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 <span id="projectnumber">1.7.1</span>
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<div class="title">eigen-with-viennacl.cpp</div> </div>
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<div class="contents">
<p>This tutorial shows how data can be directly transferred from the <a href="http://eigen.tuxfamily.org/">Eigen Library</a> to ViennaCL objects using the built-in convenience wrappers.</p>
<p>The first step is to include the necessary headers and activate the Eigen convenience functions in ViennaCL: </p>
<div class="fragment"><div class="line"><span class="comment">// System headers</span></div>
<div class="line"><span class="preprocessor">#include <iostream></span></div>
<div class="line"></div>
<div class="line"><span class="comment">// Eigen headers</span></div>
<div class="line"><span class="preprocessor">#include <Eigen/Core></span></div>
<div class="line"><span class="preprocessor">#include <Eigen/Sparse></span></div>
<div class="line"></div>
<div class="line"><span class="comment">// IMPORTANT: Must be set prior to any ViennaCL includes if you want to use ViennaCL algorithms on Eigen objects</span></div>
<div class="line"><span class="preprocessor">#define VIENNACL_WITH_EIGEN 1</span></div>
<div class="line"></div>
<div class="line"></div>
<div class="line"><span class="comment">// ViennaCL includes</span></div>
<div class="line"><span class="preprocessor">#include "<a class="code" href="vector_8hpp.html">viennacl/vector.hpp</a>"</span></div>
<div class="line"><span class="preprocessor">#include "<a class="code" href="matrix_8hpp.html">viennacl/matrix.hpp</a>"</span></div>
<div class="line"><span class="preprocessor">#include "<a class="code" href="compressed__matrix_8hpp.html">viennacl/compressed_matrix.hpp</a>"</span></div>
<div class="line"><span class="preprocessor">#include "<a class="code" href="prod_8hpp.html">viennacl/linalg/prod.hpp</a>"</span></div>
<div class="line"></div>
<div class="line"></div>
<div class="line"><span class="comment">// Helper functions for this tutorial:</span></div>
<div class="line"><span class="preprocessor">#include "<a class="code" href="vector-io_8hpp.html">vector-io.hpp</a>"</span></div>
</div><!-- fragment --><p> The following is a set of auxiliary dispatchers for obtaining the right Eigen types for a given floating point type. This is merely an implementation detail, so feel free to skip over it. </p>
<div class="fragment"><div class="line"><span class="comment">//dense matrix:</span></div>
<div class="line"><span class="keyword">template</span><<span class="keyword">typename</span> T></div>
<div class="line"><span class="keyword">struct </span>Eigen_dense_matrix</div>
<div class="line">{</div>
<div class="line"> <span class="keyword">typedef</span> <span class="keyword">typename</span> T::ERROR_NO_EIGEN_TYPE_AVAILABLE error_type;</div>
<div class="line">};</div>
<div class="line"></div>
<div class="line"><span class="keyword">template</span><></div>
<div class="line"><span class="keyword">struct </span>Eigen_dense_matrix<float></div>
<div class="line">{</div>
<div class="line"> <span class="keyword">typedef</span> Eigen::MatrixXf type;</div>
<div class="line">};</div>
<div class="line"></div>
<div class="line"><span class="keyword">template</span><></div>
<div class="line"><span class="keyword">struct </span>Eigen_dense_matrix<double></div>
<div class="line">{</div>
<div class="line"> <span class="keyword">typedef</span> Eigen::MatrixXd type;</div>
<div class="line">};</div>
<div class="line"></div>
<div class="line"></div>
<div class="line"><span class="comment">//sparse matrix</span></div>
