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<h1>
<span class="m-breadcrumb"><a href="cudaFlowAlgorithms.html">cudaFlow Algorithms</a> »</span>
Parallel Reduction
</h1>
<div class="m-block m-default">
<h3>Contents</h3>
<ul>
<li><a href="#CUDAParallelReductionIncludeTheHeader">Include the Header</a></li>
<li><a href="#CUDAReduceItemsWithAnInitialValue">Reduce Items with an Initial Value</a></li>
<li><a href="#CUDAReduceItemsWithoutAnInitialValue">Reduce Items without an Initial Value</a></li>
<li><a href="#cudaFlowReduceTransformedItemsWithAnInitialValue">Reduce a Range of Transformed Items with an Initial Value</a></li>
<li><a href="#cudaFlowReduceTransformedItemsWithoutAnInitialValue">Reduce a Range of Transformed Items without an Initial Value</a></li>
<li><a href="#CUDAReduceMiscellaneousItems">Miscellaneous Items</a></li>
</ul>
</div>
<p>cudaFlow provides template methods to create parallel reduction tasks on a CUDA GPU.</p><section id="CUDAParallelReductionIncludeTheHeader"><h2><a href="#CUDAParallelReductionIncludeTheHeader">Include the Header</a></h2><p>You need to include the header file, <code>taskflow/cuda/algorithm/reduce.hpp</code>, for creating a parallel-reduction task.</p></section><section id="CUDAReduceItemsWithAnInitialValue"><h2><a href="#CUDAReduceItemsWithAnInitialValue">Reduce Items with an Initial Value</a></h2><p>The reduction task created by tf::cudaFlow::reduce(I first, I last, T* result, C&& bop) performs parallel reduction over a range of elements specified by <code>[first, last)</code> using the binary operator <code>bop</code> and stores the reduced result in <code>result</code>. It represents the parallel execution of the following reduction loop on a GPU:</p><pre class="m-code"><span class="k">while</span> <span class="p">(</span><span class="n">first</span> <span class="o">!=</span> <span class="n">last</span><span class="p">)</span> <span class="p">{</span>
<span class="o">*</span><span class="n">result</span> <span class="o">=</span> <span class="n">bop</span><span class="p">(</span><span class="o">*</span><span class="n">result</span><span class="p">,</span> <span class="o">*</span><span class="n">first</span><span class="o">++</span><span class="p">);</span>
<span class="p">}</span></pre><p>The variable <code>result</code> participates in the reduction loop and must be initialized with an initial value. The following code performs a parallel reduction to sum all the numbers in the given range with an initial value <code>1000</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">1000000</span><span class="p">;</span>
<span class="n">std</span><span class="o">::</span><span class="n">vector</span><span class="o"><</span><span class="kt">int</span><span class="o">></span> <span class="n">cpu</span><span class="p">(</span><span class="n">N</span><span class="p">,</span> <span class="mi">-1</span><span class="p">);</span>
<span class="kt">int</span> <span class="n">sol</span><span class="p">;</span> <span class="c1">// solution on CPU</span>
<span class="kt">int</span><span class="o">*</span> <span class="n">res</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="kt">int</span><span class="o">></span><span class="p">(</span><span class="mi">1</span><span class="p">);</span> <span class="c1">// solution on GPU</span>
<span class="kt">int</span><span class="o">*</span> <span class="n">gpu</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="kt">int</span><span class="o">></span><span class="p">(</span><span class="n">N</span><span class="p">);</span> <span class="c1">// data on GPU</span>
<span class="n">tf</span><span class="o">::</span><span class="n">cudaFlow</span> <span class="n">cf</span><span class="p">;</span>
<span class="n">tf</span><span class="o">::</span><span class="n">cudaTask</span> <span class="n">d2h</span> <span class="o">=</span> <span class="n">cf</span><span class="p">.</span><span class="n">copy</span><span class="p">(</span><span class="o">&</span><span class="n">sol</span><span class="p">,</span> <span class="n">res</span><span class="p">,</span> <span class="mi">1</span><span class="p">);</span>
<span class="n">tf</span><span class="o">::</span><span class="n">cudaTask</span> <span class="n">h2d</span> <span class="o">=</span> <span class="n">cf</span><span class="p">.</span><span class="n">copy</span><span class="p">(</span><span class="n">gpu</span><span class="p">,</span> <span class="n">cpu</span><span class="p">.</span><span class="n">data</span><span class="p">(),</span> <span class="n">N</span><span class="p">);</span>
