void run_tutorial()
{
typedef mtl::dense2D<ScalarType> MTL4DenseMatrix;
typedef mtl::compressed2D<ScalarType> MTL4SparseMatrix;
/**
* Create and fill dense matrices from the MTL4 library:
**/
mtl::dense2D<ScalarType> mtl4_densemat(5, 5);
mtl::dense2D<ScalarType> mtl4_densemat2(5, 5);
mtl4_densemat(0,0) = 2.0; mtl4_densemat(0,1) = -1.0;
mtl4_densemat(1,0) = -1.0; mtl4_densemat(1,1) = 2.0; mtl4_densemat(1,2) = -1.0;
mtl4_densemat(2,1) = -1.0; mtl4_densemat(2,2) = -1.0; mtl4_densemat(2,3) = -1.0;
mtl4_densemat(3,2) = -1.0; mtl4_densemat(3,3) = 2.0; mtl4_densemat(3,4) = -1.0;
mtl4_densemat(4,4) = -1.0; mtl4_densemat(4,4) = -1.0;
/**
* Create and fill sparse matrices from the MTL4 library:
**/
MTL4SparseMatrix mtl4_sparsemat;
set_to_zero(mtl4_sparsemat);
mtl4_sparsemat.change_dim(5, 5);
MTL4SparseMatrix mtl4_sparsemat2;
set_to_zero(mtl4_sparsemat2);
mtl4_sparsemat2.change_dim(5, 5);
{
mtl::matrix::inserter< MTL4SparseMatrix > ins(mtl4_sparsemat);
typedef typename mtl::Collection<MTL4SparseMatrix>::value_type ValueType;
ins(0,0) << ValueType(2.0); ins(0,1) << ValueType(-1.0);
ins(1,1) << ValueType(2.0); ins(1,2) << ValueType(-1.0);
ins(2,2) << ValueType(-1.0); ins(2,3) << ValueType(-1.0);
ins(3,3) << ValueType(2.0); ins(3,4) << ValueType(-1.0);
ins(4,4) << ValueType(-1.0);
}
/**
* Create and fill a few vectors from the MTL4 library:
**/
mtl::dense_vector<ScalarType> mtl4_rhs(5, 0.0);
mtl::dense_vector<ScalarType> mtl4_result(5, 0.0);
mtl::dense_vector<ScalarType> mtl4_temp(5, 0.0);
mtl4_rhs(0) = 10.0;
mtl4_rhs(1) = 11.0;
mtl4_rhs(2) = 12.0;
mtl4_rhs(3) = 13.0;
mtl4_rhs(4) = 14.0;
/**
* Create the corresponding ViennaCL objects:
**/
viennacl::vector<ScalarType> vcl_rhs(5);
viennacl::vector<ScalarType> vcl_result(5);
viennacl::matrix<ScalarType> vcl_densemat(5, 5);
viennacl::compressed_matrix<ScalarType> vcl_sparsemat(5, 5);
/**
* Directly copy the MTL4 objects to ViennaCL objects
**/
viennacl::copy(&(mtl4_rhs[0]), &(mtl4_rhs[0]) + 5, vcl_rhs.begin()); //method 1: via iterator interface (cf. std::copy())
viennacl::copy(mtl4_rhs, vcl_rhs); //method 2: via built-in wrappers (convenience layer)
viennacl::copy(mtl4_densemat, vcl_densemat);
viennacl::copy(mtl4_sparsemat, vcl_sparsemat);
// For completeness: Copy matrices from ViennaCL back to Eigen:
viennacl::copy(vcl_densemat, mtl4_densemat2);
viennacl::copy(vcl_sparsemat, mtl4_sparsemat2);
/**
* Run dense matrix-vector products and compare results:
**/
mtl4_result = mtl4_densemat * mtl4_rhs;
vcl_result = viennacl::linalg::prod(vcl_densemat, vcl_rhs);
viennacl::copy(vcl_result, mtl4_temp);
mtl4_result -= mtl4_temp;
std::cout << "Difference for dense matrix-vector product: " << mtl::two_norm(mtl4_result) << std::endl;
mtl4_result = mtl4_densemat2 * mtl4_rhs - mtl4_temp;
std::cout << "Difference for dense matrix-vector product (MTL4->ViennaCL->MTL4): "
<< mtl::two_norm(mtl4_result) << std::endl;
/**
* Run sparse matrix-vector products and compare results:
**/
mtl4_result = mtl4_sparsemat * mtl4_rhs;
vcl_result = viennacl::linalg::prod(vcl_sparsemat, vcl_rhs);
viennacl::copy(vcl_result, mtl4_temp);
mtl4_result -= mtl4_temp;
std::cout << "Difference for sparse matrix-vector product: " << mtl::two_norm(mtl4_result) << std::endl;
mtl4_result = mtl4_sparsemat2 * mtl4_rhs - mtl4_temp;
std::cout << "Difference for sparse matrix-vector product (MTL4->ViennaCL->MTL4): "
<< mtl::two_norm(mtl4_result) << std::endl;
}
void run_tutorial()
{
/**
* Get Eigen matrix and vector types for the provided ScalarType.
