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https://github.com/bloomen/transwarp

A header-only C++ library for task concurrency
https://github.com/bloomen/transwarp

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A header-only C++ library for task concurrency

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README 

        
# transwarp 



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Doxygen documentation



transwarp is a header-only C++ library for task concurrency. It

allows you to easily create a graph of tasks where every task can be executed

asynchronously. transwarp is written in C++17 and only depends on the standard

library. Just copy `include/transwarp.h` to your project and off you go!

Tested with GCC, Clang, ICC, and Visual Studio.



C++11 support can be enabled by defining `TRANSWARP_CPP11` at compile time.



**Important:** Only use tagged releases of transwarp in production code!



**Table of contents**



  * [Example](#example)

  * [API doc](#api-doc)

     * [Creating tasks](#creating-tasks)

     * [Scheduling tasks](#scheduling-tasks)

     * [Executors](#executors)

     * [Range functions](#range-functions)

     * [Canceling tasks](#canceling-tasks)

     * [Event system](#event-system)

     * [Task pool](#task-pool)

     * [Timing tasks](#timing-tasks)

     * [Optimizing efficiency](#optimizing-efficiency)

  * [Feedback](#feedback)

  * [Contributors](#contributors)



## Example



This example creates three tasks and connects them with each other to form

a two-level graph. The tasks are then scheduled twice for computation 

while using 4 threads.

```cpp

#include 

#include 

#include "transwarp.h"



namespace tw = transwarp;



int main() {

    double x = 0;

    int y = 0;



    // Building the task graph

    auto parent1 = tw::make_task(tw::root, [&x]{ return 13.3 + x; })->named("something");

    auto parent2 = tw::make_task(tw::root, [&y]{ return 42 + y; })->named("something else");

    auto child = tw::make_task(tw::consume, [](double a, int b) { return a + b;

                                            }, parent1, parent2)->named("adder");



    tw::parallel executor{4};  // Parallel execution with 4 threads



    child->schedule_all(executor);  // Schedules all tasks for execution

    std::cout << "result = " << child->get() << std::endl;  // result = 55.3



    // Modifying data input

    x += 2.5;

    y += 1;



    child->schedule_all(executor);  // Re-schedules all tasks for execution

    std::cout << "result = " << child->get() << std::endl;  // result = 58.8



    // Creating a dot-style graph for visualization

    std::ofstream{"basic_with_three_tasks.dot"} << tw::to_string(child->edges());

}

```



The resulting graph of this example looks like this:



![graph](https://raw.githubusercontent.com/bloomen/transwarp/master/examples/basic_with_three_tasks.png)



Every bubble represents a task and every arrow an edge between two tasks. 

The first line within a bubble is the task name. The second line denotes the task

type followed by the task id and the task level in the graph.



## API doc



This is a brief API doc of transwarp. 

For more details check out the doxygen documentation

and the transwarp examples.



In the following we will use `tw` as a namespace alias for `transwarp`.



### Creating tasks



transwarp supports seven different task types:

```cpp

root, // The task has no parents

accept, // The task's functor accepts all parent futures

accept_any, // The task's functor accepts the first parent future that becomes ready

consume, // The task's functor consumes all parent results

consume_any, // The task's functor consumes the first parent result that becomes ready

wait, // The task's functor takes no arguments but waits for all parents to finish

wait_any, // The task's functor takes no arguments but waits for the first parent to finish

```

The task type is passed as the first parameter to `make_task`, e.g., to create 

a `consume` task simply do this:

```cpp

auto task = tw::make_task(tw::consume, functor, parent1, parent2);

```

where `functor` denotes some callable and `parent1/2` the parent tasks. 



The functor as passed to `make_task` needs to fulfill certain requirements based

on the task type and the given parents:



**_root_**: A task at the root (top) of the graph. This task gets executed first.

A functor to a `root` task cannot have any parameters since this task does not

have parent tasks, e.g.:

```cpp

auto task = tw::make_task(tw::root, []{ return 42; });

```

Another way of defining a`root` task is a _value task_ which can be created as:

```cpp

auto task = tw::make_value_task(42);  

```

A value task doesn't require scheduling and always returns the same value or exception.



**_accept_**: This task is required to have at least one parent. It _accepts_

the resulting parent futures as they are without unwrapping. Hence, the child

can decide how to proceed since a call to `get()` can potentially throw an

exception. Here's an example:

```cpp

auto task = tw::make_task(tw::accept, [](auto f1, auto f2) { return f1.get() + f2.get(); }, parent1, parent2);

```



**_accept_any_**: This task is required to have at least one parent but its

functor takes exactly one future, namely the future of the parent that

first finishes. All other parents are abandoned and canceled. Here's an example:

```cpp

auto task = tw::make_task(tw::accept_any, [](auto f1) { return f1.get(); }, parent1, parent2);

```

Note that canceling only works for already running tasks when the functor is 

sub-classed from `transwarp::functor`.



