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<div class="title">iterative_operations.hpp File Reference</div> </div>
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<p>Implementations of specialized kernels for fast iterative solvers using OpenMP on the CPU.
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<div class="textblock"><code>#include <cmath></code><br/>
<code>#include <algorithm></code><br/>
<code>#include "<a class="el" href="forwards_8h_source.html">viennacl/forwards.h</a>"</code><br/>
<code>#include "<a class="el" href="scalar_8hpp_source.html">viennacl/scalar.hpp</a>"</code><br/>
<code>#include "<a class="el" href="tools_8hpp_source.html">viennacl/tools/tools.hpp</a>"</code><br/>
<code>#include "<a class="el" href="predicate_8hpp_source.html">viennacl/meta/predicate.hpp</a>"</code><br/>
<code>#include "<a class="el" href="enable__if_8hpp_source.html">viennacl/meta/enable_if.hpp</a>"</code><br/>
<code>#include "<a class="el" href="size_8hpp_source.html">viennacl/traits/size.hpp</a>"</code><br/>
<code>#include "<a class="el" href="start_8hpp_source.html">viennacl/traits/start.hpp</a>"</code><br/>
<code>#include "<a class="el" href="linalg_2host__based_2common_8hpp_source.html">viennacl/linalg/host_based/common.hpp</a>"</code><br/>
<code>#include "<a class="el" href="op__applier_8hpp_source.html">viennacl/linalg/detail/op_applier.hpp</a>"</code><br/>
<code>#include "<a class="el" href="stride_8hpp_source.html">viennacl/traits/stride.hpp</a>"</code><br/>
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<p><a href="host__based_2iterative__operations_8hpp_source.html">Go to the source code of this file.</a></p>
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<tr class="memdesc:namespaceviennacl"><td class="mdescLeft"> </td><td class="mdescRight">Main namespace in ViennaCL. Holds all the basic types such as vector, matrix, etc. and defines operations upon them. <br/></td></tr>
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<tr class="memitem:namespaceviennacl_1_1linalg"><td class="memItemLeft" align="right" valign="top">  </td><td class="memItemRight" valign="bottom"><a class="el" href="namespaceviennacl_1_1linalg.html">viennacl::linalg</a></td></tr>
<tr class="memdesc:namespaceviennacl_1_1linalg"><td class="mdescLeft"> </td><td class="mdescRight">Provides all linear algebra operations which are not covered by operator overloads. <br/></td></tr>
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<tr class="memdesc:namespaceviennacl_1_1linalg_1_1host__based"><td class="mdescLeft"> </td><td class="mdescRight">Holds all compute kernels with conventional host-based execution (buffers in CPU RAM). <br/></td></tr>
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<tr class="memdesc:namespaceviennacl_1_1linalg_1_1host__based_1_1detail"><td class="mdescLeft"> </td><td class="mdescRight">Helper functions for the host-based linear algebra backend. <br/></td></tr>
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<tr class="memitem:aa1b0719a06924fc82f8d8ed6c739a04d"><td class="memItemLeft" align="right" valign="top">#define </td><td class="memItemRight" valign="bottom"><a class="el" href="host__based_2iterative__operations_8hpp.html#aa1b0719a06924fc82f8d8ed6c739a04d">VIENNACL_OPENMP_VECTOR_MIN_SIZE</a>   5000</td></tr>
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Functions</h2></td></tr>
<tr class="memitem:a6a0b43a49ee675bc1f60fcc5a8e4f884"><td class="memTemplParams" colspan="2">template<typename NumericT > </td></tr>
<tr class="memitem:a6a0b43a49ee675bc1f60fcc5a8e4f884"><td class="memTemplItemLeft" align="right" valign="top">void </td><td class="memTemplItemRight" valign="bottom"><a class="el" href="namespaceviennacl_1_1linalg_1_1host__based_1_1detail.html#a6a0b43a49ee675bc1f60fcc5a8e4f884">viennacl::linalg::host_based::detail::pipelined_prod_impl</a> (compressed_matrix< <a class="el" href="tests_2src_2bisect_8cpp.html#a52b5d30a2d7b064678644a3bf49b7f6c">NumericT</a> > const &A, vector_base< <a class="el" href="tests_2src_2bisect_8cpp.html#a52b5d30a2d7b064678644a3bf49b7f6c">NumericT</a> > const &p, vector_base< <a class="el" href="tests_2src_2bisect_8cpp.html#a52b5d30a2d7b064678644a3bf49b7f6c">NumericT</a> > &Ap, <a class="el" href="tests_2src_2bisect_8cpp.html#a52b5d30a2d7b064678644a3bf49b7f6c">NumericT</a> const *r0star, vector_base< <a class="el" href="tests_2src_2bisect_8cpp.html#a52b5d30a2d7b064678644a3bf49b7f6c">NumericT</a> > &inner_prod_buffer, vcl_size_t buffer_chunk_size, vcl_size_t buffer_chunk_offset)</td></tr>
<tr class="memdesc:a6a0b43a49ee675bc1f60fcc5a8e4f884"><td class="mdescLeft"> </td><td class="mdescRight">Implementation of a fused matrix-vector product with a <a class="el" href="classviennacl_1_1compressed__matrix.html" title="A sparse square matrix in compressed sparse rows format. ">compressed_matrix</a> for an efficient pipelined CG algorithm. <a href="#a6a0b43a49ee675bc1f60fcc5a8e4f884">More...</a><br/></td></tr>
