs_data[ i ] += aux;
return aux;
};
- return Algorithms::Reduction< DeviceType >::reduce( lhs.getSize(), reduction, fetch, zero );
+ return Algorithms::Reduction< DeviceType >::reduce( lhs.getSize(), fetch, reduction, zero );
}
template< typename Vector,
@@ -1141,7 +1141,7 @@ Result addAndReduce( Vector& lhs,
lhs_data[ i ] += aux;
return aux;
};
- return Algorithms::Reduction< DeviceType >::reduce( lhs.getSize(), reduction, fetch, zero );
+ return Algorithms::Reduction< DeviceType >::reduce( lhs.getSize(), fetch, reduction, zero );
}
////
@@ -1167,7 +1167,7 @@ Result addAndReduceAbs( Vector& lhs,
lhs_data[ i ] += aux;
return TNL::abs( aux );
};
- return Algorithms::Reduction< DeviceType >::reduce( lhs.getSize(), reduction, fetch, zero );
+ return Algorithms::Reduction< DeviceType >::reduce( lhs.getSize(), fetch, reduction, zero );
}
template< typename Vector,
@@ -1190,7 +1190,7 @@ Result addAndReduceAbs( Vector& lhs,
lhs_data[ i ] += aux;
return TNL::abs( aux );
};
- return Algorithms::Reduction< DeviceType >::reduce( lhs.getSize(), reduction, fetch, zero );
+ return Algorithms::Reduction< DeviceType >::reduce( lhs.getSize(), fetch, reduction, zero );
}
} // namespace TNL
diff --git a/src/TNL/Containers/Expressions/ExpressionTemplates.h b/src/TNL/Containers/Expressions/ExpressionTemplates.h
index 7baf37572e..93d7e802d3 100644
--- a/src/TNL/Containers/Expressions/ExpressionTemplates.h
+++ b/src/TNL/Containers/Expressions/ExpressionTemplates.h
@@ -896,7 +896,7 @@ Result evaluateAndReduce( Vector& lhs,
RealType* lhs_data = lhs.getData();
auto fetch = [=] __cuda_callable__ ( IndexType i ) -> RealType { return ( lhs_data[ i ] = expression[ i ] ); };
- return Algorithms::Reduction< DeviceType >::reduce( ( IndexType ) 0, lhs.getSize(), reduction, fetch, zero );
+ return Algorithms::Reduction< DeviceType >::reduce( ( IndexType ) 0, lhs.getSize(), fetch, reduction, zero );
}
template< typename Vector,
@@ -915,7 +915,7 @@ Result evaluateAndReduce( Vector& lhs,
RealType* lhs_data = lhs.getData();
auto fetch = [=] __cuda_callable__ ( IndexType i ) -> RealType { return ( lhs_data[ i ] = expression[ i ] ); };
- return Algorithms::Reduction< DeviceType >::reduce( ( IndexType ) 0, lhs.getSize(), reduction, fetch, zero );
+ return Algorithms::Reduction< DeviceType >::reduce( ( IndexType ) 0, lhs.getSize(), fetch, reduction, zero );
}
////
@@ -941,7 +941,7 @@ Result addAndReduce( Vector& lhs,
lhs_data[ i ] += aux;
return aux;
};
- return Algorithms::Reduction< DeviceType >::reduce( ( IndexType ) 0, lhs.getSize(), reduction, fetch, zero );
+ return Algorithms::Reduction< DeviceType >::reduce( ( IndexType ) 0, lhs.getSize(), fetch, reduction, zero );
}
template< typename Vector,
@@ -964,7 +964,7 @@ Result addAndReduce( Vector& lhs,
lhs_data[ i ] += aux;
return aux;
};
- return Algorithms::Reduction< DeviceType >::reduce( ( IndexType ) 0, lhs.getSize(), reduction, fetch, zero );
+ return Algorithms::Reduction< DeviceType >::reduce( ( IndexType ) 0, lhs.getSize(), fetch, reduction, zero );
}
////
@@ -990,7 +990,7 @@ Result addAndReduceAbs( Vector& lhs,
lhs_data[ i ] += aux;
return TNL::abs( aux );
};
- return Algorithms::Reduction< DeviceType >::reduce( ( IndexType ) 0, lhs.getSize(), reduction, fetch, zero );