<div class="line"><span class="keyword">template</span><<span class="keyword">typename</span> T></div>
<div class="line"><span class="keyword">struct </span>Eigen_vector</div>
<div class="line">{</div>
<div class="line"> <span class="keyword">typedef</span> <span class="keyword">typename</span> T::ERROR_NO_EIGEN_TYPE_AVAILABLE error_type;</div>
<div class="line">};</div>
<div class="line"></div>
<div class="line"><span class="keyword">template</span><></div>
<div class="line"><span class="keyword">struct </span>Eigen_vector<float></div>
<div class="line">{</div>
<div class="line"> <span class="keyword">typedef</span> Eigen::VectorXf type;</div>
<div class="line">};</div>
<div class="line"></div>
<div class="line"><span class="keyword">template</span><></div>
<div class="line"><span class="keyword">struct </span>Eigen_vector<double></div>
<div class="line">{</div>
<div class="line"> <span class="keyword">typedef</span> Eigen::VectorXd type;</div>
<div class="line">};</div>
</div><!-- fragment --><p> The following function contains the main code for this tutorial. It consists of the following steps:</p>
<ul>
<li>Creates Eigen matrices and vectors</li>
<li>Initializes them with data</li>
<li>Create ViennaCL objects</li>
<li>Copy them over to the respective ViennaCL objects</li>
<li>Compute matrix-vector products in both Eigen and ViennaCL and compare results.</li>
</ul>
<div class="fragment"><div class="line"><span class="keyword">template</span><<span class="keyword">typename</span> ScalarType></div>
<div class="line"><span class="keywordtype">void</span> run_tutorial()</div>
<div class="line">{</div>
</div><!-- fragment --><p> Get Eigen matrix and vector types for the provided ScalarType. Involves a little bit of template-metaprogramming. </p>
<div class="fragment"><div class="line"><span class="keyword">typedef</span> <span class="keyword">typename</span> Eigen_dense_matrix<ScalarType>::type EigenMatrix;</div>
<div class="line"><span class="keyword">typedef</span> <span class="keyword">typename</span> Eigen_vector<ScalarType>::type EigenVector;</div>
</div><!-- fragment --><p> Create and fill dense matrices from the Eigen library: </p>
<div class="fragment"><div class="line">EigenMatrix eigen_densemat(6, 5);</div>
<div class="line">EigenMatrix eigen_densemat2(6, 5);</div>
<div class="line">eigen_densemat(0,0) = 2.0; eigen_densemat(0,1) = -1.0;</div>
<div class="line">eigen_densemat(1,0) = -1.0; eigen_densemat(1,1) = 2.0; eigen_densemat(1,2) = -1.0;</div>
<div class="line">eigen_densemat(2,1) = -1.0; eigen_densemat(2,2) = -1.0; eigen_densemat(2,3) = -1.0;</div>
<div class="line">eigen_densemat(3,2) = -1.0; eigen_densemat(3,3) = 2.0; eigen_densemat(3,4) = -1.0;</div>
<div class="line"> eigen_densemat(5,4) = -1.0; eigen_densemat(4,4) = -1.0;</div>
<div class="line">Eigen::Map<EigenMatrix> eigen_densemat_map(eigen_densemat.data(), 6, 5); <span class="comment">// same as eigen_densemat, but emulating user-provided buffer</span></div>
</div><!-- fragment --><p> Create and fill sparse matrices from the Eigen library: </p>
<div class="fragment"><div class="line">Eigen::SparseMatrix<ScalarType, Eigen::RowMajor> eigen_sparsemat(6, 5);</div>
<div class="line">Eigen::SparseMatrix<ScalarType, Eigen::RowMajor> eigen_sparsemat2(6, 5);</div>
<div class="line">eigen_sparsemat.reserve(5*2);</div>
<div class="line">eigen_sparsemat.insert(0,0) = 2.0; eigen_sparsemat.insert(0,1) = -1.0;</div>
<div class="line">eigen_sparsemat.insert(1,1) = 2.0; eigen_sparsemat.insert(1,2) = -1.0;</div>
<div class="line">eigen_sparsemat.insert(2,2) = -1.0; eigen_sparsemat.insert(2,3) = -1.0;</div>
<div class="line">eigen_sparsemat.insert(3,3) = 2.0; eigen_sparsemat.insert(3,4) = -1.0;</div>
