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<!DOCTYPE html>

<head>

<title>Cookbook » Executor | Taskflow QuickStart</title>

</head>

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<a href="https://taskflow.github.io"><img src="taskflow_logo.png" alt="" />Taskflow</a> | <a href="index.html" class="m-thin">QuickStart</a>

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<h1>

<a href="Cookbook.html">Cookbook</a> »

Executor

</h1>

<h3>Contents</h3>

<ul>

<li><a href="#CreateAnExecutor">Create an Executor</a></li>

<li><a href="#ExecuteATaskflow">Execute a Taskflow</a></li>

<li><a href="#ExecuteATaskflowWithTransferredOwnership">Execute a Taskflow with Transferred Ownership</a></li>

<li><a href="#ExecuteATaskflowFromAnInternalWorker">Execute a Taskflow from an Internal Worker</a></li>

<li><a href="#ThreadSafety">Touch an Executor from Multiple Threads</a></li>

<li><a href="#QueryTheWorkerID">Query the Worker ID</a></li>

<li><a href="#ObserveThreadActivities">Observe Thread Activities</a></li>

<li><a href="#ModifyWorkerProperty">Modify Worker Property</a></li>

</ul>

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After you create a task dependency graph, you need to submit it to threads for execution. In this chapter, we will show you how to execute a task dependency graph.<section id="CreateAnExecutor"><h2><a href="#CreateAnExecutor">Create an Executor</a></h2>To execute a taskflow, you need to create an executor of type <a href="classtf_1_1Executor.html" class="m-doc">tf::Executor</a>. An executor is a thread-safe object that manages a set of worker threads and executes tasks through an efficient work-stealing algorithm. Issuing a call to run a taskflow creates a topology, a data structure to keep track of the execution status of a running graph. <a href="classtf_1_1Executor.html" class="m-doc">tf::Executor</a> takes an unsigned integer to construct with <code>N</code> worker threads. The default value is <a href="http://en.cppreference.com/w/cpp/thread/thread/hardware_concurrency.html" class="m-doc-external">std::thread::hardware_concurrency</a>.<pre class="m-code">tf::Executor executor1; // create an executor with the number of workers

// equal to std::thread::hardware_concurrency

tf::Executor executor2(4); // create an executor of 4 worker threads</pre>An executor can be reused to execute multiple taskflows. In most workloads, you may need only one executor to run multiple taskflows where each taskflow represents a part of a parallel decomposition.</section><section id="ExecuteATaskflow"><h2><a href="#ExecuteATaskflow">Execute a Taskflow</a></h2><a href="classtf_1_1Executor.html" class="m-doc">tf::Executor</a> provides a set of <code>run_*</code> methods, <a href="classtf_1_1Executor.html#a8d08f0cb79e7b3780087975d13368a96" class="m-doc">tf::Executor::run</a>, <a href="classtf_1_1Executor.html#af15db5f7dde8e7ff1f86ef8fe825e9e2" class="m-doc">tf::Executor::run_n</a>, and <a href="classtf_1_1Executor.html#ae4f9e214ea5ee873e8d90a70bc1c77e8" class="m-doc">tf::Executor::run_until</a> to run a taskflow for one time, multiple times, or until a given predicate evaluates to true. All methods accept an optional callback to invoke after the execution completes, and return a <a href="classtf_1_1Future.html" class="m-doc">tf::Future</a> for users to access the execution status. The code below shows several ways to run a taskflow.<pre class="m-code"> 1: // Declare an executor and a taskflow

2: tf::Executor executor;

3: tf::Taskflow taskflow;

4:

5: // Add three tasks into the taskflow

6: tf::Task A = taskflow.emplace([] () { std::cout << "This is TaskA\n"; });

7: tf::Task B = taskflow.emplace([] () { std::cout << "This is TaskB\n"; });

8: tf::Task C = taskflow.emplace([] () { std::cout << "This is TaskC\n"; });

9:

10: // Build precedence between tasks

11: A.precede(B, C);

12:

13: tf::Future<void> fu = executor.run(taskflow);

14: fu.wait(); // block until the execution completes

15:

16: executor.run(taskflow, [](){ std::cout << "end of 1 run"; }).wait();

17: executor.run_n(taskflow, 4);

18: executor.wait_for_all(); // block until all associated executions finish

19: executor.run_n(taskflow, 4, [](){ std::cout << "end of 4 runs"; }).wait();

