mesytec-mnode/external/taskflow-3.8.0/examples/scalable_pipeline.cpp
2025-01-04 01:25:05 +01:00

114 lines
3 KiB
C++

// This program demonstrates how to create a pipeline scheduling framework
// that propagates a series of integers and adds one to the result at each
// stage, using a range of pipes provided by the application.
//
// The pipeline has the following structure:
//
// o -> o -> o
// | | |
// v v v
// o -> o -> o
// | | |
// v v v
// o -> o -> o
// | | |
// v v v
// o -> o -> o
//
// Then, the program resets the pipeline to a new range of five pipes.
//
// o -> o -> o -> o -> o
// | | | | |
// v v v v v
// o -> o -> o -> o -> o
// | | | | |
// v v v v v
// o -> o -> o -> o -> o
// | | | | |
// v v v v v
// o -> o -> o -> o -> o
#include <taskflow/taskflow.hpp>
#include <taskflow/algorithm/pipeline.hpp>
int main() {
tf::Taskflow taskflow("pipeline");
tf::Executor executor;
const size_t num_lines = 4;
// create data storage
std::array<size_t, num_lines> buffer;
// define the pipe callable
auto pipe_callable = [&buffer] (tf::Pipeflow& pf) mutable {
switch(pf.pipe()) {
// first stage generates only 5 scheduling tokens and saves the
// token number into the buffer.
case 0: {
if(pf.token() == 5) {
pf.stop();
}
else {
printf("stage 1: input token = %zu\n", pf.token());
buffer[pf.line()] = pf.token();
}
return;
}
break;
// other stages propagate the previous result to this pipe and
// increment it by one
default: {
printf(
"stage %zu: input buffer[%zu] = %zu\n", pf.pipe(), pf.line(), buffer[pf.line()]
);
buffer[pf.line()] = buffer[pf.line()] + 1;
}
break;
}
};
// create a vector of three pipes
std::vector< tf::Pipe<std::function<void(tf::Pipeflow&)>> > pipes;
for(size_t i=0; i<3; i++) {
pipes.emplace_back(tf::PipeType::SERIAL, pipe_callable);
}
// create a pipeline of four parallel lines using the given vector of pipes
tf::ScalablePipeline<decltype(pipes)::iterator> pl(num_lines, pipes.begin(), pipes.end());
// 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();
// reset the pipeline to a new range of five pipes and starts from
// the initial state (i.e., token counts from zero)
for(size_t i=0; i<2; i++) {
pipes.emplace_back(tf::PipeType::SERIAL, pipe_callable);
}
pl.reset(pipes.begin(), pipes.end());
executor.run(taskflow).wait();
return 0;
}