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<span class="m-breadcrumb"><a href="cudaFlowAlgorithms.html">cudaFlow Algorithms</a> »</span>
Parallel Sort
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<h3>Contents</h3>
<ul>
<li><a href="#CUDAParallelSortIncludeTheHeader">Include the Header</a></li>
<li><a href="#cudaFlowSortARangeofItems">Sort a Range of Items</a></li>
<li><a href="#cudaFlowSortKeyValueItems">Sort a Range of Key-Value Items</a></li>
<li><a href="#cudaFlowSortMiscellaneousItems">Miscellaneous Items</a></li>
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<p>cudaFlow provides template methods to create parallel sort tasks on a CUDA GPU.</p><section id="CUDAParallelSortIncludeTheHeader"><h2><a href="#CUDAParallelSortIncludeTheHeader">Include the Header</a></h2><p>You need to include the header file, <code>taskflow/cuda/algorithm/sort.hpp</code>, for creating a parallel-sort task.</p></section><section id="cudaFlowSortARangeofItems"><h2><a href="#cudaFlowSortARangeofItems">Sort a Range of Items</a></h2><p><a href="classtf_1_1cudaFlow.html#ae462d455fed06dfcdbd1e25a2c9c5da6" class="m-doc">tf::<wbr />cudaFlow::<wbr />sort</a> performs an in-place parallel sort over a range of elements specified by <code>[first, last)</code> using the given comparator. The following code sorts one million random integers in an increasing order on a GPU.</p><pre class="m-code"><span class="k">const</span><span class="w"> </span><span class="kt">size_t</span><span class="w"> </span><span class="n">N</span><span class="w"> </span><span class="o">=</span><span class="w"> </span><span class="mi">1000000</span><span class="p">;</span><span class="w"></span>
<span class="kt">int</span><span class="o">*</span><span class="w"> </span><span class="n">vec</span><span class="w"> </span><span class="o">=</span><span class="w"> </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="w"> </span><span class="c1">// 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="w"> </span><span class="n">i</span><span class="o">=</span><span class="mi">0</span><span class="p">;</span><span class="w"> </span><span class="n">i</span><span class="o"><</span><span class="n">N</span><span class="p">;</span><span class="w"> </span><span class="n">vec</span><span class="p">[</span><span class="n">i</span><span class="o">++</span><span class="p">]</span><span class="o">=</span><span class="n">rand</span><span class="p">());</span><span class="w"></span>
<span class="c1">// create a cudaFlow of one task to perform parallel sort</span>
<span class="n">tf</span><span class="o">::</span><span class="n">cudaFlow</span><span class="w"> </span><span class="n">cf</span><span class="p">;</span><span class="w"></span>
<span class="n">tf</span><span class="o">::</span><span class="n">cudaTask</span><span class="w"> </span><span class="n">task</span><span class="w"> </span><span class="o">=</span><span class="w"> </span><span class="n">cf</span><span class="p">.</span><span class="n">sort</span><span class="p">(</span><span class="w"></span>
<span class="w"> </span><span class="n">vec</span><span class="p">,</span><span class="w"> </span><span class="n">vec</span><span class="w"> </span><span class="o">+</span><span class="w"> </span><span class="n">N</span><span class="p">,</span><span class="w"> </span><span class="p">[]</span><span class="n">__device__</span><span class="p">(</span><span class="kt">int</span><span class="w"> </span><span class="n">a</span><span class="p">,</span><span class="w"> </span><span class="kt">int</span><span class="w"> </span><span class="n">b</span><span class="p">)</span><span class="w"> </span><span class="p">{</span><span class="w"> </span><span class="k">return</span><span class="w"> </span><span class="n">a</span><span class="w"> </span><span class="o"><</span><span class="w"> </span><span class="n">b</span><span class="p">;</span><span class="w"> </span><span class="p">}</span><span class="w"></span>
<span class="p">);</span><span class="w"></span>
