335 lines
7.9 KiB
Text
335 lines
7.9 KiB
Text
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namespace tf {
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/** @page TaskflowProcessingPipeline Taskflow Processing Pipeline
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We study a taskflow processing pipeline that propagates a sequence of tokens through
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linearly dependent taskflows.
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The pipeline embeds a taskflow in each pipe to run a parallel algorithm using
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task graph parallelism.
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@tableofcontents
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@section FormulateTheTaskflowProcessingPipelineProblem Formulate the Taskflow Processing Pipeline Problem
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Many complex and irregular pipeline applications require each pipe to run a parallel algorithm
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using task graph parallelism.
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We can formulate such applications as scheduling a sequence of tokens through
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linearly dependent taskflows.
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The following example illustrates the pipeline propagation of three scheduling tokens
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through three linearly dependent taskflows:
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@dotfile images/taskflow_processing_pipeline_2.dot
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@dotfile images/taskflow_processing_pipeline_1.dot
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Each pipe (stage) in the pipeline embeds a taskflow to perform a stage-specific parallel algorithm
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on an input scheduling token.
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Parallelism exhibits both inside and outside the three taskflows, combining both
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<i>task graph parallelism</i> and <i>pipeline parallelism</i>.
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@section CreateATaskflowProcessingPipeline Create a Taskflow Processing Pipeline
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Using the example from the previous section, we create a pipeline of three @em serial pipes
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each running a taskflow on a sequence of five scheduling tokens.
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The overall implementation is shown below:
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@code{.cpp}
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#include <taskflow/taskflow.hpp>
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#include <taskflow/algorithm/pipeline.hpp>
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// taskflow on the first pipe
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void make_taskflow1(tf::Taskflow& tf) {
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auto [A1, B1, C1, D1] = tf.emplace(
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[](){ printf("A1\n"); },
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[](){ printf("B1\n"); },
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[](){ printf("C1\n"); },
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[](){ printf("D1\n"); }
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);
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A1.precede(B1, C1);
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D1.succeed(B1, C1);
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}
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// taskflow on the second pipe
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void make_taskflow2(tf::Taskflow& tf) {
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auto [A2, B2, C2, D2] = tf.emplace(
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[](){ printf("A2\n"); },
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[](){ printf("B2\n"); },
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[](){ printf("C2\n"); },
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[](){ printf("D2\n"); }
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);
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tf.linearize({A2, B2, C2, D2});
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}
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// taskflow on the third pipe
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void make_taskflow3(tf::Taskflow& tf) {
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auto [A3, B3, C3, D3] = tf.emplace(
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[](){ printf("A3\n"); },
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[](){ printf("B3\n"); },
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[](){ printf("C3\n"); },
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[](){ printf("D3\n"); }
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);
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A3.precede(B3, C3, D3);
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}
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int main() {
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tf::Taskflow taskflow("taskflow processing pipeline");
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tf::Executor executor;
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const size_t num_lines = 2;
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const size_t num_pipes = 3;
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// define the taskflow storage
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// we use the pipe dimension because we create three 'serial' pipes
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std::array<tf::Taskflow, num_pipes> taskflows;
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// create three different taskflows for the three pipes
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make_taskflow1(taskflows[0]);
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make_taskflow2(taskflows[1]);
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make_taskflow3(taskflows[2]);
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// the pipeline consists of three serial pipes
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// and up to two concurrent scheduling tokens
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tf::Pipeline pl(num_lines,
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// first pipe runs taskflow1
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tf::Pipe{tf::PipeType::SERIAL, [&](tf::Pipeflow& pf) {
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if(pf.token() == 5) {
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pf.stop();
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return;
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}
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printf("begin token %zu\n", pf.token());
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executor.corun(taskflows[pf.pipe()]);
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}},
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// second pipe runs taskflow2
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tf::Pipe{tf::PipeType::SERIAL, [&](tf::Pipeflow& pf) {
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executor.corun(taskflows[pf.pipe()]);
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}},
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// third pipe calls taskflow3
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tf::Pipe{tf::PipeType::SERIAL, [&](tf::Pipeflow& pf) {
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executor.corun(taskflows[pf.pipe()]);
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}}
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);
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// build the pipeline graph using composition
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tf::Task init = taskflow.emplace([](){ std::cout << "ready\n"; })
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.name("starting pipeline");
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tf::Task task = taskflow.composed_of(pl)
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.name("pipeline");
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tf::Task stop = taskflow.emplace([](){ std::cout << "stopped\n"; })
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.name("pipeline stopped");
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// create task dependency
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init.precede(task);
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task.precede(stop);
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// dump the pipeline graph structure (with composition)
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taskflow.dump(std::cout);
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// run the pipeline
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executor.run(taskflow).wait();
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return 0;
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}
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@endcode
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@subsection TaskflowPipelineDefineTaskflows Define Taskflows
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First, we define three taskflows for the three pipes in the pipeline:
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@code{.cpp}
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// taskflow on the first pipe
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void make_taskflow1(tf::Taskflow& tf) {
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auto [A1, B1, C1, D1] = tf.emplace(
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[](){ printf("A1\n"); },
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[](){ printf("B1\n"); },
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[](){ printf("C1\n"); },
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[](){ printf("D1\n"); }
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);
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A1.precede(B1, C1);
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D1.succeed(B1, C1);
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}
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// taskflow on the second pipe
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void make_taskflow2(tf::Taskflow& tf) {
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auto [A2, B2, C2, D2] = tf.emplace(
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[](){ printf("A2\n"); },
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[](){ printf("B2\n"); },
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[](){ printf("C2\n"); },