20: executor.run_until(taskflow, [cnt=0] () mutable { return ++cnt == 10; });</pre>Debrief:<ul><li>Lines 6-8 create a taskflow of three tasks A, B, and C</li><li>Lines 13-14 run the taskflow once and wait for completion</li><li>Line 16 runs the taskflow once with a callback to invoke when the execution finishes</li><li>Lines 17-18 run the taskflow four times and use <a href="classtf_1_1Executor.html#ab9aa252f70e9a40020a1e5a89d485b85" class="m-doc">tf::Executor::wait_for_all</a> to wait for completion</li><li>Line 19 runs the taskflow four times and invokes a callback at the end of the fourth execution</li><li>Line 20 keeps running the taskflow until the predicate returns true</li></ul>Issuing multiple runs on the same taskflow will automatically synchronize to a sequential chain of executions in the order of run calls.<pre class="m-code">executor.run(taskflow); // execution 1

executor.run_n(taskflow, 10); // execution 2

executor.run(taskflow); // execution 3

executor.wait_for_all(); // execution 1 -> execution 2 -> execution 3</pre><aside class="m-note m-warning"><h4>Attention</h4>A running taskflow must remain alive during its execution. It is your responsibility to ensure a taskflow not being destructed when it is running. For example, the code below can result undefined behavior.</aside><pre class="m-code">tf::Executor executor; // create an executor

// create a taskflow whose lifetime is restricted by the scope

{

tf::Taskflow taskflow;

// add tasks to the taskflow

// ...

// run the taskflow

executor.run(taskflow);

} // leaving the scope will destroy taskflow while it is running,

// resulting in undefined behavior</pre>Similarly, you should avoid touching a taskflow while it is running.<pre class="m-code">tf::Taskflow taskflow;

// Add tasks into the taskflow

// ...

// Declare an executor

tf::Executor executor;

tf::Future<void> future = executor.run(taskflow); // non-blocking return

// alter the taskflow while running leads to undefined behavior

taskflow.emplace([](){ std::cout << "Add a new task\n"; });</pre>You must always keep a taskflow alive and must not modify it while it is running on an executor.</section><section id="ExecuteATaskflowWithTransferredOwnership"><h2><a href="#ExecuteATaskflowWithTransferredOwnership">Execute a Taskflow with Transferred Ownership</a></h2>You can transfer the ownership of a taskflow to an executor and run it without wrangling with the lifetime issue of that taskflow. Each <code>run_*</code> method discussed in the previous section comes with an overload that takes a moved taskflow object.<pre class="m-code">tf::Taskflow taskflow;

tf::Executor executor;

taskflow.emplace([](){});

// let the executor manage the lifetime of the submitted taskflow

// now taskflow has no tasks

assert(taskflow.num_tasks() == 0);</pre>However, you should avoid moving a running taskflow which can result in undefined behavior.<pre class="m-code">tf::Taskflow taskflow;

tf::Executor executor;

taskflow.emplace([](){});

// executor does not manage the lifetime of taskflow

executor.run(taskflow);

// error! you cannot move a taskflow while it is running

executor.run(std::move(taskflow)); </pre>The correct way to submit a taskflow with moved ownership to an executor is to ensure all previous runs have completed. The executor will automatically release the resources of a moved taskflow right after its execution completes.<pre class="m-code">// submit the taskflow and wait until it completes

// now it's safe to move the taskflow to the executor and run it

executor.run(std::move(taskflow)); </pre>Likewise, you cannot move a taskflow that is running on an executor. You must wait until all the previous fires of runs on that taskflow complete before calling move.<pre class="m-code">// submit the taskflow and wait until it completes

// now it's safe to move the taskflow to another

tf::Taskflow moved_taskflow(std::move(taskflow)); </pre></section><section id="ExecuteATaskflowFromAnInternalWorker"><h2><a href="#ExecuteATaskflowFromAnInternalWorker">Execute a Taskflow from an Internal Worker</a></h2>Each run variant of <a href="classtf_1_1Executor.html" class="m-doc">tf::Executor</a> returns a <a href="classtf_1_1Future.html" class="m-doc">tf::Future</a> object which allows you to wait for the result to complete. When calling <code>tf::Future::wait</code>, the caller blocks without doing anything until the associated state is written to be ready. This design, however, can introduce deadlock problem especially when you need to run multiple taskflows from the internal workers of an executor. For example, the code below creates a taskflow of 1000 tasks with each task running a taskflow of 500 tasks in a blocking fashion:<pre class="m-code">tf::Executor executor(2);

tf::Taskflow taskflow;

std::array<tf::Taskflow, 1000> others;

std::atomic<size_t> counter{0};

for(size_t n=0; n<1000; n++) {

for(size_t i=0; i<500; i++) {

others[n].emplace([&](){ counter++; });

