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PartitioningAlgorithm.html

PartitioningAlgorithm.html 15.18 KB

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 <a href="Algorithms.html">Taskflow Algorithms</a> &raquo;
 Partitioning Algorithm
 </h1>
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 <h3>Contents</h3>
 <ul>
 <li><a href="#DefineAPartitionerForParallelAlgorithms">Define a Partitioner for Parallel Algorithms</a></li>
 <li><a href="#DefineAStaticPartitioner">Define a Static Partitioner</a></li>
 <li><a href="#DefineADynamicPartitioner">Define a Dynamic Partitioner</a></li>
 <li><a href="#DefineAGuidedPartitioner">Define a Guided Partitioner</a></li>
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A partitioning algorithm allows applications to optimize parallel algorithms using different scheduling methods, such as static partitioning, dynamic partitioning, and guided partitioning.<section id="DefineAPartitionerForParallelAlgorithms"><h2><a href="#DefineAPartitionerForParallelAlgorithms">Define a Partitioner for Parallel Algorithms</a></h2>A partitioner defines how to partition and distribute iterations to different workers when running parallel algorithms in Taskflow, such as <a href="classtf_1_1FlowBuilder.html#a025717373e424a6ccf9a61163bdaa585" class="m-doc">tf::Taskflow::for_each</a> and <a href="classtf_1_1FlowBuilder.html#a822a75c597ae42cacc28d6f8cefb8c7c" class="m-doc">tf::Taskflow::transform</a>. The following example shows how to create parallel-iteration tasks with different execution policies:<pre class="m-code">std::vector&lt;int&gt; data = {1, 2, 3, 4, 5, 6, 7, 8, 9, 10}

// create different partitioners
tf::GuidedPartitioner guided_partitioner;
tf::StaticPartitioner static_partitioner;
tf::RandomPartitioner random_partitioner;
tf::DynamicPartitioner dynamic_partitioner;

