Theoretical and practical exploration of C++ concurrent programming

C++ concurrent programming implements multi-task concurrent execution through mechanisms such as threads, mutexes, condition variables, and atomic operations. In practical cases, multi-threaded image processing programs divide the image into blocks and process these blocks in parallel through a thread pool, shortening the processing time.

Theoretical and practical exploration of concurrent programming in C++

Introduction
Concurrent programming involves simultaneous execution Multiple tasks, it has become an integral part of modern software development. The C++ language provides a wealth of concurrency features, and this article will delve into the theoretical foundations and practical applications of these features.

Theoretical basis

• Thread: A lightweight execution unit that shares the resources of a process, such as memory and files Descriptor.
• Mutex (Mutex): A synchronization primitive used to protect the critical area and ensure that only one thread can access the area at the same time.
• Condition Variable: Another synchronization primitive that allows a thread to wait for certain conditions to be met.
• Atomic operation: The operation of shared data ensures that the operation cannot be interrupted, thereby ensuring the integrity of the data.

Practical Case: Multi-threaded Image Processing
To demonstrate the practical application of concurrent programming, we will implement a multi-threaded image processing program that divides the image into multiple blocks and perform image processing tasks in parallel on each block.

Code implementation

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

using namespace std;

// 图像块结构体
struct ImageBlock {
    int start_row;  // 块的起始行
    int start_col;  // 块的起始列
    int width;      // 块的宽度
    int height;     // 块的高度
};

// 共享变量保护
mutex m;
condition_variable cv;

// 是否已处理完所有块
bool done = false;

// 图片处理函数
void processImageBlock(ImageBlock block) {
    // ... 实际的图像处理操作 ...
}

// 线程处理函数
void threadFunc(vector<ImageBlock>& blocks) {
    while (!done) {
        // 获取一个未处理的块
        unique_lock<mutex> lk(m);
        ImageBlock block;
        for (auto& b : blocks) {
            if (!b.processed) {
                block = b;
                b.processed = true;
                break;
            }
        }
        // 如果没有未处理的块，则等待
        if (!block.processed) {
            cv.wait(lk);
        }
        // 对块进行处理
        processImageBlock(block);
    }
}

int main() {
    // 划分图像为块
    vector<ImageBlock> blocks;
    // ... 省略分割图像的代码 ...

    // 创建线程池
    vector<thread> threads;
    for (int i = 0; i < 4; i++) {
        threads.emplace_back(threadFunc, ref(blocks));
    }

    // 等待所有线程完成
    {
        unique_lock<mutex> lk(m);
        done = true;
        cv.notify_all();
    }
    for (auto& t : threads) {
        t.join();
    }

    return 0;
}

Running results
The program will process the blocks in the image in parallel, thus shortening the overall processing time. The output will show details of the blocks processed by each thread.

Summary
This article explores the theoretical foundation and practical application of C++ concurrent programming. By introducing concepts such as mutexes, condition variables, and atomic operations, we show how to implement thread-safe and efficient multi-threaded programs. Practical cases demonstrate the practical application of concurrent programming in the field of image processing.

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