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<h1>
<span class="m-breadcrumb"><a href="cudaStandardAlgorithms.html">CUDA Standard Algorithms</a> »</span>
Parallel Merge
</h1>
<div class="m-block m-default">
<h3>Contents</h3>
<ul>
<li><a href="#CUDASTDMergeIncludeTheHeader">Include the Header</a></li>
<li><a href="#CUDASTDMergeItems">Merge Two Sorted Ranges of Items</a></li>
<li><a href="#CUDASTDMergeKeyValueItems">Merge Two Sorted Ranges of Key-Value Items</a></li>
</ul>
</div>
<p>Taskflow provides standalone template methods for merging two sorted ranges of items into a sorted range of items.</p><section id="CUDASTDMergeIncludeTheHeader"><h2><a href="#CUDASTDMergeIncludeTheHeader">Include the Header</a></h2><p>You need to include the header file, <code>taskflow/cuda/algorithm/merge.hpp</code>, for using the parallel-merge algorithm.</p></section><section id="CUDASTDMergeItems"><h2><a href="#CUDASTDMergeItems">Merge Two Sorted Ranges of Items</a></h2><p><a href="namespacetf.html#a37ec481149c2f01669353033d75ed72a" class="m-doc">tf::<wbr />cuda_merge</a> merges two sorted ranges of items into a sorted range. The following code merges two sorted arrays <code>input_1</code> and <code>input_2</code>, each of 1000 items, into a sorted array <code>output</code> of 2000 items.</p><pre class="m-code"><span class="k">const</span> <span class="kt">size_t</span> <span class="n">N</span> <span class="o">=</span> <span class="mi">1000</span><span class="p">;</span>
<span class="kt">int</span><span class="o">*</span> <span class="n">input_1</span> <span class="o">=</span> <span class="n">tf</span><span class="o">::</span><span class="n">cuda_malloc_shared</span><span class="o"><</span><span class="kt">int</span><span class="o">></span><span class="p">(</span><span class="n">N</span><span class="p">);</span> <span class="c1">// input vector 1</span>
<span class="kt">int</span><span class="o">*</span> <span class="n">input_2</span> <span class="o">=</span> <span class="n">tf</span><span class="o">::</span><span class="n">cuda_malloc_shared</span><span class="o"><</span><span class="kt">int</span><span class="o">></span><span class="p">(</span><span class="n">N</span><span class="p">);</span> <span class="c1">// input vector 2</span>
<span class="kt">int</span><span class="o">*</span> <span class="n">output</span> <span class="o">=</span> <span class="n">tf</span><span class="o">::</span><span class="n">cuda_malloc_shared</span><span class="o"><</span><span class="kt">int</span><span class="o">></span><span class="p">(</span><span class="mi">2</span><span class="o">*</span><span class="n">N</span><span class="p">);</span> <span class="c1">// output vector</span>
<span class="c1">// initializes the data</span>
<span class="k">for</span><span class="p">(</span><span class="kt">size_t</span> <span class="n">i</span><span class="o">=</span><span class="mi">0</span><span class="p">;</span> <span class="n">i</span><span class="o"><</span><span class="n">N</span><span class="p">;</span> <span class="n">i</span><span class="o">++</span><span class="p">)</span> <span class="p">{</span>
<span class="n">input_1</span><span class="p">[</span><span class="n">i</span><span class="p">]</span> <span class="o">=</span> <span class="n">rand</span><span class="p">()</span><span class="o">%</span><span class="mi">100</span><span class="p">;</span>
<span class="n">input_2</span><span class="p">[</span><span class="n">i</span><span class="p">]</span> <span class="o">=</span> <span class="n">rand</span><span class="p">()</span><span class="o">%</span><span class="mi">100</span><span class="p">;</span>
<span class="p">}</span>
<span class="n">std</span><span class="o">::</span><span class="n">sort</span><span class="p">(</span><span class="n">input_1</span><span class="p">,</span> <span class="n">input1</span> <span class="o">+</span> <span class="n">N</span><span class="p">);</span>
<span class="n">std</span><span class="o">::</span><span class="n">sort</span><span class="p">(</span><span class="n">input_2</span><span class="p">,</span> <span class="n">input2</span> <span class="o">+</span> <span class="n">N</span><span class="p">);</span>