<span class="n">tf</span><span class="o">::</span><span class="n">cudaTask</span> <span class="n">set</span> <span class="o">=</span> <span class="n">cf</span><span class="p">.</span><span class="n">single_task</span><span class="p">([</span><span class="n">res</span><span class="p">]</span> <span class="n">__device__</span> <span class="p">()</span> <span class="k">mutable</span> <span class="p">{</span> <span class="o">*</span><span class="n">res</span> <span class="o">=</span> <span class="mi">1000</span><span class="p">;</span> <span class="p">});</span>
<span class="n">tf</span><span class="o">::</span><span class="n">cudaTask</span> <span class="n">kernel</span> <span class="o">=</span> <span class="n">cf</span><span class="p">.</span><span class="n">reduce</span><span class="p">(</span>
<span class="n">gpu</span><span class="p">,</span> <span class="n">gpu</span><span class="o">+</span><span class="n">N</span><span class="p">,</span> <span class="n">res</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="p">);</span>
<span class="n">kernel</span><span class="p">.</span><span class="n">succeed</span><span class="p">(</span><span class="n">h2d</span><span class="p">,</span> <span class="n">set</span><span class="p">)</span>
<span class="p">.</span><span class="n">precede</span><span class="p">(</span><span class="n">d2h</span><span class="p">);</span>
<span class="n">cf</span><span class="p">.</span><span class="n">offload</span><span class="p">();</span>
<span class="n">assert</span><span class="p">(</span><span class="n">sol</span> <span class="o">==</span> <span class="n">N</span> <span class="o">+</span> <span class="mi">1000</span><span class="p">);</span></pre></section><section id="CUDAReduceItemsWithoutAnInitialValue"><h2><a href="#CUDAReduceItemsWithoutAnInitialValue">Reduce Items without an Initial Value</a></h2><p>You can use <a href="classtf_1_1cudaFlow.html#a3a5189b9c9494d2a63a983c32cf234f6" class="m-doc">tf::<wbr />cudaFlow::<wbr />uninitialized_reduce</a> to perform parallel reduction without an initial value. This method represents a parallel execution of the following reduction loop on a GPU, thus in no need of <a href="classtf_1_1cudaFlow.html#ac2906cb0002fc411a983d100a3d58d62" class="m-doc">tf::<wbr />cudaFlow::<wbr />single_task</a> to initialize the variable:</p><pre class="m-code"><span class="o">*</span><span class="n">result</span> <span class="o">=</span> <span class="o">*</span><span class="n">first</span><span class="o">++</span><span class="p">;</span> <span class="c1">// no initial values to participate in the reduction loop</span>
<span class="k">while</span> <span class="p">(</span><span class="n">first</span> <span class="o">!=</span> <span class="n">last</span><span class="p">)</span> <span class="p">{</span>
<span class="o">*</span><span class="n">result</span> <span class="o">=</span> <span class="n">bop</span><span class="p">(</span><span class="o">*</span><span class="n">result</span><span class="p">,</span> <span class="o">*</span><span class="n">first</span><span class="o">++</span><span class="p">);</span>
<span class="p">}</span></pre><p>The variable <code>result</code> is directly assigned the reduced value without any initial value to participate in the reduction loop. The following code performs a parallel reduction to sum all the numbers in the given range without any initial value:</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">1000000</span><span class="p">;</span>
<span class="n">std</span><span class="o">::</span><span class="n">vector</span><span class="o"><</span><span class="kt">int</span><span class="o">></span> <span class="n">cpu</span><span class="p">(</span><span class="n">N</span><span class="p">,</span> <span class="mi">-1</span><span class="p">);</span>
<span class="kt">int</span> <span class="n">sol</span><span class="p">;</span> <span class="c1">// solution on CPU</span>
<span class="kt">int</span><span class="o">*</span> <span class="n">res</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="kt">int</span><span class="o">></span><span class="p">(</span><span class="mi">1</span><span class="p">);</span> <span class="c1">// solution on GPU</span>
<span class="kt">int</span><span class="o">*</span> <span class="n">gpu</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="kt">int</span><span class="o">></span><span class="p">(</span><span class="n">N</span><span class="p">);</span> <span class="c1">// data on GPU</span>
