C++多線程在Linux下的線程池任務(wù)調(diào)度

在Linux下，使用C++多線程和線程池可以有效地執(zhí)行并發(fā)任務(wù)

1. 引入頭文件：

#include <iostream>
#include <vector>
#include <queue>
#include <thread>
#include <mutex>
#include <condition_variable>
#include <functional>
#include <future>

2. 定義線程池類：

class ThreadPool {
public:
    ThreadPool(size_t num_threads);
    ~ThreadPool();

    template<class F, class... Args>
    auto enqueue(F&& f, Args&&... args) -> std::future<typename std::result_of<F(Args...)>::type>;

private:
    // 工作線程函數(shù)
    void worker(size_t id);

    std::vector<std::thread> workers;
    std::queue<std::function<void()>> tasks;
    std::mutex task_queue_mutex;
    std::condition_variable condition;
    bool stop = false;
};

3. 實(shí)現(xiàn)線程池類：

ThreadPool::ThreadPool(size_t num_threads) {
    for (size_t i = 0; i < num_threads; ++i) {
        workers.emplace_back(&ThreadPool::worker, this, i);
    }
}

ThreadPool::~ThreadPool() {
    {
        std::unique_lock<std::mutex> lock(task_queue_mutex);
        stop = true;
    }
    condition.notify_all();
    for (auto& worker : workers) {
        worker.join();
    }
}

template<class F, class... Args>
auto ThreadPool::enqueue(F&& f, Args&&... args) -> std::future<typename std::result_of<F(Args...)>::type> {
    using return_type = typename std::result_of<F(Args...)>::type;

    auto task = std::make_shared<std::packaged_task<return_type()>>(std::bind(std::forward<F>(f), std::forward<Args>(args)...));
    std::future<return_type> result = task->get_future();
    {
        std::unique_lock<std::mutex> lock(task_queue_mutex);
        if (stop) {
            throw std::runtime_error("enqueue on stopped ThreadPool");
        }
        tasks.emplace([task]() { (*task)(); });
    }
    condition.notify_one();
    return result;
}

void ThreadPool::worker(size_t id) {
    while (true) {
        std::function<void()> task;
        {
            std::unique_lock<std::mutex> lock(task_queue_mutex);
            condition.wait(lock, [this]() { return stop || !tasks.empty(); });
            if (stop && tasks.empty()) {
                return;
            }
            task = std::move(tasks.front());
            tasks.pop();
        }
        task();
    }
}

4. 使用線程池執(zhí)行任務(wù)：

int main() {
    ThreadPool pool(4);

    auto result1 = pool.enqueue([](int a, int b) { return a + b; }, 5, 3);
    std::cout << "Result 1: " << result1.get() << std::endl;

    auto result2 = pool.enqueue([](int a, int b) { return a * b; }, 4, 6);
    std::cout << "Result 2: " << result2.get() << std::endl;

    return 0;
}

這個簡單的線程池實(shí)現(xiàn)可以處理并發(fā)任務(wù)，并在Linux下使用C++多線程進(jìn)行任務(wù)調(diào)度。當(dāng)然，你可以根據(jù)實(shí)際需求對這個實(shí)現(xiàn)進(jìn)行擴(kuò)展和優(yōu)化。