<span class="c1">// queries the required buffer size to merge two N-element vectors </span>
<span class="n">tf</span><span class="o">::</span><span class="n">cudaDefaultExecutionPolicy</span> <span class="n">policy</span><span class="p">;</span>
<span class="k">auto</span> <span class="n">bytes</span> <span class="o">=</span> <span class="n">tf</span><span class="o">::</span><span class="n">cuda_merge_buffer_size</span><span class="o"><</span><span class="n">tf</span><span class="o">::</span><span class="n">cudaDefaultExecutionPolicy</span><span class="o">></span><span class="p">(</span><span class="n">N</span><span class="p">,</span> <span class="n">N</span><span class="p">);</span>
<span class="k">auto</span> <span class="n">buffer</span> <span class="o">=</span> <span class="n">tf</span><span class="o">::</span><span class="n">cuda_malloc_device</span><span class="o"><</span><span class="n">std</span><span class="o">::</span><span class="n">byte</span><span class="o">></span><span class="p">(</span><span class="n">bytes</span><span class="p">);</span>
<span class="c1">// merge input_1 and input_2 to output</span>
<span class="n">tf</span><span class="o">::</span><span class="n">cuda_merge</span><span class="p">(</span><span class="n">policy</span><span class="p">,</span>
<span class="n">input_1</span><span class="p">,</span> <span class="n">input_1</span> <span class="o">+</span> <span class="n">N</span><span class="p">,</span> <span class="n">input_2</span><span class="p">,</span> <span class="n">input_2</span> <span class="o">+</span> <span class="n">N</span><span class="p">,</span> <span class="n">output</span><span class="p">,</span>
<span class="p">[]</span><span class="n">__device__</span> <span class="p">(</span><span class="kt">int</span> <span class="n">a</span><span class="p">,</span> <span class="kt">int</span> <span class="n">b</span><span class="p">)</span> <span class="p">{</span> <span class="k">return</span> <span class="n">a</span> <span class="o"><</span> <span class="n">b</span><span class="p">;</span> <span class="p">},</span> <span class="c1">// comparator</span>
<span class="n">buffer</span>
<span class="p">);</span>
<span class="c1">// synchronizes the execution and verifies the result</span>
<span class="n">policy</span><span class="p">.</span><span class="n">synchronize</span><span class="p">();</span>
<span class="n">assert</span><span class="p">(</span><span class="n">std</span><span class="o">::</span><span class="n">is_sorted</span><span class="p">(</span><span class="n">output</span><span class="p">,</span> <span class="n">output</span> <span class="o">+</span> <span class="mi">2</span><span class="o">*</span><span class="n">N</span><span class="p">));</span>
<span class="c1">// delete the buffer</span>
<span class="n">tf</span><span class="o">::</span><span class="n">cuda_free</span><span class="p">(</span><span class="n">buffer</span><span class="p">);</span></pre><p>The merge algorithm runs <em>asynchronously</em> through the stream specified in the execution policy. You need to synchronize the stream to obtain correct results. Since the GPU merge algorithm may require extra buffer to store the temporary results, you must provide a buffer of size at least bytes returned from <a href="namespacetf.html#a3db5b8f718e7f6a6fa2da3a689bd8828" class="m-doc">tf::<wbr />cuda_merge_buffer_size</a>.</p><aside class="m-note m-warning"><h4>Attention</h4><p>You must keep the buffer alive before the merge call completes.</p></aside></section><section id="CUDASTDMergeKeyValueItems"><h2><a href="#CUDASTDMergeKeyValueItems">Merge Two Sorted Ranges of Key-Value Items</a></h2><p><a href="namespacetf.html#aa84d4c68d2cbe9f6efc4a1eb1a115458" class="m-doc">tf::<wbr />cuda_merge_by_key</a> performs key-value merge over two sorted ranges in a similar way to <a href="namespacetf.html#a37ec481149c2f01669353033d75ed72a" class="m-doc">tf::<wbr />cuda_merge</a>; additionally, it copies elements from the two ranges of values associated with the two input keys, respectively. The following code performs key-value merge over <code>a</code> and <code>b:</code></p><pre class="m-code"><span class="k">const</span> <span class="kt">size_t</span> <span class="n">N</span> <span class="o">=</span> <span class="mi">2</span><span class="p">;</span>
