tf::DataPipeline< Ps > Class Template Reference

class to create a data-parallel pipeline scheduling framework More...

#include <taskflow/algorithm/data_pipeline.hpp>

Public Types
using	data_t
	internal storage type for each data token (default std::variant)

Public Member Functions
	DataPipeline (size_t num_lines, Ps &&... ps)
	constructs a data-parallel pipeline object
	DataPipeline (size_t num_lines, std::tuple< Ps... > &&ps)
	constructs a data-parallel pipeline object
size_t	num_lines () const noexcept
	queries the number of parallel lines
constexpr size_t	num_pipes () const noexcept
	queries the number of pipes
void	reset ()
	resets the pipeline
size_t	num_tokens () const noexcept
	queries the number of generated tokens in the pipeline
Graph &	graph ()
	obtains the graph object associated with the pipeline construct

Detailed Description

template<typename... Ps>
class tf::DataPipeline< Ps >

class to create a data-parallel pipeline scheduling framework

Template Parameters

Ps	data pipe types

Similar to tf::Pipeline, a tf::DataPipeline is a composable graph object for users to create a data-parallel pipeline scheduling framework using a module task in a taskflow. The only difference is that tf::DataPipeline provides a data abstraction for users to quickly express dataflow in a pipeline. The following example creates a data-parallel pipeline of three stages that generate dataflow from void to int, std::string, and void.

#include <taskflow/taskflow.hpp>
#include <taskflow/algorithm/data_pipeline.hpp>
 
int main() {
 
  // data flow => void -> int -> std::string -> void
  tf::Taskflow taskflow("pipeline");
  tf::Executor executor;
 
  const size_t num_lines = 4;
 
  tf::DataPipeline pl(num_lines,
    tf::make_data_pipe<void, int>(tf::PipeType::SERIAL, [&](tf::Pipeflow& pf) -> int{
      if(pf.token() == 5) {
        pf.stop();
        return 0;
      }
      else {
        return pf.token();
      }
    }),
    tf::make_data_pipe<int, std::string>(tf::PipeType::SERIAL, [](int& input) {
      return std::to_string(input + 100);
    }),
    tf::make_data_pipe<std::string, void>(tf::PipeType::SERIAL, [](std::string& input) {
      std::cout << input << std::endl;
    })
  );
 
  // build the pipeline graph using composition
  taskflow.composed_of(pl).name("pipeline");
 
  // dump the pipeline graph structure (with composition)
  taskflow.dump(std::cout);
 
  // run the pipeline
  executor.run(taskflow).wait();
 
  return 0;
}

The pipeline schedules five tokens over four parallel lines in a circular fashion, as depicted below:

o -> o -> o
|    |    |
v    v    v
o -> o -> o
|    |    |
v    v    v
o -> o -> o
|    |    |
v    v    v
o -> o -> o

Member Typedef Documentation

data_t

template<typename... Ps>

using tf::DataPipeline< Ps >::data_t

Initial value:

 unique_variant_t<std::variant<std::conditional_t<
    std::is_void_v<typename Ps::output_t>, 
    std::monostate, 
    std::decay_t<typename Ps::output_t>>...
  >>

internal storage type for each data token (default std::variant)

Constructor & Destructor Documentation

DataPipeline() [1/2]

template<typename... Ps>

tf::DataPipeline< Ps >::DataPipeline	(	size_t	num_lines,
		Ps &&...	ps )

constructs a data-parallel pipeline object

Parameters

num_lines	the number of parallel lines
ps	a list of pipes

Constructs a data-parallel pipeline of up to num_lines parallel lines to schedule tokens through the given linear chain of pipes. The first pipe must define a serial direction (tf::PipeType::SERIAL) or an exception will be thrown.

DataPipeline() [2/2]

template<typename... Ps>

tf::DataPipeline< Ps >::DataPipeline	(	size_t	num_lines,
		std::tuple< Ps... > &&	ps )

constructs a data-parallel pipeline object

Parameters

num_lines	the number of parallel lines
ps	a tuple of pipes

Constructs a data-parallel pipeline of up to num_lines parallel lines to schedule tokens through the given linear chain of pipes stored in a std::tuple. The first pipe must define a serial direction (tf::PipeType::SERIAL) or an exception will be thrown.

Member Function Documentation

graph()

template<typename... Ps>

Graph & tf::DataPipeline< Ps >::graph

(

)

obtains the graph object associated with the pipeline construct

This method is primarily used as an opaque data structure for creating a module task of this pipeline.

num_lines()

template<typename... Ps>

size_t tf::DataPipeline< Ps >::num_lines ( ) const

noexcept

queries the number of parallel lines

The function returns the number of parallel lines given by the user upon the construction of the pipeline. The number of lines represents the maximum parallelism this pipeline can achieve.

num_pipes()

template<typename... Ps>

size_t tf::DataPipeline< Ps >::num_pipes ( ) const

constexprnoexcept

queries the number of pipes

The Function returns the number of pipes given by the user upon the construction of the pipeline.

num_tokens()

template<typename... Ps>

size_t tf::DataPipeline< Ps >::num_tokens ( ) const

noexcept

queries the number of generated tokens in the pipeline

The number represents the total scheduling tokens that has been generated by the pipeline so far.

reset()

template<typename... Ps>

void tf::DataPipeline< Ps >::reset

(

)

resets the pipeline

Resetting the pipeline to the initial state. After resetting a pipeline, its token identifier will start from zero as if the pipeline was just constructed.

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The documentation for this class was generated from the following file:

• taskflow/algorithm/data_pipeline.hpp