How to use STL for distributed computing in C++?

How to use STL for distributed computing in C++? By using STL algorithm parallelization, working with executors and developing practical cases such as image processing pipelines.

How to use STL for distributed computing in C++

Introduction

Distributed computing involves distributing tasks across multiple computer nodes to increase processing speed. The C++ Standard Template Library (STL) provides concurrency tools that enable you to develop distributed computing applications.

Parallelizing STL Algorithms

You can parallelize STL algorithms by using the std::async and std::future functions change. std::async Starts an asynchronous task and returns a handle to the std::future object generated by the task.

// 计算无序向量中所有整数的总和
std::vector<int> numbers = {1, 2, 3, 4, 5};
int sum = 0;

// 并行化 for_each 算法
std::for_each(numbers.begin(), numbers.end(), [&](int n) {
  std::future<int> result = std::async(std::launch::async, [] { return n * n; });

  // 在另一个线程中执行的计算
  sum += result.get();
});

std::cout << "Sum: " << sum << std::endl;

Using Executors

Executors are part of the concurrency library and provide an abstraction for managing tasks across thread pools. STL algorithms can be parallelized on the executor using the std::execution::parallel_unsequenced strategy.

// 查找向量中所有奇数
std::vector<int> numbers = {1, 2, 3, 4, 5};
std::vector<int> oddNumbers;

// 使用执行器上的 parallel_unsequenced 策略
std::execution::parallel_unsequenced(numbers.begin(), numbers.end(),
                                    [&](int n) { if (n % 2) oddNumbers.push_back(n); });

std::cout << "Odd numbers: ";
for (int n : oddNumbers) {
  std::cout << n << " ";
}
std::cout << std::endl;

Practical case

Parallelized image processing pipeline

Imagine that you have a pipeline to process images, including Resize, convert and save image operations. By parallelizing these operations, you can significantly increase pipeline throughput.

// 图像处理管道
struct ImageProcessingPipeline {
  // 调整大小
  std::vector<std::future<cv::Mat>> resizeTasks;

  // 转换
  std::vector<std::future<cv::Mat>> convertTasks;

  // 保存
  std::vector<std::future<void>> saveTasks;

  // 执行管道
  std::vector<cv::Mat> execute(const std::vector<cv::Mat>& images) {
    for (const cv::Mat& image : images) {
      // 并行化调整大小
      resizeTasks.emplace_back(std::async(std::launch::async,
                                         [&image] { return resize(image, 500, 500); }));
    }

    // 等待所有调整大小的任务完成
    for (auto& task : resizeTasks) task.get();

    // 并行化转换
    for (auto& resizedImage : resizeTasks) {
      convertTasks.emplace_back(
          std::async(std::launch::async, [&resizedImage] { return convert(resizedImage); }));
    }

    // 等待所有转换任务完成
    for (auto& task : convertTasks) task.get();

    // 并行化保存
    for (auto& convertedImage : convertTasks) {
      saveTasks.emplace_back(std::async(std::launch::async,
                                     [&convertedImage](const std::string& path) { return save(convertedImage, path); },
                                     "output/image_" + std::to_string(i) + ".jpg"));
    }

    // 等待所有保存任务完成
    for (auto& task : saveTasks) task.get();
  }
};

You can easily develop efficient distributed computing applications in C++ by using STL's concurrency tools and executors.

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