<div class="line">eigen_sparsemat.insert(5,4) = -1.0;</div>
<div class="line"><span class="comment">//eigen_sparsemat.endFill();</span></div>
</div><!-- fragment --><p> Create and fill a few vectors from the Eigen library: </p>
<div class="fragment"><div class="line">EigenVector eigen_rhs(5);</div>
<div class="line">Eigen::Map<EigenVector> eigen_rhs_map(eigen_rhs.data(), 5);</div>
<div class="line">EigenVector eigen_result(6);</div>
<div class="line">EigenVector eigen_temp(6);</div>
<div class="line"></div>
<div class="line">eigen_rhs(0) = 10.0;</div>
<div class="line">eigen_rhs(1) = 11.0;</div>
<div class="line">eigen_rhs(2) = 12.0;</div>
<div class="line">eigen_rhs(3) = 13.0;</div>
<div class="line">eigen_rhs(4) = 14.0;</div>
</div><!-- fragment --><p> Create the corresponding ViennaCL objects: </p>
<div class="fragment"><div class="line"><a name="_a0"></a><a class="code" href="classviennacl_1_1vector.html">viennacl::vector<ScalarType></a> vcl_rhs(5);</div>
<div class="line"><a class="code" href="classviennacl_1_1vector.html">viennacl::vector<ScalarType></a> vcl_result(6);</div>
<div class="line"><a name="_a1"></a><a class="code" href="classviennacl_1_1matrix.html">viennacl::matrix<ScalarType></a> vcl_densemat(6, 5);</div>
<div class="line"><a name="_a2"></a><a class="code" href="classviennacl_1_1compressed__matrix.html">viennacl::compressed_matrix<ScalarType></a> vcl_sparsemat(6, 5);</div>
</div><!-- fragment --><p> Directly copy the Eigen objects to ViennaCL objects </p>
<div class="fragment"><div class="line"><a name="a3"></a><a class="code" href="namespaceviennacl.html#a10b7f8cf6b8864a7aa196d670481a453">viennacl::copy</a>(&(eigen_rhs[0]), &(eigen_rhs[0]) + 5, vcl_rhs.begin()); <span class="comment">// Method 1: via iterator interface (cf. std::copy())</span></div>
<div class="line"><a class="code" href="namespaceviennacl.html#a10b7f8cf6b8864a7aa196d670481a453">viennacl::copy</a>(eigen_rhs, vcl_rhs); <span class="comment">// Method 2: via built-in wrappers (convenience layer)</span></div>
<div class="line"><a class="code" href="namespaceviennacl.html#a10b7f8cf6b8864a7aa196d670481a453">viennacl::copy</a>(eigen_rhs_map, vcl_rhs); <span class="comment">// Same as method 2, but for a mapped vector</span></div>
<div class="line"></div>
<div class="line"><a class="code" href="namespaceviennacl.html#a10b7f8cf6b8864a7aa196d670481a453">viennacl::copy</a>(eigen_densemat, vcl_densemat);</div>
<div class="line"><a class="code" href="namespaceviennacl.html#a10b7f8cf6b8864a7aa196d670481a453">viennacl::copy</a>(eigen_densemat_map, vcl_densemat); <span class="comment">//same as above, using mapped matrix</span></div>
<div class="line"><a class="code" href="namespaceviennacl.html#a10b7f8cf6b8864a7aa196d670481a453">viennacl::copy</a>(eigen_sparsemat, vcl_sparsemat);</div>
<div class="line">std::cout << <span class="stringliteral">"VCL sparsematrix dimensions: "</span> << vcl_sparsemat.size1() << <span class="stringliteral">", "</span> << vcl_sparsemat.size2() << std::endl;</div>
<div class="line"></div>
<div class="line"><span class="comment">// For completeness: Copy matrices from ViennaCL back to Eigen:</span></div>
<div class="line"><a class="code" href="namespaceviennacl.html#a10b7f8cf6b8864a7aa196d670481a453">viennacl::copy</a>(vcl_densemat, eigen_densemat2);</div>
<div class="line"><a class="code" href="namespaceviennacl.html#a10b7f8cf6b8864a7aa196d670481a453">viennacl::copy</a>(vcl_sparsemat, eigen_sparsemat2);</div>
</div><!-- fragment --><p> Run dense matrix-vector products and compare results: </p>
<div class="fragment"><div class="line">eigen_result = eigen_densemat * eigen_rhs;</div>
