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Add support for replicate in CUDA

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This commit is contained in:
Gael Guennebaud 2015-07-20 10:53:03 +02:00
parent 6799c26cd6
commit 88e352adac
4 changed files with 29 additions and 1 deletions
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2
Eigen/src/Core/DenseBase.h
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@ -517,7 +517,9 @@ template<typename Derived> class DenseBase
template<int p> RealScalar lpNorm() const; template<int p> RealScalar lpNorm() const;
template<int RowFactor, int ColFactor> template<int RowFactor, int ColFactor>
EIGEN_DEVICE_FUNC
const Replicate<Derived,RowFactor,ColFactor> replicate() const; const Replicate<Derived,RowFactor,ColFactor> replicate() const;
EIGEN_DEVICE_FUNC
const Replicate<Derived,Dynamic,Dynamic> replicate(Index rowFacor,Index colFactor) const; const Replicate<Derived,Dynamic,Dynamic> replicate(Index rowFacor,Index colFactor) const;
typedef Reverse<Derived, BothDirections> ReverseReturnType; typedef Reverse<Derived, BothDirections> ReverseReturnType;

5
Eigen/src/Core/Replicate.h
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@ -69,6 +69,7 @@ template<typename MatrixType,int RowFactor,int ColFactor> class Replicate
typedef typename internal::remove_all<MatrixType>::type NestedExpression; typedef typename internal::remove_all<MatrixType>::type NestedExpression;
template<typename OriginalMatrixType> template<typename OriginalMatrixType>
EIGEN_DEVICE_FUNC
inline explicit Replicate(const OriginalMatrixType& matrix) inline explicit Replicate(const OriginalMatrixType& matrix)
: m_matrix(matrix), m_rowFactor(RowFactor), m_colFactor(ColFactor) : m_matrix(matrix), m_rowFactor(RowFactor), m_colFactor(ColFactor)
{ {
@ -78,6 +79,7 @@ template<typename MatrixType,int RowFactor,int ColFactor> class Replicate
} }
template<typename OriginalMatrixType> template<typename OriginalMatrixType>
EIGEN_DEVICE_FUNC
inline Replicate(const OriginalMatrixType& matrix, Index rowFactor, Index colFactor) inline Replicate(const OriginalMatrixType& matrix, Index rowFactor, Index colFactor)
: m_matrix(matrix), m_rowFactor(rowFactor), m_colFactor(colFactor) : m_matrix(matrix), m_rowFactor(rowFactor), m_colFactor(colFactor)
{ {
@ -85,9 +87,12 @@ template<typename MatrixType,int RowFactor,int ColFactor> class Replicate
THE_MATRIX_OR_EXPRESSION_THAT_YOU_PASSED_DOES_NOT_HAVE_THE_EXPECTED_TYPE) THE_MATRIX_OR_EXPRESSION_THAT_YOU_PASSED_DOES_NOT_HAVE_THE_EXPECTED_TYPE)
} }
EIGEN_DEVICE_FUNC
inline Index rows() const { return m_matrix.rows() * m_rowFactor.value(); } inline Index rows() const { return m_matrix.rows() * m_rowFactor.value(); }
EIGEN_DEVICE_FUNC
inline Index cols() const { return m_matrix.cols() * m_colFactor.value(); } inline Index cols() const { return m_matrix.cols() * m_colFactor.value(); }
EIGEN_DEVICE_FUNC
const _MatrixTypeNested& nestedExpression() const const _MatrixTypeNested& nestedExpression() const
{ {
return m_matrix; return m_matrix;

1
Eigen/src/Core/VectorwiseOp.h
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@ -461,6 +461,7 @@ template<typename ExpressionType, int Direction> class VectorwiseOp
*/ */
// NOTE implemented here because of sunstudio's compilation errors // NOTE implemented here because of sunstudio's compilation errors
template<int Factor> const Replicate<ExpressionType,(IsVertical?Factor:1),(IsHorizontal?Factor:1)> template<int Factor> const Replicate<ExpressionType,(IsVertical?Factor:1),(IsHorizontal?Factor:1)>
EIGEN_DEVICE_FUNC
replicate(Index factor = Factor) const replicate(Index factor = Factor) const
{ {
return Replicate<ExpressionType,Direction==Vertical?Factor:1,Direction==Horizontal?Factor:1> return Replicate<ExpressionType,Direction==Vertical?Factor:1,Direction==Horizontal?Factor:1>

22
test/cuda_basic.cu
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@ -47,6 +47,23 @@ struct coeff_wise {
} }
}; };
template<typename T>
struct replicate {
EIGEN_DEVICE_FUNC
void operator()(int i, const typename T::Scalar* in, typename T::Scalar* out) const
{
using namespace Eigen;
T x1(in+i);
int step = x1.size() * 4;
int stride = 3 * step;
typedef Map<Array<typename T::Scalar,Dynamic,Dynamic> > MapType;
MapType(out+i*stride+0*step, x1.rows()*2, x1.cols()*2) = x1.replicate(2,2);
MapType(out+i*stride+1*step, x1.rows()*3, x1.cols()) = in[i] * x1.colwise().replicate(3);
MapType(out+i*stride+2*step, x1.rows(), x1.cols()*3) = in[i] * x1.rowwise().replicate(3);
}
};
template<typename T> template<typename T>
struct redux { struct redux {
EIGEN_DEVICE_FUNC EIGEN_DEVICE_FUNC
@ -117,7 +134,7 @@ void test_cuda_basic()
Eigen::VectorXf in, out; Eigen::VectorXf in, out;
#ifndef __CUDA_ARCH__ #ifndef __CUDA_ARCH__
int data_size = nthreads * 16; int data_size = nthreads * 512;
in.setRandom(data_size); in.setRandom(data_size);
out.setRandom(data_size); out.setRandom(data_size);
#endif #endif
@ -125,6 +142,9 @@ void test_cuda_basic()
CALL_SUBTEST( run_and_compare_to_cuda(coeff_wise<Vector3f>(), nthreads, in, out) ); CALL_SUBTEST( run_and_compare_to_cuda(coeff_wise<Vector3f>(), nthreads, in, out) );
CALL_SUBTEST( run_and_compare_to_cuda(coeff_wise<Array44f>(), nthreads, in, out) ); CALL_SUBTEST( run_and_compare_to_cuda(coeff_wise<Array44f>(), nthreads, in, out) );
CALL_SUBTEST( run_and_compare_to_cuda(replicate<Array4f>(), nthreads, in, out) );
CALL_SUBTEST( run_and_compare_to_cuda(replicate<Array33f>(), nthreads, in, out) );
CALL_SUBTEST( run_and_compare_to_cuda(redux<Array4f>(), nthreads, in, out) ); CALL_SUBTEST( run_and_compare_to_cuda(redux<Array4f>(), nthreads, in, out) );
CALL_SUBTEST( run_and_compare_to_cuda(redux<Matrix3f>(), nthreads, in, out) ); CALL_SUBTEST( run_and_compare_to_cuda(redux<Matrix3f>(), nthreads, in, out) );