<tr class="separator:a6a0b43a49ee675bc1f60fcc5a8e4f884"><td class="memSeparator" colspan="2"> </td></tr>
<tr class="memitem:a60401098f6a2dea052342d94f14f1bb4"><td class="memTemplParams" colspan="2">template<typename NumericT > </td></tr>
<tr class="memitem:a60401098f6a2dea052342d94f14f1bb4"><td class="memTemplItemLeft" align="right" valign="top">void </td><td class="memTemplItemRight" valign="bottom"><a class="el" href="namespaceviennacl_1_1linalg_1_1host__based_1_1detail.html#a60401098f6a2dea052342d94f14f1bb4">viennacl::linalg::host_based::detail::pipelined_prod_impl</a> (coordinate_matrix< <a class="el" href="tests_2src_2bisect_8cpp.html#a52b5d30a2d7b064678644a3bf49b7f6c">NumericT</a> > const &A, vector_base< <a class="el" href="tests_2src_2bisect_8cpp.html#a52b5d30a2d7b064678644a3bf49b7f6c">NumericT</a> > const &p, vector_base< <a class="el" href="tests_2src_2bisect_8cpp.html#a52b5d30a2d7b064678644a3bf49b7f6c">NumericT</a> > &Ap, <a class="el" href="tests_2src_2bisect_8cpp.html#a52b5d30a2d7b064678644a3bf49b7f6c">NumericT</a> const *r0star, vector_base< <a class="el" href="tests_2src_2bisect_8cpp.html#a52b5d30a2d7b064678644a3bf49b7f6c">NumericT</a> > &inner_prod_buffer, vcl_size_t buffer_chunk_size, vcl_size_t buffer_chunk_offset)</td></tr>
<tr class="memdesc:a60401098f6a2dea052342d94f14f1bb4"><td class="mdescLeft"> </td><td class="mdescRight">Implementation of a fused matrix-vector product with a <a class="el" href="classviennacl_1_1coordinate__matrix.html" title="A sparse square matrix, where entries are stored as triplets (i,j, val), where i and j are the row an...">coordinate_matrix</a> for an efficient pipelined CG algorithm. <a href="#a60401098f6a2dea052342d94f14f1bb4">More...</a><br/></td></tr>
<tr class="separator:a60401098f6a2dea052342d94f14f1bb4"><td class="memSeparator" colspan="2"> </td></tr>
<tr class="memitem:a4c47c67ddce12fd93fe84c15f351b389"><td class="memTemplParams" colspan="2">template<typename NumericT > </td></tr>
<tr class="memitem:a4c47c67ddce12fd93fe84c15f351b389"><td class="memTemplItemLeft" align="right" valign="top">void </td><td class="memTemplItemRight" valign="bottom"><a class="el" href="namespaceviennacl_1_1linalg_1_1host__based_1_1detail.html#a4c47c67ddce12fd93fe84c15f351b389">viennacl::linalg::host_based::detail::pipelined_prod_impl</a> (ell_matrix< <a class="el" href="tests_2src_2bisect_8cpp.html#a52b5d30a2d7b064678644a3bf49b7f6c">NumericT</a> > const &A, vector_base< <a class="el" href="tests_2src_2bisect_8cpp.html#a52b5d30a2d7b064678644a3bf49b7f6c">NumericT</a> > const &p, vector_base< <a class="el" href="tests_2src_2bisect_8cpp.html#a52b5d30a2d7b064678644a3bf49b7f6c">NumericT</a> > &Ap, <a class="el" href="tests_2src_2bisect_8cpp.html#a52b5d30a2d7b064678644a3bf49b7f6c">NumericT</a> const *r0star, vector_base< <a class="el" href="tests_2src_2bisect_8cpp.html#a52b5d30a2d7b064678644a3bf49b7f6c">NumericT</a> > &inner_prod_buffer, vcl_size_t buffer_chunk_size, vcl_size_t buffer_chunk_offset)</td></tr>
<tr class="memdesc:a4c47c67ddce12fd93fe84c15f351b389"><td class="mdescLeft"> </td><td class="mdescRight">Implementation of a fused matrix-vector product with an <a class="el" href="classviennacl_1_1ell__matrix.html" title="Sparse matrix class using the ELLPACK format for storing the nonzeros. ">ell_matrix</a> for an efficient pipelined CG algorithm. <a href="#a4c47c67ddce12fd93fe84c15f351b389">More...</a><br/></td></tr>
<tr class="separator:a4c47c67ddce12fd93fe84c15f351b389"><td class="memSeparator" colspan="2"> </td></tr>
<tr class="memitem:a407ff4933c3fcf3fd01a2a3a34857ebe"><td class="memTemplParams" colspan="2">template<typename NumericT , typename IndexT > </td></tr>
<tr class="memitem:a407ff4933c3fcf3fd01a2a3a34857ebe"><td class="memTemplItemLeft" align="right" valign="top">void </td><td class="memTemplItemRight" valign="bottom"><a class="el" href="namespaceviennacl_1_1linalg_1_1host__based_1_1detail.html#a407ff4933c3fcf3fd01a2a3a34857ebe">viennacl::linalg::host_based::detail::pipelined_prod_impl</a> (sliced_ell_matrix< <a class="el" href="tests_2src_2bisect_8cpp.html#a52b5d30a2d7b064678644a3bf49b7f6c">NumericT</a>, IndexT > const &A, vector_base< <a class="el" href="tests_2src_2bisect_8cpp.html#a52b5d30a2d7b064678644a3bf49b7f6c">NumericT</a> > const &p, vector_base< <a class="el" href="tests_2src_2bisect_8cpp.html#a52b5d30a2d7b064678644a3bf49b7f6c">NumericT</a> > &Ap, <a class="el" href="tests_2src_2bisect_8cpp.html#a52b5d30a2d7b064678644a3bf49b7f6c">NumericT</a> const *r0star, vector_base< <a class="el" href="tests_2src_2bisect_8cpp.html#a52b5d30a2d7b064678644a3bf49b7f6c">NumericT</a> > &inner_prod_buffer, vcl_size_t buffer_chunk_size, vcl_size_t buffer_chunk_offset)</td></tr>