+ return Algorithms::Reduction< DeviceType >::reduce( ( IndexType ) 0, lhs.getSize(), fetch, reduction, zero );
}
template< typename Vector,
@@ -1013,7 +1013,7 @@ Result addAndReduceAbs( Vector& lhs,
lhs_data[ i ] += aux;
return TNL::abs( aux );
};
- return Algorithms::Reduction< DeviceType >::reduce( ( IndexType ) 0, lhs.getSize(), reduction, fetch, zero );
+ return Algorithms::Reduction< DeviceType >::reduce( ( IndexType ) 0, lhs.getSize(), fetch, reduction, zero );
}
} // namespace TNL
diff --git a/src/TNL/Containers/Expressions/VerticalOperations.h b/src/TNL/Containers/Expressions/VerticalOperations.h
index 8de97f06cb..6e5f5624b2 100644
--- a/src/TNL/Containers/Expressions/VerticalOperations.h
+++ b/src/TNL/Containers/Expressions/VerticalOperations.h
@@ -43,7 +43,7 @@ auto ExpressionMin( const Expression& expression )
};
static_assert( std::numeric_limits< ResultType >::is_specialized,
"std::numeric_limits is not specialized for the reduction's result type" );
- return Algorithms::Reduction< typename Expression::DeviceType >::reduce( ( IndexType ) 0, expression.getSize(), reduction, fetch, std::numeric_limits< ResultType >::max() );
+ return Algorithms::Reduction< typename Expression::DeviceType >::reduce( ( IndexType ) 0, expression.getSize(), fetch, reduction, std::numeric_limits< ResultType >::max() );
}
template< typename Expression >
@@ -65,7 +65,7 @@ auto ExpressionArgMin( const Expression& expression )
};
static_assert( std::numeric_limits< ResultType >::is_specialized,
"std::numeric_limits is not specialized for the reduction's result type" );
- return Algorithms::Reduction< typename Expression::DeviceType >::reduceWithArgument( ( IndexType ) 0, expression.getSize(), reduction, fetch, std::numeric_limits< ResultType >::max() );
+ return Algorithms::Reduction< typename Expression::DeviceType >::reduceWithArgument( ( IndexType ) 0, expression.getSize(), fetch, reduction, std::numeric_limits< ResultType >::max() );
}
template< typename Expression >
@@ -85,7 +85,7 @@ auto ExpressionMax( const Expression& expression )
};
static_assert( std::numeric_limits< ResultType >::is_specialized,
"std::numeric_limits is not specialized for the reduction's result type" );
- return Algorithms::Reduction< typename Expression::DeviceType >::reduce( ( IndexType ) 0, expression.getSize(), reduction, fetch, std::numeric_limits< ResultType >::lowest() );
+ return Algorithms::Reduction< typename Expression::DeviceType >::reduce( ( IndexType ) 0, expression.getSize(), fetch, reduction, std::numeric_limits< ResultType >::lowest() );
}
template< typename Expression >
@@ -107,7 +107,7 @@ auto ExpressionArgMax( const Expression& expression )
};
static_assert( std::numeric_limits< ResultType >::is_specialized,
"std::numeric_limits is not specialized for the reduction's result type" );
- return Algorithms::Reduction< typename Expression::DeviceType >::reduceWithArgument( ( IndexType ) 0, expression.getSize(), reduction, fetch, std::numeric_limits< ResultType >::lowest() );
+ return Algorithms::Reduction< typename Expression::DeviceType >::reduceWithArgument( ( IndexType ) 0, expression.getSize(), fetch, reduction, std::numeric_limits< ResultType >::lowest() );
}
template< typename Expression >