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[](){ printf("D2\n"); }
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);
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tf.linearize({A2, B2, C2, D2});
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}
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// taskflow on the third pipe
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void make_taskflow3(tf::Taskflow& tf) {
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auto [A3, B3, C3, D3] = tf.emplace(
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[](){ printf("A3\n"); },
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[](){ printf("B3\n"); },
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[](){ printf("C3\n"); },
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[](){ printf("D3\n"); }
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);
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A3.precede(B3, C3, D3);
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}
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@endcode
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As each taskflow corresponds to a pipe in the pipeline, we create a linear
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array to store the three taskflows:
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@code{.cpp}
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std::array<tf::Taskflow, num_pipes> taskflows;
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make_taskflow1(taskflows[0]);
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make_taskflow2(taskflows[1]);
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make_taskflow3(taskflows[2]);
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@endcode
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Since the three taskflows are linearly dependent, at most one taskflow will run at a pipe.
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We can store the three taskflows in a linear array of dimension
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equal to the number of pipes.
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If there is a parallel pipe, we need to use two-dimensional array,
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as multiple taskflows at a stage can run simultaneously across parallel lines.
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@subsection TaskflowPipelineDefineThePipes Define the Pipes
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The pipe definition is straightforward. Each pipe runs the corresponding taskflow,
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which can be indexed at `taskflows` with the pipe's identifier,
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tf::Pipeflow::pipe().
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The first pipe will cease the pipeline scheduling when it has processed
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five scheduling tokens:
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@code{.cpp}
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// first pipe runs taskflow1
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tf::Pipe{tf::PipeType::SERIAL, [&](tf::Pipeflow& pf) {
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if(pf.token() == 5) {
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pf.stop();
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return;
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}
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printf("begin token %zu\n", pf.token());
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executor.corun(taskflows[pf.pipe()]);
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}},
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// second pipe runs taskflow2
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tf::Pipe{tf::PipeType::SERIAL, [&](tf::Pipeflow& pf) {
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executor.corun(taskflows[pf.pipe()]);
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}},
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// third pipe calls taskflow3
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tf::Pipe{tf::PipeType::SERIAL, [&](tf::Pipeflow& pf) {
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executor.corun(taskflows[pf.pipe()]);
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}}
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@endcode
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At each pipe, we use tf::Executor::corun to execute the corresponding taskflow
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and wait until the execution completes.
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This is important because we want the caller thread, which is the worker that invokes
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the pipe callable, to not block (i.e., `executor.run(taskflows[pf.pipe()]).wait()`)
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but participate in the work-stealing loop of the scheduler to avoid deadlock.
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@subsection TaskflowPipelineDefineTheTaskGraph Define the Task Graph
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To build up the taskflow for the pipeline,
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we create a module task with the defined pipeline structure and connect it with
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two tasks that output helper messages before and after the pipeline:
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@code{.cpp}
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tf::Task init = taskflow.emplace([](){ std::cout << "ready\n"; })
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.name("starting pipeline");
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tf::Task task = taskflow.composed_of(pl)
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.name("pipeline");
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tf::Task stop = taskflow.emplace([](){ std::cout << "stopped\n"; })
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.name("pipeline stopped");
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init.precede(task);
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task.precede(stop);
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@endcode
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@dotfile images/taskflow_processing_pipeline_3.dot
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@subsection TaskflowPipelineSubmitTheTaskGraph Submit the Task Graph
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Finally, we submit the taskflow to the execution and run it once:
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@code{.cpp}
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executor.run(taskflow).wait();
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@endcode
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One possible output is shown below:
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@code{.shell-session}
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ready
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begin token 0
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A1
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C1
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B1
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D1
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begin token 1
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A2
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B2
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A1
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C1
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B1
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D1
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C2
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D2
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A3
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D3
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C3
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B3
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begin token 2
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A2
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B2
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C2
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D2
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A1
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C1
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B1
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D1
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A3
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D3
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C3
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B3
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A2
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B2
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C2
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D2
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begin token 3
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A3
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D3
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C3
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B3
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A1
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C1
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B1
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D1
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begin token 4
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A2
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A1
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C1
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B1
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D1
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B2
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C2
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D2
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A3
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D3
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C3
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B3
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A2
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B2
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C2
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D2
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A3
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D3
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C3
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B3
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stopped
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@endcode
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*/
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}
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