<tr class="memdesc:a407ff4933c3fcf3fd01a2a3a34857ebe"><td class="mdescLeft"> </td><td class="mdescRight">Implementation of a fused matrix-vector product with an <a class="el" href="classviennacl_1_1sliced__ell__matrix.html" title="Sparse matrix class using the sliced ELLPACK with parameters C, . ">sliced_ell_matrix</a> for an efficient pipelined CG algorithm. <a href="#a407ff4933c3fcf3fd01a2a3a34857ebe">More...</a><br/></td></tr>
<tr class="separator:a407ff4933c3fcf3fd01a2a3a34857ebe"><td class="memSeparator" colspan="2"> </td></tr>
<tr class="memitem:ac9d1c21f37de06e65be9d2002fb3b45e"><td class="memTemplParams" colspan="2">template<typename NumericT > </td></tr>
<tr class="memitem:ac9d1c21f37de06e65be9d2002fb3b45e"><td class="memTemplItemLeft" align="right" valign="top">void </td><td class="memTemplItemRight" valign="bottom"><a class="el" href="namespaceviennacl_1_1linalg_1_1host__based_1_1detail.html#ac9d1c21f37de06e65be9d2002fb3b45e">viennacl::linalg::host_based::detail::pipelined_prod_impl</a> (hyb_matrix< <a class="el" href="tests_2src_2bisect_8cpp.html#a52b5d30a2d7b064678644a3bf49b7f6c">NumericT</a> > const &A, vector_base< <a class="el" href="tests_2src_2bisect_8cpp.html#a52b5d30a2d7b064678644a3bf49b7f6c">NumericT</a> > const &p, vector_base< <a class="el" href="tests_2src_2bisect_8cpp.html#a52b5d30a2d7b064678644a3bf49b7f6c">NumericT</a> > &Ap, <a class="el" href="tests_2src_2bisect_8cpp.html#a52b5d30a2d7b064678644a3bf49b7f6c">NumericT</a> const *r0star, vector_base< <a class="el" href="tests_2src_2bisect_8cpp.html#a52b5d30a2d7b064678644a3bf49b7f6c">NumericT</a> > &inner_prod_buffer, vcl_size_t buffer_chunk_size, vcl_size_t buffer_chunk_offset)</td></tr>
<tr class="memdesc:ac9d1c21f37de06e65be9d2002fb3b45e"><td class="mdescLeft"> </td><td class="mdescRight">Implementation of a fused matrix-vector product with an <a class="el" href="classviennacl_1_1hyb__matrix.html" title="Sparse matrix class using a hybrid format composed of the ELL and CSR format for storing the nonzeros...">hyb_matrix</a> for an efficient pipelined CG algorithm. <a href="#ac9d1c21f37de06e65be9d2002fb3b45e">More...</a><br/></td></tr>
<tr class="separator:ac9d1c21f37de06e65be9d2002fb3b45e"><td class="memSeparator" colspan="2"> </td></tr>
<tr class="memitem:aa1f80e9f52a434ea819a0a38c91223a4"><td class="memTemplParams" colspan="2">template<typename NumericT > </td></tr>
<tr class="memitem:aa1f80e9f52a434ea819a0a38c91223a4"><td class="memTemplItemLeft" align="right" valign="top">void </td><td class="memTemplItemRight" valign="bottom"><a class="el" href="namespaceviennacl_1_1linalg_1_1host__based.html#aa1f80e9f52a434ea819a0a38c91223a4">viennacl::linalg::host_based::pipelined_cg_vector_update</a> (vector_base< <a class="el" href="tests_2src_2bisect_8cpp.html#a52b5d30a2d7b064678644a3bf49b7f6c">NumericT</a> > &result, <a class="el" href="tests_2src_2bisect_8cpp.html#a52b5d30a2d7b064678644a3bf49b7f6c">NumericT</a> alpha, vector_base< <a class="el" href="tests_2src_2bisect_8cpp.html#a52b5d30a2d7b064678644a3bf49b7f6c">NumericT</a> > &p, vector_base< <a class="el" href="tests_2src_2bisect_8cpp.html#a52b5d30a2d7b064678644a3bf49b7f6c">NumericT</a> > &r, vector_base< <a class="el" href="tests_2src_2bisect_8cpp.html#a52b5d30a2d7b064678644a3bf49b7f6c">NumericT</a> > const &Ap, <a class="el" href="tests_2src_2bisect_8cpp.html#a52b5d30a2d7b064678644a3bf49b7f6c">NumericT</a> beta, vector_base< <a class="el" href="tests_2src_2bisect_8cpp.html#a52b5d30a2d7b064678644a3bf49b7f6c">NumericT</a> > &inner_prod_buffer)</td></tr>
<tr class="memdesc:aa1f80e9f52a434ea819a0a38c91223a4"><td class="mdescLeft"> </td><td class="mdescRight">Performs a joint vector update operation needed for an efficient pipelined CG algorithm. <a href="#aa1f80e9f52a434ea819a0a38c91223a4">More...</a><br/></td></tr>
<tr class="separator:aa1f80e9f52a434ea819a0a38c91223a4"><td class="memSeparator" colspan="2"> </td></tr>
<tr class="memitem:a9cca5888f4345b3b4f3339c74dbf9042"><td class="memTemplParams" colspan="2">template<typename NumericT > </td></tr>
<tr class="memitem:a9cca5888f4345b3b4f3339c74dbf9042"><td class="memTemplItemLeft" align="right" valign="top">void </td><td class="memTemplItemRight" valign="bottom"><a class="el" href="namespaceviennacl_1_1linalg_1_1host__based.html#a9cca5888f4345b3b4f3339c74dbf9042">viennacl::linalg::host_based::pipelined_cg_prod</a> (compressed_matrix< <a class="el" href="tests_2src_2bisect_8cpp.html#a52b5d30a2d7b064678644a3bf49b7f6c">NumericT</a> > const &A, vector_base< <a class="el" href="tests_2src_2bisect_8cpp.html#a52b5d30a2d7b064678644a3bf49b7f6c">NumericT</a> > const &p, vector_base< <a class="el" href="tests_2src_2bisect_8cpp.html#a52b5d30a2d7b064678644a3bf49b7f6c">NumericT</a> > &Ap, vector_base< <a class="el" href="tests_2src_2bisect_8cpp.html#a52b5d30a2d7b064678644a3bf49b7f6c">NumericT</a> > &inner_prod_buffer)</td></tr>
