# Taskflow Algorithms » Task-parallel Scalable Pipeline

Unlike tf::Pipeline (see Task-parallel Pipeline) that instantiates all pipes at the construction time, Taskflow provides a scalable alternative called tf::ScalablePipeline to allow variable assignments of pipes using range iterators. A scalable pipeline is thus more flexible for applications to create a pipeline scheduling framework whose pipeline structure depends on runtime variables.

## Include the Header

You need to include the header file, `taskflow/algorithm/pipeline.hpp`, for creating a scalable pipeline scheduling framework.

    #include <taskflow/algorithm/pipeline.hpp>

## Create a Scalable Pipeline Module Task

Similar to tf::Pipeline, tf::ScalablePipeline is a composable graph object to implement a pipeline scheduling framework in a taskflow. The key difference between tf::Pipeline and tf::ScalablePipeline is that a scalable pipeline can accept variable assignments of pipes rather than instantiating all pipes at construction or programming time. Users define a linear range of pipes, each of the same callable type, and apply that range to construct a scalable pipeline. Between successive runs, users can reset the pipeline to a different range of pipes. The following code creates a scalable pipeline that uses four parallel lines to schedule tokens through three serial pipes in the given vector, then resetting that pipeline to a new range of five serial pipes:

    tf::Taskflow taskflow("pipeline");
    tf::Executor executor;

    const size_t num_lines = 4;

    // create data storage
    std::array<int, 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] = %d\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 based on the given vector of pipes
    tf::ScalablePipeline 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();

The program defines a uniform pipe type of `tf::Pipe<std::function<void(tf::Pipeflow&)>>` and keep all pipes in a vector that is amenable to change. Then, it constructs a scalable pipeline using two range iterators, `[first, last)`, that point to the beginning and the end of the pipe vector, resulting in a pipeline of three serial stages:

Then, the program appends another two pipes into the vector and resets the pipeline to the new range of two additional pipes, resulting in a pipeline of five serial stages:

When resetting a scalable pipeline to a new range, it will start from the initial state as if it has just been constructed, i.e., the token number counts from zero.

#### Attention

Unlike tf::Pipeline that keeps the given pipes in a std::tuple object, tf::ScalablePipeline does not own the given pipe but maintains a vector of iterators to each pipe in the given range. It is your responsibility to keep those pipe objects alive during the execution of the pipeline task.

## Reset a Placeholder Scalable Pipeline

It is possible to create a scalable pipeline as a placeholder using the constructor tf::ScalablePipeline(size_t num_lines) and reset it to another range later in the application. The following code creates a task to emplace a range of pipes and reset the pipeline to that range, before running the pipeline task:

    tf::Executor executor;
    tf::Taskflow taskflow;

    size_t num_pipes = 10;
    size_t num_lines = 10;

    std::vector<tf::Pipe<std::function<void(tf::Pipeflow&)>>> pipes;
    tf::ScalablePipeline<typename decltype(pipes)::iterator> spl(num_lines);

    tf::Task init = taskflow.emplace([&](){
      for(size_t i=0; i<num_pipes; i++) {
        pipes.emplace_back(tf::PipeType::SERIAL, [&](tf::Pipeflow& pf) {
          if(pf.pipe() == 0 && pf.token() == 1024) {
            pf.stop();
            return;
          }
        });
      }
      spl.reset(pipes.begin(), pipes.end());
    }).name("init");

    tf::Task pipeline = taskflow.composed_of(spl).name("pipeline");
    pipeline.succeed(init);
    executor.run(taskflow).wait();

The task graph of this program is shown below:

#### Attention

It is your responsibility to ensure a scalable pipeline has a valid structure before running it. A valid pipeline must have at least one parallel line and one pipe, where the first pipe is a serial type.

Similarly, you can create an empty scalable pipeline using the default constructor tf::ScalablePipeline() and reset it later in your program.

    std::vector<tf::Pipe<std::function<void(tf::Pipeflow&)>>> pipes;
    tf::ScalablePipeline<typename decltype(pipes)::iterator> spl;
    // create pipes ...
    spl.reset(num_lines, pipes.begin(), pipes.end());

## Use Other Iterator Types

When assigning a range to a scalable pipeline, the pipeline fetches all pipe iterators in that range to an internal vector. This organization allows invoking a pipe callable to be a random accessible operation, regardless of the pipe container type. Taskflow does not have much restriction on the iterator type, as long as these pipes can be iterated in a sequential order using the postfix increment operator, `++`.

    // use vector to store pipes
    std::vector<tf::Pipe<std::function<void(tf::Pipeflow&)>>> vector;
    tf::ScalablePipeline spl1(num_lines, vector.begin(), vector.end());

    // use list to store pipes
    std::list<tf::Pipe<std::function<void(tf::Pipeflow&)>>> list;
    tf::ScalablePipeline spl2(num_lines, list.begin(), list.end());

## Learn More about Taskflow Pipeline

Visit the following pages to learn more about pipeline:

- Task-parallel Pipeline
- Data-parallel Pipeline
- Text Processing Pipeline
- Graph Processing Pipeline
- Taskflow Processing Pipeline

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