docs/classtf_1_1DataPipeline.html
| | Taskflow: A General-purpose Task-parallel Programming System |
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Public Types | Public Member Functions | List of all members
tf::DataPipeline< Ps > Class Template Reference
class to create a data-parallel pipeline scheduling framework More...
#include <taskflow/algorithm/data_pipeline.hpp>
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| using | data_t |
| | internal storage type for each data token (default std::variant)
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| | DataPipeline (size_t num_lines, Ps &&... ps) |
| | constructs a data-parallel pipeline object
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| | DataPipeline (size_t num_lines, std::tuple< Ps... > &&ps) |
| | constructs a data-parallel pipeline object
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| size_t | num_lines () const noexcept |
| | queries the number of parallel lines
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| constexpr size_t | num_pipes () const noexcept |
| | queries the number of pipes
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| void | reset () |
| | resets the pipeline
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| size_t | num_tokens () const noexcept |
| | queries the number of generated tokens in the pipeline
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| Graph & | graph () |
| | obtains the graph object associated with the pipeline construct
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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;
}
class to create a data-parallel pipeline scheduling framework
Definition data_pipeline.hpp:254
size_t num_lines() const noexcept
queries the number of parallel lines
Definition data_pipeline.hpp:428
class to create an executor
Definition executor.hpp:62
tf::Future< void > run(Taskflow &taskflow)
runs a taskflow once
class to create a pipeflow object used by the pipe callable
Definition pipeline.hpp:43
size_t token() const
queries the token identifier
Definition pipeline.hpp:78
void stop()
stops the pipeline scheduling
Definition pipeline.hpp:88
class to create a taskflow object
Definition taskflow.hpp:64
auto make_data_pipe(PipeType d, C &&callable)
function to construct a data pipe (tf::DataPipe)
Definition data_pipeline.hpp:171
@ SERIAL
serial type
Definition pipeline.hpp:117
The pipeline schedules five tokens over four parallel lines in a circular fashion, as depicted below:
o -> o -> o
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v v v
o -> o -> o
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v v v
o -> o -> o
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v v v
o -> o -> o
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)
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.
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.
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.
template<typename... Ps>
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| 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.
template<typename... Ps>
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| 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.
template<typename... Ps>
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| 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.
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.
The documentation for this class was generated from the following file:
taskflow/algorithm/data_pipeline.hpp
Maintained by Dr. Tsung-Wei Huang — Generated by 1.13.1