<tr class="memdesc:a9cca5888f4345b3b4f3339c74dbf9042"><td class="mdescLeft"> </td><td class="mdescRight">Performs a fused matrix-vector product with a <a class="el" href="classviennacl_1_1compressed__matrix.html" title="A sparse square matrix in compressed sparse rows format. ">compressed_matrix</a> for an efficient pipelined CG algorithm. <a href="#a9cca5888f4345b3b4f3339c74dbf9042">More...</a><br/></td></tr>
<tr class="separator:a9cca5888f4345b3b4f3339c74dbf9042"><td class="memSeparator" colspan="2"> </td></tr>
<tr class="memitem:ab3c4e15706647da60ecf0d9bf375bd20"><td class="memTemplParams" colspan="2">template<typename NumericT > </td></tr>
<tr class="memitem:ab3c4e15706647da60ecf0d9bf375bd20"><td class="memTemplItemLeft" align="right" valign="top">void </td><td class="memTemplItemRight" valign="bottom"><a class="el" href="namespaceviennacl_1_1linalg_1_1host__based.html#ab3c4e15706647da60ecf0d9bf375bd20">viennacl::linalg::host_based::pipelined_cg_prod</a> (coordinate_matrix< <a class="el" href="tests_2src_2bisect_8cpp.html#a52b5d30a2d7b064678644a3bf49b7f6c">NumericT</a> > const &A, vector_base< <a class="el" href="tests_2src_2bisect_8cpp.html#a52b5d30a2d7b064678644a3bf49b7f6c">NumericT</a> > const &p, vector_base< <a class="el" href="tests_2src_2bisect_8cpp.html#a52b5d30a2d7b064678644a3bf49b7f6c">NumericT</a> > &Ap, vector_base< <a class="el" href="tests_2src_2bisect_8cpp.html#a52b5d30a2d7b064678644a3bf49b7f6c">NumericT</a> > &inner_prod_buffer)</td></tr>
<tr class="memdesc:ab3c4e15706647da60ecf0d9bf375bd20"><td class="mdescLeft"> </td><td class="mdescRight">Performs a fused matrix-vector product with a <a class="el" href="classviennacl_1_1coordinate__matrix.html" title="A sparse square matrix, where entries are stored as triplets (i,j, val), where i and j are the row an...">coordinate_matrix</a> for an efficient pipelined CG algorithm. <a href="#ab3c4e15706647da60ecf0d9bf375bd20">More...</a><br/></td></tr>
<tr class="separator:ab3c4e15706647da60ecf0d9bf375bd20"><td class="memSeparator" colspan="2"> </td></tr>
<tr class="memitem:aecdb028789f4419f1404533749109529"><td class="memTemplParams" colspan="2">template<typename NumericT > </td></tr>
<tr class="memitem:aecdb028789f4419f1404533749109529"><td class="memTemplItemLeft" align="right" valign="top">void </td><td class="memTemplItemRight" valign="bottom"><a class="el" href="namespaceviennacl_1_1linalg_1_1host__based.html#aecdb028789f4419f1404533749109529">viennacl::linalg::host_based::pipelined_cg_prod</a> (ell_matrix< <a class="el" href="tests_2src_2bisect_8cpp.html#a52b5d30a2d7b064678644a3bf49b7f6c">NumericT</a> > const &A, vector_base< <a class="el" href="tests_2src_2bisect_8cpp.html#a52b5d30a2d7b064678644a3bf49b7f6c">NumericT</a> > const &p, vector_base< <a class="el" href="tests_2src_2bisect_8cpp.html#a52b5d30a2d7b064678644a3bf49b7f6c">NumericT</a> > &Ap, vector_base< <a class="el" href="tests_2src_2bisect_8cpp.html#a52b5d30a2d7b064678644a3bf49b7f6c">NumericT</a> > &inner_prod_buffer)</td></tr>
<tr class="memdesc:aecdb028789f4419f1404533749109529"><td class="mdescLeft"> </td><td class="mdescRight">Performs a fused matrix-vector product with an <a class="el" href="classviennacl_1_1ell__matrix.html" title="Sparse matrix class using the ELLPACK format for storing the nonzeros. ">ell_matrix</a> for an efficient pipelined CG algorithm. <a href="#aecdb028789f4419f1404533749109529">More...</a><br/></td></tr>
<tr class="separator:aecdb028789f4419f1404533749109529"><td class="memSeparator" colspan="2"> </td></tr>
<tr class="memitem:a6824118f240d2035a829f3ad6f7a0550"><td class="memTemplParams" colspan="2">template<typename NumericT , typename IndexT > </td></tr>
<tr class="memitem:a6824118f240d2035a829f3ad6f7a0550"><td class="memTemplItemLeft" align="right" valign="top">void </td><td class="memTemplItemRight" valign="bottom"><a class="el" href="namespaceviennacl_1_1linalg_1_1host__based.html#a6824118f240d2035a829f3ad6f7a0550">viennacl::linalg::host_based::pipelined_cg_prod</a> (sliced_ell_matrix< <a class="el" href="tests_2src_2bisect_8cpp.html#a52b5d30a2d7b064678644a3bf49b7f6c">NumericT</a>, IndexT > const &A, vector_base< <a class="el" href="tests_2src_2bisect_8cpp.html#a52b5d30a2d7b064678644a3bf49b7f6c">NumericT</a> > const &p, vector_base< <a class="el" href="tests_2src_2bisect_8cpp.html#a52b5d30a2d7b064678644a3bf49b7f6c">NumericT</a> > &Ap, vector_base< <a class="el" href="tests_2src_2bisect_8cpp.html#a52b5d30a2d7b064678644a3bf49b7f6c">NumericT</a> > &inner_prod_buffer)</td></tr>
<tr class="memdesc:a6824118f240d2035a829f3ad6f7a0550"><td class="mdescLeft"> </td><td class="mdescRight">Performs a fused matrix-vector product with an <a class="el" href="classviennacl_1_1sliced__ell__matrix.html" title="Sparse matrix class using the sliced ELLPACK with parameters C, . ">sliced_ell_matrix</a> for an efficient pipelined CG algorithm. <a href="#a6824118f240d2035a829f3ad6f7a0550">More...</a><br/></td></tr>
