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<title>CUDA Standard Algorithms » Parallel Transforms | Taskflow QuickStart</title>

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<h1>

<a href="cudaStandardAlgorithms.html">CUDA Standard Algorithms</a> »

Parallel Transforms

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<h3>Contents</h3>

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<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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Taskflow provides template methods for transforming ranges of items to different outputs.<section id="CUDASTDParallelTransformsIncludeTheHeader"><h2><a href="#CUDASTDParallelTransformsIncludeTheHeader">Include the Header</a></h2>You need to include the header file, <code>taskflow/cuda/algorithm/transform.hpp</code>, for using the parallel-transform algorithm.<pre class="m-code">#include <taskflow/cuda/algorithm/transform.hpp></pre></section><section id="CUDASTDTransformARangeOfItems"><h2><a href="#CUDASTDTransformARangeOfItems">Transform a Range of Items</a></h2>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::cuda_transform(P&& p, I first, I last, O output, C op)</a> represents a parallel execution for the following loop:<pre class="m-code">while (first != last) {

*output++ = op(*first++);

}</pre>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.<pre class="m-code">tf::cudaDefaultExecutionPolicy policy;

// output[i] = input[i]*10

tf::cuda_transform(

policy, input, input + N, output, [] __device__ (int x) { return x*10; }

);

// synchronize the execution

policy.synchronize();</pre>Each iteration is independent of each other and is assigned one kernel thread to run the callable. The transform algorithm runs asynchronously through the stream specified in the execution policy. You need to synchronize the stream to obtain correct results.</section><section id="CUDASTDTransformTwoRangesOfItems"><h2><a href="#CUDASTDTransformTwoRangesOfItems">Transform Two Ranges of Items</a></h2>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::cuda_transform(P&& p, I1 first1, I1 last1, I2 first2, O output, C op)</a> represents a parallel execution for the following loop:<pre class="m-code">while (first1 != last1) {

*output++ = op(*first1++, *first2++);

}</pre>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.<pre class="m-code">tf::cudaDefaultExecutionPolicy policy;

// output[i] = input1[i] + inpu2[i]

tf::cuda_transform(policy,

input1, input1+N, input2, output, []__device__(int a, int b) { return a+b; }

);

// synchronize the execution

policy.synchronize();</pre></section>

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