}

taskflow.emplace([&executor, &tf=others[n]](){

// blocking the worker can introduce deadlock where

// all workers are waiting for their taskflows to finish

executor.run(tf).wait();

});

}

executor.run(taskflow).wait();</pre>To avoid this problem, the executor has a method, <a href="classtf_1_1Executor.html#a8fcd9e0557922bb8194999f0cd433ea8" class="m-doc">tf::Executor::corun</a>, to execute a taskflow from a worker of that executor. The worker will not block but co-run the taskflow with other tasks in its work-stealing loop.<pre class="m-code">tf::Executor executor(2);

tf::Taskflow taskflow;

}

// the caller worker will not block but corun these

// taskflows through its work-stealing loop

executor.corun(tf);

});

}

executor.run(taskflow).wait();</pre>Similar to <a href="classtf_1_1Executor.html#a8fcd9e0557922bb8194999f0cd433ea8" class="m-doc">tf::Executor::corun</a>, the method <a href="classtf_1_1Executor.html#a0fc6eb19f168dc4a9cd0a7c6187c1d2d" class="m-doc">tf::Executor::corun_until</a> is another variant that keeps the calling worker in the work-stealing loop until the given predicate becomes true. You can use this method to prevent blocking a worker from doing useful things, such as being blocked when submitting an outstanding task (e.g., a GPU operation).<pre class="m-code">taskflow.emplace([&](){

auto fu = std::async([](){ std::sleep(100s); });

executor.corun_until([](){

return fu.wait_for(std::chrono::seconds(0)) == future_status::ready;

});

});</pre><aside class="m-note m-warning"><h4>Attention</h4>You must call <a href="classtf_1_1Executor.html#a0fc6eb19f168dc4a9cd0a7c6187c1d2d" class="m-doc">tf::Executor::corun_until</a> and <a href="classtf_1_1Executor.html#a8fcd9e0557922bb8194999f0cd433ea8" class="m-doc">tf::Executor::corun</a> from a worker of the calling executor or an exception will be thrown.</aside></section><section id="ThreadSafety"><h2><a href="#ThreadSafety">Touch an Executor from Multiple Threads</a></h2>All <code>run_*</code> methods are thread-safe. You can have multiple threads call these methods from an executor to run different taskflows. However, the order which taskflow runs first is non-deterministic and is up to the runtime.<pre class="m-code"> 1: tf::Executor executor;

2:

3: for(int i=0; i<10; ++i) {

4: std::thread([i, &](){

5: // ... modify my taskflow at i

6: executor.run(taskflows[i]); // run my taskflow at i

7: }).detach();

8: }

9:

10: executor.wait_for_all();</pre></section><section id="QueryTheWorkerID"><h2><a href="#QueryTheWorkerID">Query the Worker ID</a></h2>Each worker in an executor has an unique integer identifier in the range <code>[0, N)</code> that can be queried by the caller thread using <a href="classtf_1_1Executor.html#a6487d589cb1f6b078b69fd3bb1082345" class="m-doc">tf::Executor::this_worker_id</a>. If the caller thread is not a worker in the executor, <code>-1</code> is returned. This method is convenient for users to maintain a one-to-one mapping between a worker and its application data structure.<pre class="m-code">std::vector<int> worker_vectors[8]; // one vector per worker

tf::Taskflow taskflow;

tf::Executor executor(8); // an executor of eight workers

assert(executor.this_worker_id() == -1); // master thread is not a worker

taskflow.emplace([&](){

int id = executor.this_worker_id(); // in the range [0, 8)

auto& vec = worker_vectors[worker_id];

// ...