<tr class="separator:a6824118f240d2035a829f3ad6f7a0550"><td class="memSeparator" colspan="2"> </td></tr>
<tr class="memitem:a007721c3f2ab15ac50194467853ab87e"><td class="memTemplParams" colspan="2">template<typename NumericT > </td></tr>
<tr class="memitem:a007721c3f2ab15ac50194467853ab87e"><td class="memTemplItemLeft" align="right" valign="top">void </td><td class="memTemplItemRight" valign="bottom"><a class="el" href="namespaceviennacl_1_1linalg_1_1host__based.html#a007721c3f2ab15ac50194467853ab87e">viennacl::linalg::host_based::pipelined_cg_prod</a> (hyb_matrix< <a class="el" href="tests_2src_2bisect_8cpp.html#a52b5d30a2d7b064678644a3bf49b7f6c">NumericT</a> > const &A, vector_base< <a class="el" href="tests_2src_2bisect_8cpp.html#a52b5d30a2d7b064678644a3bf49b7f6c">NumericT</a> > const &p, vector_base< <a class="el" href="tests_2src_2bisect_8cpp.html#a52b5d30a2d7b064678644a3bf49b7f6c">NumericT</a> > &Ap, vector_base< <a class="el" href="tests_2src_2bisect_8cpp.html#a52b5d30a2d7b064678644a3bf49b7f6c">NumericT</a> > &inner_prod_buffer)</td></tr>
<tr class="memdesc:a007721c3f2ab15ac50194467853ab87e"><td class="mdescLeft"> </td><td class="mdescRight">Performs a fused matrix-vector product with an <a class="el" href="classviennacl_1_1hyb__matrix.html" title="Sparse matrix class using a hybrid format composed of the ELL and CSR format for storing the nonzeros...">hyb_matrix</a> for an efficient pipelined CG algorithm. <a href="#a007721c3f2ab15ac50194467853ab87e">More...</a><br/></td></tr>
<tr class="separator:a007721c3f2ab15ac50194467853ab87e"><td class="memSeparator" colspan="2"> </td></tr>
<tr class="memitem:ac1b61075d4b2a3d605fb08dc0dca95ea"><td class="memTemplParams" colspan="2">template<typename NumericT > </td></tr>
<tr class="memitem:ac1b61075d4b2a3d605fb08dc0dca95ea"><td class="memTemplItemLeft" align="right" valign="top">void </td><td class="memTemplItemRight" valign="bottom"><a class="el" href="namespaceviennacl_1_1linalg_1_1host__based.html#ac1b61075d4b2a3d605fb08dc0dca95ea">viennacl::linalg::host_based::pipelined_bicgstab_update_s</a> (vector_base< <a class="el" href="tests_2src_2bisect_8cpp.html#a52b5d30a2d7b064678644a3bf49b7f6c">NumericT</a> > &s, vector_base< <a class="el" href="tests_2src_2bisect_8cpp.html#a52b5d30a2d7b064678644a3bf49b7f6c">NumericT</a> > &r, vector_base< <a class="el" href="tests_2src_2bisect_8cpp.html#a52b5d30a2d7b064678644a3bf49b7f6c">NumericT</a> > const &Ap, vector_base< <a class="el" href="tests_2src_2bisect_8cpp.html#a52b5d30a2d7b064678644a3bf49b7f6c">NumericT</a> > &inner_prod_buffer, vcl_size_t buffer_chunk_size, vcl_size_t buffer_chunk_offset)</td></tr>
<tr class="memdesc:ac1b61075d4b2a3d605fb08dc0dca95ea"><td class="mdescLeft"> </td><td class="mdescRight">Performs a joint vector update operation needed for an efficient pipelined BiCGStab algorithm. <a href="#ac1b61075d4b2a3d605fb08dc0dca95ea">More...</a><br/></td></tr>
<tr class="separator:ac1b61075d4b2a3d605fb08dc0dca95ea"><td class="memSeparator" colspan="2"> </td></tr>
<tr class="memitem:ae379e200baf8decb11531e9284252bd8"><td class="memTemplParams" colspan="2">template<typename NumericT > </td></tr>
<tr class="memitem:ae379e200baf8decb11531e9284252bd8"><td class="memTemplItemLeft" align="right" valign="top">void </td><td class="memTemplItemRight" valign="bottom"><a class="el" href="namespaceviennacl_1_1linalg_1_1host__based.html#ae379e200baf8decb11531e9284252bd8">viennacl::linalg::host_based::pipelined_bicgstab_vector_update</a> (vector_base< <a class="el" href="tests_2src_2bisect_8cpp.html#a52b5d30a2d7b064678644a3bf49b7f6c">NumericT</a> > &result, <a class="el" href="tests_2src_2bisect_8cpp.html#a52b5d30a2d7b064678644a3bf49b7f6c">NumericT</a> alpha, vector_base< <a class="el" href="tests_2src_2bisect_8cpp.html#a52b5d30a2d7b064678644a3bf49b7f6c">NumericT</a> > &p, <a class="el" href="tests_2src_2bisect_8cpp.html#a52b5d30a2d7b064678644a3bf49b7f6c">NumericT</a> omega, vector_base< <a class="el" href="tests_2src_2bisect_8cpp.html#a52b5d30a2d7b064678644a3bf49b7f6c">NumericT</a> > const &s, vector_base< <a class="el" href="tests_2src_2bisect_8cpp.html#a52b5d30a2d7b064678644a3bf49b7f6c">NumericT</a> > &residual, vector_base< <a class="el" href="tests_2src_2bisect_8cpp.html#a52b5d30a2d7b064678644a3bf49b7f6c">NumericT</a> > const &As, <a class="el" href="tests_2src_2bisect_8cpp.html#a52b5d30a2d7b064678644a3bf49b7f6c">NumericT</a> beta, vector_base< <a class="el" href="tests_2src_2bisect_8cpp.html#a52b5d30a2d7b064678644a3bf49b7f6c">NumericT</a> > const &Ap, vector_base< <a class="el" href="tests_2src_2bisect_8cpp.html#a52b5d30a2d7b064678644a3bf49b7f6c">NumericT</a> > const &r0star, vector_base< <a class="el" href="tests_2src_2bisect_8cpp.html#a52b5d30a2d7b064678644a3bf49b7f6c">NumericT</a> > &inner_prod_buffer, vcl_size_t buffer_chunk_size)</td></tr>
