Engineering and design patterns in C++ concurrent programming?

C++ Concurrent programming involves shared resources and synchronized operations, requiring engineering and design patterns to solve the challenges. Engineering modes include multithreading, processes, thread pools, semaphores, and atomic operations for efficient thread management. Design patterns include producer-consumer queues, reader-writer locks, deadlock avoidance, starvation prevention, and divide-and-conquer to coordinate data access and processing. These patterns can be applied to real-world problems such as image processing and logging services to implement efficient concurrent programs.

Engineering and Design Patterns in C++ Concurrent Programming

Introduction

Concurrency Programming requires proper handling of shared resources and synchronization operations to avoid data consistency issues. C++ provides various engineering and design patterns for solving these challenges, which this article will explore in depth.

Engineering Mode

• Multi-threading: Perform multiple tasks at the same time to improve performance.
• Process: Isolated execution environment, sharing operating system resources with other processes.
• Thread pool: A collection of pre-allocated threads to reduce thread creation overhead.
• Semaphore: Synchronization mechanism to limit concurrent access to shared resources.
• Atomic operations: Perform atomic operations on a single memory location in a single-threaded environment.

Practical case:

Consider using the thread pool for image processing. Image reading and processing can be distributed among multiple threads in the pool.

#include <vector>
#include <future>
#include <thread>

void process_image(const std::string& filename) {
  // Image processing logic here
}

int main() {
  // 创建线程池
  std::vector<std::thread> pool;
  int num_threads = 8;
  for (int i = 0; i < num_threads; ++i) {
    pool.push_back(std::thread([] {
      // 该线程将执行 image_processing()
    }));
  }

  // 提交任务到池
  std::vector<std::future<void>> results;
  std::vector<std::string> filenames = {"image1.jpg", "image2.jpg", ...};
  for (const auto& filename : filenames) {
    results.push_back(std::async(std::launch::async, process_image, filename));
  }

  // 等待任务完成
  for (auto& result : results) {
    result.wait();
  }

  // 关闭线程池
  for (auto& thread : pool) {
    thread.join();
  }

  return 0;
}

Design Pattern

• Producer-Consumer Queue: Queue abstraction, allowing producers to write data to the queue, and Consumers read data from the queue.
• Reader-writer lock: Synchronization mechanism that limits concurrent read and write access to shared data.
• Deadlock avoidance: Prevent deadlocks through careful resource acquisition and release order.
• Prevent starvation: Ensure that each thread has a chance to obtain resources and avoid some threads from being hungry for a long time.
• Divide and Conquer: Decompose the problem into smaller concurrent subtasks and then merge the results.

Practical case:

Consider using a producer-consumer queue to implement a log service. The producer thread logs events, while the consumer thread processes the log and writes it to a file.

#include <queue>
#include <mutex>
#include <thread>

std::queue<std::string> log_queue;
std::mutex log_queue_mutex;

void write_log(const std::string& entry) {
  std::lock_guard<std::mutex> lock(log_queue_mutex);
  log_queue.push(entry);
}

void process_logs() {
  while (true) {
    std::string entry;
    {
      std::lock_guard<std::mutex> lock(log_queue_mutex);
      if (log_queue.empty()) {
        // 队列为空时，防止忙等待
        std::this_thread::sleep_for(std::chrono::milliseconds(1));
        continue;
      }
      entry = log_queue.front();
      log_queue.pop();
    }
    // 处理日志项
  }
}

int main() {
  // 创建生产者线程
  std::thread producer(write_log, "Log entry 1");
  // 创建消费者线程
  std::thread consumer(process_logs);

  producer.join();
  consumer.join();

  return 0;
}

Conclusion

By adopting appropriate engineering and design patterns, C++ programmers can effectively implement concurrent programs, maximize performance and reduce data consistency issues .

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