for (unsigned int vv = 0; vv < macro_size; ++vv)
offsets_nm[vv] = nm * vv;
- vectorized_transpose_and_store(false,
- nm,
- &(mass_matrix[dir](0, 0)),
- offsets_nm.cbegin(),
- mass_matrix_flat.data());
- vectorized_transpose_and_store(false,
- nm,
- &(derivative_matrix[dir](0, 0)),
- offsets_nm.cbegin(),
- deriv_matrix_flat.data());
+ vectorized_transpose_and_store<Number, n_lanes>(
+ false,
+ nm,
+ &(mass_matrix[dir](0, 0)),
+ offsets_nm.data(),
+ mass_matrix_flat.data());
+ vectorized_transpose_and_store<Number, n_lanes>(
+ false,
+ nm,
+ &(derivative_matrix[dir](0, 0)),
+ offsets_nm.data(),
+ deriv_matrix_flat.data());
const Number *mass_cbegin = mass_matrix_flat.data();
const Number *deriv_cbegin = deriv_matrix_flat.data();
eigenvectors[dir].reinit(n_rows, n_cols);
for (unsigned int vv = 0; vv < macro_size; ++vv)
offsets_n[vv] = n_rows * vv;
- vectorized_load_and_transpose(n_rows,
- eigenvalues_flat.data(),
- offsets_n.cbegin(),
- eigenvalues[dir].begin());
- vectorized_load_and_transpose(nm,
- eigenvectors_flat.data(),
- offsets_nm.cbegin(),
- &(eigenvectors[dir](0, 0)));
+ vectorized_load_and_transpose<Number, n_lanes>(
+ n_rows,
+ eigenvalues_flat.data(),
+ offsets_n.data(),
+ eigenvalues[dir].begin());
+ vectorized_load_and_transpose<Number, n_lanes>(
+ nm,
+ eigenvectors_flat.data(),
+ offsets_nm.data(),
+ &(eigenvectors[dir](0, 0)));
}
}