* Involves a little bit of template-metaprogramming.
**/
typedef typename Eigen_dense_matrix<ScalarType>::type EigenMatrix;
typedef typename Eigen_vector<ScalarType>::type EigenVector;
/**
* Create and fill dense matrices from the Eigen library:
**/
EigenMatrix eigen_densemat(6, 5);
EigenMatrix eigen_densemat2(6, 5);
eigen_densemat(0,0) = 2.0; eigen_densemat(0,1) = -1.0;
eigen_densemat(1,0) = -1.0; eigen_densemat(1,1) = 2.0; eigen_densemat(1,2) = -1.0;
eigen_densemat(2,1) = -1.0; eigen_densemat(2,2) = -1.0; eigen_densemat(2,3) = -1.0;
eigen_densemat(3,2) = -1.0; eigen_densemat(3,3) = 2.0; eigen_densemat(3,4) = -1.0;
eigen_densemat(5,4) = -1.0; eigen_densemat(4,4) = -1.0;
Eigen::Map<EigenMatrix> eigen_densemat_map(eigen_densemat.data(), 6, 5); // same as eigen_densemat, but emulating user-provided buffer
/**
* Create and fill sparse matrices from the Eigen library:
**/
Eigen::SparseMatrix<ScalarType, Eigen::RowMajor> eigen_sparsemat(6, 5);
Eigen::SparseMatrix<ScalarType, Eigen::RowMajor> eigen_sparsemat2(6, 5);
eigen_sparsemat.reserve(5*2);
eigen_sparsemat.insert(0,0) = 2.0; eigen_sparsemat.insert(0,1) = -1.0;
eigen_sparsemat.insert(1,1) = 2.0; eigen_sparsemat.insert(1,2) = -1.0;
eigen_sparsemat.insert(2,2) = -1.0; eigen_sparsemat.insert(2,3) = -1.0;
eigen_sparsemat.insert(3,3) = 2.0; eigen_sparsemat.insert(3,4) = -1.0;
eigen_sparsemat.insert(5,4) = -1.0;
//eigen_sparsemat.endFill();
/**
* Create and fill a few vectors from the Eigen library:
**/
EigenVector eigen_rhs(5);
Eigen::Map<EigenVector> eigen_rhs_map(eigen_rhs.data(), 5);
EigenVector eigen_result(6);
EigenVector eigen_temp(6);
eigen_rhs(0) = 10.0;
eigen_rhs(1) = 11.0;
eigen_rhs(2) = 12.0;
eigen_rhs(3) = 13.0;
eigen_rhs(4) = 14.0;
/**
* Create the corresponding ViennaCL objects:
**/
viennacl::vector<ScalarType> vcl_rhs(5);
viennacl::vector<ScalarType> vcl_result(6);
viennacl::matrix<ScalarType> vcl_densemat(6, 5);
viennacl::compressed_matrix<ScalarType> vcl_sparsemat(6, 5);
/**
* Directly copy the Eigen objects to ViennaCL objects
**/
viennacl::copy(&(eigen_rhs[0]), &(eigen_rhs[0]) + 5, vcl_rhs.begin()); // Method 1: via iterator interface (cf. std::copy())
viennacl::copy(eigen_rhs, vcl_rhs); // Method 2: via built-in wrappers (convenience layer)
viennacl::copy(eigen_rhs_map, vcl_rhs); // Same as method 2, but for a mapped vector
viennacl::copy(eigen_densemat, vcl_densemat);
viennacl::copy(eigen_densemat_map, vcl_densemat); //same as above, using mapped matrix
viennacl::copy(eigen_sparsemat, vcl_sparsemat);
std::cout << "VCL sparsematrix dimensions: " << vcl_sparsemat.size1() << ", " << vcl_sparsemat.size2() << std::endl;
// For completeness: Copy matrices from ViennaCL back to Eigen:
viennacl::copy(vcl_densemat, eigen_densemat2);
viennacl::copy(vcl_sparsemat, eigen_sparsemat2);
/**
* Run dense matrix-vector products and compare results:
**/
eigen_result = eigen_densemat * eigen_rhs;
vcl_result = viennacl::linalg::prod(vcl_densemat, vcl_rhs);
viennacl::copy(vcl_result, eigen_temp);
std::cout << "Difference for dense matrix-vector product: " << (eigen_result - eigen_temp).norm() << std::endl;
std::cout << "Difference for dense matrix-vector product (Eigen->ViennaCL->Eigen): "
<< (eigen_densemat2 * eigen_rhs - eigen_temp).norm() << std::endl;
/**
* Run sparse matrix-vector products and compare results:
**/
eigen_result = eigen_sparsemat * eigen_rhs;
vcl_result = viennacl::linalg::prod(vcl_sparsemat, vcl_rhs);
viennacl::copy(vcl_result, eigen_temp);
std::cout << "Difference for sparse matrix-vector product: " << (eigen_result - eigen_temp).norm() << std::endl;