// create four parallel-iteration tasks from the four execution policies
taskflow.for_each(data.begin(), data.end(), [](int i){}, guided_partitioner);
taskflow.for_each(data.begin(), data.end(), [](int i){}, static_partitioner);
taskflow.for_each(data.begin(), data.end(), [](int i){}, random_partitioner);
taskflow.for_each(data.begin(), data.end(), [](int i){}, dynamic_partitioner);</pre>Each partitioner has a specific algorithm to partition iterations into a set of chunks and distribute chunks to workers. A chunk is the basic unit of work that will be run by a worker during the execution of parallel iterations. The following figure illustrates the scheduling diagram for three major partitioners, <a href="classtf_1_1StaticPartitioner.html" class="m-doc">tf::StaticPartitioner</a>, <a href="classtf_1_1DynamicPartitioner.html" class="m-doc">tf::DynamicPartitioner</a>, and <a href="classtf_1_1GuidedPartitioner.html" class="m-doc">tf::GuidedPartitioner</a>:<img class="m-image" src="parallel_for_partition_algorithms.png" alt="Image" />Depending on applications, partitioning algorithms can impact the performance a lot. For example, if a parallel-iteration workload contains a regular work unit per iteration, <a href="classtf_1_1StaticPartitioner.html" class="m-doc">tf::StaticPartitioner</a> may deliver the best performance. On the other hand, if the work unit per iteration is irregular and unbalanced, <a href="classtf_1_1GuidedPartitioner.html" class="m-doc">tf::GuidedPartitioner</a> or <a href="classtf_1_1DynamicPartitioner.html" class="m-doc">tf::DynamicPartitioner</a> can outperform <a href="classtf_1_1StaticPartitioner.html" class="m-doc">tf::StaticPartitioner</a>.<aside class="m-note m-info"><h4>Note</h4>By default, all parallel algorithms in Taskflow use tf::DefaultExecutionPolicy, which is based on guided scheduling via <a href="classtf_1_1GuidedPartitioner.html" class="m-doc">tf::GuidedPartitioner</a>.</aside></section><section id="DefineAStaticPartitioner"><h2><a href="#DefineAStaticPartitioner">Define a Static Partitioner</a></h2>Static partitioner splits iterations into <code>iter_size/chunk_size</code> chunks and distribute chunks to workers in order. If no chunk size is given (<code>chunk_size</code> is <code>1</code>), Taskflow will partition iterations into chunks that are approximately equal in size. The following code creates a static partitioner with chunk size equal to 100:<pre class="m-code">tf::StaticPartitioner static_partitioner(100);</pre></section><section id="DefineADynamicPartitioner"><h2><a href="#DefineADynamicPartitioner">Define a Dynamic Partitioner</a></h2>Dynamic partitioner splits iterations into <code>iter_size/chunk_size</code> chunks and distribute chunks to workers without any specific order. The default chunk size is <code>1</code>, if not specified. The following code creates a dynamic partitioner with chunk size equal to 2:<pre class="m-code">tf::DynamicPartitioner dynamic_partitioner(2);</pre></section><section id="DefineAGuidedPartitioner"><h2><a href="#DefineAGuidedPartitioner">Define a Guided Partitioner</a></h2>Guided partitioner dynamically decides the chunk size. The size of a chunk is proportional to the number of unassigned iterations divided by the number of the threads, and the size will gradually decrease to the specified chunk size (default <code>1</code>). The last chunk may be smaller than the specified chunk size. The following code creates a guided partitioner with chunk size equal to 10:<pre class="m-code">tf::GuidedPartitioner guided_partitioner(10);</pre>In most situations, guided partitioner can achieve decent performance due to adaptive parallelism, especially for those with irregular and unbalanced workload per iteration. As a result, guided partitioner is used as the default partitioner for our parallel algorithms.</section>
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 <span class="m-breadcrumb"><a href="Algorithms.html">Taskflow Algorithms</a> &raquo;</span>
 Partitioning Algorithm
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 <h3>Contents</h3>
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 <li><a href="#DefineAPartitionerForParallelAlgorithms">Define a Partitioner for Parallel Algorithms</a></li>
 <li><a href="#DefineAStaticPartitioner">Define a Static Partitioner</a></li>
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<p>A partitioning algorithm allows applications to optimize parallel algorithms using different scheduling methods, such as static partitioning, dynamic partitioning, and guided partitioning.</p><section id="DefineAPartitionerForParallelAlgorithms"><h2><a href="#DefineAPartitionerForParallelAlgorithms">Define a Partitioner for Parallel Algorithms</a></h2><p>A partitioner defines how to partition and distribute iterations to different workers when running parallel algorithms in Taskflow, such as <a href="classtf_1_1FlowBuilder.html#a025717373e424a6ccf9a61163bdaa585" class="m-doc">tf::<wbr />Taskflow::<wbr />for_each</a> and <a href="classtf_1_1FlowBuilder.html#a822a75c597ae42cacc28d6f8cefb8c7c" class="m-doc">tf::<wbr />Taskflow::<wbr />transform</a>. The following example shows how to create parallel-iteration tasks with different execution policies:</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">data</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><span class="mi">6</span><span class="p">,</span><span class="w"> </span><span class="mi">7</span><span class="p">,</span><span class="w"> </span><span class="mi">8</span><span class="p">,</span><span class="w"> </span><span class="mi">9</span><span class="p">,</span><span class="w"> </span><span class="mi">10</span><span class="p">}</span><span class="w"></span>

<span class="c1">// create different partitioners</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">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">RandomPartitioner</span><span class="w"> </span><span class="n">random_partitioner</span><span class="p">;</span><span class="w"></span>
<span class="n">tf</span><span class="o">::</span><span class="n">DynamicPartitioner</span><span class="w"> </span><span class="n">dynamic_partitioner</span><span class="p">;</span><span class="w"></span>

