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<h1>
<span class="m-breadcrumb"><a href="cudaStandardAlgorithms.html">CUDA Standard Algorithms</a> &raquo;</span>
Parallel Iterations
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<h3>Contents</h3>
<ul>
<li><a href="#CUDASTDParallelIterationIncludeTheHeader">Include the Header</a></li>
<li><a href="#CUDASTDIndexBasedParallelFor">Index-based Parallel Iterations</a></li>
<li><a href="#CUDASTDIteratorBasedParallelFor">Iterator-based Parallel Iterations</a></li>
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<p>Taskflow provides standard template methods for performing parallel iterations over a range of items a CUDA GPU.</p><section id="CUDASTDParallelIterationIncludeTheHeader"><h2><a href="#CUDASTDParallelIterationIncludeTheHeader">Include the Header</a></h2><p>You need to include the header file, <code>taskflow/cuda/algorithm/for_each.hpp</code>, for using the parallel-iteration algorithm.</p><pre class="m-code"><span class="cp">#include</span><span class="w"> </span><span class="cpf">&lt;taskflow/cuda/algorithm/for_each.hpp&gt;</span></pre></section><section id="CUDASTDIndexBasedParallelFor"><h2><a href="#CUDASTDIndexBasedParallelFor">Index-based Parallel Iterations</a></h2><p>Index-based parallel-for performs parallel iterations over a range <code>[first, last)</code> with the given <code>step</code> size. The task created by <a href="namespacetf.html#a01ad7ce62fa6f42f2f2fbff3659b7884" class="m-doc">tf::<wbr />cuda_for_each_index</a> represents a kernel of parallel execution for the following loop:</p><pre class="m-code"><span class="c1">// positive step: first, first+step, first+2*step, ...</span>
<span class="k">for</span><span class="p">(</span><span class="k">auto</span><span class="w"> </span><span class="n">i</span><span class="o">=</span><span class="n">first</span><span class="p">;</span><span class="w"> </span><span class="n">i</span><span class="o">&lt;</span><span class="n">last</span><span class="p">;</span><span class="w"> </span><span class="n">i</span><span class="o">+=</span><span class="n">step</span><span class="p">)</span><span class="w"> </span><span class="p">{</span>
<span class="w"> </span><span class="n">callable</span><span class="p">(</span><span class="n">i</span><span class="p">);</span>
<span class="p">}</span>
<span class="c1">// negative step: first, first-step, first-2*step, ...</span>
<span class="k">for</span><span class="p">(</span><span class="k">auto</span><span class="w"> </span><span class="n">i</span><span class="o">=</span><span class="n">first</span><span class="p">;</span><span class="w"> </span><span class="n">i</span><span class="o">&gt;</span><span class="n">last</span><span class="p">;</span><span class="w"> </span><span class="n">i</span><span class="o">+=</span><span class="n">step</span><span class="p">)</span><span class="w"> </span><span class="p">{</span>
<span class="w"> </span><span class="n">callable</span><span class="p">(</span><span class="n">i</span><span class="p">);</span>
<span class="p">}</span></pre><p>Each iteration <code>i</code> is independent of each other and is assigned one kernel thread to run the callable. The following example creates a kernel that assigns each entry of <code>data</code> to 1 over the range [0, 100) with step size 1.</p><pre class="m-code"><span class="n">tf</span><span class="o">::</span><span class="n">cudaDefaultExecutionPolicy</span><span class="w"> </span><span class="n">policy</span><span class="p">;</span>
<span class="k">auto</span><span class="w"> </span><span class="n">data</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">&lt;</span><span class="kt">int</span><span class="o">&gt;</span><span class="p">(</span><span class="mi">100</span><span class="p">);</span>
<span class="c1">// assigns each element in data to 1 over the range [0, 100) with step size 1</span>
<span class="n">tf</span><span class="o">::</span><span class="n">cuda_for_each_index</span><span class="p">(</span>
<span class="w"> </span><span class="n">policy</span><span class="p">,</span><span class="w"> </span><span class="mi">0</span><span class="p">,</span><span class="w"> </span><span class="mi">100</span><span class="p">,</span><span class="w"> </span><span class="mi">1</span><span class="p">,</span><span class="w"> </span><span class="p">[</span><span class="n">data</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">idx</span><span class="p">)</span><span class="w"> </span><span class="p">{</span><span class="w"> </span><span class="n">data</span><span class="p">[</span><span class="n">idx</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="p">}</span>
<span class="p">);</span>
<span class="c1">// synchronize the execution</span>
<span class="n">policy</span><span class="p">.</span><span class="n">synchronize</span><span class="p">();</span></pre><p>The parallel-iteration algorithm runs <em>asynchronously</em> through the stream specified in the execution policy. You need to synchronize the stream to obtain correct results.</p></section><section id="CUDASTDIteratorBasedParallelFor"><h2><a href="#CUDASTDIteratorBasedParallelFor">Iterator-based Parallel Iterations</a></h2><p>Iterator-based parallel-for performs parallel iterations over a range specified by two STL-styled iterators, <code>first</code> and <code>last</code>. The task created by <a href="namespacetf.html#a7c449cec0b93503b8280d05add35e9f4" class="m-doc">tf::<wbr />cuda_for_each</a> represents a parallel execution of the following loop:</p><pre class="m-code"><span class="k">for</span><span class="p">(</span><span class="k">auto</span><span class="w"> </span><span class="n">i</span><span class="o">=</span><span class="n">first</span><span class="p">;</span><span class="w"> </span><span class="n">i</span><span class="o">&lt;</span><span class="n">last</span><span class="p">;</span><span class="w"> </span><span class="n">i</span><span class="o">++</span><span class="p">)</span><span class="w"> </span><span class="p">{</span>
<span class="w"> </span><span class="n">callable</span><span class="p">(</span><span class="o">*</span><span class="n">i</span><span class="p">);</span>
<span class="p">}</span></pre><p>The two iterators, <code>first</code> and <code>last</code>, are typically two raw pointers to the first element and the next to the last element in the range in GPU memory space. The following example creates a <code>for_each</code> kernel that assigns each element in <code>gpu_data</code> to 1 over the range <code>[data, data + 1000)</code>.</p><pre class="m-code"><span class="n">tf</span><span class="o">::</span><span class="n">cudaDefaultExecutionPolicy</span><span class="w"> </span><span class="n">policy</span><span class="p">;</span>
<span class="k">auto</span><span class="w"> </span><span class="n">data</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">&lt;</span><span class="kt">int</span><span class="o">&gt;</span><span class="p">(</span><span class="mi">1000</span><span class="p">);</span>
<span class="c1">// assigns each element in data to 1 over the range [0, 1000) with step size 1</span>
<span class="n">tf</span><span class="o">::</span><span class="n">cuda_for_each</span><span class="p">(</span>
<span class="w"> </span><span class="n">policy</span><span class="p">,</span><span class="w"> </span><span class="n">data</span><span class="p">,</span><span class="w"> </span><span class="n">data</span><span class="w"> </span><span class="o">+</span><span class="w"> </span><span class="mi">1000</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="o">&amp;</span><span class="w"> </span><span class="n">item</span><span class="p">)</span><span class="w"> </span><span class="p">{</span><span class="w"> </span><span class="n">item</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="p">}</span>
<span class="p">);</span>
<span class="c1">// synchronize the execution</span>
<span class="n">policy</span><span class="p">.</span><span class="n">synchronize</span><span class="p">();</span></pre><p>Each iteration is independent of each other and is assigned one kernel thread to run the callable. Since the callable runs on GPU, it must be declared with a <code>__device__</code> specifier.</p></section>
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