<span class="n">cf</span><span class="p">.</span><span class="n">offload</span><span class="p">();</span><span class="w"></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">vec</span><span class="p">,</span><span class="w"> </span><span class="n">vec</span><span class="o">+</span><span class="n">N</span><span class="p">));</span><span class="w"></span></pre><p>You can specify a different comparator to <a href="classtf_1_1cudaFlow.html#ae462d455fed06dfcdbd1e25a2c9c5da6" class="m-doc">tf::<wbr />cudaFlow::<wbr />sort</a> to alter the sorting order. For example, the following code sorts one million random integers in an decreasing order on a GPU.</p><pre class="m-code"><span class="k">const</span><span class="w"> </span><span class="kt">size_t</span><span class="w"> </span><span class="n">N</span><span class="w"> </span><span class="o">=</span><span class="w"> </span><span class="mi">1000000</span><span class="p">;</span><span class="w"></span>
<span class="kt">int</span><span class="o">*</span><span class="w"> </span><span class="n">vec</span><span class="w"> </span><span class="o">=</span><span class="w"> </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="w"> </span><span class="c1">// 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="w"> </span><span class="n">i</span><span class="o">=</span><span class="mi">0</span><span class="p">;</span><span class="w"> </span><span class="n">i</span><span class="o"><</span><span class="n">N</span><span class="p">;</span><span class="w"> </span><span class="n">vec</span><span class="p">[</span><span class="n">i</span><span class="o">++</span><span class="p">]</span><span class="o">=</span><span class="n">rand</span><span class="p">());</span><span class="w"></span>
<span class="c1">// create a cudaFlow of one task to perform parallel sort</span>
<span class="n">tf</span><span class="o">::</span><span class="n">cudaFlow</span><span class="w"> </span><span class="n">cf</span><span class="p">;</span><span class="w"></span>
<span class="n">tf</span><span class="o">::</span><span class="n">cudaTask</span><span class="w"> </span><span class="n">task</span><span class="w"> </span><span class="o">=</span><span class="w"> </span><span class="n">cf</span><span class="p">.</span><span class="n">sort</span><span class="p">(</span><span class="w"></span>
<span class="w"> </span><span class="n">vec</span><span class="p">,</span><span class="w"> </span><span class="n">vec</span><span class="w"> </span><span class="o">+</span><span class="w"> </span><span class="n">N</span><span class="p">,</span><span class="w"> </span><span class="p">[]</span><span class="w"> </span><span class="n">__device__</span><span class="w"> </span><span class="p">(</span><span class="kt">int</span><span class="w"> </span><span class="n">a</span><span class="p">,</span><span class="w"> </span><span class="kt">int</span><span class="w"> </span><span class="n">b</span><span class="p">)</span><span class="w"> </span><span class="p">{</span><span class="w"> </span><span class="k">return</span><span class="w"> </span><span class="n">a</span><span class="w"> </span><span class="o">></span><span class="w"> </span><span class="n">b</span><span class="p">;</span><span class="w"> </span><span class="p">}</span><span class="w"></span>
<span class="p">);</span><span class="w"></span>
<span class="n">cf</span><span class="p">.</span><span class="n">offload</span><span class="p">();</span><span class="w"></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">vec</span><span class="p">,</span><span class="w"> </span><span class="n">vec</span><span class="o">+</span><span class="n">N</span><span class="p">,</span><span class="w"> </span><span class="p">[](</span><span class="kt">int</span><span class="w"> </span><span class="n">a</span><span class="p">,</span><span class="w"> </span><span class="kt">int</span><span class="w"> </span><span class="n">b</span><span class="p">){</span><span class="w"> </span><span class="k">return</span><span class="w"> </span><span class="n">a</span><span class="w"> </span><span class="o">></span><span class="w"> </span><span class="n">b</span><span class="p">;</span><span class="w"> </span><span class="p">}));</span><span class="w"></span></pre></section><section id="cudaFlowSortKeyValueItems"><h2><a