<span class="c1">// std::cout &lt;&lt; &quot;parallel iteration on item &quot; &lt;&lt; i &lt;&lt; &#39;\n&#39;;</span>

<span class="c1">// });</span>

<span class="n">init</span><span class="p">.</span><span class="n">precede</span><span class="p">(</span><span class="n">pf</span><span class="p">);</span><span class="w"></span></pre><p>When <code>init</code> finishes, the parallel-for task <code>pf</code> will see <code>first</code> pointing to the beginning of <code>vec</code> and <code>last</code> pointing to the end of <code>vec</code> and performs parallel iterations over the 1000 items. The two tasks form an end-to-end task graph where the parameters of parallel-for are computed on the fly.</p></section><section id="ParallelIterationsConfigureAPartitioner"><h2><a href="#ParallelIterationsConfigureAPartitioner">Configure a Partitioner</a></h2><p>You can configure a partitioner for parallel-iteration tasks to run with different scheduling methods, such as guided partitioning, dynamic partitioning, and static partitioning. The following example creates two parallel-iteration tasks using two different partitioners, one with the static partitioning algorithm and another one with the guided partitioning algorithm:</p><pre class="m-code"><span class="n">std</span><span class="o">::</span><span class="n">vector</span><span class="o">&lt;</span><span class="kt">int</span><span class="o">&gt;</span><span class="w"> </span><span class="n">vec</span><span class="p">(</span><span class="mi">1024</span><span class="p">,</span><span class="w"> </span><span class="mi">0</span><span class="p">);</span><span class="w"></span>

<span class="n">tf</span><span class="o">::</span><span class="n">ExecutionPolicy</span><span class="o">&lt;</span><span class="n">tf</span><span class="o">::</span><span class="n">StaticPartitioner</span><span class="o">&gt;</span><span class="w"> </span><span class="n">static_partitioner</span><span class="p">;</span><span class="w"></span>

<span class="n">tf</span><span class="o">::</span><span class="n">ExecutionPolicy</span><span class="o">&lt;</span><span class="n">tf</span><span class="o">::</span><span class="n">GuidedPartitioner</span><span class="o">&gt;</span><span class="w"> </span><span class="n">guided_partitioner</span><span class="p">;</span><span class="w"></span>

<span class="w"> </span><span class="p">}</span><span class="w"> </span>

<span class="p">);</span><span class="w"> </span>

<span class="n">executor</span><span class="p">.</span><span class="n">run</span><span class="p">(</span><span class="n">taskflow</span><span class="p">).</span><span class="n">wait</span><span class="p">();</span><span class="w"> </span>

<span class="n">assert</span><span class="p">(</span><span class="n">sum</span><span class="w"> </span><span class="o">==</span><span class="w"> </span><span class="mi">1</span><span class="w"> </span><span class="o">+</span><span class="w"> </span><span class="mi">2</span><span class="w"> </span><span class="o">+</span><span class="w"> </span><span class="mi">3</span><span class="w"> </span><span class="o">+</span><span class="w"> </span><span class="mi">4</span><span class="w"> </span><span class="o">+</span><span class="w"> </span><span class="mi">5</span><span class="w"> </span><span class="o">+</span><span class="w"> </span><span class="mi">6</span><span class="w"> </span><span class="o">+</span><span class="w"> </span><span class="mi">7</span><span class="w"> </span><span class="o">+</span><span class="w"> </span><span class="mi">8</span><span class="p">);</span><span class="w"> </span><span class="c1">// sum will be 36 </span></pre><p>The order in which we apply the binary operator on the transformed elements is <em>unspecified</em>. It is possible that the binary operator will take <em>r-value</em> in both arguments, for example, <code>bop(uop(*itr1), uop(*itr2))</code>, due to the transformed temporaries. When data passing is expensive, you may define the result type <code>T</code> to be move-constructible.</p></section><section id="ParallelReductionCfigureAPartitioner"><h2><a href="#ParallelReductionCfigureAPartitioner">Configure a Partitioner</a></h2><p>You can configure a partitioner for parallel-reduction tasks to run with different scheduling methods, such as guided partitioning, dynamic partitioning, and static partitioning. The following example creates two parallel-reduction tasks using two different partitioners, one with the static partitioning algorithm and another one with the guided partitioning algorithm:</p><pre class="m-code"><span class="n">tf</span><span class="o">::</span><span class="n">StaticPartitioner</span><span class="w"> </span><span class="n">static_partitioner</span><span class="p">;</span><span class="w"></span>

<span class="n">tf</span><span class="o">::</span><span class="n">GuidedPartitioner</span><span class="w"> </span><span class="n">guided_partitioner</span><span class="p">;</span><span class="w"></span>

<span class="kt">int</span><span class="w"> </span><span class="n">sum1</span><span class="w"> </span><span class="o">=</span><span class="w"> </span><span class="mi">100</span><span class="p">,</span><span class="w"> </span><span class="n">sum2</span><span class="w"> </span><span class="o">=</span><span class="w"> </span><span class="mi">100</span><span class="p">;</span><span class="w"></span>

<span class="w"> </span><span class="p">[](</span><span class="kt">int</span><span class="w"> </span><span class="n">i</span><span class="p">,</span><span class="w"> </span><span class="kt">int</span><span class="w"> </span><span class="n">j</span><span class="p">){</span><span class="w"> </span>

<span class="w"> </span><span class="k">return</span><span class="w"> </span><span class="n">i</span><span class="w"> </span><span class="o">+</span><span class="w"> </span><span class="n">j</span><span class="p">;</span><span class="w"></span>

<span class="w"> </span><span class="p">}</span><span class="w"></span>

<span class="p">);</span><span class="w"></span></pre></section><section id="ParallelTransformsCfigureAPartitioner"><h2><a href="#ParallelTransformsCfigureAPartitioner">Configure a Partitioner</a></h2><p>You can configure a partitioner for parallel-transform tasks to run with different scheduling methods, such as guided partitioning, dynamic partitioning, and static partitioning. The following example creates two parallel-transform tasks using two different partitioners, one with the static partitioning algorithm and another one with the guided partitioning algorithm:</p><pre class="m-code"><span class="n">tf</span><span class="o">::</span><span class="n">StaticPartitioner</span><span class="w"> </span><span class="n">static_partitioner</span><span class="p">;</span><span class="w"></span>

<span class="n">tf</span><span class="o">::</span><span class="n">GuidedPartitioner</span><span class="w"> </span><span class="n">guided_partitioner</span><span class="p">;</span><span class="w"></span>

<span class="n">std</span><span class="o">::</span><span class="n">vector</span><span class="o">&lt;</span><span class="kt">int</span><span class="o">&gt;</span><span class="w"> </span><span class="n">src1</span><span class="w"> </span><span class="o">=</span><span class="w"> </span><span class="p">{</span><span class="mi">1</span><span class="p">,</span><span class="w"> </span><span class="mi">2</span><span class="p">,</span><span class="w"> </span><span class="mi">3</span><span class="p">,</span><span class="w"> </span><span class="mi">4</span><span class="p">,</span><span class="w"> </span><span class="mi">5</span><span class="p">};</span><span class="w"></span>