<tr class="memdesc:ae379e200baf8decb11531e9284252bd8"><td class="mdescLeft"> </td><td class="mdescRight">Performs a joint vector update operation needed for an efficient pipelined BiCGStab algorithm. <a href="#ae379e200baf8decb11531e9284252bd8">More...</a><br/></td></tr>
<tr class="separator:ae379e200baf8decb11531e9284252bd8"><td class="memSeparator" colspan="2"> </td></tr>
<tr class="memitem:ac78885423ca2ebe2205b29a213883c61"><td class="memTemplParams" colspan="2">template<typename NumericT > </td></tr>
<tr class="memitem:ac78885423ca2ebe2205b29a213883c61"><td class="memTemplItemLeft" align="right" valign="top">void </td><td class="memTemplItemRight" valign="bottom"><a class="el" href="namespaceviennacl_1_1linalg_1_1host__based.html#ac78885423ca2ebe2205b29a213883c61">viennacl::linalg::host_based::pipelined_bicgstab_prod</a> (compressed_matrix< <a class="el" href="tests_2src_2bisect_8cpp.html#a52b5d30a2d7b064678644a3bf49b7f6c">NumericT</a> > const &A, vector_base< <a class="el" href="tests_2src_2bisect_8cpp.html#a52b5d30a2d7b064678644a3bf49b7f6c">NumericT</a> > const &p, vector_base< <a class="el" href="tests_2src_2bisect_8cpp.html#a52b5d30a2d7b064678644a3bf49b7f6c">NumericT</a> > &Ap, vector_base< <a class="el" href="tests_2src_2bisect_8cpp.html#a52b5d30a2d7b064678644a3bf49b7f6c">NumericT</a> > const &r0star, vector_base< <a class="el" href="tests_2src_2bisect_8cpp.html#a52b5d30a2d7b064678644a3bf49b7f6c">NumericT</a> > &inner_prod_buffer, vcl_size_t buffer_chunk_size, vcl_size_t buffer_chunk_offset)</td></tr>
<tr class="memdesc:ac78885423ca2ebe2205b29a213883c61"><td class="mdescLeft"> </td><td class="mdescRight">Performs a fused matrix-vector product with a <a class="el" href="classviennacl_1_1compressed__matrix.html" title="A sparse square matrix in compressed sparse rows format. ">compressed_matrix</a> for an efficient pipelined BiCGStab algorithm. <a href="#ac78885423ca2ebe2205b29a213883c61">More...</a><br/></td></tr>
<tr class="separator:ac78885423ca2ebe2205b29a213883c61"><td class="memSeparator" colspan="2"> </td></tr>
<tr class="memitem:a2f7dc9950987cea3c1cedc017c8cebef"><td class="memTemplParams" colspan="2">template<typename NumericT > </td></tr>
<tr class="memitem:a2f7dc9950987cea3c1cedc017c8cebef"><td class="memTemplItemLeft" align="right" valign="top">void </td><td class="memTemplItemRight" valign="bottom"><a class="el" href="namespaceviennacl_1_1linalg_1_1host__based.html#a2f7dc9950987cea3c1cedc017c8cebef">viennacl::linalg::host_based::pipelined_bicgstab_prod</a> (coordinate_matrix< <a class="el" href="tests_2src_2bisect_8cpp.html#a52b5d30a2d7b064678644a3bf49b7f6c">NumericT</a> > const &A, vector_base< <a class="el" href="tests_2src_2bisect_8cpp.html#a52b5d30a2d7b064678644a3bf49b7f6c">NumericT</a> > const &p, vector_base< <a class="el" href="tests_2src_2bisect_8cpp.html#a52b5d30a2d7b064678644a3bf49b7f6c">NumericT</a> > &Ap, vector_base< <a class="el" href="tests_2src_2bisect_8cpp.html#a52b5d30a2d7b064678644a3bf49b7f6c">NumericT</a> > const &r0star, vector_base< <a class="el" href="tests_2src_2bisect_8cpp.html#a52b5d30a2d7b064678644a3bf49b7f6c">NumericT</a> > &inner_prod_buffer, vcl_size_t buffer_chunk_size, vcl_size_t buffer_chunk_offset)</td></tr>
<tr class="memdesc:a2f7dc9950987cea3c1cedc017c8cebef"><td class="mdescLeft"> </td><td class="mdescRight">Performs a fused matrix-vector product with a <a class="el" href="classviennacl_1_1coordinate__matrix.html" title="A sparse square matrix, where entries are stored as triplets (i,j, val), where i and j are the row an...">coordinate_matrix</a> for an efficient pipelined BiCGStab algorithm. <a href="#a2f7dc9950987cea3c1cedc017c8cebef">More...</a><br/></td></tr>
<tr class="separator:a2f7dc9950987cea3c1cedc017c8cebef"><td class="memSeparator" colspan="2"> </td></tr>
<tr class="memitem:afab7aa8f81633f10c702f617e27a9b79"><td class="memTemplParams" colspan="2">template<typename NumericT > </td></tr>
<tr class="memitem:afab7aa8f81633f10c702f617e27a9b79"><td class="memTemplItemLeft" align="right" valign="top">void </td><td class="memTemplItemRight" valign="bottom"><a class="el" href="namespaceviennacl_1_1linalg_1_1host__based.html#afab7aa8f81633f10c702f617e27a9b79">viennacl::linalg::host_based::pipelined_bicgstab_prod</a> (ell_matrix< <a class="el" href="tests_2src_2bisect_8cpp.html#a52b5d30a2d7b064678644a3bf49b7f6c">NumericT</a> > const &A, vector_base< <a class="el" href="tests_2src_2bisect_8cpp.html#a52b5d30a2d7b064678644a3bf49b7f6c">NumericT</a> > const &p, vector_base< <a class="el" href="tests_2src_2bisect_8cpp.html#a52b5d30a2d7b064678644a3bf49b7f6c">NumericT</a> > &Ap, vector_base< <a class="el" href="tests_2src_2bisect_8cpp.html#a52b5d30a2d7b064678644a3bf49b7f6c">NumericT</a> > const &r0star, vector_base< <a class="el" href="tests_2src_2bisect_8cpp.html#a52b5d30a2d7b064678644a3bf49b7f6c">NumericT</a> > &inner_prod_buffer, vcl_size_t buffer_chunk_size, vcl_size_t buffer_chunk_offset)</td></tr>