<span class="n">tf</span><span class="o">::</span><span class="n">cudaFlow</span> <span class="n">cf</span><span class="p">;</span>
<span class="n">tf</span><span class="o">::</span><span class="n">cudaTask</span> <span class="n">d2h</span> <span class="o">=</span> <span class="n">cf</span><span class="p">.</span><span class="n">copy</span><span class="p">(</span><span class="o">&</span><span class="n">sol</span><span class="p">,</span> <span class="n">res</span><span class="p">,</span> <span class="mi">1</span><span class="p">);</span>
<span class="n">tf</span><span class="o">::</span><span class="n">cudaTask</span> <span class="n">h2d</span> <span class="o">=</span> <span class="n">cf</span><span class="p">.</span><span class="n">copy</span><span class="p">(</span><span class="n">gpu</span><span class="p">,</span> <span class="n">cpu</span><span class="p">.</span><span class="n">data</span><span class="p">(),</span> <span class="n">N</span><span class="p">);</span>
<span class="n">tf</span><span class="o">::</span><span class="n">cudaTask</span> <span class="n">kernel</span> <span class="o">=</span> <span class="n">cf</span><span class="p">.</span><span class="n">uninitialized_reduce</span><span class="p">(</span>
<span class="n">gpu</span><span class="p">,</span> <span class="n">gpu</span><span class="o">+</span><span class="n">N</span><span class="p">,</span> <span class="n">res</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="p">);</span>
<span class="n">kernel</span><span class="p">.</span><span class="n">succeed</span><span class="p">(</span><span class="n">h2d</span><span class="p">)</span>
<span class="p">.</span><span class="n">precede</span><span class="p">(</span><span class="n">d2h</span><span class="p">);</span>
<span class="n">cf</span><span class="p">.</span><span class="n">offload</span><span class="p">();</span>
<span class="n">assert</span><span class="p">(</span><span class="n">sol</span> <span class="o">==</span> <span class="n">N</span><span class="p">);</span></pre></section><section id="cudaFlowReduceTransformedItemsWithAnInitialValue"><h2><a href="#cudaFlowReduceTransformedItemsWithAnInitialValue">Reduce a Range of Transformed Items with an Initial Value</a></h2><p><a href="classtf_1_1cudaFlow.html#a1af5d4e8a3820f9b9d8abbe4f4f6c4a4" class="m-doc">tf::<wbr />cudaFlow::<wbr />transform_reduce</a> performs a parallel reduction over a range of <em>transformed</em> elements specified by <code>[first, last)</code> using a binary reduce operator <code>bop</code> and a unary transform operator <code>uop</code>. It represents the parallel execution of the following reduction loop on a GPU:</p><pre class="m-code"><span class="k">while</span> <span class="p">(</span><span class="n">first</span> <span class="o">!=</span> <span class="n">last</span><span class="p">)</span> <span class="p">{</span>
<span class="o">*</span><span class="n">result</span> <span class="o">=</span> <span class="n">bop</span><span class="p">(</span><span class="o">*</span><span class="n">result</span><span class="p">,</span> <span class="n">uop</span><span class="p">(</span><span class="o">*</span><span class="n">first</span><span class="o">++</span><span class="p">));</span>
<span class="p">}</span></pre><p>The variable <code>result</code> participates in the reduction loop and must be initialized with an initial value. The following code performs a parallel reduction to sum all the transformed numbers multiplied by <code>10</code> in the given range with an initial value <code>1000</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">1000000</span><span class="p">;</span>
<span class="kt">int</span><span class="o">*</span> <span class="n">res</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">1</span><span class="p">);</span> <span class="c1">// result</span>
<span class="kt">int</span><span class="o">*</span> <span class="n">vec</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">// vector</span>
<span class="c1">// initializes the data</span>
<span class="o">*</span><span class="n">res</span> <span class="o">=</span> <span class="mi">1000</span><span class="p">;</span>
<span class="n">std</span><span class="o">::</span><span class="n">iota</span><span class="p">(</span><span class="n">vec</span><span class="p">,</span> <span class="n">vec</span> <span class="o">+</span> <span class="n">N</span><span class="p">,</span> <span class="mi">0</span><span class="p">);</span>
<span class="n">tf</span><span class="o">::</span><span class="n">cudaFlow</span> <span class="n">cf</span><span class="p">;</span>
