cpp-taskflow/examples/parallel_graph_pipeline.cpp at master · EMinsight/cpp-taskflow · GitHub

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// This program demonstrates how to pipeline a sequence of linearly dependent

// tasks (stage function) over a directed acyclic graph.

#include <taskflow/taskflow.hpp>

#include <taskflow/algorithm/pipeline.hpp>

// 1st-stage function

void f1(const std::string& node) {

printf("f1(%s)\n", node.c_str());

}

// 2nd-stage function

void f2(const std::string& node) {

printf("f2(%s)\n", node.c_str());

}

// 3rd-stage function

void f3(const std::string& node) {

printf("f3(%s)\n", node.c_str());

}

int main() {

tf::Taskflow taskflow("graph processing pipeline");

tf::Executor executor;

const size_t num_lines = 2;

// a topological order of the graph

// |-> B

// A--|

// |-> C

const std::vector<std::string> nodes = {"A", "B", "C"};

// the pipeline consists of three serial pipes

// and up to two concurrent scheduling tokens

tf::Pipeline pl(num_lines,

// first pipe calls f1

tf::Pipe{tf::PipeType::SERIAL, [&](tf::Pipeflow& pf) {

if(pf.token() == nodes.size()) {

pf.stop();

}

else {

f1(nodes[pf.token()]);

}

}},

// second pipe calls f2

tf::Pipe{tf::PipeType::SERIAL, [&](tf::Pipeflow& pf) {

f2(nodes[pf.token()]);

}},

// third pipe calls f3

tf::Pipe{tf::PipeType::SERIAL, [&](tf::Pipeflow& pf) {

f3(nodes[pf.token()]);

}}

);

// build the pipeline graph using composition

tf::Task init = taskflow.emplace([](){ std::cout << "ready\n"; })

.name("starting pipeline");

tf::Task task = taskflow.composed_of(pl)

.name("pipeline");

tf::Task stop = taskflow.emplace([](){ std::cout << "stopped\n"; })

.name("pipeline stopped");

// create task dependency

init.precede(task);

task.precede(stop);

// dump the pipeline graph structure (with composition)

taskflow.dump(std::cout);

// run the pipeline

executor.run(taskflow).wait();

return 0;

}

cpp-taskflow/examples/parallel_graph_pipeline.cpp at master · EMinsight/cpp-taskflow · GitHub

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