How to use C++ for high-performance concurrent data operations?

How to use C for high-performance concurrent data operations?

In today's highly concurrent computing environment, achieving high-performance concurrent data operations is one of the important tasks of software development. As a powerful programming language, C provides a wealth of concurrent programming libraries and features, allowing developers to use it to achieve efficient concurrent data operations. This article will introduce some basic principles and common techniques of concurrent data operations in C, and provide some code examples for readers' reference.

1. Use mutex lock (Mutex) to protect shared data

Mutex lock is one of the most basic and commonly used concurrent programming techniques. It can protect shared data by Access is restricted to only one thread at a time, thus avoiding race conditions. The following example shows how to use the mutex provided by the C standard library to protect read and write operations on shared data.

#include <iostream>
#include <thread>
#include <mutex>

std::mutex mtx; // 互斥锁对象

int counter = 0;

void increment()
{
    std::lock_guard<std::mutex> lock(mtx); // 上锁
    counter++;
} 

int main()
{
    std::thread t1(increment);
    std::thread t2(increment);
    
    t1.join();
    t2.join();
    
    std::cout << "Counter value: " << counter << std::endl;
    
    return 0;
}

2. Use condition variables (Condition Variable) for thread synchronization

Condition variable is a thread synchronization mechanism that allows threads to wait when a certain condition is met. When the conditions are met, it is awakened to continue execution. The C standard library provides the std::condition_variable class and the std::condition_variable_any class to implement condition variables. The following example shows how to use condition variables to synchronize operations between threads.

#include <iostream>
#include <thread>
#include <mutex>
#include <condition_variable>

std::mutex mtx;
std::condition_variable cv;
bool ready = false;

void worker()
{
    std::unique_lock<std::mutex> lock(mtx);
    cv.wait(lock, [](){ return ready; }); // 等待条件满足
    
    std::cout << "Worker thread is working..." << std::endl;
    
    // 执行一些具体的操作
    
    lock.unlock();
}

int main()
{
    std::thread t(worker);
    
    // 做一些其他的操作
    
    {
        std::lock_guard<std::mutex> lock(mtx);
        ready = true; // 设置条件为true
    }
    
    cv.notify_one(); // 唤醒一个线程
    
    t.join();
    
    return 0;
}

3. Use atomic operation (Atomic Operation)

Atomic operation is another important tool in concurrent programming, which can ensure that an operation is executed in a multi-threaded environment. Atomicity, that is, it will not be interrupted. The C standard library provides the std::atomic class to implement atomic operations. The following example shows how to use atomic operations to protect the operation of a shared counter.

#include <iostream>
#include <thread>
#include <atomic>

std::atomic<int> counter(0);

void increment()
{
    counter++;
}

int main()
{
    std::thread t1(increment);
    std::thread t2(increment);
    
    t1.join();
    t2.join();
    
    std::cout << "Counter value: " << counter << std::endl;
    
    return 0;
}

Summary:

This article introduces some basic principles and common techniques for using C to perform high-performance concurrent data operations, and provides corresponding code examples. Readers can choose the concurrent programming technology that suits them based on actual needs and practice it according to the sample code. In practical applications, other programming technologies and optimization methods can also be combined, such as thread pools, asynchronous programming, etc., to further improve the performance of concurrent data operations. I hope this article can be helpful to readers in the field of concurrent programming.

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