});</pre></section><section id="ObserveThreadActivities"><h2><a href="#ObserveThreadActivities">Observe Thread Activities</a></h2>You can observe thread activities in an executor when a worker thread participates in executing a task and leaves the execution using <a href="classtf_1_1ObserverInterface.html" class="m-doc">tf::ObserverInterface</a> – an interface class that provides a set of methods for you to define what to do when a thread enters and leaves the execution context of a task.<pre class="m-code">class ObserverInterface {

virtual ~ObserverInterface() = default;

virtual void set_up(size_t num_workers) = 0;

virtual void on_entry(tf::WorkerView worker_view, tf::TaskView task_view) = 0;

virtual void on_exit(tf::WorkerView worker_view, tf::TaskView task_view) = 0;

};</pre>There are three methods you must define in your derived class, <a href="classtf_1_1ObserverInterface.html#a41e6e62f12bf9d9dc4fa74632f6825d9" class="m-doc">tf::ObserverInterface::set_up</a>, <a href="classtf_1_1ObserverInterface.html#a8225fcacb03089677a1efc4b16b734cc" class="m-doc">tf::ObserverInterface::on_entry</a>, and <a href="classtf_1_1ObserverInterface.html#aa22f5378154653f08d9a58326bda4754" class="m-doc">tf::ObserverInterface::on_exit</a>. The method, <a href="classtf_1_1ObserverInterface.html#a41e6e62f12bf9d9dc4fa74632f6825d9" class="m-doc">tf::ObserverInterface::set_up</a>, is a constructor-like method that will be called by the executor when the observer is constructed. It passes an argument of the number of workers to observer in the executor. You may use it to preallocate or initialize data storage, e.g., an independent vector for each worker. The methods, <a href="classtf_1_1ObserverInterface.html#a8225fcacb03089677a1efc4b16b734cc" class="m-doc">tf::ObserverInterface::on_entry</a> and <a href="classtf_1_1ObserverInterface.html#aa22f5378154653f08d9a58326bda4754" class="m-doc">tf::ObserverInterface::on_exit</a>, are called by a worker thread before and after the execution context of a task, respectively. Both methods provide immutable access to the underlying worker and the running task using <a href="classtf_1_1WorkerView.html" class="m-doc">tf::WorkerView</a> and <a href="classtf_1_1TaskView.html" class="m-doc">tf::TaskView</a>. You may use them to record timepoints and calculate the elapsed time of a task.You can associate an executor with one or multiple observers (though one is common) using <a href="classtf_1_1Executor.html#ab5a793a0f2ce41cd49ca45e9f0a6962c" class="m-doc">tf::Executor::make_observer</a>. We use <a href="http://en.cppreference.com/w/cpp/memory/shared_ptr.html" class="m-doc-external">std::shared_ptr</a> to manage the ownership of an observer. The executor loops through each observer and invoke the corresponding methods accordingly.<pre class="m-code">#include <taskflow/taskflow.hpp>

struct MyObserver : public tf::ObserverInterface {

MyObserver(const std::string& name) {

std::cout << "constructing observer " << name << '\n';

}

void set_up(size_t num_workers) override final {

std::cout << "setting up observer with " << num_workers << " workers\n";

}

void on_entry(tf::WorkerView w, tf::TaskView tv) override final {

std::ostringstream oss;

oss << "worker " << w.id() << " ready to run " << tv.name() << '\n';

}

void on_exit(tf::WorkerView w, tf::TaskView tv) override final {

std::ostringstream oss;

oss << "worker " << w.id() << " finished running " << tv.name() << '\n';

}

};

int main(){

// Create a taskflow of eight tasks

tf::Taskflow taskflow;

auto A = taskflow.emplace([] () { std::cout << "1\n"; }).name("A");

auto B = taskflow.emplace([] () { std::cout << "2\n"; }).name("B");

auto C = taskflow.emplace([] () { std::cout << "3\n"; }).name("C");

auto D = taskflow.emplace([] () { std::cout << "4\n"; }).name("D");

auto E = taskflow.emplace([] () { std::cout << "5\n"; }).name("E");

auto F = taskflow.emplace([] () { std::cout << "6\n"; }).name("F");

auto G = taskflow.emplace([] () { std::cout << "7\n"; }).name("G");

auto H = taskflow.emplace([] () { std::cout << "8\n"; }).name("H");

// create an observer

std::shared_ptr<MyObserver> observer = executor.make_observer<MyObserver>(

"MyObserver"

);

// run the taskflow

executor.run(taskflow).get();

// remove the observer (optional)

executor.remove_observer(std::move(observer));

return 0;

