पैरेलल एल्गोरिदम और एक्ज़ीक्यूशन पॉलिसी

#include <algorithm>
#include <execution>
#include <vector>
#include <iostream>
#include <chrono>

int main() {
    std::vector<int> data(10'000'000);
    std::iota(data.begin(), data.end(), 0);

    // Sequential — same as no policy
    auto t1 = std::chrono::high_resolution_clock::now();
    std::sort(std::execution::seq, data.begin(), data.end(),
              std::greater<>{});
    auto t2 = std::chrono::high_resolution_clock::now();

    // Parallel — may use multiple threads
    std::sort(std::execution::par, data.begin(), data.end());
    auto t3 = std::chrono::high_resolution_clock::now();

    // Parallel + vectorized — threads AND SIMD
    std::sort(std::execution::par_unseq, data.begin(), data.end(),
              std::greater<>{});
    auto t4 = std::chrono::high_resolution_clock::now();

    auto ms = [](auto a, auto b) {
        return std::chrono::duration_cast<std::chrono::milliseconds>(b - a).count();
    };
    std::cout << "seq: " << ms(t1, t2) << "ms\n";
    std::cout << "par: " << ms(t2, t3) << "ms\n";
    std::cout << "par_unseq: " << ms(t3, t4) << "ms\n";
    return 0;
}

#include <numeric>
#include <execution>
#include <vector>
#include <iostream>

int main() {
    std::vector<double> data(1'000'000, 1.0);

    // Sequential accumulate — always left-to-right
    double sum1 = std::accumulate(data.begin(), data.end(), 0.0);

    // Parallel reduce — may split and combine in any order
    double sum2 = std::reduce(std::execution::par,
                              data.begin(), data.end(), 0.0);

    // Transform-reduce: parallel dot product
    std::vector<double> a(1'000'000, 2.0);
    std::vector<double> b(1'000'000, 3.0);
    double dot = std::transform_reduce(
        std::execution::par,
        a.begin(), a.end(), b.begin(),
        0.0);  // default: multiply + add

    std::cout << "accumulate: " << sum1 << "\n";
    std::cout << "reduce:     " << sum2 << "\n";
    std::cout << "dot product: " << dot << "\n";  // 6'000'000
    return 0;
}

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

class ThreadPool {
    std::vector<std::thread> workers_;
    std::queue<std::function<void()>> tasks_;
    std::mutex mtx_;
    std::condition_variable cv_;
    bool stop_ = false;

public:
    explicit ThreadPool(size_t n) {
        for (size_t i = 0; i < n; ++i) {
            workers_.emplace_back([this] {
                while (true) {
                    std::function<void()> task;
                    {
                        std::unique_lock lock(mtx_);
                        cv_.wait(lock, [this] {
                            return stop_ || !tasks_.empty();
                        });
                        if (stop_ && tasks_.empty()) return;
                        task = std::move(tasks_.front());
                        tasks_.pop();
                    }
                    task();  // run outside the lock
                }
            });
        }
    }

    void enqueue(std::function<void()> task) {
        {
            std::lock_guard lock(mtx_);
            tasks_.push(std::move(task));
        }
        cv_.notify_one();
    }

    ~ThreadPool() {
        {
            std::lock_guard lock(mtx_);
            stop_ = true;
        }
        cv_.notify_all();
        for (auto& w : workers_) w.join();
    }
};

int main() {
    ThreadPool pool(4);
    for (int i = 0; i < 20; ++i) {
        pool.enqueue([i] {
            std::cout << "Task " << i << " on thread "
                      << std::this_thread::get_id() << "\n";
        });
    }
    // pool destructor waits for all tasks
    return 0;
}