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<title>cudaFlow Algorithms » Parallel Transforms | Taskflow QuickStart</title>
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<h1>
<span class="m-breadcrumb"><a href="cudaFlowAlgorithms.html">cudaFlow Algorithms</a> »</span>
Parallel Transforms
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<h3>Contents</h3>
<ul>
<li><a href="#CUDAParallelTransformsIncludeTheHeader">Include the Header</a></li>
<li><a href="#cudaFlowTransformARangeOfItems">Transform a Range of Items</a></li>
<li><a href="#cudaFlowTransformTwoRangesOfItems">Transform Two Ranges of Items</a></li>
<li><a href="#ParallelTransformCUDAMiscellaneousItems">Miscellaneous Items</a></li>
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<p><a href="classtf_1_1cudaFlow.html" class="m-doc">tf::<wbr />cudaFlow</a> provides template methods for transforming ranges of items to different outputs.</p><section id="CUDAParallelTransformsIncludeTheHeader"><h2><a href="#CUDAParallelTransformsIncludeTheHeader">Include the Header</a></h2><p>You need to include the header file, <code>taskflow/cuda/algorithm/transform.hpp</code>, for creating a parallel-transform task.</p><pre class="m-code"><span class="cp">#include</span><span class="w"> </span><span class="cpf"><taskflow/cuda/algorithm/transform.hpp></span><span class="cp"></span></pre></section><section id="cudaFlowTransformARangeOfItems"><h2><a href="#cudaFlowTransformARangeOfItems">Transform a Range of Items</a></h2><p>Iterator-based parallel-transform applies the given transform function to a range of items and store the result in another range specified by two iterators, <code>first</code> and <code>last</code>. The task created by <a href="classtf_1_1cudaFlow.html#af89a9bda182272462a0eda2581536cd8" class="m-doc">tf::<wbr />cudaFlow::<wbr />transform(I first, I last, O output, C op)</a> represents a parallel execution for the following loop:</p><pre class="m-code"><span class="k">while</span><span class="w"> </span><span class="p">(</span><span class="n">first</span><span class="w"> </span><span class="o">!=</span><span class="w"> </span><span class="n">last</span><span class="p">)</span><span class="w"> </span><span class="p">{</span><span class="w"></span>
<span class="w"> </span><span class="o">*</span><span class="n">output</span><span class="o">++</span><span class="w"> </span><span class="o">=</span><span class="w"> </span><span class="n">op</span><span class="p">(</span><span class="o">*</span><span class="n">first</span><span class="o">++</span><span class="p">);</span><span class="w"></span>
<span class="p">}</span><span class="w"></span></pre><p>The following example creates a transform kernel that transforms an input range of <code>N</code> items to an output range by multiplying each item by 10.</p><pre class="m-code"><span class="c1">// output[i] = input[i] * 10</span>
<span class="n">cudaflow</span><span class="p">.</span><span class="n">transform</span><span class="p">(</span><span class="w"></span>
<span class="w"> </span><span class="n">input</span><span class="p">,</span><span class="w"> </span><span class="n">input</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="n">output</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">x</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">x</span><span class="w"> </span><span class="o">*</span><span class="w"> </span><span class="mi">10</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></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><section id="cudaFlowTransformTwoRangesOfItems"><h2><a href="#cudaFlowTransformTwoRangesOfItems">Transform Two Ranges of Items</a></h2><p>You can transform two ranges of items to an output range through a binary operator. The task created by <a href="classtf_1_1cudaFlow.html#abab2bfdfc86ef3a764ece4743fdede76" class="m-doc">tf::<wbr />cudaFlow::<wbr />transform(I1 first1, I1 last1, I2 first2, O output, C op)</a> represents a parallel execution for the following loop:</p><pre class="m-code"><span class="k">while</span><span class="w"> </span><span class="p">(</span><span class="n">first1</span><span class="w"> </span><span class="o">!=</span><span class="w"> </span><span class="n">last1</span><span class="p">)</span><span class="w"> </span><span class="p">{</span><span class="w"></span>
<span class="w"> </span><span class="o">*</span><span class="n">output</span><span class="o">++</span><span class="w"> </span><span class="o">=</span><span class="w"> </span><span class="n">op</span><span class="p">(</span><span class="o">*</span><span class="n">first1</span><span class="o">++</span><span class="p">,</span><span class="w"> </span><span class="o">*</span><span class="n">first2</span><span class="o">++</span><span class="p">);</span><span class="w"></span>
<span class="p">}</span><span class="w"></span></pre><p>The following example creates a transform kernel that transforms two input ranges of <code>N</code> items to an output range by summing each pair of items in the input ranges.</p><pre class="m-code"><span class="c1">// output[i] = input1[i] + inpu2[i]</span>
<span class="n">cudaflow</span><span class="p">.</span><span class="n">transform</span><span class="p">(</span><span class="w"></span>
<span class="w"> </span><span class="n">input1</span><span class="p">,</span><span class="w"> </span><span class="n">input1</span><span class="o">+</span><span class="n">N</span><span class="p">,</span><span class="w"> </span><span class="n">input2</span><span class="p">,</span><span class="w"> </span><span class="n">output</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="o">+</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></pre></section><section id="ParallelTransformCUDAMiscellaneousItems"><h2><a href="#ParallelTransformCUDAMiscellaneousItems">Miscellaneous Items</a></h2><p>The parallel-transform algorithms are also available in <a href="classtf_1_1cudaFlowCapturer.html" class="m-doc">tf::<wbr />cudaFlowCapturer</a>.</p></section>
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