}</pre>The above code produces the following output:<pre class="m-code">constructing observer MyObserver

setting up observer with 4 workers

worker 2 ready to run A

1

worker 2 finished running A

worker 2 ready to run B

2

worker 1 ready to run C

worker 2 finished running B

3

worker 2 ready to run D

worker 3 ready to run E

worker 1 finished running C

4

5

worker 1 ready to run F

worker 2 finished running D

worker 3 finished running E

6

worker 2 ready to run G

worker 3 ready to run H

worker 1 finished running F

7

8

worker 2 finished running G

worker 3 finished running H</pre>It is expected each line of <a href="http://en.cppreference.com/w/cpp/io/basic_ostream.html" class="m-doc-external">std::cout</a> interleaves with each other as there are four workers participating in task scheduling. However, the ready message always appears before the corresponding task message (e.g., numbers) and then the finished message.</section><section id="ModifyWorkerProperty"><h2><a href="#ModifyWorkerProperty">Modify Worker Property</a></h2>You can change the property of each worker thread from its executor, such as assigning thread-processor affinity before the worker enters the scheduler loop and post-processing additional information after the worker leaves the scheduler loop, by passing an instance derived from <a href="classtf_1_1WorkerInterface.html" class="m-doc">tf::WorkerInterface</a> to the executor. The example demonstrates the usage of <a href="classtf_1_1WorkerInterface.html" class="m-doc">tf::WorkerInterface</a> to affine a worker to a specific CPU core equal to its id on a linux platform:<pre class="m-code">// affine the given thread to the given core index (linux-specific)

bool affine(std::thread& thread, unsigned int core_id) {

cpu_set_t cpuset;

CPU_ZERO(&cpuset);

CPU_SET(core_id, &cpuset);

pthread_t native_handle = thread.native_handle();

return pthread_setaffinity_np(native_handle, sizeof(cpu_set_t), &cpuset) == 0;

}

class CustomWorkerBehavior : public tf::WorkerInterface {

public:

// to call before the worker enters the scheduling loop

void scheduler_prologue(tf::Worker& w) override {

printf("worker %lu prepares to enter the work-stealing loop\n", w.id());

// now affine the worker to a particular CPU core equal to its id

if(affine(w.thread(), w.id())) {

printf("successfully affines worker %lu to CPU core %lu\n", w.id(), w.id());

}

else {

printf("failed to affine worker %lu to CPU core %lu\n", w.id(), w.id());

}

// to call after the worker leaves the scheduling loop

void scheduler_epilogue(tf::Worker& w, std::exception_ptr) override {

printf("worker %lu left the work-stealing loop\n", w.id());

}

};

int main() {

tf::Executor executor(4, tf::make_worker_interface<CustomWorkerBehavior>());

return 0;

}</pre>When running the program, we see the following one possible output:<pre class="m-console">worker 3 prepares to enter the work-stealing loop

successfully affines worker 3 to CPU core 3

worker 3 left the work-stealing loop

worker 0 prepares to enter the work-stealing loop

successfully affines worker 0 to CPU core 0

worker 0 left the work-stealing loop

worker 1 prepares to enter the work-stealing loop

worker 2 prepares to enter the work-stealing loop

successfully affines worker 1 to CPU core 1

worker 1 left the work-stealing loop

successfully affines worker 2 to CPU core 2

worker 2 left the work-stealing loop</pre>When you create an executor, it spawns a set of worker threads to run tasks using a work-stealing scheduling algorithm. The execution logic of the scheduler and its interaction with each spawned worker via <a href="classtf_1_1WorkerInterface.html" class="m-doc">tf::WorkerInterface</a> is given below:<pre class="m-code">for(size_t n=0; n<num_workers; n++) {

create_thread([](Worker& worker)

// pre-processing executor-specific worker information

// ...

// enter the scheduling loop

// Here, WorkerInterface::scheduler_prologue is invoked, if any

worker_interface->scheduler_prologue(worker);

try {

while(1) {

perform_work_stealing_algorithm();

if(stop) {

break;

}

} catch(...) {

exception_ptr = std::current_exception();

}

// leaves the scheduling loop and joins this worker thread

// Here, WorkerInterface::scheduler_epilogue is invoked, if any

worker_interface->scheduler_epilogue(worker, exception_ptr);

);

}</pre><aside class="m-note m-warning"><h4>Attention</h4><a href="classtf_1_1WorkerInterface.html#a41c3b931a36bde8eff4aa8d375e8888a" class="m-doc">tf::WorkerInterface::scheduler_prologue</a> and tf::WorkerInterface::scheduler_eiplogue are invoked by each worker simultaneously.</aside></section>

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