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<title>CUDA Standard Algorithms » Parallel Transforms | Taskflow QuickStart</title>
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<h1>
<span class="m-breadcrumb"><a href="cudaStandardAlgorithms.html">CUDA Standard Algorithms</a> »</span>
Parallel Transforms
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<h3>Contents</h3>
<ul>
<li><a href="#CUDASTDParallelTransformsIncludeTheHeader">Include the Header</a></li>
<li><a href="#CUDASTDTransformARangeOfItems">Transform a Range of Items</a></li>
<li><a href="#CUDASTDTransformTwoRangesOfItems">Transform Two Ranges of Items</a></li>
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<p>Taskflow provides template methods for transforming ranges of items to different outputs.</p><section id="CUDASTDParallelTransformsIncludeTheHeader"><h2><a href="#CUDASTDParallelTransformsIncludeTheHeader">Include the Header</a></h2><p>You need to include the header file, <code>taskflow/cuda/algorithm/transform.hpp</code>, for using the parallel-transform algorithm.</p></section><section id="CUDASTDTransformARangeOfItems"><h2><a href="#CUDASTDTransformARangeOfItems">Transform a Range of Items</a></h2><p>Parallel-transform algorithm 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="namespacetf.html#a3ed764530620a419e3400e1f9ab6c956" class="m-doc">tf::<wbr />cuda_transform(P&& p, 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="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">output</span><span class="o">++</span> <span class="o">=</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="p">}</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="n">tf</span><span class="o">::</span><span class="n">cudaDefaultExecutionPolicy</span> <span class="n">policy</span><span class="p">;</span>
<span class="c1">// output[i] = input[i]*10</span>
<span class="n">tf</span><span class="o">::</span><span class="n">cuda_transform</span><span class="p">(</span>
<span class="n">policy</span><span class="p">,</span> <span class="n">input</span><span class="p">,</span> <span class="n">input</span> <span class="o">+</span> <span class="n">N</span><span class="p">,</span> <span class="n">output</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">x</span><span class="p">)</span> <span class="p">{</span> <span class="k">return</span> <span class="n">x</span><span class="o">*</span><span class="mi">10</span><span class="p">;</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. The transform 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="CUDASTDTransformTwoRangesOfItems"><h2><a href="#CUDASTDTransformTwoRangesOfItems">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="namespacetf.html#abdcb5b755f7ace2aa452541d5bf93b5f" class="m-doc">tf::<wbr />cuda_transform(P&& p, 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="p">(</span><span class="n">first1</span> <span class="o">!=</span> <span class="n">last1</span><span class="p">)</span> <span class="p">{</span>
<span class="o">*</span><span class="n">output</span><span class="o">++</span> <span class="o">=</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="o">*</span><span class="n">first2</span><span class="o">++</span><span class="p">);</span>
<span class="p">}</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="n">tf</span><span class="o">::</span><span class="n">cudaDefaultExecutionPolicy</span> <span class="n">policy</span><span class="p">;</span>
<span class="c1">// output[i] = input1[i] + inpu2[i]</span>
<span class="n">tf</span><span class="o">::</span><span class="n">cuda_transform</span><span class="p">(</span><span class="n">policy</span><span class="p">,</span>
<span class="n">input1</span><span class="p">,</span> <span class="n">input1</span><span class="o">+</span><span class="n">N</span><span class="p">,</span> <span class="n">input2</span><span class="p">,</span> <span class="n">output</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="c1">// synchronize the execution</span>
<span class="n">policy</span><span class="p">.</span><span class="n">synchronize</span><span class="p">();</span></pre></section>
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