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 <tr class="memitem:af55f617654efc870d5cd468dd4cf98fd"><td class="memTemplItemLeft" align="right" valign="top">void&#160;</td><td class="memTemplItemRight" valign="bottom"><a class="el" href="namespacemxnet_1_1common_1_1cuda_1_1rtc.html#af55f617654efc870d5cd468dd4cf98fd">VectorizedKernelRTCLauncher</a> (const std::string &amp;parameters, const std::string &amp;kernel_name, const std::string &amp;code, int nvec, const <a class="el" href="namespacemxnet.html#abadedc1b42ee3e5b8852e63783b982ef">index_t</a> lead_dim, const <a class="el" href="namespacemxnet.html#abadedc1b42ee3e5b8852e63783b982ef">index_t</a> other_dim, <a class="el" href="structmshadow_1_1Stream.html">mshadow::Stream</a>&lt; <a class="el" href="namespacemxnet.html#a31ef33571a4f6ba8847eeee1f4907141">gpu</a> &gt; *s, const Params params, const std::vector&lt; <a class="el" href="classmxnet_1_1TBlob.html">TBlob</a> &gt; &amp;inputs, const std::vector&lt; <a class="el" href="classmxnet_1_1TBlob.html">TBlob</a> &gt; &amp;outputs, const int dev_id, const int lead_input_num=0, const <a class="el" href="namespacemxnet.html#abadedc1b42ee3e5b8852e63783b982ef">index_t</a> blocks=0)</td></tr>
 <tr class="memdesc:af55f617654efc870d5cd468dd4cf98fd"><td class="mdescLeft">&#160;</td><td class="mdescRight">Launcher helper for the kernels using vectorization.  <a href="namespacemxnet_1_1common_1_1cuda_1_1rtc.html#af55f617654efc870d5cd468dd4cf98fd">More...</a><br /></td></tr>
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 <tr class="memdesc:aa376584cbd919b0c3cf23f22bbe948fd"><td class="mdescLeft">&#160;</td><td class="mdescRight">Compile and get the GPU kernel. Uses cache in order to eliminate the overhead of compilation.  <a href="namespacemxnet_1_1common_1_1cuda_1_1rtc.html#aa376584cbd919b0c3cf23f22bbe948fd">More...</a><br /></td></tr>
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 <tr class="memdesc:a9732a1d386bd52d6e11f19ab02be34b1"><td class="mdescLeft">&#160;</td><td class="mdescRight">Launch a GPU kernel.  <a href="namespacemxnet_1_1common_1_1cuda_1_1rtc.html#a9732a1d386bd52d6e11f19ab02be34b1">More...</a><br /></td></tr>
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 <h2 class="memtitle"><span class="permalink"><a href="#aa376584cbd919b0c3cf23f22bbe948fd">&#9670;&nbsp;</a></span>get_function()</h2>

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           <td class="memname">CUfunction mxnet::common::cuda::rtc::get_function </td>
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 <p>Compile and get the GPU kernel. Uses cache in order to eliminate the overhead of compilation. </p>
 <dl class="params"><dt>Parameters</dt><dd>
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     <tr><td class="paramname">parameters</td><td>of the kernel (e.g. values of the template arguments, types used) </td></tr>
     <tr><td class="paramname">kernel_name</td><td>name of the kernel </td></tr>
     <tr><td class="paramname">code</td><td>used for compilation of the kernel if not found in cache </td></tr>
     <tr><td class="paramname">dev_id</td><td>id of the device which the kernel will be launched on </td></tr>
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 </dl>

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 <a id="a6e2afffb9b744b04aa64c8ceafd51aae"></a>
 <h2 class="memtitle"><span class="permalink"><a href="#a6e2afffb9b744b04aa64c8ceafd51aae">&#9670;&nbsp;</a></span>GetMaxSupportedArch()</h2>

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           <td class="memname">int mxnet::common::cuda::rtc::GetMaxSupportedArch </td>
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 </div><div class="memdoc">

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 <a id="a9732a1d386bd52d6e11f19ab02be34b1"></a>
 <h2 class="memtitle"><span class="permalink"><a href="#a9732a1d386bd52d6e11f19ab02be34b1">&#9670;&nbsp;</a></span>launch()</h2>

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           <td class="memname">void mxnet::common::cuda::rtc::launch </td>
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 <p>Launch a GPU kernel. </p>
 <dl class="params"><dt>Parameters</dt><dd>
   <table class="params">
     <tr><td class="paramname">function</td><td>to launch </td></tr>
     <tr><td class="paramname">grid_dim</td><td>grid dimensions </td></tr>
     <tr><td class="paramname">block_dim</td><td>block dimensions </td></tr>
     <tr><td class="paramname">shared_mem_bytes</td><td>amount of dynamic shared memory needed by the kernel </td></tr>
     <tr><td class="paramname">stream</td><td>used for launching the kernel </td></tr>
     <tr><td class="paramname">args</td><td>arguments of the kernel </td></tr>
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 <a id="af55f617654efc870d5cd468dd4cf98fd"></a>
 <h2 class="memtitle"><span class="permalink"><a href="#af55f617654efc870d5cd468dd4cf98fd">&#9670;&nbsp;</a></span>VectorizedKernelRTCLauncher()</h2>

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 <p>Launcher helper for the kernels using vectorization. </p>
 <dl class="params"><dt>Parameters</dt><dd>
   <table class="params">
     <tr><td class="paramname">parameters</td><td>of the kernel (e.g. values of the template arguments) </td></tr>
     <tr><td class="paramname">kernel_name</td><td>name of the kernel </td></tr>
     <tr><td class="paramname">code</td><td>used for compilation of the kernel if not found in cache </td></tr>
     <tr><td class="paramname">nvec</td><td>length of the vector used for loading/storing data </td></tr>
     <tr><td class="paramname">lead_dim</td><td>size of leading dimension of the tensors </td></tr>
     <tr><td class="paramname">other_dim</td><td>maximum of the total size of all the other dimensions of the tensors </td></tr>
     <tr><td class="paramname">s</td><td>stream used to launch the kernel </td></tr>
     <tr><td class="paramname">inputs</td><td>to the kernel </td></tr>
     <tr><td class="paramname">outputs</td><td>of the kernel </td></tr>
     <tr><td class="paramname">dev_id</td><td>id of the devide which the kernel will be launched on </td></tr>
     <tr><td class="paramname">lead_input_num</td><td>number of input to use for checking alignment (in case only a subset of inputs is used vectorized). Default is 0. </td></tr>
     <tr><td class="paramname">blocks</td><td>if provided and not 0, will launch the specified number of thread blocks. Default is 0. </td></tr>
   </table>
   </dd>
 </dl>

 </div>
 </div>
 <h2 class="groupheader">Variable Documentation</h2>
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 <h2 class="memtitle"><span class="permalink"><a href="#af9ebdc3671fe5eb11c4a5aec4cfecdbe">&#9670;&nbsp;</a></span>backward_function_definitions</h2>

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