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

99 lines
3.1 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;
//1. How can I put a placeholder in the first pipe, i.e. [] (void, tf::Pipeflow&) in order to match the pipe vector?
auto pipe_callable1 = [] (tf::Pipeflow& pf) mutable -> int {
if(pf.token() == 5) {
pf.stop();
return 0;
}
else {
printf("stage 1: input token = %zu\n", pf.token());
return pf.token();
}
};
auto pipe_callable2 = [] (int input, tf::Pipeflow& pf) mutable -> float {
return input + 1.0;
};
auto pipe_callable3 = [] (float input, tf::Pipeflow& pf) mutable -> int {
return input + 1;
};
//2. Is this ok when the type in vector definition is different from the exact types of emplaced elements?
std::vector< ScalableDataPipeBase* > pipes;
pipes.emplace_back(tf::make_scalable_datapipe<void, int>(tf::PipeType::SERIAL, pipe_callable1));
pipes.emplace_back(tf::make_scalable_datapipe<int, float>(tf::PipeType::SERIAL, pipe_callable2));
pipes.emplace_back(tf::make_scalable_datapipe<float, int>(tf::PipeType::SERIAL, pipe_callable3));
// 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)
pipes.emplace_back(tf::make_scalable_datapipe<int, float>(tf::PipeType::SERIAL, pipe_callable1));
pipes.emplace_back(tf::make_scalable_datapipe<float, int>(tf::PipeType::SERIAL, pipe_callable1));
pl.reset(pipes.begin(), pipes.end());
executor.run(taskflow).wait();
return 0;
}