<div class="line">vcl_result = <a name="a4"></a><a class="code" href="namespaceviennacl_1_1linalg.html#aa18d10f8a90e38bd9ff43c650fc670ef">viennacl::linalg::prod</a>(vcl_densemat, vcl_rhs);</div>
<div class="line"><a class="code" href="namespaceviennacl.html#a10b7f8cf6b8864a7aa196d670481a453">viennacl::copy</a>(vcl_result, eigen_temp);</div>
<div class="line">std::cout << <span class="stringliteral">"Difference for dense matrix-vector product: "</span> << (eigen_result - eigen_temp).norm() << std::endl;</div>
<div class="line">std::cout << <span class="stringliteral">"Difference for dense matrix-vector product (Eigen->ViennaCL->Eigen): "</span></div>
<div class="line"> << (eigen_densemat2 * eigen_rhs - eigen_temp).norm() << std::endl;</div>
</div><!-- fragment --><p> Run sparse matrix-vector products and compare results: </p>
<div class="fragment"><div class="line"> eigen_result = eigen_sparsemat * eigen_rhs;</div>
<div class="line"> vcl_result = <a class="code" href="namespaceviennacl_1_1linalg.html#aa18d10f8a90e38bd9ff43c650fc670ef">viennacl::linalg::prod</a>(vcl_sparsemat, vcl_rhs);</div>
<div class="line"> <a class="code" href="namespaceviennacl.html#a10b7f8cf6b8864a7aa196d670481a453">viennacl::copy</a>(vcl_result, eigen_temp);</div>
<div class="line"> std::cout << <span class="stringliteral">"Difference for sparse matrix-vector product: "</span> << (eigen_result - eigen_temp).norm() << std::endl;</div>
<div class="line"> std::cout << <span class="stringliteral">"Difference for sparse matrix-vector product (Eigen->ViennaCL->Eigen): "</span></div>
<div class="line"> << (eigen_sparsemat2 * eigen_rhs - eigen_temp).norm() << std::endl;</div>
<div class="line">}</div>
</div><!-- fragment --><p> In the <a class="el" href="tests_2src_2bisect_8cpp.html#ae66f6b31b5ad750f1fe042a706a4e3d4">main()</a> routine we only call the worker function defined above with both single and double precision arithmetic. </p>
<div class="fragment"><div class="line"><span class="keywordtype">int</span> <a name="a5"></a><a class="code" href="tests_2src_2bisect_8cpp.html#ae66f6b31b5ad750f1fe042a706a4e3d4">main</a>(<span class="keywordtype">int</span>, <span class="keywordtype">char</span> *[])</div>
<div class="line">{</div>
<div class="line"> std::cout << <span class="stringliteral">"----------------------------------------------"</span> << std::endl;</div>
<div class="line"> std::cout << <span class="stringliteral">"## Single precision"</span> << std::endl;</div>
<div class="line"> std::cout << <span class="stringliteral">"----------------------------------------------"</span> << std::endl;</div>
<div class="line"> run_tutorial<float>();</div>
<div class="line"></div>
<div class="line"><span class="preprocessor">#ifdef VIENNACL_HAVE_OPENCL</span></div>
<div class="line"> <span class="keywordflow">if</span> ( <a name="a6"></a><a class="code" href="namespaceviennacl_1_1ocl.html#ac54d59a74deaccec81f64e738b856348">viennacl::ocl::current_device</a>().<a name="a7"></a><a class="code" href="classviennacl_1_1ocl_1_1device.html#a5182025f56c76de6770e26b14165b00a">double_support</a>() )</div>
<div class="line">#endif</div>
<div class="line"> {</div>
<div class="line"> std::cout << <span class="stringliteral">"----------------------------------------------"</span> << std::endl;</div>
<div class="line"> std::cout << <span class="stringliteral">"## Double precision"</span> << std::endl;</div>
<div class="line"> std::cout << <span class="stringliteral">"----------------------------------------------"</span> << std::endl;</div>
<div class="line"> run_tutorial<double>();</div>
<div class="line"> }</div>
</div><!-- fragment --><p> That's it. Print a success message and exit. </p>