@@ -119,7 +119,7 @@ auto ExpressionSum( const Expression& expression )
const auto view = expression.getConstView();
auto fetch = [=] __cuda_callable__ ( IndexType i ) { return view[ i ]; };
- return Algorithms::Reduction< typename Expression::DeviceType >::reduce( ( IndexType ) 0, expression.getSize(), std::plus<>{}, fetch, (ResultType) 0 );
+ return Algorithms::Reduction< typename Expression::DeviceType >::reduce( ( IndexType ) 0, expression.getSize(), fetch, std::plus<>{}, (ResultType) 0 );
}
template< typename Expression >
@@ -131,7 +131,7 @@ auto ExpressionProduct( const Expression& expression )
const auto view = expression.getConstView();
auto fetch = [=] __cuda_callable__ ( IndexType i ) { return view[ i ]; };
- return Algorithms::Reduction< typename Expression::DeviceType >::reduce( ( IndexType ) 0, expression.getSize(), std::multiplies<>{}, fetch, (ResultType) 1 );
+ return Algorithms::Reduction< typename Expression::DeviceType >::reduce( ( IndexType ) 0, expression.getSize(), fetch, std::multiplies<>{}, (ResultType) 1 );
}
template< typename Expression >
@@ -145,7 +145,7 @@ auto ExpressionLogicalAnd( const Expression& expression )
auto fetch = [=] __cuda_callable__ ( IndexType i ) { return view[ i ]; };
static_assert( std::numeric_limits< ResultType >::is_specialized,
"std::numeric_limits is not specialized for the reduction's result type" );
- return Algorithms::Reduction< typename Expression::DeviceType >::reduce( ( IndexType ) 0, expression.getSize(), std::logical_and<>{}, fetch, std::numeric_limits< ResultType >::max() );
+ return Algorithms::Reduction< typename Expression::DeviceType >::reduce( ( IndexType ) 0, expression.getSize(), fetch, std::logical_and<>{}, std::numeric_limits< ResultType >::max() );
}
template< typename Expression >
@@ -157,7 +157,7 @@ auto ExpressionLogicalOr( const Expression& expression )
const auto view = expression.getConstView();
auto fetch = [=] __cuda_callable__ ( IndexType i ) { return view[ i ]; };
- return Algorithms::Reduction< typename Expression::DeviceType >::reduce( ( IndexType ) 0, expression.getSize(), std::logical_or<>{}, fetch, (ResultType) 0 );
+ return Algorithms::Reduction< typename Expression::DeviceType >::reduce( ( IndexType ) 0, expression.getSize(), fetch, std::logical_or<>{}, (ResultType) 0 );
}
template< typename Expression >
@@ -171,7 +171,7 @@ auto ExpressionBinaryAnd( const Expression& expression )
auto fetch = [=] __cuda_callable__ ( IndexType i ) { return view[ i ]; };
static_assert( std::numeric_limits< ResultType >::is_specialized,
"std::numeric_limits is not specialized for the reduction's result type" );
- return Algorithms::Reduction< typename Expression::DeviceType >::reduce( ( IndexType ) 0, expression.getSize(), std::bit_and<>{}, fetch, std::numeric_limits< ResultType >::max() );
+ return Algorithms::Reduction< typename Expression::DeviceType >::reduce( ( IndexType ) 0, expression.getSize(), fetch, std::bit_and<>{}, std::numeric_limits< ResultType >::max() );
}
template< typename Expression >
@@ -183,7 +183,7 @@ auto ExpressionBinaryOr( const Expression& expression )
const auto view = expression.getConstView();
auto fetch = [=] __cuda_callable__ ( IndexType i ) { return view[ i ]; };