<span class="n">cf</span><span class="p">.</span><span class="n">transform_reduce</span><span class="p">(</span>
<span class="n">vec</span><span class="p">,</span> <span class="n">vec</span> <span class="o">+</span> <span class="n">N</span><span class="p">,</span> <span class="n">res</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="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="p">{</span> <span class="k">return</span> <span class="n">a</span><span class="o">*</span><span class="mi">10</span><span class="p">;</span> <span class="p">}</span>
<span class="p">);</span>
<span class="n">cf</span><span class="p">.</span><span class="n">offload</span><span class="p">();</span>
<span class="c1">// *res = 1000 + (0*10 + 1*10 + 2*10 + 3*10 + 4*10 + ... + (N-1)*10)</span></pre></section><section id="cudaFlowReduceTransformedItemsWithoutAnInitialValue"><h2><a href="#cudaFlowReduceTransformedItemsWithoutAnInitialValue">Reduce a Range of Transformed Items without an Initial Value</a></h2><p><a href="namespacetf.html#a747a41c0474fd34da370839b60ddc4ca" class="m-doc">tf::<wbr />cuda_transform_uninitialized_reduce</a> performs a parallel reduction over a range of transformed items without an initial value. This method represents a parallel execution of the following reduction loop on a GPU:</p><pre class="m-code"><span class="o">*</span><span class="n">result</span> <span class="o">=</span> <span class="o">*</span><span class="n">first</span><span class="o">++</span><span class="p">;</span> <span class="c1">// no initial values to participate in the reduction loop</span>
<span class="k">while</span> <span class="p">(</span><span class="n">first</span> <span class="o">!=</span> <span class="n">last</span><span class="p">)</span> <span class="p">{</span>
<span class="o">*</span><span class="n">result</span> <span class="o">=</span> <span class="n">bop</span><span class="p">(</span><span class="o">*</span><span class="n">result</span><span class="p">,</span> <span class="n">uop</span><span class="p">(</span><span class="o">*</span><span class="n">first</span><span class="o">++</span><span class="p">));</span>
<span class="p">}</span></pre><p>The variable <code>result</code> is directly assigned the reduced value without any initial value participating in the reduction loop. The following code performs a parallel reduction to sum all the transformed numbers multiplied by <code>10</code> in the given range without any initial value:</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">1000000</span><span class="p">;</span>
<span class="kt">int</span><span class="o">*</span> <span class="n">res</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">1</span><span class="p">);</span> <span class="c1">// result</span>
<span class="kt">int</span><span class="o">*</span> <span class="n">vec</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">// vector</span>
<span class="c1">// initializes the data</span>
<span class="o">*</span><span class="n">res</span> <span class="o">=</span> <span class="mi">1000</span><span class="p">;</span>
<span class="n">std</span><span class="o">::</span><span class="n">iota</span><span class="p">(</span><span class="n">vec</span><span class="p">,</span> <span class="n">vec</span> <span class="o">+</span> <span class="n">N</span><span class="p">,</span> <span class="mi">0</span><span class="p">);</span>
<span class="n">tf</span><span class="o">::</span><span class="n">cudaFlow</span> <span class="n">cf</span><span class="p">;</span>
<span class="n">cf</span><span class="p">.</span><span class="n">transform_reduce</span><span class="p">(</span>
<span class="n">vec</span><span class="p">,</span> <span class="n">vec</span> <span class="o">+</span> <span class="n">N</span><span class="p">,</span> <span class="n">res</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="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="p">{</span> <span class="k">return</span> <span class="n">a</span><span class="o">*</span><span class="mi">10</span><span class="p">;</span> <span class="p">}</span>
<span class="p">);</span>
<span class="n">cf</span><span class="p">.</span><span class="n">offload</span><span class="p">();</span>
<span class="c1">// *res = 0*10 + 1*10 + 2*10 + 3*10 + 4*10 + ... + (N-1)*10</span></pre></section><section id="CUDAReduceMiscellaneousItems"><h2><a href="#CUDAReduceMiscellaneousItems">Miscellaneous Items</a></h2><p>Parallel-reduction 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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