diff --git a/Eigen/src/Core/DenseBase.h b/Eigen/src/Core/DenseBase.h
index 58f682dce..c603b5a6d 100644
--- a/Eigen/src/Core/DenseBase.h
+++ b/Eigen/src/Core/DenseBase.h
@@ -517,7 +517,9 @@ template<typename Derived> class DenseBase
     template<int p> RealScalar lpNorm() const;
 
     template<int RowFactor, int ColFactor>
+    EIGEN_DEVICE_FUNC
     const Replicate<Derived,RowFactor,ColFactor> replicate() const;
+    EIGEN_DEVICE_FUNC
     const Replicate<Derived,Dynamic,Dynamic> replicate(Index rowFacor,Index colFactor) const;
 
     typedef Reverse<Derived, BothDirections> ReverseReturnType;
diff --git a/Eigen/src/Core/Replicate.h b/Eigen/src/Core/Replicate.h
index d105de62b..518c52e57 100644
--- a/Eigen/src/Core/Replicate.h
+++ b/Eigen/src/Core/Replicate.h
@@ -69,6 +69,7 @@ template<typename MatrixType,int RowFactor,int ColFactor> class Replicate
     typedef typename internal::remove_all<MatrixType>::type NestedExpression;
 
     template<typename OriginalMatrixType>
+    EIGEN_DEVICE_FUNC
     inline explicit Replicate(const OriginalMatrixType& matrix)
       : 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>
+    EIGEN_DEVICE_FUNC
     inline Replicate(const OriginalMatrixType& matrix, Index rowFactor, Index 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)
     }
 
+    EIGEN_DEVICE_FUNC
     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(); }
 
+    EIGEN_DEVICE_FUNC
     const _MatrixTypeNested& nestedExpression() const
     { 
       return m_matrix; 
diff --git a/Eigen/src/Core/VectorwiseOp.h b/Eigen/src/Core/VectorwiseOp.h
index 5ebcdc100..0cc5eff16 100644
--- a/Eigen/src/Core/VectorwiseOp.h
+++ b/Eigen/src/Core/VectorwiseOp.h
@@ -461,6 +461,7 @@ template<typename ExpressionType, int Direction> class VectorwiseOp
       */
     // NOTE implemented here because of sunstudio's compilation errors
     template<int Factor> const Replicate<ExpressionType,(IsVertical?Factor:1),(IsHorizontal?Factor:1)>
+    EIGEN_DEVICE_FUNC
     replicate(Index factor = Factor) const
     {
       return Replicate<ExpressionType,Direction==Vertical?Factor:1,Direction==Horizontal?Factor:1>
diff --git a/test/cuda_basic.cu b/test/cuda_basic.cu
index 6ec9446c6..571a52299 100644
--- a/test/cuda_basic.cu
+++ b/test/cuda_basic.cu
@@ -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>
 struct redux {
   EIGEN_DEVICE_FUNC
@@ -117,7 +134,7 @@ void test_cuda_basic()
   Eigen::VectorXf in, out;
   
   #ifndef __CUDA_ARCH__
-  int data_size = nthreads * 16;
+  int data_size = nthreads * 512;
   in.setRandom(data_size);
   out.setRandom(data_size);
   #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<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<Matrix3f>(), nthreads, in, out) );