**_consume_**: This task follows the same rules as `accept` with the difference

that the resulting parent futures are unwrapped (have `get()` called on them).

The results are then passed to the child, hence, consumed by the child task.

The child task will not be invoked if any parent throws an exception.

For example:

```cpp

auto task = tw::make_task(tw::consume, [](int x, int y) { return x + y; }, parent1, parent2);

```



**_consume_any_**: This task follows the same rules as `accept_any` with the difference

that the resulting parent futures are unwrapped (have `get()` called on them).

For example:

```cpp

auto task = tw::make_task(tw::consume_any, [](int x) { return x; }, parent1, parent2);

``` 



**_wait_**: This task's functor does not take any parameters but the task

must have at least one parent. It simply waits for completion of all parents

while unwrapping futures before calling the child's functor. For example:

```cpp

auto task = tw::make_task(tw::wait, []{ return 42; }, parent1, parent2);

``` 



**_wait_any_**: This task works similar to the `wait` task but calls its functor

as soon as the first parent completes. It abandons and cancels all remaining

parent tasks. For example:

```cpp

auto task = tw::make_task(tw::wait_any, []{ return 42; }, parent1, parent2);

``` 



Generally, tasks are created using `make_task` which allows for any number 

of parents. However, it is a common use case for a child to only have one parent.

For this, `then()` can be directly called on the parent object to create a _continuation_:

```cpp

auto child = tw::make_task(tw::root, []{ return 42; })->then(tw::consume, functor);

```

`child` is now a single-parent task whose functor consumes an integer.



### Scheduling tasks



Once a task is created it can be scheduled just by itself:

```cpp

auto task = tw::make_task(tw::root, functor);

task->schedule();

```

which, if nothing else is specified, will run the task on the current thread. 

However, using the built-in `parallel` executor the task can be pushed into a 

thread pool and executed asynchronously:

```cpp

tw::parallel executor{4};  // Thread pool with 4 threads

auto task = tw::make_task(tw::root, functor);

task->schedule(executor);

```

Regardless of how you schedule, the task result can be retrieved through:

```cpp

std::cout << task->get() << std::endl;

```  

When chaining multiple tasks together a directed acyclic graph is built in which

every task can be scheduled individually. Though, in many scenarios it is useful

to compute all tasks in the right order with a single call:

```cpp

auto parent1 = tw::make_task(tw::root, foo);  // foo is a functor

auto parent2 = tw::make_task(tw::root, bar);  // bar is a functor

auto task = tw::make_task(tw::consume, functor, parent1, parent2);

task->schedule_all();  // Schedules all parents and itself

```

which can also be scheduled using an executor, for instance:

```cpp

tw::parallel executor{4};

task->schedule_all(executor);

```

which will run those tasks in parallel that do not depend on each other.



### Executors



We have seen that we can pass executors to `schedule()` and `schedule_all()`.

Additionally, they can be assigned to a task directly:

```cpp

auto exec1 = std::make_shared(2);

task->set_executor(exec1);

tw::sequential exec2;

task->schedule(exec2);  // exec1 will be used to schedule the task

``` 

The task-specific executor will always be preferred over other executors when

scheduling tasks.



transwarp defines an executor interface which can be implemented to perform custom 

behavior when scheduling tasks. The interface looks like this:

```cpp

class executor {

public:

    virtual ~executor() = default;

    

    // The name of the executor

    virtual std::string name() const = 0;

    

    // Only ever called on the thread of the caller to schedule()

    virtual void execute(const std::function& functor, tw::itask& task) = 0;

};



``` 

where `functor` denotes the function to be run and `task` the task the functor belongs to.



### Range functions



There are convenience functions that can be applied to an iterator range:

* `tw::for_each`

* `tw::transform`



These are very similar to their standard library counterparts except that they 

return a task for deferred, possibly asynchronous execution. Here's an example:



```cpp

std::vector vec = {1, 2, 3, 4, 5, 6, 7};

tw::parallel exec{4};

auto task = tw::for_each(exec, vec.begin(), vec.end(), [](int& x){ x *= 2; });

task->wait();  // all values in vec will have doubled

```



### Canceling tasks



A task can be canceled by calling `task->cancel(true)` which will, by default, 

only affect tasks that are not currently running yet. However, if you create a functor

that inherits from `transwarp::functor` you can terminate tasks while they're

running. `transwarp::functor` looks like this:

```cpp



class functor {

public:

    virtual ~functor() = default;



protected:

    // The associated task (only to be called after the task was constructed)

    const tw::itask& transwarp_task() const noexcept;



    // The associated task (only to be called after the task was constructed)

    tw::itask& transwarp_task() noexcept;



    // If the associated task is canceled then this will throw transwarp::task_canceled

    // which will stop the task while it's running (only to be called after the task was constructed)

    void transwarp_cancel_point() const;



private:

    ...

};

```

By placing calls to `transwarp_cancel_point()` in strategic places of your functor

you can denote well defined points where the functor will exit when the associated task is canceled.