<div class="fragment"><div class="line"> std::cout << std::endl;</div>
<div class="line"> std::cout << <span class="stringliteral">"!!!! TUTORIAL COMPLETED SUCCESSFULLY !!!!"</span> << std::endl;</div>
<div class="line"> std::cout << std::endl;</div>
<div class="line"></div>
<div class="line">}</div>
</div><!-- fragment --> <h2>Full Example Code</h2>
<div class="fragment"><div class="line"><span class="comment">/* =========================================================================</span></div>
<div class="line"><span class="comment"> Copyright (c) 2010-2016, Institute for Microelectronics,</span></div>
<div class="line"><span class="comment"> Institute for Analysis and Scientific Computing,</span></div>
<div class="line"><span class="comment"> TU Wien.</span></div>
<div class="line"><span class="comment"> Portions of this software are copyright by UChicago Argonne, LLC.</span></div>
<div class="line"><span class="comment"></span></div>
<div class="line"><span class="comment"> -----------------</span></div>
<div class="line"><span class="comment"> ViennaCL - The Vienna Computing Library</span></div>
<div class="line"><span class="comment"> -----------------</span></div>
<div class="line"><span class="comment"></span></div>
<div class="line"><span class="comment"> Project Head: Karl Rupp rupp@iue.tuwien.ac.at</span></div>
<div class="line"><span class="comment"></span></div>
<div class="line"><span class="comment"> (A list of authors and contributors can be found in the PDF manual)</span></div>
<div class="line"><span class="comment"></span></div>
<div class="line"><span class="comment"> License: MIT (X11), see file LICENSE in the base directory</span></div>
<div class="line"><span class="comment">============================================================================= */</span></div>
<div class="line"></div>
<div class="line"><span class="comment">// System headers</span></div>
<div class="line"><span class="preprocessor">#include <iostream></span></div>
<div class="line"></div>
<div class="line"><span class="comment">// Eigen headers</span></div>
<div class="line"><span class="preprocessor">#include <Eigen/Core></span></div>
<div class="line"><span class="preprocessor">#include <Eigen/Sparse></span></div>
<div class="line"></div>
<div class="line"><span class="comment">// IMPORTANT: Must be set prior to any ViennaCL includes if you want to use ViennaCL algorithms on Eigen objects</span></div>
<div class="line"><span class="preprocessor">#define VIENNACL_WITH_EIGEN 1</span></div>
<div class="line"></div>
<div class="line"></div>
<div class="line"><span class="comment">// ViennaCL includes</span></div>
<div class="line"><span class="preprocessor">#include "<a class="code" href="vector_8hpp.html">viennacl/vector.hpp</a>"</span></div>
<div class="line"><span class="preprocessor">#include "<a class="code" href="matrix_8hpp.html">viennacl/matrix.hpp</a>"</span></div>
<div class="line"><span class="preprocessor">#include "<a class="code" href="compressed__matrix_8hpp.html">viennacl/compressed_matrix.hpp</a>"</span></div>
<div class="line"><span class="preprocessor">#include "<a class="code" href="prod_8hpp.html">viennacl/linalg/prod.hpp</a>"</span></div>
<div class="line"></div>
<div class="line"></div>
<div class="line"><span class="comment">// Helper functions for this tutorial:</span></div>
<div class="line"><span class="preprocessor">#include "<a class="code" href="vector-io_8hpp.html">vector-io.hpp</a>"</span></div>
<div class="line"></div>
<div class="line"><span class="comment">//dense matrix:</span></div>
<div class="line"><span class="keyword">template</span><<span class="keyword">typename</span> T></div>