- return Algorithms::Reduction< typename Expression::DeviceType >::reduce( ( IndexType ) 0, expression.getSize(), std::bit_or<>{}, fetch, (ResultType) 0 );
+ return Algorithms::Reduction< typename Expression::DeviceType >::reduce( ( IndexType ) 0, expression.getSize(), fetch, std::bit_or<>{}, (ResultType) 0 );
}
} // namespace Expressions
diff --git a/src/TNL/Matrices/DenseMatrixView.hpp b/src/TNL/Matrices/DenseMatrixView.hpp
index f2532a47bb..97e82af0e3 100644
--- a/src/TNL/Matrices/DenseMatrixView.hpp
+++ b/src/TNL/Matrices/DenseMatrixView.hpp
@@ -156,7 +156,7 @@ getNonzeroElementsCount() const
auto fetch = [=] __cuda_callable__ ( const IndexType i ) -> IndexType {
return ( values_view[ i ] != 0.0 );
};
- return Algorithms::Reduction< DeviceType >::reduce( ( IndexType ) 0, this->values.getSize(), std::plus<>{}, fetch, 0 );
+ return Algorithms::Reduction< DeviceType >::reduce( ( IndexType ) 0, this->values.getSize(), fetch, std::plus<>{}, 0 );
}
template< typename Real,
diff --git a/src/TNL/Matrices/Matrix.hpp b/src/TNL/Matrices/Matrix.hpp
index 5122879350..57c79cd769 100644
--- a/src/TNL/Matrices/Matrix.hpp
+++ b/src/TNL/Matrices/Matrix.hpp
@@ -85,7 +85,7 @@ Index Matrix< Real, Device, Index, RealAllocator >::getNonzeroElementsCount() co
auto fetch = [=] __cuda_callable__ ( const IndexType i ) -> IndexType {
return ( values_view[ i ] != 0.0 );
};
- return Algorithms::Reduction< DeviceType >::reduce( 0, this->values.getSize(), std::plus<>{}, fetch, 0 );
+ return Algorithms::Reduction< DeviceType >::reduce( 0, this->values.getSize(), fetch, std::plus<>{}, 0 );
}
template< typename Real,
diff --git a/src/TNL/Matrices/MatrixView.hpp b/src/TNL/Matrices/MatrixView.hpp
index 8c20d07d1a..83563a8257 100644
--- a/src/TNL/Matrices/MatrixView.hpp
+++ b/src/TNL/Matrices/MatrixView.hpp
@@ -63,7 +63,7 @@ getNonzeroElementsCount() const
auto fetch = [=] __cuda_callable__ ( const IndexType i ) -> IndexType {
return ( values_view[ i ] != 0.0 );
};
- return Algorithms::Reduction< DeviceType >::reduce( ( IndexType ) 0, this->values.getSize(), std::plus<>{}, fetch, 0 );
+ return Algorithms::Reduction< DeviceType >::reduce( ( IndexType ) 0, this->values.getSize(), fetch, std::plus<>{}, 0 );
}
template< typename Real,
diff --git a/src/TNL/Matrices/MultidiagonalMatrixView.hpp b/src/TNL/Matrices/MultidiagonalMatrixView.hpp
index 844e1721f1..44c43da7f9 100644
--- a/src/TNL/Matrices/MultidiagonalMatrixView.hpp
+++ b/src/TNL/Matrices/MultidiagonalMatrixView.hpp
@@ -173,7 +173,7 @@ getNonzeroElementsCount() const
auto fetch = [=] __cuda_callable__ ( const IndexType i ) -> IndexType {
return ( values_view[ i ] != 0.0 );
};
- return Algorithms::Reduction< DeviceType >::reduce( ( IndexType ) 0, this->values.getSize(), std::plus<>{}, fetch, 0 );
+ return Algorithms::Reduction< DeviceType >::reduce( ( IndexType ) 0, this->values.getSize(), fetch, std::plus<>{}, 0 );
}
template< typename Real,
diff --git a/src/TNL/Matrices/SparseMatrixView.hpp b/src/TNL/Matrices/SparseMatrixView.hpp
index c26b3ee05f..e7842a50a5 100644
--- a/src/TNL/Matrices/SparseMatrixView.hpp
+++ b/src/TNL/Matrices/SparseMatrixView.hpp
@@ -191,7 +191,7 @@ getNonzeroElementsCount() const
auto fetch = [=] __cuda_callable__ ( const IndexType i ) -> IndexType {