href="#cudaFlowSortKeyValueItems">Sort a Range of Key-Value Items</a></h2><p><a href="classtf_1_1cudaFlow.html#a979739fcf70fbd760ad1a7682a8dfea8" class="m-doc">tf::<wbr />cudaFlow::<wbr />sort_by_key</a> sorts a range of key-value items into ascending key order. If <code>i</code> and <code>j</code> are any two valid iterators in <code>[k_first, k_last)</code> such that <code>i</code> precedes <code>j</code>, and <code>p</code> and <code>q</code> are iterators in <code>[v_first, v_first + (k_last - k_first))</code> corresponding to <code>i</code> and <code>j</code> respectively, then <code>comp(*j, *i)</code> evaluates to <code>false</code>. The following example sorts a range of items into ascending key order and swaps their corresponding values:</p><pre class="m-code"><span class="k">const</span><span class="w"> </span><span class="kt">size_t</span><span class="w"> </span><span class="n">N</span><span class="w"> </span><span class="o">=</span><span class="w"> </span><span class="mi">4</span><span class="p">;</span><span class="w"></span>
<span class="k">auto</span><span class="w"> </span><span class="n">vec</span><span class="w"> </span><span class="o">=</span><span class="w"> </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="w"> </span><span class="c1">// keys</span>
<span class="k">auto</span><span class="w"> </span><span class="n">idx</span><span class="w"> </span><span class="o">=</span><span class="w"> </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="w"> </span><span class="c1">// values</span>
<span class="c1">// initializes the data</span>
<span class="n">vec</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span><span class="w"> </span><span class="o">=</span><span class="w"> </span><span class="mi">1</span><span class="p">,</span><span class="w"> </span><span class="n">vec</span><span class="p">[</span><span class="mi">1</span><span class="p">]</span><span class="w"> </span><span class="o">=</span><span class="w"> </span><span class="mi">4</span><span class="p">,</span><span class="w"> </span><span class="n">vec</span><span class="p">[</span><span class="mi">2</span><span class="p">]</span><span class="w"> </span><span class="o">=</span><span class="w"> </span><span class="mi">-5</span><span class="p">,</span><span class="w"> </span><span class="n">vec</span><span class="p">[</span><span class="mi">3</span><span class="p">]</span><span class="w"> </span><span class="o">=</span><span class="w"> </span><span class="mi">2</span><span class="p">;</span><span class="w"></span>
<span class="n">idx</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span><span class="w"> </span><span class="o">=</span><span class="w"> </span><span class="mi">0</span><span class="p">,</span><span class="w"> </span><span class="n">idx</span><span class="p">[</span><span class="mi">1</span><span class="p">]</span><span class="w"> </span><span class="o">=</span><span class="w"> </span><span class="mi">1</span><span class="p">,</span><span class="w"> </span><span class="n">idx</span><span class="p">[</span><span class="mi">2</span><span class="p">]</span><span class="w"> </span><span class="o">=</span><span class="w"> </span><span class="mi">2</span><span class="p">,</span><span class="w"> </span><span class="n">idx</span><span class="p">[</span><span class="mi">3</span><span class="p">]</span><span class="w"> </span><span class="o">=</span><span class="w"> </span><span class="mi">3</span><span class="p">;</span><span class="w"></span>
<span class="c1">// sort keys (vec) and swap their corresponding values (idx)</span>
<span class="n">tf</span><span class="o">::</span><span class="n">cudaFlow</span><span class="w"> </span><span class="n">cf</span><span class="p">;</span><span class="w"></span>