<tr class="memdesc:afab7aa8f81633f10c702f617e27a9b79"><td class="mdescLeft"> </td><td class="mdescRight">Performs a fused matrix-vector product with an <a class="el" href="classviennacl_1_1ell__matrix.html" title="Sparse matrix class using the ELLPACK format for storing the nonzeros. ">ell_matrix</a> for an efficient pipelined BiCGStab algorithm. <a href="#afab7aa8f81633f10c702f617e27a9b79">More...</a><br/></td></tr>
<tr class="separator:afab7aa8f81633f10c702f617e27a9b79"><td class="memSeparator" colspan="2"> </td></tr>
<tr class="memitem:a9adec962a1a08ddb24ab4a9d0a9a19ab"><td class="memTemplParams" colspan="2">template<typename NumericT , typename IndexT > </td></tr>
<tr class="memitem:a9adec962a1a08ddb24ab4a9d0a9a19ab"><td class="memTemplItemLeft" align="right" valign="top">void </td><td class="memTemplItemRight" valign="bottom"><a class="el" href="namespaceviennacl_1_1linalg_1_1host__based.html#a9adec962a1a08ddb24ab4a9d0a9a19ab">viennacl::linalg::host_based::pipelined_bicgstab_prod</a> (sliced_ell_matrix< <a class="el" href="tests_2src_2bisect_8cpp.html#a52b5d30a2d7b064678644a3bf49b7f6c">NumericT</a>, IndexT > const &A, vector_base< <a class="el" href="tests_2src_2bisect_8cpp.html#a52b5d30a2d7b064678644a3bf49b7f6c">NumericT</a> > const &p, vector_base< <a class="el" href="tests_2src_2bisect_8cpp.html#a52b5d30a2d7b064678644a3bf49b7f6c">NumericT</a> > &Ap, vector_base< <a class="el" href="tests_2src_2bisect_8cpp.html#a52b5d30a2d7b064678644a3bf49b7f6c">NumericT</a> > const &r0star, vector_base< <a class="el" href="tests_2src_2bisect_8cpp.html#a52b5d30a2d7b064678644a3bf49b7f6c">NumericT</a> > &inner_prod_buffer, vcl_size_t buffer_chunk_size, vcl_size_t buffer_chunk_offset)</td></tr>
<tr class="memdesc:a9adec962a1a08ddb24ab4a9d0a9a19ab"><td class="mdescLeft"> </td><td class="mdescRight">Performs a fused matrix-vector product with a <a class="el" href="classviennacl_1_1sliced__ell__matrix.html" title="Sparse matrix class using the sliced ELLPACK with parameters C, . ">sliced_ell_matrix</a> for an efficient pipelined BiCGStab algorithm. <a href="#a9adec962a1a08ddb24ab4a9d0a9a19ab">More...</a><br/></td></tr>
<tr class="separator:a9adec962a1a08ddb24ab4a9d0a9a19ab"><td class="memSeparator" colspan="2"> </td></tr>
<tr class="memitem:a7b99fb1a7b869068376e649386b0f6bd"><td class="memTemplParams" colspan="2">template<typename NumericT > </td></tr>
<tr class="memitem:a7b99fb1a7b869068376e649386b0f6bd"><td class="memTemplItemLeft" align="right" valign="top">void </td><td class="memTemplItemRight" valign="bottom"><a class="el" href="namespaceviennacl_1_1linalg_1_1host__based.html#a7b99fb1a7b869068376e649386b0f6bd">viennacl::linalg::host_based::pipelined_bicgstab_prod</a> (hyb_matrix< <a class="el" href="tests_2src_2bisect_8cpp.html#a52b5d30a2d7b064678644a3bf49b7f6c">NumericT</a> > const &A, vector_base< <a class="el" href="tests_2src_2bisect_8cpp.html#a52b5d30a2d7b064678644a3bf49b7f6c">NumericT</a> > const &p, vector_base< <a class="el" href="tests_2src_2bisect_8cpp.html#a52b5d30a2d7b064678644a3bf49b7f6c">NumericT</a> > &Ap, vector_base< <a class="el" href="tests_2src_2bisect_8cpp.html#a52b5d30a2d7b064678644a3bf49b7f6c">NumericT</a> > const &r0star, vector_base< <a class="el" href="tests_2src_2bisect_8cpp.html#a52b5d30a2d7b064678644a3bf49b7f6c">NumericT</a> > &inner_prod_buffer, vcl_size_t buffer_chunk_size, vcl_size_t buffer_chunk_offset)</td></tr>
<tr class="memdesc:a7b99fb1a7b869068376e649386b0f6bd"><td class="mdescLeft"> </td><td class="mdescRight">Performs a fused matrix-vector product with a <a class="el" href="classviennacl_1_1hyb__matrix.html" title="Sparse matrix class using a hybrid format composed of the ELL and CSR format for storing the nonzeros...">hyb_matrix</a> for an efficient pipelined BiCGStab algorithm. <a href="#a7b99fb1a7b869068376e649386b0f6bd">More...</a><br/></td></tr>
<tr class="separator:a7b99fb1a7b869068376e649386b0f6bd"><td class="memSeparator" colspan="2"> </td></tr>
<tr class="memitem:abb2b03970007010e3989b6474d5c8061"><td class="memTemplParams" colspan="2">template<typename T > </td></tr>
<tr class="memitem:abb2b03970007010e3989b6474d5c8061"><td class="memTemplItemLeft" align="right" valign="top">void </td><td class="memTemplItemRight" valign="bottom"><a class="el" href="namespaceviennacl_1_1linalg_1_1host__based.html#abb2b03970007010e3989b6474d5c8061">viennacl::linalg::host_based::pipelined_gmres_normalize_vk</a> (vector_base< T > &v_k, vector_base< T > const &residual, vector_base< T > &R_buffer, vcl_size_t offset_in_R, vector_base< T > const &inner_prod_buffer, vector_base< T > &r_dot_vk_buffer, vcl_size_t buffer_chunk_size, vcl_size_t buffer_chunk_offset)</td></tr>
<tr class="memdesc:abb2b03970007010e3989b6474d5c8061"><td class="mdescLeft"> </td><td class="mdescRight">Performs a vector normalization needed for an efficient pipelined GMRES algorithm. <a href="#abb2b03970007010e3989b6474d5c8061">More...</a><br/></td></tr>