std::cout << "Difference for sparse matrix-vector product (Eigen->ViennaCL->Eigen): "
<< (eigen_sparsemat2 * eigen_rhs - eigen_temp).norm() << std::endl;
}
void run_test()
{
//
// get Eigen matrix and vector types for the provided ScalarType:
//
typedef typename Eigen_dense_matrix<ScalarType>::type EigenMatrix;
typedef typename Eigen_vector<ScalarType>::type EigenVector;
//
// Create and fill dense matrices from the Eigen library:
//
EigenMatrix eigen_densemat(6, 5);
EigenMatrix eigen_densemat2(6, 5);
eigen_densemat(0,0) = 2.0; eigen_densemat(0,1) = -1.0;
eigen_densemat(1,0) = -1.0; eigen_densemat(1,1) = 2.0; eigen_densemat(1,2) = -1.0;
eigen_densemat(2,1) = -1.0; eigen_densemat(2,2) = -1.0; eigen_densemat(2,3) = -1.0;
eigen_densemat(3,2) = -1.0; eigen_densemat(3,3) = 2.0; eigen_densemat(3,4) = -1.0;
eigen_densemat(5,4) = -1.0; eigen_densemat(4,4) = -1.0;
//
// Create and fill sparse matrices from the Eigen library:
//
Eigen::SparseMatrix<ScalarType, Eigen::RowMajor> eigen_sparsemat(6, 5);
Eigen::SparseMatrix<ScalarType, Eigen::RowMajor> eigen_sparsemat2(6, 5);
eigen_sparsemat.reserve(5*2);
eigen_sparsemat.insert(0,0) = 2.0; eigen_sparsemat.insert(0,1) = -1.0;
eigen_sparsemat.insert(1,1) = 2.0; eigen_sparsemat.insert(1,2) = -1.0;
eigen_sparsemat.insert(2,2) = -1.0; eigen_sparsemat.insert(2,3) = -1.0;
eigen_sparsemat.insert(3,3) = 2.0; eigen_sparsemat.insert(3,4) = -1.0;
eigen_sparsemat.insert(5,4) = -1.0;
//eigen_sparsemat.endFill();
//
// Create and fill a few vectors from the Eigen library:
//
EigenVector eigen_rhs(5);
EigenVector eigen_result(6);
EigenVector eigen_temp(6);
eigen_rhs(0) = 10.0;
eigen_rhs(1) = 11.0;
eigen_rhs(2) = 12.0;
eigen_rhs(3) = 13.0;
eigen_rhs(4) = 14.0;
//
// Let us create the ViennaCL analogues:
//
viennacl::vector<ScalarType> vcl_rhs(5);
viennacl::vector<ScalarType> vcl_result(6);
viennacl::matrix<ScalarType> vcl_densemat(6, 5);
viennacl::compressed_matrix<ScalarType> vcl_sparsemat(6, 5);
//
// Directly copy the Eigen objects to ViennaCL objects
//
viennacl::copy(&(eigen_rhs[0]), &(eigen_rhs[0]) + 5, vcl_rhs.begin()); //method 1: via iterator interface (cf. std::copy())
viennacl::copy(eigen_rhs, vcl_rhs); //method 2: via built-in wrappers (convenience layer)
viennacl::copy(eigen_densemat, vcl_densemat);
viennacl::copy(eigen_sparsemat, vcl_sparsemat);
std::cout << "VCL sparsematrix dimensions: " << vcl_sparsemat.size1() << ", " << vcl_sparsemat.size2() << std::endl;
// For completeness: Copy matrices from ViennaCL back to Eigen:
viennacl::copy(vcl_densemat, eigen_densemat2);
viennacl::copy(vcl_sparsemat, eigen_sparsemat2);
//
// Run matrix-vector products and compare results:
//
eigen_result = eigen_densemat * eigen_rhs;
vcl_result = viennacl::linalg::prod(vcl_densemat, vcl_rhs);
viennacl::copy(vcl_result, eigen_temp);
std::cout << "Difference for dense matrix-vector product: " << (eigen_result - eigen_temp).norm() << std::endl;
std::cout << "Difference for dense matrix-vector product (Eigen->ViennaCL->Eigen): "
<< (eigen_densemat2 * eigen_rhs - eigen_temp).norm() << std::endl;
//
// Same for sparse matrix:
//
eigen_result = eigen_sparsemat * eigen_rhs;
vcl_result = viennacl::linalg::prod(vcl_sparsemat, vcl_rhs);
viennacl::copy(vcl_result, eigen_temp);
std::cout << "Difference for sparse matrix-vector product: " << (eigen_result - eigen_temp).norm() << std::endl;
std::cout << "Difference for sparse matrix-vector product (Eigen->ViennaCL->Eigen): "
<< (eigen_sparsemat2 * eigen_rhs - eigen_temp).norm() << std::endl;
//
// Please have a look at the other tutorials on how to use the ViennaCL types
//
}