<span class="n">cf</span><span class="p">.</span><span class="n">sort_by_key</span><span class="p">(</span><span class="n">vec</span><span class="p">,</span><span class="w"> </span><span class="n">vec</span><span class="o">+</span><span class="n">N</span><span class="p">,</span><span class="w"> </span><span class="n">idx</span><span class="p">,</span><span class="w"> </span><span class="p">[]</span><span class="w"> </span><span class="n">__device__</span><span class="w"> </span><span class="p">(</span><span class="kt">int</span><span class="w"> </span><span class="n">a</span><span class="p">,</span><span class="w"> </span><span class="kt">int</span><span class="w"> </span><span class="n">b</span><span class="p">)</span><span class="w"> </span><span class="p">{</span><span class="w"> </span><span class="k">return</span><span class="w"> </span><span class="n">a</span><span class="w"> </span><span class="o"><</span><span class="w"> </span><span class="n">b</span><span class="p">;</span><span class="w"> </span><span class="p">});</span><span class="w"></span>
<span class="n">cf</span><span class="p">.</span><span class="n">offload</span><span class="p">();</span><span class="w"></span>
<span class="c1">// now vec = {-5, 1, 2, 4}</span>
<span class="c1">// now idx = { 2, 0, 3, 1}</span>
<span class="c1">// deletes the 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="w"></span>
<span class="n">tf</span><span class="o">::</span><span class="n">cuda_free</span><span class="p">(</span><span class="n">vec</span><span class="p">);</span><span class="w"></span>
<span class="n">tf</span><span class="o">::</span><span class="n">cuda_free</span><span class="p">(</span><span class="n">idx</span><span class="p">);</span><span class="w"></span></pre><p>While you can capture the values into the lambda and sort them indirectly using plain <a href="classtf_1_1cudaFlow.html#ae462d455fed06dfcdbd1e25a2c9c5da6" class="m-doc">tf::<wbr />cudaFlow::<wbr />sort</a>, this organization will result in frequent and costly access to the global memory. For example, we can sort <code>idx</code> indirectly using the captured keys in <code>vec:</code></p><pre class="m-code"><span class="n">cf</span><span class="p">.</span><span class="n">sort</span><span class="p">(</span><span class="n">idx</span><span class="p">,</span><span class="w"> </span><span class="n">idx</span><span class="o">+</span><span class="n">N</span><span class="p">,</span><span class="w"> </span><span class="p">[</span><span class="n">vec</span><span class="p">]</span><span class="w"> </span><span class="n">__device__</span><span class="w"> </span><span class="p">(</span><span class="kt">int</span><span class="w"> </span><span class="n">a</span><span class="p">,</span><span class="w"> </span><span class="kt">int</span><span class="w"> </span><span class="n">b</span><span class="p">)</span><span class="w"> </span><span class="p">{</span><span class="w"> </span><span class="k">return</span><span class="w"> </span><span class="n">vec</span><span class="p">[</span><span class="n">a</span><span class="p">]</span><span class="w"> </span><span class="o"><</span><span class="w"> </span><span class="n">vec</span><span class="p">[</span><span class="n">b</span><span class="p">];</span><span class="w"> </span><span class="p">});</span><span class="w"></span></pre><p>The comparator here will frequently access the global memory of <code>vec</code>, resulting in high memory latency. Instead, you should use <a href="classtf_1_1cudaFlow.html#a979739fcf70fbd760ad1a7682a8dfea8" class="m-doc">tf::<wbr />cudaFlow::<wbr />sort_by_key</a> that has been optimized for this purpose.</p></section><section id="cudaFlowSortMiscellaneousItems"><h2><a href="#cudaFlowSortMiscellaneousItems">Miscellaneous Items</a></h2><p>Parallel sort algorithms are also available in <a href="classtf_1_1cudaFlowCapturer.html" class="m-doc">tf::<wbr />cudaFlowCapturer</a> with the same API.</p></section>
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<p class="m-noindent">Use <span class="m-label m-dim">↓</span>
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<span class="m-label m-dim">Tab</span> autocompletes common prefix, you can
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<span class="m-label m-dim">L</span> while <span class="m-label m-dim">⌘</span>
<span class="m-label m-dim">M</span> produces a Markdown link.</p>
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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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