// The contents of this file are in the public domain. See LICENSE_FOR_EXAMPLE_PROGRAMS.txt /* This is an example illustrating the use of the thread_pool object from the dlib C++ Library. In this example we will crate a thread pool with 3 threads and then show a few different ways to send tasks to the pool. */ #include #include // for dlib::sleep #include #include using namespace dlib; // We will be using the dlib logger object to print messages in this example // because its output is timestamped and labeled with the thread that the log // message came from. This will make it easier to see what is going on in this // example. Here we make an instance of the logger. See the logger // documentation and examples for detailed information regarding its use. logger dlog("main"); // Here we make an instance of the thread pool object. You could also use the // global dlib::default_thread_pool(), which automatically selects the number of // threads based on your hardware. But here let's make our own. thread_pool tp(3); // ---------------------------------------------------------------------------------------- class test { /* The thread_pool accepts "tasks" from the user and schedules them for execution in one of its threads when one becomes available. Each task is just a request to call a function. So here we create a class called test with a few member functions, which we will have the thread pool call as tasks. */ public: void mytask() { dlog << LINFO << "mytask start"; dlib::future var; var = 1; // Here we ask the thread pool to call this->subtask() and this->subtask2(). // Note that calls to add_task() will return immediately if there is an // available thread. However, if there isn't a thread ready then // add_task() blocks until there is such a thread. Also, note that if // mytask() is executed within the thread pool then calls to add_task() // will execute the requested task within the calling thread in cases // where the thread pool is full. This means it is always safe to spawn // subtasks from within another task, which is what we are doing here. tp.add_task(*this,&test::subtask,var); // schedule call to this->subtask(var) tp.add_task(*this,&test::subtask2); // schedule call to this->subtask2() // Since var is a future, this line will wait for the test::subtask task to // finish before allowing us to access the contents of var. Then var will // return the integer it contains. In this case result will be assigned // the value 2 since var was incremented by subtask(). int result = var; dlog << LINFO << "var = " << result; // Wait for all the tasks we have started to finish. Note that // wait_for_all_tasks() only waits for tasks which were started by the // calling thread. So you don't have to worry about other unrelated // parts of your application interfering. In this case it just waits // for subtask2() to finish. tp.wait_for_all_tasks(); dlog << LINFO << "mytask end" ; } void subtask(int& a) { dlib::sleep(200); a = a + 1; dlog << LINFO << "subtask end "; } void subtask2() { dlib::sleep(300); dlog << LINFO << "subtask2 end "; } }; // ---------------------------------------------------------------------------------------- int main() try { // tell the logger to print out everything dlog.set_level(LALL); dlog << LINFO << "schedule a few tasks"; test taskobj; // Schedule the thread pool to call taskobj.mytask(). Note that all forms of // add_task() pass in the task object by reference. This means you must make sure, // in this case, that taskobj isn't destructed until after the task has finished // executing. tp.add_task(taskobj, &test::mytask); // This behavior of add_task() enables it to guarantee that no memory allocations // occur after the thread_pool has been constructed, so long as the user doesn't // call any of the add_task_by_value() routines. The future object also doesn't // perform any memory allocations or contain any system resources such as mutex // objects. If you don't care about memory allocations then you will likely find // the add_task_by_value() interface more convenient to use, which is shown below. // If we call add_task_by_value() we pass task objects to a thread pool by value. // So in this case we don't have to worry about keeping our own instance of the // task. Here we create a lambda function and pass it right in and everything // works like it should. dlib::future num = 3; tp.add_task_by_value([](int& val){val += 7;}, num); // adds 7 to num int result = num.get(); dlog << LINFO << "result = " << result; // prints result = 10 // dlib also contains dlib::async(), which is essentially identical to std::async() // except that it launches tasks to a dlib::thread_pool (using add_task_by_value) // rather than starting an unbounded number of threads. As an example, here we // make 10 different tasks, each assigns a different value into the elements of the // vector vect. std::vector> vect(10); for (unsigned long i = 0; i < vect.size(); ++i) vect[i] = dlib::async(tp, [i]() { return i*i; }); // Print the results for (unsigned long i = 0; i < vect.size(); ++i) dlog << LINFO << "vect["<