<div class="line"><span class="keyword">struct </span>Eigen_dense_matrix</div>
<div class="line">{</div>
<div class="line"> <span class="keyword">typedef</span> <span class="keyword">typename</span> T::ERROR_NO_EIGEN_TYPE_AVAILABLE error_type;</div>
<div class="line">};</div>
<div class="line"></div>
<div class="line"><span class="keyword">template</span><></div>
<div class="line"><span class="keyword">struct </span>Eigen_dense_matrix<float></div>
<div class="line">{</div>
<div class="line"> <span class="keyword">typedef</span> Eigen::MatrixXf type;</div>
<div class="line">};</div>
<div class="line"></div>
<div class="line"><span class="keyword">template</span><></div>
<div class="line"><span class="keyword">struct </span>Eigen_dense_matrix<double></div>
<div class="line">{</div>
<div class="line"> <span class="keyword">typedef</span> Eigen::MatrixXd type;</div>
<div class="line">};</div>
<div class="line"></div>
<div class="line"></div>
<div class="line"><span class="comment">//sparse matrix</span></div>
<div class="line"><span class="keyword">template</span><<span class="keyword">typename</span> T></div>
<div class="line"><span class="keyword">struct </span>Eigen_vector</div>
<div class="line">{</div>
<div class="line"> <span class="keyword">typedef</span> <span class="keyword">typename</span> T::ERROR_NO_EIGEN_TYPE_AVAILABLE error_type;</div>
<div class="line">};</div>
<div class="line"></div>
<div class="line"><span class="keyword">template</span><></div>
<div class="line"><span class="keyword">struct </span>Eigen_vector<float></div>
<div class="line">{</div>
<div class="line"> <span class="keyword">typedef</span> Eigen::VectorXf type;</div>
<div class="line">};</div>
<div class="line"></div>
<div class="line"><span class="keyword">template</span><></div>
<div class="line"><span class="keyword">struct </span>Eigen_vector<double></div>
<div class="line">{</div>
<div class="line"> <span class="keyword">typedef</span> Eigen::VectorXd type;</div>
<div class="line">};</div>
<div class="line"></div>
<div class="line"></div>
<div class="line"></div>
<div class="line"><span class="keyword">template</span><<span class="keyword">typename</span> ScalarType></div>
<div class="line"><span class="keywordtype">void</span> run_tutorial()</div>
<div class="line">{</div>
<div class="line"> <span class="keyword">typedef</span> <span class="keyword">typename</span> Eigen_dense_matrix<ScalarType>::type EigenMatrix;</div>
<div class="line"> <span class="keyword">typedef</span> <span class="keyword">typename</span> Eigen_vector<ScalarType>::type EigenVector;</div>
<div class="line"></div>
<div class="line"> EigenMatrix eigen_densemat(6, 5);</div>
<div class="line"> EigenMatrix eigen_densemat2(6, 5);</div>
<div class="line"> eigen_densemat(0,0) = 2.0; eigen_densemat(0,1) = -1.0;</div>
<div class="line"> eigen_densemat(1,0) = -1.0; eigen_densemat(1,1) = 2.0; eigen_densemat(1,2) = -1.0;</div>
<div class="line"> eigen_densemat(2,1) = -1.0; eigen_densemat(2,2) = -1.0; eigen_densemat(2,3) = -1.0;</div>
<div class="line"> eigen_densemat(3,2) = -1.0; eigen_densemat(3,3) = 2.0; eigen_densemat(3,4) = -1.0;</div>
<div class="line"> eigen_densemat(5,4) = -1.0; eigen_densemat(4,4) = -1.0;</div>
<div class="line"> Eigen::Map<EigenMatrix> eigen_densemat_map(eigen_densemat.data(), 6, 5); <span class="comment">// same as eigen_densemat, but emulating user-provided buffer</span></div>
<div class="line"></div>
<div class="line"> Eigen::SparseMatrix<ScalarType, Eigen::RowMajor> eigen_sparsemat(6, 5);</div>
<div class="line"> Eigen::SparseMatrix<ScalarType, Eigen::RowMajor> eigen_sparsemat2(6, 5);</div>
<div class="line"> eigen_sparsemat.reserve(5*2);</div>