<span class="kt">int</span><span class="o">*</span> <span class="n">a_keys</span> <span class="o">=</span> <span class="n">tf</span><span class="o">::</span><span class="n">cuda_malloc_shared</span><span class="o"><</span><span class="kt">int</span><span class="o">></span><span class="p">(</span><span class="n">N</span><span class="p">);</span>
<span class="kt">int</span><span class="o">*</span> <span class="n">a_vals</span> <span class="o">=</span> <span class="n">tf</span><span class="o">::</span><span class="n">cuda_malloc_shared</span><span class="o"><</span><span class="kt">int</span><span class="o">></span><span class="p">(</span><span class="n">N</span><span class="p">);</span>
<span class="kt">int</span><span class="o">*</span> <span class="n">b_keys</span> <span class="o">=</span> <span class="n">tf</span><span class="o">::</span><span class="n">cuda_malloc_shared</span><span class="o"><</span><span class="kt">int</span><span class="o">></span><span class="p">(</span><span class="n">N</span><span class="p">);</span>
<span class="kt">int</span><span class="o">*</span> <span class="n">b_vals</span> <span class="o">=</span> <span class="n">tf</span><span class="o">::</span><span class="n">cuda_malloc_shared</span><span class="o"><</span><span class="kt">int</span><span class="o">></span><span class="p">(</span><span class="n">N</span><span class="p">);</span>
<span class="kt">int</span><span class="o">*</span> <span class="n">c_keys</span> <span class="o">=</span> <span class="n">tf</span><span class="o">::</span><span class="n">cuda_malloc_shared</span><span class="o"><</span><span class="kt">int</span><span class="o">></span><span class="p">(</span><span class="mi">2</span><span class="o">*</span><span class="n">N</span><span class="p">);</span>
<span class="kt">int</span><span class="o">*</span> <span class="n">c_vals</span> <span class="o">=</span> <span class="n">tf</span><span class="o">::</span><span class="n">cuda_malloc_shared</span><span class="o"><</span><span class="kt">int</span><span class="o">></span><span class="p">(</span><span class="mi">2</span><span class="o">*</span><span class="n">N</span><span class="p">);</span>
<span class="c1">// initializes the data</span>
<span class="n">a_keys</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span> <span class="o">=</span> <span class="mi">8</span><span class="p">,</span> <span class="n">a_keys</span><span class="p">[</span><span class="mi">1</span><span class="p">]</span> <span class="o">=</span> <span class="mi">1</span><span class="p">;</span>
<span class="n">a_vals</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span> <span class="o">=</span> <span class="mi">1</span><span class="p">,</span> <span class="n">a_vals</span><span class="p">[</span><span class="mi">1</span><span class="p">]</span> <span class="o">=</span> <span class="mi">2</span><span class="p">;</span>
<span class="n">b_keys</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span> <span class="o">=</span> <span class="mi">3</span><span class="p">,</span> <span class="n">b_keys</span><span class="p">[</span><span class="mi">1</span><span class="p">]</span> <span class="o">=</span> <span class="mi">7</span><span class="p">;</span>
<span class="n">b_vals</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span> <span class="o">=</span> <span class="mi">3</span><span class="p">,</span> <span class="n">b_vals</span><span class="p">[</span><span class="mi">1</span><span class="p">]</span> <span class="o">=</span> <span class="mi">4</span><span class="p">;</span>
<span class="c1">// queries the required buffer size to merge the two key-value items</span>
<span class="n">tf</span><span class="o">::</span><span class="n">cudaDefaultExecutionPolicy</span> <span class="n">policy</span><span class="p">;</span>
