Technical insider knowledge of C++ concurrent programming

Concurrent programming can be implemented in C++ through a variety of technologies, including: Threads: allow multiple tasks to execute simultaneously and share the same memory space. Parallel algorithms: Use multiple processing cores to perform different chunks of data simultaneously on the same operation. These techniques can be applied to a variety of real-world scenarios, such as multi-threaded image processing, to improve performance and responsiveness.

Technical Insider of C++ Concurrent Programming

Concurrent programming is a vital part of modern software development that involves simultaneous execution Multiple tasks to improve performance and responsiveness. In C++, concurrency can be achieved through a variety of techniques, including threads and parallel algorithms.

Threads

Threads are lightweight concurrency units scheduled by the operating system. Each thread has its own stack and set of registers, but shares the same memory space. This means that threads can perform different tasks at the same time and access the same global variables. The following code snippet demonstrates how to use threads:

#include <thread>
#include <iostream>

using namespace std;

void thread_function() {
    cout << "Hello from a thread!" << endl;
}

int main() {
    thread t1(thread_function);
    t1.join();
    
    return 0;
}

This example creates a new thread to execute the thread_function and then waits for it to complete.

Parallel Algorithms

Parallel algorithms use multiple processing cores to perform different data blocks of the same operation simultaneously. The standard library in C++ provides the std::thread library, which contains convenience functions for parallel algorithms, such as std::parallel_for:

#include <iostream>
#include <vector>
#include <parallel/algorithm>

using namespace std;

int main() {
    vector<int> v(1000000);
    
    parallel_for(v.begin(), v.end(), [](int& i) { i *= 2; });
    
    return 0;
}

this The example uses parallel_for to multiply each element of the given vector by 2 in parallel.

Practical Case: Multi-Threaded Image Processing

Concurrent programming has many applications in the real world. Here is an example of using threads to speed up image processing:

#include <thread>
#include <vector>
#include <opencv2/opencv.hpp>

using namespace cv;
using namespace std;

void process_image(Mat& image, int start_row, int end_row) {
    for (int i = start_row; i < end_row; i++) {
        for (int j = 0; j < image.cols; j++) {
            // 执行图像处理操作...
        }
    }
}

int main() {
    Mat image = imread("image.jpg");
    
    vector<thread> threads;
    int num_threads = 4;
    int rows_per_thread = image.rows / num_threads;
    
    for (int i = 0; i < num_threads; i++) {
        threads.push_back(thread(process_image, ref(image), i * rows_per_thread, (i + 1) * rows_per_thread));
    }
    
    for (auto& thread : threads) {
        thread.join();
    }
    
    return 0;
}

This example divides the image into row blocks and uses threads to process each block in parallel.

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