<div class="line"> eigen_sparsemat.insert(0,0) = 2.0; eigen_sparsemat.insert(0,1) = -1.0;</div>
<div class="line"> eigen_sparsemat.insert(1,1) = 2.0; eigen_sparsemat.insert(1,2) = -1.0;</div>
<div class="line"> eigen_sparsemat.insert(2,2) = -1.0; eigen_sparsemat.insert(2,3) = -1.0;</div>
<div class="line"> eigen_sparsemat.insert(3,3) = 2.0; eigen_sparsemat.insert(3,4) = -1.0;</div>
<div class="line"> eigen_sparsemat.insert(5,4) = -1.0;</div>
<div class="line"> <span class="comment">//eigen_sparsemat.endFill();</span></div>
<div class="line"></div>
<div class="line"> EigenVector eigen_rhs(5);</div>
<div class="line"> Eigen::Map<EigenVector> eigen_rhs_map(eigen_rhs.data(), 5);</div>
<div class="line"> EigenVector eigen_result(6);</div>
<div class="line"> EigenVector eigen_temp(6);</div>
<div class="line"></div>
<div class="line"> eigen_rhs(0) = 10.0;</div>
<div class="line"> eigen_rhs(1) = 11.0;</div>
<div class="line"> eigen_rhs(2) = 12.0;</div>
<div class="line"> eigen_rhs(3) = 13.0;</div>
<div class="line"> eigen_rhs(4) = 14.0;</div>
<div class="line"></div>
<div class="line"></div>
<div class="line"> <a class="code" href="classviennacl_1_1vector.html">viennacl::vector<ScalarType></a> vcl_rhs(5);</div>
<div class="line"> <a class="code" href="classviennacl_1_1vector.html">viennacl::vector<ScalarType></a> vcl_result(6);</div>
<div class="line"> <a class="code" href="classviennacl_1_1matrix.html">viennacl::matrix<ScalarType></a> vcl_densemat(6, 5);</div>
<div class="line"> <a class="code" href="classviennacl_1_1compressed__matrix.html">viennacl::compressed_matrix<ScalarType></a> vcl_sparsemat(6, 5);</div>
<div class="line"></div>
<div class="line"></div>
<div class="line"> <a class="code" href="namespaceviennacl.html#a10b7f8cf6b8864a7aa196d670481a453">viennacl::copy</a>(&(eigen_rhs[0]), &(eigen_rhs[0]) + 5, vcl_rhs.begin()); <span class="comment">// Method 1: via iterator interface (cf. std::copy())</span></div>
<div class="line"> <a class="code" href="namespaceviennacl.html#a10b7f8cf6b8864a7aa196d670481a453">viennacl::copy</a>(eigen_rhs, vcl_rhs); <span class="comment">// Method 2: via built-in wrappers (convenience layer)</span></div>
<div class="line"> <a class="code" href="namespaceviennacl.html#a10b7f8cf6b8864a7aa196d670481a453">viennacl::copy</a>(eigen_rhs_map, vcl_rhs); <span class="comment">// Same as method 2, but for a mapped vector</span></div>
<div class="line"></div>
<div class="line"> <a class="code" href="namespaceviennacl.html#a10b7f8cf6b8864a7aa196d670481a453">viennacl::copy</a>(eigen_densemat, vcl_densemat);</div>
<div class="line"> <a class="code" href="namespaceviennacl.html#a10b7f8cf6b8864a7aa196d670481a453">viennacl::copy</a>(eigen_densemat_map, vcl_densemat); <span class="comment">//same as above, using mapped matrix</span></div>
<div class="line"> <a class="code" href="namespaceviennacl.html#a10b7f8cf6b8864a7aa196d670481a453">viennacl::copy</a>(eigen_sparsemat, vcl_sparsemat);</div>
<div class="line"> std::cout << <span class="stringliteral">"VCL sparsematrix dimensions: "</span> << vcl_sparsemat.size1() << <span class="stringliteral">", "</span> << vcl_sparsemat.size2() << std::endl;</div>
<div class="line"></div>
<div class="line"> <span class="comment">// For completeness: Copy matrices from ViennaCL back to Eigen:</span></div>
<div class="line"> <a class="code" href="namespaceviennacl.html#a10b7f8cf6b8864a7aa196d670481a453">viennacl::copy</a>(vcl_densemat, eigen_densemat2);</div>
<div class="line"> <a class="code" href="namespaceviennacl.html#a10b7f8cf6b8864a7aa196d670481a453">viennacl::copy</a>(vcl_sparsemat, eigen_sparsemat2);</div>
<div class="line"></div>
<div class="line"></div>