<span class="c1">// create four parallel-iteration tasks from the four execution policies</span>
<span class="n">taskflow</span><span class="p">.</span><span class="n">for_each</span><span class="p">(</span><span class="n">data</span><span class="p">.</span><span class="n">begin</span><span class="p">(),</span><span class="w"> </span><span class="n">data</span><span class="p">.</span><span class="n">end</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">i</span><span class="p">){},</span><span class="w"> </span><span class="n">guided_partitioner</span><span class="p">);</span><span class="w"></span>
<span class="n">taskflow</span><span class="p">.</span><span class="n">for_each</span><span class="p">(</span><span class="n">data</span><span class="p">.</span><span class="n">begin</span><span class="p">(),</span><span class="w"> </span><span class="n">data</span><span class="p">.</span><span class="n">end</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">i</span><span class="p">){},</span><span class="w"> </span><span class="n">static_partitioner</span><span class="p">);</span><span class="w"></span>
<span class="n">taskflow</span><span class="p">.</span><span class="n">for_each</span><span class="p">(</span><span class="n">data</span><span class="p">.</span><span class="n">begin</span><span class="p">(),</span><span class="w"> </span><span class="n">data</span><span class="p">.</span><span class="n">end</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">i</span><span class="p">){},</span><span class="w"> </span><span class="n">random_partitioner</span><span class="p">);</span><span class="w"></span>
<span class="n">taskflow</span><span class="p">.</span><span class="n">for_each</span><span class="p">(</span><span class="n">data</span><span class="p">.</span><span class="n">begin</span><span class="p">(),</span><span class="w"> </span><span class="n">data</span><span class="p">.</span><span class="n">end</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">i</span><span class="p">){},</span><span class="w"> </span><span class="n">dynamic_partitioner</span><span class="p">);</span><span class="w"></span></pre><p>Each partitioner has a specific algorithm to partition iterations into a set of <em>chunks</em> and distribute chunks to workers. A chunk is the basic unit of work that will be run by a worker during the execution of parallel iterations. The following figure illustrates the scheduling diagram for three major partitioners, <a href="classtf_1_1StaticPartitioner.html" class="m-doc">tf::<wbr />StaticPartitioner</a>, <a href="classtf_1_1DynamicPartitioner.html" class="m-doc">tf::<wbr />DynamicPartitioner</a>, and <a href="classtf_1_1GuidedPartitioner.html" class="m-doc">tf::<wbr />GuidedPartitioner</a>:</p><img class="m-image" src="parallel_for_partition_algorithms.png" alt="Image" /><p>Depending on applications, partitioning algorithms can impact the performance a lot. For example, if a parallel-iteration workload contains a regular work unit per iteration, <a href="classtf_1_1StaticPartitioner.html" class="m-doc">tf::<wbr />StaticPartitioner</a> may deliver the best performance. On the other hand, if the work unit per iteration is irregular and unbalanced, <a href="classtf_1_1GuidedPartitioner.html" class="m-doc">tf::<wbr />GuidedPartitioner</a> or <a href="classtf_1_1DynamicPartitioner.html" class="m-doc">tf::<wbr />DynamicPartitioner</a> can outperform <a href="classtf_1_1StaticPartitioner.html" class="m-doc">tf::<wbr />StaticPartitioner</a>.</p><aside class="m-note m-info"><h4>Note</h4><p>By default, all parallel algorithms in Taskflow use tf::DefaultExecutionPolicy, which is based on guided scheduling via <a href="classtf_1_1GuidedPartitioner.html" class="m-doc">tf::<wbr />GuidedPartitioner</a>.</p></aside></section><section id="DefineAStaticPartitioner"><h2><a href="#DefineAStaticPartitioner">Define a Static Partitioner</a></h2><p>Static partitioner splits iterations into <code>iter_size/chunk_size</code> chunks and distribute chunks to workers in order. If no chunk size is given (<code>chunk_size</code> is <code>1</code>), Taskflow will partition iterations into chunks that are approximately equal in size. The following code creates a static partitioner with chunk size equal to 100:</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="nf">static_partitioner</span><span class="p">(</span><span class="mi">100</span><span class="p">);</span><span class="w"></span></pre></section><section id="DefineADynamicPartitioner"><h2><a href="#DefineADynamicPartitioner">Define a Dynamic Partitioner</a></h2><p>Dynamic partitioner splits iterations into <code>iter_size/chunk_size</code> chunks and distribute chunks to workers without any specific order. The default chunk size is <code>1</code>, if not specified. The following code creates a dynamic partitioner with chunk size equal to 2:</p><pre class="m-code"><span class="n">tf</span><span class="o">::</span><span class="n">DynamicPartitioner</span><span class="w"> </span><span class="nf">dynamic_partitioner</span><span class="p">(</span><span class="mi">2</span><span class="p">);</span><span class="w"></span></pre></section><section id="DefineAGuidedPartitioner"><h2><a href="#DefineAGuidedPartitioner">Define a Guided Partitioner</a></h2><p>Guided partitioner dynamically decides the chunk size. The size of a chunk is proportional to the number of unassigned iterations divided by the number of the threads, and the size will gradually decrease to the specified chunk size (default <code>1</code>). The last chunk may be smaller than the specified chunk size. The following code creates a guided partitioner with chunk size equal to 10:</p><pre class="m-code"><span class="n">tf</span><span class="o">::</span><span class="n">GuidedPartitioner</span><span class="w"> </span><span class="nf">guided_partitioner</span><span class="p">(</span><span class="mi">10</span><span class="p">);</span><span class="w"></span></pre><p>In most situations, guided partitioner can achieve decent performance due to adaptive parallelism, especially for those with irregular and unbalanced workload per iteration. As a result, guided partitioner is used as the default partitioner for our parallel algorithms.</p></section>
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