<span class="k">auto</span> <span class="n">bytes</span> <span class="o">=</span> <span class="n">tf</span><span class="o">::</span><span class="n">cuda_merge_buffer_size</span><span class="o"><</span><span class="n">tf</span><span class="o">::</span><span class="n">cudaDefaultExecutionPolicy</span><span class="o">></span><span class="p">(</span><span class="n">N</span><span class="p">,</span> <span class="n">N</span><span class="p">);</span>
<span class="k">auto</span> <span class="n">buffer</span> <span class="o">=</span> <span class="n">tf</span><span class="o">::</span><span class="n">cuda_malloc_device</span><span class="o"><</span><span class="n">std</span><span class="o">::</span><span class="n">byte</span><span class="o">></span><span class="p">(</span><span class="n">bytes</span><span class="p">);</span>
<span class="c1">// merge keys and values of a and b to c</span>
<span class="n">tf</span><span class="o">::</span><span class="n">cuda_merge_by_key</span><span class="p">(</span>
<span class="n">policy</span><span class="p">,</span>
<span class="n">a_keys</span><span class="p">,</span> <span class="n">a_keys</span><span class="o">+</span><span class="n">N</span><span class="p">,</span> <span class="n">a_vals</span><span class="p">,</span>
<span class="n">b_keys</span><span class="p">,</span> <span class="n">b_keys</span><span class="o">+</span><span class="n">N</span><span class="p">,</span> <span class="n">b_vals</span><span class="p">,</span>
<span class="n">c_keys</span><span class="p">,</span> <span class="n">c_vals</span><span class="p">,</span>
<span class="p">[]</span><span class="n">__device__</span> <span class="p">(</span><span class="kt">int</span> <span class="n">a</span><span class="p">,</span> <span class="kt">int</span> <span class="n">b</span><span class="p">)</span> <span class="p">{</span> <span class="k">return</span> <span class="n">a</span> <span class="o"><</span> <span class="n">b</span><span class="p">;</span> <span class="p">},</span> <span class="c1">// comparator</span>
<span class="n">buffer</span>
<span class="p">);</span>
<span class="n">policy</span><span class="p">.</span><span class="n">synchronize</span><span class="p">();</span>
<span class="c1">// now, c_keys = {1, 3, 7, 8}</span>
<span class="c1">// now, c_vals = {2, 3, 4, 1}</span>
<span class="c1">// delete the device memory</span>
<span class="n">tf</span><span class="o">::</span><span class="n">cuda_free</span><span class="p">(</span><span class="n">buffer</span><span class="p">);</span>
<span class="n">tf</span><span class="o">::</span><span class="n">cuda_free</span><span class="p">(</span><span class="n">a_keys</span><span class="p">);</span>
<span class="n">tf</span><span class="o">::</span><span class="n">cuda_free</span><span class="p">(</span><span class="n">b_keys</span><span class="p">);</span>
<span class="n">tf</span><span class="o">::</span><span class="n">cuda_free</span><span class="p">(</span><span class="n">c_keys</span><span class="p">);</span>
<span class="n">tf</span><span class="o">::</span><span class="n">cuda_free</span><span class="p">(</span><span class="n">a_vals</span><span class="p">);</span>
<span class="n">tf</span><span class="o">::</span><span class="n">cuda_free</span><span class="p">(</span><span class="n">b_vals</span><span class="p">);</span>
<span class="n">tf</span><span class="o">::</span><span class="n">cuda_free</span><span class="p">(</span><span class="n">c_vals</span><span class="p">);</span></pre><p>The buffer size required by <a href="namespacetf.html#aa84d4c68d2cbe9f6efc4a1eb1a115458" class="m-doc">tf::<wbr />cuda_merge_by_key</a> is the same as <a href="namespacetf.html#a37ec481149c2f01669353033d75ed72a" class="m-doc">tf::<wbr />cuda_merge</a> and must be at least equal to or larger than the value returned by <a href="namespacetf.html#a3db5b8f718e7f6a6fa2da3a689bd8828" class="m-doc">tf::<wbr />cuda_merge_buffer_size</a>.</p></section>
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<p>Taskflow handbook is part of the <a href="https://taskflow.github.io">Taskflow project</a>, copyright © <a href="https://tsung-wei-huang.github.io/">Dr. Tsung-Wei Huang</a>, 2018–2022.<br />Generated by <a href="https://doxygen.org/">Doxygen</a> 1.8.14 and <a href="https://mcss.mosra.cz/">m.css</a>.</p>
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