<div class="line"> eigen_result = eigen_densemat * eigen_rhs;</div>
<div class="line"> vcl_result = <a class="code" href="namespaceviennacl_1_1linalg.html#aa18d10f8a90e38bd9ff43c650fc670ef">viennacl::linalg::prod</a>(vcl_densemat, vcl_rhs);</div>
<div class="line"> <a class="code" href="namespaceviennacl.html#a10b7f8cf6b8864a7aa196d670481a453">viennacl::copy</a>(vcl_result, eigen_temp);</div>
<div class="line"> std::cout << <span class="stringliteral">"Difference for dense matrix-vector product: "</span> << (eigen_result - eigen_temp).norm() << std::endl;</div>
<div class="line"> std::cout << <span class="stringliteral">"Difference for dense matrix-vector product (Eigen->ViennaCL->Eigen): "</span></div>
<div class="line"> << (eigen_densemat2 * eigen_rhs - eigen_temp).norm() << std::endl;</div>
<div class="line"></div>
<div class="line"> eigen_result = eigen_sparsemat * eigen_rhs;</div>
<div class="line"> vcl_result = <a class="code" href="namespaceviennacl_1_1linalg.html#aa18d10f8a90e38bd9ff43c650fc670ef">viennacl::linalg::prod</a>(vcl_sparsemat, vcl_rhs);</div>
<div class="line"> <a class="code" href="namespaceviennacl.html#a10b7f8cf6b8864a7aa196d670481a453">viennacl::copy</a>(vcl_result, eigen_temp);</div>
<div class="line"> std::cout << <span class="stringliteral">"Difference for sparse matrix-vector product: "</span> << (eigen_result - eigen_temp).norm() << std::endl;</div>
<div class="line"> std::cout << <span class="stringliteral">"Difference for sparse matrix-vector product (Eigen->ViennaCL->Eigen): "</span></div>
<div class="line"> << (eigen_sparsemat2 * eigen_rhs - eigen_temp).norm() << std::endl;</div>
<div class="line">}</div>
<div class="line"></div>
<div class="line"></div>
<div class="line"><span class="keywordtype">int</span> <a class="code" href="tests_2src_2bisect_8cpp.html#ae66f6b31b5ad750f1fe042a706a4e3d4">main</a>(<span class="keywordtype">int</span>, <span class="keywordtype">char</span> *[])</div>
<div class="line">{</div>
<div class="line"> std::cout << <span class="stringliteral">"----------------------------------------------"</span> << std::endl;</div>
<div class="line"> std::cout << <span class="stringliteral">"## Single precision"</span> << std::endl;</div>
<div class="line"> std::cout << <span class="stringliteral">"----------------------------------------------"</span> << std::endl;</div>
<div class="line"> run_tutorial<float>();</div>
<div class="line"></div>
<div class="line"><span class="preprocessor">#ifdef VIENNACL_HAVE_OPENCL</span></div>
<div class="line"> <span class="keywordflow">if</span> ( <a class="code" href="namespaceviennacl_1_1ocl.html#ac54d59a74deaccec81f64e738b856348">viennacl::ocl::current_device</a>().<a class="code" href="classviennacl_1_1ocl_1_1device.html#a5182025f56c76de6770e26b14165b00a">double_support</a>() )</div>
<div class="line">#endif</div>
<div class="line"> {</div>
<div class="line"> std::cout << <span class="stringliteral">"----------------------------------------------"</span> << std::endl;</div>
<div class="line"> std::cout << <span class="stringliteral">"## Double precision"</span> << std::endl;</div>
<div class="line"> std::cout << <span class="stringliteral">"----------------------------------------------"</span> << std::endl;</div>
<div class="line"> run_tutorial<double>();</div>
<div class="line"> }</div>
<div class="line"></div>
<div class="line"> std::cout << std::endl;</div>
<div class="line"> std::cout << <span class="stringliteral">"!!!! TUTORIAL COMPLETED SUCCESSFULLY !!!!"</span> << std::endl;</div>
<div class="line"> std::cout << std::endl;</div>
<div class="line"></div>
<div class="line">}</div>
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