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‎docs/Cookbook.html‎

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<div class="title">C6: CPU-GPU Tasking </div> </div>
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<div class="textblock"><p>Modern scientific computing typically leverages GPU-powered parallel processing cores to speed up large-scale applications. This chapters discusses how to implement heterogeneous decomposition algorithms using CPU-GPU collaborative tasking.</p>
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<h1><a class="anchor" id="C6_Create_a_cudaFlow"></a>
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Create a cudaFlow</h1>
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<p>Cpp-Taskflow enables concurrent CPU-GPU tasking by leveraging <a href="https://docs.nvidia.com/cuda/cuda-runtime-api/group__CUDART__GRAPH.html">Nvidia CUDA Graph</a>. The tasking interface is referred to as <em>cudaFlow</em>. A <a class="el" href="classtf_1_1cudaFlow.html" title="Building methods of a cuda task dependency graph. ">tf::cudaFlow</a> is a graph object created at runtime similar to dynamic tasking. It manages a task node in a taskflow and associates it with a CUDA Graph. To create a cudaFlow, emplace a callable with an argument of type <a class="el" href="classtf_1_1cudaFlow.html" title="Building methods of a cuda task dependency graph. ">tf::cudaFlow</a>. The following example implements the canonical saxpy (A·X Plus Y) task graph.</p>
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<div class="fragment"><div class="line"> 1: #include &lt;taskflow/taskflow.hpp&gt;</div><div class="line"> 2: </div><div class="line"> 3: <span class="comment">// saxpy (single-precision A·X Plus Y) kernel</span></div><div class="line"> 4: __global__ <span class="keywordtype">void</span> saxpy(<span class="keywordtype">int</span> n, <span class="keywordtype">float</span> a, <span class="keywordtype">float</span> *x, <span class="keywordtype">float</span> *y) {</div><div class="line"> 5: <span class="keywordtype">int</span> i = blockIdx.x*blockDim.x + threadIdx.x;</div><div class="line"> 6: <span class="keywordflow">if</span> (i &lt; n) {</div><div class="line"> 7: y[i] = a*x[i] + y[i];</div><div class="line"> 8: }</div><div class="line"> 9: }</div><div class="line">10:</div><div class="line">11: <span class="comment">// main function begins</span></div><div class="line">12: <span class="keywordtype">int</span> main() {</div><div class="line">13:</div><div class="line">14: <a class="code" href="classtf_1_1Taskflow.html">tf::Taskflow</a> taskflow;</div><div class="line">15: <a class="code" href="classtf_1_1Executor.html">tf::Executor</a> executor;</div><div class="line">16: </div><div class="line">17: <span class="keyword">const</span> <span class="keywordtype">unsigned</span> N = 1&lt;&lt;20; 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<p>Debrief:</p>
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<li>Lines 3-9 define a saxpy kernel using CUDA </li>
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<li>Lines 19-20 declare two host vectors, <code>hx</code> and <code>hy</code> </li>
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<li>Lines 46-48 define the task dependency between host tasks and the cudaFlow tasks and execute the taskflow</li>
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<p>Cpp-Taskflow does not expend unnecessary efforts on kernel programming but focus on tasking CUDA operations with CPU work. We give users full privileges to craft a CUDA kernel that is commensurate with their domain knowledge. Users focus on developing high-performance kernels using a native CUDA toolkit, while leaving difficult task parallelism to Cpp-Taskflow. </p>
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