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<!DOCTYPE html>

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<title>syclFlow Algorithms » Parallel Iterations | Taskflow QuickStart</title>

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<h1>

<a href="syclFlowAlgorithms.html">syclFlow Algorithms</a> »

Parallel Iterations

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<h3>Contents</h3>

<ul>

<li><a href="#ForEachSYCLIndexBasedParallelFor">Index-based Parallel Iterations</a></li>

<li><a href="#ForEachSYCLIteratorBasedParallelIterations">Iterator-based Parallel Iterations</a></li>

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<a href="classtf_1_1syclFlow.html" class="m-doc">tf::syclFlow</a> provides two template methods, <a href="classtf_1_1syclFlow.html#a30c3f7a279d1f88b6052f7ded1ee381a" class="m-doc">tf::syclFlow::for_each</a> and <a href="classtf_1_1syclFlow.html#a25e8aa7d7efbd05ef11353317174574a" class="m-doc">tf::syclFlow::for_each_index</a>, for creating tasks to perform parallel iterations over a range of items.<section id="ForEachSYCLIndexBasedParallelFor"><h2><a href="#ForEachSYCLIndexBasedParallelFor">Index-based Parallel Iterations</a></h2>Index-based parallel-for performs parallel iterations over a range <code>[first, last)</code> with the given <code>step</code> size. These indices must be integral type. The task created by <a href="classtf_1_1syclFlow.html#a25e8aa7d7efbd05ef11353317174574a" class="m-doc">tf::syclFlow::for_each_index(I first, I last, I step, C&& callable)</a> represents a kernel of parallel execution for the following loop:<pre class="m-code">// positive step: first, first+step, first+2*step, ...

for(auto i=first; i<last; i+=step) {

callable(i);

}

// negative step: first, first-step, first-2*step, ...

for(auto i=first; i>last; i+=step) {

callable(i);

}</pre>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 element of <code>gpu_data</code> to 1 over the range [0, 100) with step size 1.<pre class="m-code">taskflow.emplace_on([&](tf::syclFlow& sf){

// ... create other gpu tasks

// assigns each element in gpu_data to 1 over the range [0, 100) with step size 1

sf.for_each_index(0, 100, 1, [gpu_data] (int idx) {

gpu_data[idx] = 1;

});

}, sycl_queue);</pre></section><section id="ForEachSYCLIteratorBasedParallelIterations"><h2><a href="#ForEachSYCLIteratorBasedParallelIterations">Iterator-based Parallel Iterations</a></h2>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="classtf_1_1syclFlow.html#a30c3f7a279d1f88b6052f7ded1ee381a" class="m-doc">tf::syclFlow::for_each(I first, I last, C&& callable)</a> represents a parallel execution of the following loop:<pre class="m-code">for(auto i=first; i<last; i++) {

callable(*i);

}</pre>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>[gpu_data, gpu_data + 1000)</code>.<pre class="m-code">taskflow.emplace_on([&](tf::syclFlow& cf){

// ... create gpu tasks

// assigns each element to 1 over the range [gpu_data, gpu_data + 1000)

cf.for_each(gpu_data, gpu_data + 1000, [] (int& item) {

item = 1;

});

}, sycl_queue);</pre>Each iteration is independent of each other and is assigned one kernel thread to run the callable.</section>

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