return ( columns_view[ i ] != paddingIndex );
};
- return Algorithms::Reduction< DeviceType >::reduce( ( IndexType ) 0, this->columnIndexes.getSize(), std::plus<>{}, fetch, 0 );
+ return Algorithms::Reduction< DeviceType >::reduce( ( IndexType ) 0, this->columnIndexes.getSize(), fetch, std::plus<>{}, 0 );
}
else
{
@@ -799,7 +799,7 @@ operator==( const Matrix& m ) const
{
return view1.getRow( i ) == view2.getRow( i );
};
- return Algorithms::Reduction< DeviceType >::reduce( 0, this->getRows(), std::logical_and<>{}, fetch, true );
+ return Algorithms::Reduction< DeviceType >::reduce( 0, this->getRows(), fetch, std::logical_and<>{}, true );
}
template< typename Real,
diff --git a/src/TNL/Matrices/TridiagonalMatrixView.hpp b/src/TNL/Matrices/TridiagonalMatrixView.hpp
index c0b6547fba..c125ffe222 100644
--- a/src/TNL/Matrices/TridiagonalMatrixView.hpp
+++ b/src/TNL/Matrices/TridiagonalMatrixView.hpp
@@ -133,7 +133,7 @@ getNonzeroElementsCount() const
auto fetch = [=] __cuda_callable__ ( const IndexType i ) -> IndexType {
return ( values_view[ i ] != 0.0 );
};
- return Algorithms::Reduction< DeviceType >::reduce( ( IndexType ) 0, this->values.getSize(), std::plus<>{}, fetch, 0 );
+ return Algorithms::Reduction< DeviceType >::reduce( ( IndexType ) 0, this->values.getSize(), fetch, std::plus<>{}, 0 );
}
template< typename Real,
diff --git a/src/TNL/Meshes/MeshDetails/layers/EntityTags/Layer.h b/src/TNL/Meshes/MeshDetails/layers/EntityTags/Layer.h
index dab80fc7e3..dc0c767b80 100644
--- a/src/TNL/Meshes/MeshDetails/layers/EntityTags/Layer.h
+++ b/src/TNL/Meshes/MeshDetails/layers/EntityTags/Layer.h
@@ -139,8 +139,8 @@ public:
{
return bool(tags_view[ entityIndex ] & EntityTags::GhostEntity);
};
- const GlobalIndexType boundaryEntities = Algorithms::Reduction< Device >::reduce( (GlobalIndexType) 0, tags.getSize(), std::plus<>{}, is_boundary, (GlobalIndexType) 0 );
- const GlobalIndexType ghostEntities = Algorithms::Reduction< Device >::reduce( (GlobalIndexType) 0, tags.getSize(), std::plus<>{}, is_ghost, (GlobalIndexType) 0 );
+ const GlobalIndexType boundaryEntities = Algorithms::Reduction< Device >::reduce( (GlobalIndexType) 0, tags.getSize(), is_boundary, std::plus<>{}, (GlobalIndexType) 0 );
+ const GlobalIndexType ghostEntities = Algorithms::Reduction< Device >::reduce( (GlobalIndexType) 0, tags.getSize(), is_ghost, std::plus<>{}, (GlobalIndexType) 0 );
interiorIndices.setSize( tags.getSize() - boundaryEntities );
boundaryIndices.setSize( boundaryEntities );
diff --git a/src/UnitTests/Matrices/DenseMatrixTest.h b/src/UnitTests/Matrices/DenseMatrixTest.h
index 3ac1f38ff1..9cd7c3db05 100644
--- a/src/UnitTests/Matrices/DenseMatrixTest.h
+++ b/src/UnitTests/Matrices/DenseMatrixTest.h
@@ -448,7 +448,7 @@ void test_SetElement()
auto fetch = [=] __cuda_callable__ ( IndexType i ) -> bool {
return ( v_view[ i ] == m_view.getElement( i, i ) );
};
- EXPECT_TRUE( TNL::Algorithms::Reduction< DeviceType >::reduce( ( IndexType ) 0, m.getRows(), std::logical_and<>{}, fetch, true ) );
+ EXPECT_TRUE( TNL::Algorithms::Reduction< DeviceType >::reduce( ( IndexType ) 0, m.getRows(), fetch, std::logical_and<>{}, true ) );
}
--
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