Parallel calling scheme of C++ functions in distributed systems?

There are three options for calling C functions in parallel in a distributed system: using threads, using the C 11 thread pool, and using third-party libraries. The thread pool provides more advanced functions and performance, which can be used to process images, scientific calculations and other practical cases, significantly improving algorithm performance.

#Parallel calling scheme of C functions in distributed systems

In distributed systems, it is often necessary to call functions on multiple nodes in parallel. There are several ways to implement this functionality in C.

Using threads

The easiest way is to use threads. The following code creates four threads, each calling a function in parallel:

#include <iostream>
#include <thread>

using namespace std;

void function(int i) {
  cout << "Thread " << i << " is running." << endl;
}

int main() {
  thread thread1(function, 1);
  thread thread2(function, 2);
  thread thread3(function, 3);
  thread thread4(function, 4);
  thread1.join();
  thread2.join();
  thread3.join();
  thread4.join();
  return 0;
}

Using the thread pool in the C 11 standard

The C 11 standard introduced std::thread Library, which provides a more advanced thread pool. A thread pool is a group of pre-created threads that can be used to perform tasks. The following code uses the thread pool to call four functions in parallel:

#include <iostream>
#include <thread>

using namespace std;

void function(int i) {
  cout << "Thread " << i << " is running." << endl;
}

int main() {
  threadpool pool(4);
  for (int i = 1; i <= 4; i++) {
    pool.enqueue(function, i);
  }
  pool.join_all();
  return 0;
}

Using third-party libraries

There are also some third-party libraries that can be used to call functions in parallel, such as Intel TBB and Boost.Asio. These libraries typically provide more advanced functionality and performance than the C standard library.

Practical case

The following is a practical case using C to call functions in parallel:

Image processing

Parallel image processing can Significantly improve the performance of image processing algorithms. The following code uses a thread pool to process four different areas on an image in parallel:

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

using namespace cv;
using namespace std;

void process_region(Mat& image, int start_x, int start_y, int end_x, int end_y) {
  // 处理图像区域
}

int main() {
  Mat image = imread("image.jpg");
  threadpool pool(4);
  int width = image.cols;
  int height = image.rows;
  int region_width = width / 4;
  int region_height = height / 4;
  for (int i = 0; i < 4; i++) {
    int start_x = i * region_width;
    int start_y = 0;
    int end_x = (i + 1) * region_width;
    int end_y = height;
    pool.enqueue(process_region, image, start_x, start_y, end_x, end_y);
  }
  pool.join_all();
  return 0;
}

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