A task can also be canceled by throwing `transwarp::task_canceled` directly.



As mentioned above, tasks can be explicitly canceled on client request. In addition,

all tasks considered abandoned by `accept_any`, `consume_any`, or `wait_any`

operations are also canceled in order to terminate them as soon as their computations

become superfluous. 



### Event system



Transwarp provides an event system that allows you to subscribe to all or specific

events of a task, such as, before started or after finished events. The task events

are enumerated in the `event_type` enum:

```cpp

enum class event_type {

    before_scheduled, // Just before a task is scheduled

    after_future_changed, // Just after the task's future was changed

    before_started, // Just before a task starts running

    before_invoked, // Just before a task's functor is invoked

    after_finished, // Just after a task has finished running

    after_canceled, // Just after a task was canceled

    after_satisfied, ///< Just after a task has satisfied all its children with results

    after_custom_data_set, // Just after custom data was assigned

}

```

Listeners are created by sub-classing from the `listener` interface:

```cpp

class listener {

public:

    virtual ~listener() = default;



    // This may be called from arbitrary threads depending on the event type

    virtual void handle_event(tw::event_type event, tw::itask& task) = 0;

};

```

A listener can then be passed to the `add_listener` functions of a task

to add a new listener or to the `remove_listener` functions to remove

an existing listener.



### Task pool



A task pool is useful when one wants to run the same graph in parallel. For this purpose,

transwarp provides a `task_pool` which manages a pool of tasks from which

one can request an idle task for parallel graph execution. For example:

```cpp

tw::parallel exec{4};



auto my_task = make_graph();

tw::task_pool pool{my_task};



for (;;) {

    auto task = pool.next_task(); // task may be null if the pool size is exhausted

    if (task) {

        task->schedule_all(exec);

    }

}

```



### Timing tasks



In order to identify bottlenecks it's often useful to know how much time is spent

in which task. transwarp provides a `timer` listener that will automatically

time the tasks it listens to:

```cpp

auto task = make_graph();

task->add_listener_all(std::make_shared()); // assigns the timer listener to all tasks

task->schedule_all();

std::ofstream{"graph.dot"} << tw::to_string(task->edges()); // the dot file now contains timing info

```



### Optimizing efficiency



**Compile time switches**



By default, transwarp provides its full functionality to its client. However,

in many cases not all of that is actually required and so transwarp provides

a few compile time switches to reduce the task size.

These switches are:

```

TRANSWARP_DISABLE_TASK_CUSTOM_DATA

TRANSWARP_DISABLE_TASK_NAME

TRANSWARP_DISABLE_TASK_PRIORITY

TRANSWARP_DISABLE_TASK_REFCOUNT

TRANSWARP_DISABLE_TASK_TIME

```



To get the minimal task size with a single switch one can define

```

TRANSWARP_MINIMUM_TASK_SIZE

```

at build time.



**Releasing unused memory**



By default, every task in a graph will keep its result until rescheduling or

a manual task reset. The `releaser` listener allows you to automatically

release a task result after that task's children have consumed the result.

For example:

```cpp

auto task = make_graph();

task->add_listener_all(std::make_shared()); // assigns the releaser listener to all tasks

task->schedule_all();

// All intermediate task results are now released (i.e. futures are invalid)

auto result = task->get(); // The final task's result remains valid

```

The `releaser` also accepts an executor that gives control over _where_ a task's

result is released.



## Using transwarp with tipi.build



`transwarp` can be easily used in [tipi.build](https://tipi.build) projects simply by adding the following entry to your `.tipi/deps`:



```json

{

    "bloomen/transwarp": { }

}

```



## Feedback



Get in touch if you have any questions or suggestions to make this a better library!

You can post on [gitter](https://gitter.im/bloomen/transwarp), submit a pull request,

create a Github issue, or simply email one of the contributors.



If you're serious about contributing code to transwarp (which would be awesome!) then 

please submit a pull request and keep in mind that:

- unit tests should be added for all new code by extending the existing unit test suite

- C++ code uses spaces throughout