<tr class="separator:abb2b03970007010e3989b6474d5c8061"><td class="memSeparator" colspan="2"> </td></tr>
<tr class="memitem:aa17b7680809f798794a5c2dc863931a6"><td class="memTemplParams" colspan="2">template<typename T > </td></tr>
<tr class="memitem:aa17b7680809f798794a5c2dc863931a6"><td class="memTemplItemLeft" align="right" valign="top">void </td><td class="memTemplItemRight" valign="bottom"><a class="el" href="namespaceviennacl_1_1linalg_1_1host__based.html#aa17b7680809f798794a5c2dc863931a6">viennacl::linalg::host_based::pipelined_gmres_gram_schmidt_stage1</a> (vector_base< T > const &device_krylov_basis, vcl_size_t v_k_size, vcl_size_t v_k_internal_size, vcl_size_t k, vector_base< T > &vi_in_vk_buffer, vcl_size_t buffer_chunk_size)</td></tr>
<tr class="memdesc:aa17b7680809f798794a5c2dc863931a6"><td class="mdescLeft"> </td><td class="mdescRight">Computes first reduction stage for multiple inner products <v_i, v_k>, i=0..k-1. <a href="#aa17b7680809f798794a5c2dc863931a6">More...</a><br/></td></tr>
<tr class="separator:aa17b7680809f798794a5c2dc863931a6"><td class="memSeparator" colspan="2"> </td></tr>
<tr class="memitem:a8e4aac801d1aeb7868dad15fc9691485"><td class="memTemplParams" colspan="2">template<typename T > </td></tr>
<tr class="memitem:a8e4aac801d1aeb7868dad15fc9691485"><td class="memTemplItemLeft" align="right" valign="top">void </td><td class="memTemplItemRight" valign="bottom"><a class="el" href="namespaceviennacl_1_1linalg_1_1host__based.html#a8e4aac801d1aeb7868dad15fc9691485">viennacl::linalg::host_based::pipelined_gmres_gram_schmidt_stage2</a> (vector_base< T > &device_krylov_basis, vcl_size_t v_k_size, vcl_size_t v_k_internal_size, vcl_size_t k, vector_base< T > const &vi_in_vk_buffer, vector_base< T > &R_buffer, vcl_size_t krylov_dim, vector_base< T > &inner_prod_buffer, vcl_size_t buffer_chunk_size)</td></tr>
<tr class="memdesc:a8e4aac801d1aeb7868dad15fc9691485"><td class="mdescLeft"> </td><td class="mdescRight">Computes the second reduction stage for multiple inner products <v_i, v_k>, i=0..k-1, then updates v_k -= <v_i, v_k> v_i and computes the first reduction stage for ||v_k||. <a href="#a8e4aac801d1aeb7868dad15fc9691485">More...</a><br/></td></tr>
<tr class="separator:a8e4aac801d1aeb7868dad15fc9691485"><td class="memSeparator" colspan="2"> </td></tr>
<tr class="memitem:af497e625d846f3e219b9467b82cda386"><td class="memTemplParams" colspan="2">template<typename T > </td></tr>
<tr class="memitem:af497e625d846f3e219b9467b82cda386"><td class="memTemplItemLeft" align="right" valign="top">void </td><td class="memTemplItemRight" valign="bottom"><a class="el" href="namespaceviennacl_1_1linalg_1_1host__based.html#af497e625d846f3e219b9467b82cda386">viennacl::linalg::host_based::pipelined_gmres_update_result</a> (vector_base< T > &result, vector_base< T > const &residual, vector_base< T > const &krylov_basis, vcl_size_t v_k_size, vcl_size_t v_k_internal_size, vector_base< T > const &coefficients, vcl_size_t k)</td></tr>
<tr class="memdesc:af497e625d846f3e219b9467b82cda386"><td class="mdescLeft"> </td><td class="mdescRight">Computes x += eta_0 r + sum_{i=1}^{k-1} eta_i v_{i-1}. <a href="#af497e625d846f3e219b9467b82cda386">More...</a><br/></td></tr>
<tr class="separator:af497e625d846f3e219b9467b82cda386"><td class="memSeparator" colspan="2"> </td></tr>
<tr class="memitem:aeb57ebff0a27bc75b9340df9a99bf155"><td class="memTemplParams" colspan="2">template<typename MatrixType , typename T > </td></tr>
<tr class="memitem:aeb57ebff0a27bc75b9340df9a99bf155"><td class="memTemplItemLeft" align="right" valign="top">void </td><td class="memTemplItemRight" valign="bottom"><a class="el" href="namespaceviennacl_1_1linalg_1_1host__based.html#aeb57ebff0a27bc75b9340df9a99bf155">viennacl::linalg::host_based::pipelined_gmres_prod</a> (MatrixType const &A, vector_base< T > const &p, vector_base< T > &Ap, vector_base< T > &inner_prod_buffer)</td></tr>
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<a name="details" id="details"></a><h2 class="groupheader">Detailed Description</h2>
<div class="textblock"><p>Implementations of specialized kernels for fast iterative solvers using OpenMP on the CPU. </p>
<p>Definition in file <a class="el" href="host__based_2iterative__operations_8hpp_source.html">iterative_operations.hpp</a>.</p>
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<td class="memname">#define VIENNACL_OPENMP_VECTOR_MIN_SIZE   5000</td>
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<p>Definition at line <a class="el" href="host__based_2iterative__operations_8hpp_source.html#l00042">42</a> of file <a class="el" href="host__based_2iterative__operations_8hpp_source.html">iterative_operations.hpp</a>.</p>
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